PMID- 28196460 OWN - NLM STAT- Publisher DA - 20170215 LR - 20170215 IS - 1557-9700 (Electronic) IS - 1075-2730 (Linking) DP - 2017 Feb 15 TI - Prescribing of Clozapine and Antipsychotic Polypharmacy for Schizophrenia in a Large Medicaid Program. PG - appips201600041 LID - 10.1176/appi.ps.201600041 [doi] AB - OBJECTIVE: Underuse of clozapine and overuse of antipsychotic polypharmacy are both indicators of poor quality of care. This study examined variation in prescribing clozapine and antipsychotic polypharmacy across providers, as well as factors associated with these practices. METHODS: Using 2010-2012 Pennsylvania Medicaid data, prescribers were identified if they wrote antipsychotic prescriptions for ten or more nonelderly adult patients with schizophrenia annually. Generalized linear mixed models with a binomial distribution and a logit link were used to examine prescriber-level annual percentages of patients with clozapine use and with long-term (>/=90 days) antipsychotic polypharmacy and associated characteristics of prescribers' patient caseloads, prescriber characteristics, and Medicaid payer (fee-for-service versus managed care plans). RESULTS: The study cohort included 645 prescribers in 2010, 632 in 2011, and 650 in 2012. In 2012, the mean prescriber-level annual percentage of patients with any clozapine use was 7% (range 0%-89%), and the mean percentage of patients with any long-term antipsychotic polypharmacy was 7% (range 0%-45%) (similar rates were found during 2010-2012). Prescribers with high prescription volume, a smaller percentage of patients from racial or ethnic minority groups, and a larger percentage of patients eligible for Supplemental Security Income were more likely to use both clozapine and antipsychotic polypharmacy for treating schizophrenia. Prescriber specialty and Medicaid payer were also associated with prescribers' practices. CONCLUSIONS: Considerable variation was found in clozapine and antipsychotic polypharmacy practices across prescribers in their treatment of schizophrenia. Targeting efforts to selected prescribers holds promise as an approach to promote evidence-based antipsychotic prescribing. FAU - Tang, Yan AU - Tang Y AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. FAU - Horvitz-Lennon, Marcela AU - Horvitz-Lennon M AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. FAU - Gellad, Walid F AU - Gellad WF AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. FAU - Lave, Judith R AU - Lave JR AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. FAU - Chang, Chung-Chou H AU - Chang CH AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. FAU - Normand, Sharon-Lise AU - Normand SL AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. FAU - Donohue, Julie M AU - Donohue JM AD - Dr. Tang, who was a doctoral student at the University of Pittsburgh when this work was conducted, is with RTI International, Research Triangle Park, North Carolina (e-mail: yantang@rti.org ). Dr. Horvitz-Lennon is with RAND Corporation, Boston. Dr. Gellad is with the U.S. Department of Veterans Affairs Pittsburgh Healthcare System and the Division of General Medicine, University of Pittsburgh, both in Pittsburgh. Dr. Lave and Dr. Donohue are with the Graduate School of Public Health and Dr. Chang is with the Department of Biostatistics, University of Pittsburgh. Dr. Normand is with Harvard Medical School, Boston. LA - eng PT - Journal Article DEP - 20170215 PL - United States TA - Psychiatr Serv JT - Psychiatric services (Washington, D.C.) JID - 9502838 OTO - NOTNLM OT - Drug treatment/psychopharmacology OT - Schizophrenia EDAT- 2017/02/16 06:00 MHDA- 2017/02/16 06:00 CRDT- 2017/02/16 06:00 AID - 10.1176/appi.ps.201600041 [doi] PST - aheadofprint SO - Psychiatr Serv. 2017 Feb 15:appips201600041. doi: 10.1176/appi.ps.201600041. PMID- 28121489 OWN - NLM STAT- MEDLINE DA - 20170125 DCOM- 20170214 LR - 20170220 IS - 1533-4406 (Electronic) IS - 0028-4793 (Linking) VI - 376 IP - 6 DP - 2017 Feb 09 TI - Registry-Based Prospective, Active Surveillance of Medical-Device Safety. PG - 526-535 LID - 10.1056/NEJMoa1516333 [doi] AB - Background The process of assuring the safety of medical devices is constrained by reliance on voluntary reporting of adverse events. We evaluated a strategy of prospective, active surveillance of a national clinical registry to monitor the safety of an implantable vascular-closure device that had a suspected association with increased adverse events after percutaneous coronary intervention (PCI). Methods We used an integrated clinical-data surveillance system to conduct a prospective, propensity-matched analysis of the safety of the Mynx vascular-closure device, as compared with alternative approved vascular-closure devices, with data from the CathPCI Registry of the National Cardiovascular Data Registry. The primary outcome was any vascular complication, which was a composite of access-site bleeding, access-site hematoma, retroperitoneal bleeding, or any vascular complication requiring intervention. Secondary safety end points were access-site bleeding requiring treatment and postprocedural blood transfusion. Results We analyzed data from 73,124 patients who had received Mynx devices after PCI procedures with femoral access from January 1, 2011, to September 30, 2013. The Mynx device was associated with a significantly greater risk of any vascular complication than were alternative vascular-closure devices (absolute risk, 1.2% vs. 0.8%; relative risk, 1.59; 95% confidence interval [CI], 1.42 to 1.78; P<0.001); there was also a significantly greater risk of access-site bleeding (absolute risk, 0.4% vs. 0.3%; relative risk, 1.34; 95% CI, 1.10 to 1.62; P=0.001) and transfusion (absolute risk, 1.8% vs. 1.5%; relative risk, 1.23; 95% CI, 1.13 to 1.34; P<0.001). The initial alerts occurred within the first 12 months of monitoring. Relative risks were greater in three prespecified high-risk subgroups: patients with diabetes, those 70 years of age or older, and women. All safety alerts were confirmed in an independent sample of 48,992 patients from April 1, 2014, to September 30, 2015. Conclusions A strategy of prospective, active surveillance of a clinical registry rapidly identified potential safety signals among recipients of an implantable vascular-closure device, with initial alerts occurring within the first 12 months of monitoring. (Funded by the Food and Drug Administration and others.). FAU - Resnic, Frederic S AU - Resnic FS AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Majithia, Arjun AU - Majithia A AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Marinac-Dabic, Danica AU - Marinac-Dabic D AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Robbins, Susan AU - Robbins S AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Ssemaganda, Henry AU - Ssemaganda H AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Hewitt, Kathleen AU - Hewitt K AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Ponirakis, Angelo AU - Ponirakis A AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Loyo-Berrios, Nilsa AU - Loyo-Berrios N AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Moussa, Issam AU - Moussa I AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Drozda, Joseph AU - Drozda J AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Normand, Sharon-Lise AU - Normand SL AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). FAU - Matheny, Michael E AU - Matheny ME AD - From the Comparative Effectiveness Research Institute, Lahey Hospital and Medical Center, Burlington (F.S.R., A.M., S.R., H.S.), and Tufts School of Medicine (F.S.R., A.M.) and Harvard Medical School and the Harvard T.H. Chan School of Public Health (S.-L.N.), Boston - all in Massachusetts; the Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD (D.M.-D., N.L.-B.); the National Cardiovascular Data Registry, American College of Cardiology, Washington, DC (K.H., A.P.); First Coast Cardiovascular Institute and University of Central Florida College of Medicine, Jacksonville (I.M.); Mercy Health, St. Louis (J.D.); and the Veterans Affairs Tennessee Valley Healthcare System and Vanderbilt University, Center for Population Health Informatics, Departments of Biomedical Informatics, Biostatistics, and Medicine, Vanderbilt University Medical Center - both in Nashville (M.E.M.). LA - eng GR - U01 FD004493/FD/FDA HHS/United States GR - U01 FD004963/FD/FDA HHS/United States PT - Comparative Study PT - Journal Article PT - Research Support, Non-U.S. Gov't PT - Research Support, U.S. Gov't, P.H.S. DEP - 20170125 PL - United States TA - N Engl J Med JT - The New England journal of medicine JID - 0255562 SB - AIM SB - IM MH - Aged MH - Equipment Design MH - *Equipment Safety/statistics & numerical data MH - Female MH - Hemorrhage/epidemiology/etiology MH - Humans MH - Incidence MH - Male MH - Middle Aged MH - Percutaneous Coronary Intervention/*instrumentation MH - Population Surveillance MH - Prospective Studies MH - Registries MH - Risk MH - Risk Assessment/methods MH - Vascular Closure Devices/*adverse effects EDAT- 2017/01/26 06:00 MHDA- 2017/02/15 06:00 CRDT- 2017/01/26 06:00 AID - 10.1056/NEJMoa1516333 [doi] PST - ppublish SO - N Engl J Med. 2017 Feb 9;376(6):526-535. doi: 10.1056/NEJMoa1516333. Epub 2017 Jan 25. PMID- 28198561 OWN - NLM STAT- In-Data-Review DA - 20170215 LR - 20170303 IS - 1532-5415 (Electronic) IS - 0002-8614 (Linking) VI - 65 IP - 2 DP - 2017 Feb TI - Nursing Home Use After Implantable Cardioverter-Defibrillator Implantation in Older Adults: Results from the National Cardiovascular Data Registry. PG - 340-347 LID - 10.1111/jgs.14520 [doi] AB - OBJECTIVES: To evaluate the incidence and characteristics of nursing home (NH) use after implantable cardioverter-defibrillator (ICD) implantation. DESIGN: Cohort study. SETTING: Medicare beneficiaries in the National Cardiovascular Data Registry-ICD Registry. PARTICIPANTS: Individuals aged 65 and older receiving ICDs between January 1, 2006, and March 31, 2010 (N = 192,483). MEASUREMENTS: Proportion of ICD recipients discharged to NHs directly after device placement, cumulative incidence of long-term NH admission, and factors associated with immediate discharge to a NH and time to long-term NH admission. RESULTS: Over 4 years, 40.6% of the cohort died, and 35,939 (18.7%) experienced at least one NH admission, including 4.0% directly discharged to a NH after ICD implantation and 2.8% admitted to long-term NH care during follow-up. The cumulative incidence of long-term NH admission, accounting for the competing risk of death, was 1.7% at 1 year, 3.8% at 3 years, and 4.6% at 4 years; 20.1% of individuals admitted to a NH died there. Factors most strongly associated with direct NH discharge and time to long-term NH care were older age (adjusted odds ratio (AOR) = 2.09, 95% confidence interval (CI) = 2.01-2.17 per 10-year increment; adjusted hazard ratio (AHR) = 1.88, 95% CI = 1.80-1.97, respectively), dementia (AOR = 2.60, 95% CI = 2.25-3.01; AHR = 2.50, 95% CI = 2.14-2.93, respectively), and Medicare Part A claim for NH stay in prior 6 months (AOR = 3.96, 95% CI = 3.70-4.25; AHR = 2.88, 95% CI = 2.65-3.14, respectively). CONCLUSION: Nearly one in five individuals are admitted to NHs over a median of 1.6 years of follow-up after ICD implantation. Understanding these outcomes may help inform the clinical care of these individuals. CI - (c) 2016, Copyright the Authors Journal compilation (c) 2016, The American Geriatrics Society. FAU - Kramer, Daniel B AU - Kramer DB AD - Hebrew SeniorLife Institute for Aging Research, Boston, MA. AD - Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA. AD - Harvard Medical School, Boston, MA. FAU - Reynolds, Matthew R AU - Reynolds MR AD - Harvard Clinical Research Institute, Boston, MA. AD - Lahey Hospital and Medical Center, Burlington, MA. FAU - Normand, Sharon-Lise AU - Normand SL AD - Department of Health Care Policy, Harvard Medical School, Boston, MA. AD - Department of Biostatistics, Havard TH Chan School of Public Health, Boston, MA. FAU - Parzynski, Craig S AU - Parzynski CS AD - Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, Yale University, New Haven, Connecticut. FAU - Spertus, John A AU - Spertus JA AD - Mid America Heart Institute, Kansas City, Missouri. FAU - Mor, Vincent AU - Mor V AD - Department of Health Services, Policy and Practice, Center for Gerontology and Health Care Research, School of Public Health, Brown University, Providence, Rhode Island. FAU - Mitchell, Susan L AU - Mitchell SL AD - Hebrew SeniorLife Institute for Aging Research, Boston, MA. AD - Harvard Medical School, Boston, MA. LA - eng GR - K23 AG045963/AG/NIA NIH HHS/United States GR - K24 AG033640/AG/NIA NIH HHS/United States GR - U01 FD004493/FD/FDA HHS/United States PT - Journal Article DEP - 20161107 PL - United States TA - J Am Geriatr Soc JT - Journal of the American Geriatrics Society JID - 7503062 PMC - PMC5325141 MID - NIHMS801654 OTO - NOTNLM OT - aging OT - implantable cardioverter-defibrillators OT - outcomes EDAT- 2017/02/16 06:00 MHDA- 2017/02/16 06:00 CRDT- 2017/02/16 06:00 PMCR- 2018/02/01 AID - 10.1111/jgs.14520 [doi] PST - ppublish SO - J Am Geriatr Soc. 2017 Feb;65(2):340-347. doi: 10.1111/jgs.14520. Epub 2016 Nov 7. PMID- 28112994 OWN - NLM STAT- Publisher DA - 20170123 LR - 20170205 IS - 1552-681X (Electronic) IS - 0272-989X (Linking) DP - 2017 Jan 01 TI - Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making. PG - 272989X16686767 LID - 10.1177/0272989X16686767 [doi] AB - BACKGROUND: Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate. OBJECTIVE: To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making. METHODS: In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals. RESULTS: The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified. LIMITATIONS: Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging. CONCLUSIONS: A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts. FAU - Hatfield, Laura A AU - Hatfield LA AD - Department of Health Care Policy, Harvard Medical School, Boston, MA, USA (LAH, VA). AD - Interfaculty Initiative in Health Policy, Harvard University, Cambridge, MA, USA (CMB). AD - Department of Health Care Policy, Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA (S-LTN). FAU - Baugh, Christine M AU - Baugh CM AD - Department of Health Care Policy, Harvard Medical School, Boston, MA, USA (LAH, VA). AD - Interfaculty Initiative in Health Policy, Harvard University, Cambridge, MA, USA (CMB). AD - Department of Health Care Policy, Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA (S-LTN). FAU - Azzone, Vanessa AU - Azzone V AD - Department of Health Care Policy, Harvard Medical School, Boston, MA, USA (LAH, VA). AD - Interfaculty Initiative in Health Policy, Harvard University, Cambridge, MA, USA (CMB). AD - Department of Health Care Policy, Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA (S-LTN). FAU - Normand, Sharon-Lise T AU - Normand ST AD - Department of Health Care Policy, Harvard Medical School, Boston, MA, USA (LAH, VA). AD - Interfaculty Initiative in Health Policy, Harvard University, Cambridge, MA, USA (CMB). AD - Department of Health Care Policy, Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA (S-LTN). LA - eng GR - T32 MH019733/MH/NIMH NIH HHS/United States PT - Journal Article DEP - 20170101 PL - United States TA - Med Decis Making JT - Medical decision making : an international journal of the Society for Medical Decision Making JID - 8109073 OTO - NOTNLM OT - Bayesian statistical methods OT - database analysis OT - decision analysis OT - hierarchical models OT - performance measures OT - simulation methods EDAT- 2017/01/24 06:00 MHDA- 2017/01/24 06:00 CRDT- 2017/01/24 06:00 AID - 10.1177/0272989X16686767 [doi] PST - aheadofprint SO - Med Decis Making. 2017 Jan 1:272989X16686767. doi: 10.1177/0272989X16686767. PMID- 27770280 OWN - NLM STAT- In-Data-Review DA - 20161022 LR - 20170224 IS - 1573-7373 (Electronic) IS - 0167-594X (Linking) VI - 131 IP - 2 DP - 2017 Jan TI - Comparative effectiveness of radiotherapy with vs. without temozolomide in older patients with glioblastoma. PG - 301-311 LID - 10.1007/s11060-016-2294-7 [doi] AB - It is unknown whether the addition of temozolomide (TMZ) to radiotherapy (RT) is associated with improved overall survival (OS) among older glioblastoma patients. We performed a retrospective cohort SEER-Medicare analysis of 1652 patients aged >/=65 years with glioblastoma who received >/=10 fractions of RT from 2005 to 2009, or from 1995 to 1999 before TMZ was available. Three cohorts were assembled based on diagnosis year and treatment initiated within 60 days of diagnosis: (1) 2005-2009 and TMZ/RT, (2) 2005-2009 and RT only, or (3) 1995-1999 and RT only. Associations with OS were estimated using Cox proportional hazards models and propensity score analyses; OS was calculated starting 60 days after diagnosis. Pre-specified sensitivity analyses were performed among patients who received long-course RT (>/=27 fractions). Median survival estimates were 7.4 (IQR, 3.3-14.7) months for TMZ/RT, 5.9 (IQR, 2.6-12.1) months for RT alone in 2005-2009, and 5.6 (IQR, 2.7-9.6) months for RT alone in 1995-1999. OS at 2 years was 10.1 % for TMZ/RT, 7.1 % for RT in 2005-2009, and 4.7 % for RT in 1995-1999. Adjusted models suggested decreased mortality risk for TMZ/RT compared to RT in 2005-2009 (AHR, 0.86; 95 % CI, 0.76-0.98) and RT in 1995-1999 (AHR, 0.71; 95 % CI, 0.57-0.90). Among patients from 2005 to 2009 who received long-course RT, however, the addition of TMZ did not significantly improve survival (AHR, 0.91; 95 % CI, 0.80-1.04). In summary, among a large cohort of older glioblastoma patients treated in a real-world setting, the addition of TMZ to RT was associated with a small survival gain. FAU - Arvold, Nils D AU - Arvold ND AD - St. Luke's Radiation Oncology Associates, Lakeview Building, St. Luke's Cancer Center, and Whiteside Institute for Clinical Research, University of Minnesota Duluth, 1001 East Superior Street, Suite L101, Duluth, MN, 55802, USA. nils.arvold@slhduluth.com. FAU - Cefalu, Matthew AU - Cefalu M AD - Department of Biostatistics, Harvard School of Public Health, Building 2, 4th Floor, 655 Huntington Ave, Boston, MA, 02115, USA. FAU - Wang, Yun AU - Wang Y AD - Department of Biostatistics, Harvard School of Public Health, Building 2, 4th Floor, 655 Huntington Ave, Boston, MA, 02115, USA. FAU - Zigler, Corwin AU - Zigler C AD - Department of Biostatistics, Harvard School of Public Health, Building 2, 4th Floor, 655 Huntington Ave, Boston, MA, 02115, USA. FAU - Schrag, Deborah AU - Schrag D AD - Department of Medicine, Dana-Farber Cancer Institute, 44 Binney St., Boston, MA, 02115, USA. FAU - Dominici, Francesca AU - Dominici F AD - Department of Biostatistics, Harvard School of Public Health, Building 2, 4th Floor, 655 Huntington Ave, Boston, MA, 02115, USA. LA - eng GR - K18 HS021991/HS/AHRQ HHS/United States GR - P01 CA134294/CA/NCI NIH HHS/United States GR - R01 GM111339/GM/NIGMS NIH HHS/United States PT - Journal Article DEP - 20161021 PL - United States TA - J Neurooncol JT - Journal of neuro-oncology JID - 8309335 PMC - PMC5303537 MID - NIHMS824800 OTO - NOTNLM OT - Elderly OT - Glioblastoma OT - Radiotherapy OT - Survival OT - Temozolomide COI - Compliance with ethical standards: Conflict of interest The authors declare that they have no conflict of interest. EDAT- 2016/10/23 06:00 MHDA- 2016/10/23 06:00 CRDT- 2016/10/23 06:00 PMCR- 2018/01/01 PHST- 2016/04/14 [received] PHST- 2016/10/09 [accepted] AID - 10.1007/s11060-016-2294-7 [doi] AID - 10.1007/s11060-016-2294-7 [pii] PST - ppublish SO - J Neurooncol. 2017 Jan;131(2):301-311. doi: 10.1007/s11060-016-2294-7. Epub 2016 Oct 21. PMID- 27417891 OWN - NLM STAT- In-Data-Review DA - 20160715 LR - 20170220 IS - 1557-9700 (Electronic) IS - 1075-2730 (Linking) VI - 67 IP - 12 DP - 2016 Dec 01 TI - Patterns of Antipsychotic Prescribing by Physicians to Young Children. PG - 1307-1314 AB - OBJECTIVE: Antipsychotic use among young children has grown rapidly despite a lack of approval by the U.S. Food and Drug Administration (FDA) for broad use in this age group. Characteristics of physicians who prescribed antipsychotics to young children were identified, and prescribing patterns involving young children and adults were compared. METHODS: Physician-level prescribing data from IMS Health's Xponent database were linked with American Medical Association Masterfile data and analyzed. The sample included all U.S. psychiatrists and a random sample of 5% of family medicine physicians who wrote at least ten antipsychotic prescriptions per year from 2008 to 2011 (N=31,713). Logistic and hierarchical binomial regression models were estimated to examine physician prescribing for children ages zero to nine, and the types and numbers of ingredients used for children versus adults ages 20 to 64 were compared. RESULTS: Among antipsychotic prescribers, 42.2% had written at least one antipsychotic prescription for young children. Such prescribing was more likely among physicians age /=60 (odds ratio [OR]=1.70) and physicians in rural versus nonrural areas (OR=1.11) and was less likely among males (OR=.93) and graduates of a top-25 versus a lower-ranked U.S. medical school (OR=.87). Among physicians who prescribed antipsychotics to young children and adults, 75.0% of prescriptions for children and 35.7% of those for adults were for drugs with an FDA-approved indication for that age. Fewer antipsychotic agents were prescribed for young children (median=2) versus adults (median=7). CONCLUSIONS: Prescribing antipsychotics for young children was relatively common, but prescribing patterns differed between young children and adults. FAU - Huskamp, Haiden A AU - Huskamp HA AD - Dr. Huskamp and Dr. Normand are with the Department of Health Care Policy, Harvard Medical School, Boston (e-mail: huskamp@hcp.med.harvard.edu ). Dr. Horvitz-Lennon is with the RAND Corporation, Boston. Dr. Berndt is with the MIT Sloan School of Management, Cambridge, Massachusetts. Dr. Donohue is with the Health Policy and Management Department, Graduate School of Public Health, University of Pittsburgh, Pittsburgh. FAU - Horvitz-Lennon, Marcela AU - Horvitz-Lennon M AD - Dr. Huskamp and Dr. Normand are with the Department of Health Care Policy, Harvard Medical School, Boston (e-mail: huskamp@hcp.med.harvard.edu ). Dr. Horvitz-Lennon is with the RAND Corporation, Boston. Dr. Berndt is with the MIT Sloan School of Management, Cambridge, Massachusetts. Dr. Donohue is with the Health Policy and Management Department, Graduate School of Public Health, University of Pittsburgh, Pittsburgh. FAU - Berndt, Ernst R AU - Berndt ER AD - Dr. Huskamp and Dr. Normand are with the Department of Health Care Policy, Harvard Medical School, Boston (e-mail: huskamp@hcp.med.harvard.edu ). Dr. Horvitz-Lennon is with the RAND Corporation, Boston. Dr. Berndt is with the MIT Sloan School of Management, Cambridge, Massachusetts. Dr. Donohue is with the Health Policy and Management Department, Graduate School of Public Health, University of Pittsburgh, Pittsburgh. FAU - Normand, Sharon-Lise T AU - Normand ST AD - Dr. Huskamp and Dr. Normand are with the Department of Health Care Policy, Harvard Medical School, Boston (e-mail: huskamp@hcp.med.harvard.edu ). Dr. Horvitz-Lennon is with the RAND Corporation, Boston. Dr. Berndt is with the MIT Sloan School of Management, Cambridge, Massachusetts. Dr. Donohue is with the Health Policy and Management Department, Graduate School of Public Health, University of Pittsburgh, Pittsburgh. FAU - Donohue, Julie M AU - Donohue JM AD - Dr. Huskamp and Dr. Normand are with the Department of Health Care Policy, Harvard Medical School, Boston (e-mail: huskamp@hcp.med.harvard.edu ). Dr. Horvitz-Lennon is with the RAND Corporation, Boston. Dr. Berndt is with the MIT Sloan School of Management, Cambridge, Massachusetts. Dr. Donohue is with the Health Policy and Management Department, Graduate School of Public Health, University of Pittsburgh, Pittsburgh. LA - eng GR - R01 MH087488/MH/NIMH NIH HHS/United States GR - R01 MH093359/MH/NIMH NIH HHS/United States PT - Journal Article DEP - 20160715 PL - United States TA - Psychiatr Serv JT - Psychiatric services (Washington, D.C.) JID - 9502838 PMC - PMC5133161 MID - NIHMS795688 EDAT- 2016/07/16 06:00 MHDA- 2016/07/16 06:00 CRDT- 2016/07/16 06:00 PMCR- 2017/12/01 AID - 10.1176/appi.ps.201500224 [doi] PST - ppublish SO - Psychiatr Serv. 2016 Dec 1;67(12):1307-1314. Epub 2016 Jul 15. PMID- 27902825 OWN - NLM STAT- Publisher DA - 20161130 LR - 20161203 IS - 2168-6262 (Electronic) IS - 2168-6254 (Linking) DP - 2016 Nov 30 TI - Association Between the Amount of Vaginal Mesh Used With Mesh Erosions and Repeated Surgery After Repairing Pelvic Organ Prolapse and Stress Urinary Incontinence. LID - 10.1001/jamasurg.2016.4200 [doi] AB - Importance: Mesh, a synthetic graft, has been used in pelvic organ prolapse (POP) repair and stress urinary incontinence (SUI) to augment and strengthen weakened tissue. Polypropylene mesh has come under scrutiny by the US Food and Drug Administration. Objective: To examine the rates of mesh complications and invasive reintervention after the placement of vaginal mesh for POP repair or SUI surgery. Design, Setting, and Participants: This investigation was an observational cohort study at inpatient and ambulatory surgery settings in New York State. Participants were women who underwent transvaginal repair for POP or SUI with mesh between January 1, 2008, and December 31, 2012, and were followed up through December 31, 2013. They were divided into the following 4 groups based on the amount of mesh exposure: transvaginal POP repair surgery with mesh and concurrent sling use (vaginal mesh plus sling group), transvaginal POP repair with mesh and no concurrent sling use (vaginal mesh group), transvaginal POP repair without mesh but concurrent sling use for SUI (POP sling group), and sling for SUI alone (SUI sling group). Main Outcomes and Measures: The primary outcome was the occurrence of mesh complications and repeated invasive intervention within 1 year after the initial mesh implantation. A time-to-event analysis was performed to examine the occurrence of mesh erosions and subsequent reintervention. Secondary analyses of an age association (<65 vs >/=65 years) were conducted. Results: The study identified 41604 women who underwent 1 of the 4 procedures. The mean (SD) age of women at their initial mesh implantation was 56.2 (13.0) years. The highest risk of erosions was found in the vaginal mesh plus sling group (2.72%; 95% CI, 2.31%-3.21%) and the lowest in the SUI sling group (1.57%; 95% CI, 1.41%-1.74%). The risk of repeated surgery with concomitant erosion diagnosis was also the highest in the vaginal mesh plus sling group (2.13%; 95% CI, 1.76%-2.56%) and the lowest in the SUI sling group (1.16%; 95% CI, 1.03%-1.31%). Conclusions and Relevance: The combined use of POP mesh and SUI mesh sling was associated with the highest erosion and repeated intervention risk, while mesh sling alone had the lowest erosion and repeated intervention risk. There is evidence for a dose-response relationship between the amount of mesh used and subsequent mesh erosions, complications, and invasive repeated intervention. FAU - Chughtai, Bilal AU - Chughtai B AD - Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York. FAU - Barber, Matthew D AU - Barber MD AD - Obstetrics, Gynecology, and Women's Health Institute, Cleveland Clinic, Cleveland, Ohio. FAU - Mao, Jialin AU - Mao J AD - Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York. FAU - Forde, James C AU - Forde JC AD - Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York. FAU - Normand, Sharon-Lise T AU - Normand ST AD - Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts5Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts. FAU - Sedrakyan, Art AU - Sedrakyan A AD - Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York. LA - eng PT - Journal Article DEP - 20161130 PL - United States TA - JAMA Surg JT - JAMA surgery JID - 101589553 EDAT- 2016/12/03 06:00 MHDA- 2016/12/03 06:00 CRDT- 2016/12/01 06:00 AID - 2587478 [pii] AID - 10.1001/jamasurg.2016.4200 [doi] PST - aheadofprint SO - JAMA Surg. 2016 Nov 30. doi: 10.1001/jamasurg.2016.4200. PMID- 28263941 OWN - NLM STAT- In-Data-Review DA - 20170306 LR - 20170309 IS - 1941-7705 (Electronic) IS - 1941-7713 (Linking) VI - 9 IP - 6 DP - 2016 Nov TI - Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data. PG - 659-669 LID - 10.1161/CIRCOUTCOMES.116.002826 [doi] AB - BACKGROUND: Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly suited to leveraging big data. METHODS AND RESULTS: We assessed hospital all-cause 30-day excess mortality risk among 8952 adults undergoing percutaneous coronary intervention between October 1, 2011, and September 30, 2012, in 24 Massachusetts hospitals using clinical registry data linked with billing data. We compared conventional logistic regression models with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estimates of hospital effects. We also compared a clinically informed and a machine-learning approach to confounder selection, using elastic net penalized regression in the latter case. Hospital excess risk estimates range from -1.4% to 2.0% across methods and confounder sets. Some hospitals were consistently classified as low or as high excess mortality outliers; others changed classification depending on the method and confounder set used. Switching from the clinically selected list of 11 confounders to a full set of 225 confounders increased the estimation uncertainty by an average of 62% across methods as measured by confidence interval length. Agreement among methods ranged from fair, with a kappa statistic of 0.39 (SE: 0.16), to perfect, with a kappa of 1 (SE: 0.0). CONCLUSIONS: Modern causal inference techniques should be more frequently adopted to leverage big data while minimizing bias in hospital performance assessments. CI - (c) 2016 American Heart Association, Inc. FAU - Spertus, Jacob V AU - Spertus JV AD - From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). FAU - T Normand, Sharon-Lise AU - T Normand SL AD - From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). sharon@hcp.med.harvard.edu. FAU - Wolf, Robert AU - Wolf R AD - From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). FAU - Cioffi, Matt AU - Cioffi M AD - From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). FAU - Lovett, Ann AU - Lovett A AD - From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). FAU - Rose, Sherri AU - Rose S AD - From the Department of Health Care Policy, Harvard Medical School, Boston, MA (J.V.S., S.-L.T.N., R.W., M.C., A.L., S.R.); and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA (S.-L.T.N.). LA - eng GR - R01 GM111339/GM/NIGMS NIH HHS/United States PT - Journal Article DEP - 20161108 PL - United States TA - Circ Cardiovasc Qual Outcomes JT - Circulation. Cardiovascular quality and outcomes JID - 101489148 PMC - PMC5341139 MID - NIHMS821847 OTO - NOTNLM OT - hospital mortality OT - logistic regression OT - percutaneous coronary intervention OT - propensity score EDAT- 2017/03/07 06:00 MHDA- 2017/03/07 06:00 CRDT- 2017/03/07 06:00 PMCR- 2017/11/08 PHST- 2016/03/04 [received] PHST- 2016/07/26 [accepted] AID - CIRCOUTCOMES.116.002826 [pii] AID - 10.1161/CIRCOUTCOMES.116.002826 [doi] PST - ppublish SO - Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):659-669. doi: 10.1161/CIRCOUTCOMES.116.002826. Epub 2016 Nov 8. PMID- 27705249 OWN - NLM STAT- MEDLINE DA - 20161005 DCOM- 20170110 LR - 20170220 IS - 1533-4406 (Electronic) IS - 0028-4793 (Linking) VI - 375 IP - 14 DP - 2016 Oct 06 TI - Life Expectancy after Myocardial Infarction, According to Hospital Performance. PG - 1332-1342 AB - Background Thirty-day risk-standardized mortality rates after acute myocardial infarction are commonly used to evaluate and compare hospital performance. However, it is not known whether differences among hospitals in the early survival of patients with acute myocardial infarction are associated with differences in long-term survival. Methods We analyzed data from the Cooperative Cardiovascular Project, a study of Medicare beneficiaries who were hospitalized for acute myocardial infarction between 1994 and 1996 and who had 17 years of follow-up. We grouped hospitals into five strata that were based on case-mix severity. Within each case-mix stratum, we compared life expectancy among patients admitted to high-performing hospitals with life expectancy among patients admitted to low-performing hospitals. Hospital performance was defined by quintiles of 30-day risk-standardized mortality rates. Cox proportional-hazards models were used to calculate life expectancy. Results The study sample included 119,735 patients with acute myocardial infarction who were admitted to 1824 hospitals. Within each case-mix stratum, survival curves of the patients admitted to hospitals in each risk-standardized mortality rate quintile separated within the first 30 days and then remained parallel over 17 years of follow-up. Estimated life expectancy declined as hospital risk-standardized mortality rate quintile increased. On average, patients treated at high-performing hospitals lived between 0.74 and 1.14 years longer, depending on hospital case mix, than patients treated at low-performing hospitals. When 30-day survivors were examined separately, there was no significant difference in unadjusted or adjusted life expectancy across hospital risk-standardized mortality rate quintiles. Conclusions In this study, patients admitted to high-performing hospitals after acute myocardial infarction had longer life expectancies than patients treated in low-performing hospitals. This survival benefit occurred in the first 30 days and persisted over the long term. (Funded by the National Heart, Lung, and Blood Institute and the National Institute of General Medical Sciences Medical Scientist Training Program.). FAU - Bucholz, Emily M AU - Bucholz EM AD - From the Department of Medicine, Boston Children's Hospital (E.M.B.), the Department of Internal Medicine, Massachusetts General Hospital (N.M.B.), the Department of Health Care Policy, Harvard Medical School (S.-L.T.N.), and the Department of Biostatistics, Harvard T.H. Chan School of Public Health (S.-L.T.N.) - all in Boston; and the Departments of Biostatistics (S.M.) and Health Policy and Management (H.M.K.), Yale School of Public Health, the Section of Cardiovascular Medicine, Department of Internal Medicine, and Robert Wood Johnson Clinical Scholars Program, Yale School of Medicine (H.M.K.), and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital (H.M.K.) - all in New Haven, CT. FAU - Butala, Neel M AU - Butala NM AD - From the Department of Medicine, Boston Children's Hospital (E.M.B.), the Department of Internal Medicine, Massachusetts General Hospital (N.M.B.), the Department of Health Care Policy, Harvard Medical School (S.-L.T.N.), and the Department of Biostatistics, Harvard T.H. Chan School of Public Health (S.-L.T.N.) - all in Boston; and the Departments of Biostatistics (S.M.) and Health Policy and Management (H.M.K.), Yale School of Public Health, the Section of Cardiovascular Medicine, Department of Internal Medicine, and Robert Wood Johnson Clinical Scholars Program, Yale School of Medicine (H.M.K.), and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital (H.M.K.) - all in New Haven, CT. FAU - Ma, Shuangge AU - Ma S AD - From the Department of Medicine, Boston Children's Hospital (E.M.B.), the Department of Internal Medicine, Massachusetts General Hospital (N.M.B.), the Department of Health Care Policy, Harvard Medical School (S.-L.T.N.), and the Department of Biostatistics, Harvard T.H. Chan School of Public Health (S.-L.T.N.) - all in Boston; and the Departments of Biostatistics (S.M.) and Health Policy and Management (H.M.K.), Yale School of Public Health, the Section of Cardiovascular Medicine, Department of Internal Medicine, and Robert Wood Johnson Clinical Scholars Program, Yale School of Medicine (H.M.K.), and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital (H.M.K.) - all in New Haven, CT. FAU - Normand, Sharon-Lise T AU - Normand ST AD - From the Department of Medicine, Boston Children's Hospital (E.M.B.), the Department of Internal Medicine, Massachusetts General Hospital (N.M.B.), the Department of Health Care Policy, Harvard Medical School (S.-L.T.N.), and the Department of Biostatistics, Harvard T.H. Chan School of Public Health (S.-L.T.N.) - all in Boston; and the Departments of Biostatistics (S.M.) and Health Policy and Management (H.M.K.), Yale School of Public Health, the Section of Cardiovascular Medicine, Department of Internal Medicine, and Robert Wood Johnson Clinical Scholars Program, Yale School of Medicine (H.M.K.), and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital (H.M.K.) - all in New Haven, CT. FAU - Krumholz, Harlan M AU - Krumholz HM AD - From the Department of Medicine, Boston Children's Hospital (E.M.B.), the Department of Internal Medicine, Massachusetts General Hospital (N.M.B.), the Department of Health Care Policy, Harvard Medical School (S.-L.T.N.), and the Department of Biostatistics, Harvard T.H. Chan School of Public Health (S.-L.T.N.) - all in Boston; and the Departments of Biostatistics (S.M.) and Health Policy and Management (H.M.K.), Yale School of Public Health, the Section of Cardiovascular Medicine, Department of Internal Medicine, and Robert Wood Johnson Clinical Scholars Program, Yale School of Medicine (H.M.K.), and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital (H.M.K.) - all in New Haven, CT. LA - eng GR - F30 HL120498/HL/NHLBI NIH HHS/United States GR - T32 GM007205/GM/NIGMS NIH HHS/United States GR - U01 HL105270/HL/NHLBI NIH HHS/United States PT - Journal Article PT - Research Support, N.I.H., Extramural PL - United States TA - N Engl J Med JT - The New England journal of medicine JID - 0255562 SB - AIM SB - IM MH - Aged MH - Aged, 80 and over MH - Female MH - Follow-Up Studies MH - Hospitals/*standards MH - Humans MH - *Life Expectancy MH - Male MH - Myocardial Infarction/*mortality MH - Quality of Health Care MH - Survival Analysis MH - United States/epidemiology PMC - PMC5118048 MID - NIHMS821920 EDAT- 2016/10/06 06:00 MHDA- 2017/01/11 06:00 CRDT- 2016/10/06 06:00 AID - 10.1056/NEJMoa1513223 [doi] PST - ppublish SO - N Engl J Med. 2016 Oct 6;375(14):1332-1342. PMID- 27261637 OWN - NLM STAT- In-Data-Review DA - 20160914 LR - 20170220 IS - 1537-1948 (Electronic) IS - 0025-7079 (Linking) VI - 54 IP - 10 DP - 2016 Oct TI - Hospital Phenotypes in the Management of Patients Admitted for Acute Myocardial Infarction. PG - 929-36 LID - 10.1097/MLR.0000000000000571 [doi] AB - OBJECTIVES: To characterize hospital phenotypes by their combined utilization pattern of percutaneous coronary interventions (PCI), coronary artery bypass grafting (CABG) procedures, and intensive care unit (ICU) admissions for patients hospitalized for acute myocardial infarction (AMI). RESEARCH DESIGN: Using the Premier Analytical Database, we identified 129,138 hospitalizations for AMI from 246 hospitals with the capacity for performing open-heart surgery during 2010-2013. We calculated year-specific, risk-standardized estimates of PCI procedure rates, CABG procedure rates, and ICU admission rates for each hospital, adjusting for patient clinical characteristics and within-hospital correlation of patients. We used a mixture modeling approach to identify groups of hospitals (ie, hospital phenotypes) that exhibit distinct longitudinal patterns of risk-standardized PCI, CABG, and ICU admission rates. RESULTS: We identified 3 distinct phenotypes among the 246 hospitals: (1) high PCI-low CABG-high ICU admission (39.2% of the hospitals), (2) high PCI-low CABG-low ICU admission (30.5%), and (3) low PCI-high CABG-moderate ICU admission (30.4%). Hospitals in the high PCI-low CABG-high ICU admission phenotype had significantly higher risk-standardized in-hospital costs and 30-day risk-standardized payment yet similar risk-standardized mortality and readmission rates compared with hospitals in the low PCI-high CABG-moderate ICU admission phenotype. Hospitals in these phenotypes differed by geographic region. CONCLUSIONS: Hospitals differ in how they manage patients hospitalized for AMI. Their distinctive practice patterns suggest that some hospital phenotypes may be more successful in producing good outcomes at lower cost. FAU - Xu, Xiao AU - Xu X AD - *Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine daggerCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital double daggerDepartment of Biostatistics, Yale School of Public Health, New Haven, CT section signDepartment of Health Care Policy, Harvard Medical School parallelDepartment of Biostatistics, Harvard T.H. Chan School of Public Health paragraph signDivision of General Medicine, Tufts University School of Medicine, Boston #Baystate Medical Center, Springfield, MA **Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT daggerdaggerPremier Inc., Charlotte, NC double daggerdouble daggerLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA section sign section signBooz Allen Hamilton Inc., McLean, VA parallel parallelRobert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine paragraph sign paragraph signDepartment of Health Policy and Management, Yale School of Public Health, New Haven, CT. FAU - Li, Shu-Xia AU - Li SX FAU - Lin, Haiqun AU - Lin H FAU - Normand, Sharon-Lise T AU - Normand SL FAU - Lagu, Tara AU - Lagu T FAU - Desai, Nihar AU - Desai N FAU - Duan, Michael AU - Duan M FAU - Kroch, Eugene A AU - Kroch EA FAU - Krumholz, Harlan M AU - Krumholz HM LA - eng GR - K01 HL114745/HL/NHLBI NIH HHS/United States GR - U01 HL105270/HL/NHLBI NIH HHS/United States PT - Journal Article PL - United States TA - Med Care JT - Medical care JID - 0230027 SB - IM PMC - PMC5305177 MID - NIHMS778065 EDAT- 2016/06/05 06:00 MHDA- 2016/06/05 06:00 CRDT- 2016/06/05 06:00 PMCR- 2017/10/01 AID - 10.1097/MLR.0000000000000571 [doi] PST - ppublish SO - Med Care. 2016 Oct;54(10):929-36. doi: 10.1097/MLR.0000000000000571. PMID- 27090498 OWN - NLM STAT- In-Data-Review DA - 20160728 LR - 20170220 IS - 1097-0258 (Electronic) IS - 0277-6715 (Linking) VI - 35 IP - 21 DP - 2016 Sep 20 TI - Handling incomplete correlated continuous and binary outcomes in meta-analysis of individual participant data. PG - 3676-89 LID - 10.1002/sim.6969 [doi] AB - Meta-analysis of individual participant data (IPD) is increasingly utilised to improve the estimation of treatment effects, particularly among different participant subgroups. An important concern in IPD meta-analysis relates to partially or completely missing outcomes for some studies, a problem exacerbated when interest is on multiple discrete and continuous outcomes. When leveraging information from incomplete correlated outcomes across studies, the fully observed outcomes may provide important information about the incompleteness of the other outcomes. In this paper, we compare two models for handling incomplete continuous and binary outcomes in IPD meta-analysis: a joint hierarchical model and a sequence of full conditional mixed models. We illustrate how these approaches incorporate the correlation across the multiple outcomes and the between-study heterogeneity when addressing the missing data. Simulations characterise the performance of the methods across a range of scenarios which differ according to the proportion and type of missingness, strength of correlation between outcomes and the number of studies. The joint model provided confidence interval coverage consistently closer to nominal levels and lower mean squared error compared with the fully conditional approach across the scenarios considered. Methods are illustrated in a meta-analysis of randomised controlled trials comparing the effectiveness of implantable cardioverter-defibrillator devices alone to implantable cardioverter-defibrillator combined with cardiac resynchronisation therapy for treating patients with chronic heart failure. (c) 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. CI - (c) 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. FAU - Gomes, Manuel AU - Gomes M AD - Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, U.K. FAU - Hatfield, Laura AU - Hatfield L AD - Department of Health Care Policy, Harvard Medical School, Boston, 02115, MA, U.S.A. FAU - Normand, Sharon-Lise AU - Normand SL AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, U.S.A. LA - eng GR - MR/K02177X/1/Medical Research Council/United Kingdom GR - U01 FD004493/FD/FDA HHS/United States PT - Journal Article DEP - 20160418 PL - England TA - Stat Med JT - Statistics in medicine JID - 8215016 SB - IM PMC - PMC4982066 OID - NLM: PMC4982066 OTO - NOTNLM OT - Bayesian analysis OT - IPD meta-analysis OT - fully conditional specification OT - joint modelling OT - missing data OT - multiple imputation EDAT- 2016/04/20 06:00 MHDA- 2016/04/20 06:00 CRDT- 2016/04/20 06:00 PHST- 2015/03/17 [received] PHST- 2016/03/15 [revised] PHST- 2016/03/24 [accepted] AID - 10.1002/sim.6969 [doi] PST - ppublish SO - Stat Med. 2016 Sep 20;35(21):3676-89. doi: 10.1002/sim.6969. Epub 2016 Apr 18. PMID- 27503972 OWN - NLM STAT- In-Data-Review DA - 20160809 LR - 20160809 IS - 1544-5208 (Electronic) IS - 0278-2715 (Linking) VI - 35 IP - 8 DP - 2016 Aug 01 TI - Accounting For Patients' Socioeconomic Status Does Not Change Hospital Readmission Rates. PG - 1461-70 LID - 10.1377/hlthaff.2015.0394 [doi] AB - There is an active public debate about whether patients' socioeconomic status should be included in the readmission measures used to determine penalties in Medicare's Hospital Readmissions Reduction Program (HRRP). Using the current Centers for Medicare and Medicaid Services methodology, we compared risk-standardized readmission rates for hospitals caring for high and low proportions of patients of low socioeconomic status (as defined by their Medicaid status or neighborhood income). We then calculated risk-standardized readmission rates after additionally adjusting for patients' socioeconomic status. Our results demonstrate that hospitals caring for large proportions of patients of low socioeconomic status have readmission rates similar to those of other hospitals. Moreover, readmission rates calculated with and without adjustment for patients' socioeconomic status are highly correlated. Readmission rates of hospitals caring for patients of low socioeconomic status changed by approximately 0.1 percent with adjustment for patients' socioeconomic status, and only 3-4 percent fewer such hospitals reached the threshold for payment penalty in Medicare's HRRP. Overall, adjustment for socioeconomic status does not change hospital results in meaningful ways. CI - Project HOPE-The People-to-People Health Foundation, Inc. FAU - Bernheim, Susannah M AU - Bernheim SM AD - Susannah M. Bernheim (susannah.bernheim@yale.edu) is director of quality measurement at the Center for Outcomes Research and Evaluation (CORE) at Yale-New Haven Hospital and an assistant clinical professor in the Department of Internal Medicine at Yale School of Medicine, both in New Haven, Connecticut. FAU - Parzynski, Craig S AU - Parzynski CS AD - Craig S. Parzynski is a senior statistician at CORE, Yale-New Haven Hospital. FAU - Horwitz, Leora AU - Horwitz L AD - Leora Horwitz is an associate professor of internal medicine, population health, at New York University School of Medicine, in New York City. FAU - Lin, Zhenqiu AU - Lin Z AD - Zhenqiu Lin is director of analytics at CORE, Yale-New Haven Hospital. FAU - Araas, Michael J AU - Araas MJ AD - Michael J. Araas is research project manager at CORE, Yale-New Haven Hospital. FAU - Ross, Joseph S AU - Ross JS AD - Joseph S. Ross is an associate professor of medicine in the Department of Internal Medicine at Yale School of Medicine. FAU - Drye, Elizabeth E AU - Drye EE AD - Elizabeth E. Drye is a director of quality measurement at CORE, Yale-New Haven Hospital. FAU - Suter, Lisa G AU - Suter LG AD - Lisa G. Suter is associate director of quality measurement at CORE, Yale-New Haven Hospital, and an associate professor of medicine in the Section of Rheumatology at Yale School of Medicine. FAU - Normand, Sharon-Lise T AU - Normand SL AD - Sharon-Lise T. Normand is a professor of health care policy and biostatistics at Harvard Medical School and at the Harvard T. H. Chan School of Public Health, both in Boston, Massachusetts. FAU - Krumholz, Harlan M AU - Krumholz HM AD - Harlan M. Krumholz is the Harold H. Hines, Jr. Professor of Medicine and Epidemiology and Public Health at Yale School of Medicine. LA - eng PT - Journal Article PL - United States TA - Health Aff (Millwood) JT - Health affairs (Project Hope) JID - 8303128 SB - IM OTO - NOTNLM OT - Disparities OT - Quality Of Care OT - Safety-Net Systems EDAT- 2016/08/10 06:00 MHDA- 2016/08/10 06:00 CRDT- 2016/08/10 06:00 AID - 35/8/1461 [pii] AID - 10.1377/hlthaff.2015.0394 [doi] PST - ppublish SO - Health Aff (Millwood). 2016 Aug 1;35(8):1461-70. doi: 10.1377/hlthaff.2015.0394. PMID- 27468928 OWN - NLM STAT- In-Data-Review DA - 20160729 LR - 20170220 IS - 2047-9980 (Electronic) IS - 2047-9980 (Linking) VI - 5 IP - 8 DP - 2016 Jul 28 TI - Geographic and Temporal Variation in Cardiac Implanted Electric Devices to Treat Heart Failure. LID - 10.1161/JAHA.116.003532 [doi] LID - e003532 [pii] AB - BACKGROUND: Cardiac implantable electric devices are commonly used to treat heart failure. Little is known about temporal and geographic variation in use of cardiac resynchronization therapy (CRT) devices in usual care settings. METHODS AND RESULTS: We identified new CRT with pacemaker (CRT-P) or defibrillator generators (CRT-D) implanted between 2008 and 2013 in the United States from a commercial claims database. For each implant, we characterized prior medication use, comorbidities, and geography. Among 17 780 patients with CRT devices (median age 69, 31% women), CRT-Ps were a small and increasing share of CRT devices, growing from 12% to 20% in this study period. Compared to CRT-D recipients, CRT-P recipients were older (median age 76 versus 67), and more likely to be female (40% versus 30%). Pre-implant use of beta-blockers and angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers was low in both CRT-D (46%) and CRT-P (31%) patients. The fraction of CRT-P devices among all new implants varied widely across states. Compared to the increasing national trend, the share of CRT-P implants was relatively increasing in Kansas and relatively decreasing in Minnesota and Oregon. CONCLUSIONS: In this large, contemporary heart failure population, CRT-D use dwarfed CRT-P, though the latter nearly doubled over 6 years. Practice patterns vary substantially across states and over time. Medical therapy appears suboptimal in real-world practice. CI - (c) 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell. FAU - Hatfield, Laura A AU - Hatfield LA AD - Harvard Medical School, Boston, MA hatfield@hcp.med.harvard.edu. FAU - Kramer, Daniel B AU - Kramer DB AD - Harvard Medical School, Boston, MA Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Boston, MA. FAU - Volya, Rita AU - Volya R AD - Harvard Medical School, Boston, MA. FAU - Reynolds, Matthew R AU - Reynolds MR AD - Lahey Hospital & Medical Center, Burlington, MA. FAU - Normand, Sharon-Lise T AU - Normand SL AD - Harvard Medical School, Boston, MA Harvard T. H. Chan School of Public Health, Boston, MA. LA - eng GR - K23 AG045963/AG/NIA NIH HHS/United States PT - Journal Article DEP - 20160728 PL - England TA - J Am Heart Assoc JT - Journal of the American Heart Association JID - 101580524 SB - IM PMC - PMC5015279 OTO - NOTNLM OT - defibrillation OT - heart failure OT - pacing OT - population OT - variation EDAT- 2016/07/30 06:00 MHDA- 2016/07/30 06:00 CRDT- 2016/07/30 06:00 AID - JAHA.116.003532 [pii] AID - 10.1161/JAHA.116.003532 [doi] PST - epublish SO - J Am Heart Assoc. 2016 Jul 28;5(8). pii: e003532. doi: 10.1161/JAHA.116.003532. PMID- 27405808 OWN - NLM STAT- In-Data-Review DA - 20160713 LR - 20170220 IS - 2047-9980 (Electronic) IS - 2047-9980 (Linking) VI - 5 IP - 7 DP - 2016 Jul 12 TI - Association Between Hospital Performance on Patient Safety and 30-Day Mortality and Unplanned Readmission for Medicare Fee-for-Service Patients With Acute Myocardial Infarction. LID - 10.1161/JAHA.116.003731 [doi] LID - e003731 [pii] AB - BACKGROUND: Little is known regarding the relationship between hospital performance on adverse event rates and hospital performance on 30-day mortality and unplanned readmission rates for Medicare fee-for-service patients hospitalized for acute myocardial infarction (AMI). METHODS AND RESULTS: Using 2009-2013 medical record-abstracted patient safety data from the Agency for Healthcare Research and Quality's Medicare Patient Safety Monitoring System and hospital mortality and readmission data from the Centers for Medicare & Medicaid Services, we fitted a mixed-effects model, adjusting for hospital characteristics, to evaluate whether hospital performance on patient safety, as measured by the hospital-specific risk-standardized occurrence rate of 21 common adverse event measures for which patients were at risk, is associated with hospital-specific 30-day all-cause risk-standardized mortality and unplanned readmission rates for Medicare patients with AMI. The unit of analysis was at the hospital level. The final sample included 793 acute care hospitals that treated 30 or more Medicare patients hospitalized for AMI and had 40 or more adverse events for which patients were at risk. The occurrence rate of adverse events for which patients were at risk was 3.8%. A 1% point change in the risk-standardized occurrence rate of adverse events was associated with average changes in the same direction of 4.86% points (95% CI, 0.79-8.94) and 3.44% points (95% CI, 0.19-6.68) for the risk-standardized mortality and unplanned readmission rates, respectively. CONCLUSIONS: For Medicare fee-for-service patients discharged with AMI, hospitals with poorer patient safety performance were also more likely to have poorer performance on 30-day all-cause mortality and on unplanned readmissions. CI - (c) 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell. FAU - Wang, Yun AU - Wang Y AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT yunwang@hsph.harvard.edu. FAU - Eldridge, Noel AU - Eldridge N AD - Agency for Healthcare Research and Quality, US Department of Health and Human Services, Rockville, MD. FAU - Metersky, Mark L AU - Metersky ML AD - Division of Pulmonary and Critical Care Medicine, University of Connecticut School of Medicine, Farmington, CT. FAU - Sonnenfeld, Nancy AU - Sonnenfeld N AD - Centers for Medicare & Medicaid Services, US Department of Health and Human Services, Rockville, MD. FAU - Fine, Jonathan M AU - Fine JM AD - Section of Pulmonary and Critical Care Medicine, Norwalk Hospital, Norwalk, CT. FAU - Pandolfi, Michelle M AU - Pandolfi MM AD - Qualidigm, Wethersfield, CT. FAU - Eckenrode, Sheila AU - Eckenrode S AD - Qualidigm, Wethersfield, CT. FAU - Bakullari, Anila AU - Bakullari A AD - Qualidigm, Wethersfield, CT. FAU - Galusha, Deron H AU - Galusha DH AD - Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT. FAU - Jaser, Lisa AU - Jaser L AD - Department of Pharmacy, Griffin Hospital, Derby, CT. FAU - Verzier, Nancy R AU - Verzier NR AD - Qualidigm, Wethersfield, CT. FAU - Nuti, Sudhakar V AU - Nuti SV AD - Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT. FAU - Hunt, David AU - Hunt D AD - Office of the National Coordinator for Health Information Technology, US Department of Health and Human Services, Rockville, MD. FAU - Normand, Sharon-Lise T AU - Normand SL AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA Department of Health Care Policy, Harvard Medical School, Boston, MA. FAU - Krumholz, Harlan M AU - Krumholz HM AD - Section of Cardiovascular Medicine and the Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT Department of Health Policy and Management, Yale School of Public Health, New Haven, CT Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT. LA - eng GR - K12 HS023000/HS/AHRQ HHS/United States PT - Journal Article DEP - 20160712 PL - England TA - J Am Heart Assoc JT - Journal of the American Heart Association JID - 101580524 SB - IM PMC - PMC5015406 OTO - NOTNLM OT - Medicare OT - mortality OT - myocardial infarction OT - patient safety OT - readmission EDAT- 2016/07/15 06:00 MHDA- 2016/07/15 06:00 CRDT- 2016/07/14 06:00 AID - JAHA.116.003731 [pii] AID - 10.1161/JAHA.116.003731 [doi] PST - epublish SO - J Am Heart Assoc. 2016 Jul 12;5(7). pii: e003731. doi: 10.1161/JAHA.116.003731. PMID- 27385247 OWN - NLM STAT- In-Data-Review DA - 20160707 LR - 20160707 IS - 1544-5208 (Electronic) IS - 0278-2715 (Linking) VI - 35 IP - 7 DP - 2016 Jul 01 TI - Declining Admission Rates And Thirty-Day Readmission Rates Positively Associated Even Though Patients Grew Sicker Over Time. PG - 1294-302 LID - 10.1377/hlthaff.2015.1614 [doi] AB - Programs from the Centers for Medicare and Medicaid Services simultaneously promote strategies to lower hospital admissions and readmissions. However, there is concern that hospitals in communities that successfully reduce admissions may be penalized, as patients that are ultimately hospitalized may be sicker and at higher risk of readmission. We therefore examined the relationship between changes from 2010 to 2013 in admission rates and thirty-day readmission rates for elderly Medicare beneficiaries. We found that communities with the greatest decline in admission rates also had the greatest decline in thirty-day readmission rates, even though hospitalized patients did grow sicker as admission rates declined. The relationship between changing admission and readmission rates persisted in models that measured observed readmission rates, risk-standardized readmission rates, and the combined rate of readmission and death. Our findings suggest that communities can reduce admission rates and readmission rates in parallel, and that federal policy incentivizing reductions in both outcomes does not create contradictory incentives. CI - Project HOPE-The People-to-People Health Foundation, Inc. FAU - Dharmarajan, Kumar AU - Dharmarajan K AD - Kumar Dharmarajan (kumar.dharmarajan@yale.edu) is an assistant professor of medicine at the Yale University School of Medicine, in New Haven, Connecticut. FAU - Qin, Li AU - Qin L AD - Li Qin is an associate research scientist in medicine at the Yale University School of Medicine. FAU - Lin, Zhenqiu AU - Lin Z AD - Zhenqiu Lin is director of analytics at the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital. FAU - Horwitz, Leora I AU - Horwitz LI AD - Leora I. Horwitz is an associate professor at the New York University School of Medicine, in New York City. FAU - Ross, Joseph S AU - Ross JS AD - Joseph S. Ross is an associate professor of medicine at the Yale University School of Medicine. FAU - Drye, Elizabeth E AU - Drye EE AD - Elizabeth E. Drye is director of quality measurement programs at the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital. FAU - Keshawarz, Amena AU - Keshawarz A AD - Amena Keshawarz is a doctoral candidate in epidemiology at the University of Colorado, Anschutz Medical Campus, in Aurora. At the time this research was performed, she was a research project coordinator at the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital. FAU - Altaf, Faseeha AU - Altaf F AD - Faseeha Altaf is a research project coordinator at the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital. FAU - Normand, Sharon-Lise T AU - Normand SL AD - Sharon-Lise T. Normand is a professor of health care policy (biostatistics) at Harvard Medical School and a professor of biostatistics at the Harvard T. H. Chan School of Public Health, both in Boston, Massachusetts. FAU - Krumholz, Harlan M AU - Krumholz HM AD - Harlan M. Krumholz is the Harold H. Hines Jr. Professor of Medicine and Epidemiology and Public Health at the Yale University School of Medicine. FAU - Bernheim, Susannah M AU - Bernheim SM AD - Susannah M. Bernheim is director of quality measurement programs at the Center for Outcomes Research and Evaluation at Yale-New Haven Hospital. LA - eng PT - Journal Article PL - United States TA - Health Aff (Millwood) JT - Health affairs (Project Hope) JID - 8303128 SB - IM OTO - NOTNLM OT - Epidemiology OT - Health Promotion/Disease Prevention OT - Hospitals OT - Medicare OT - Quality Of Care EDAT- 2016/07/08 06:00 MHDA- 2016/07/08 06:00 CRDT- 2016/07/08 06:00 AID - 35/7/1294 [pii] AID - 10.1377/hlthaff.2015.1614 [doi] PST - ppublish SO - Health Aff (Millwood). 2016 Jul 1;35(7):1294-302. doi: 10.1377/hlthaff.2015.1614. TI - League Tables for Hospital Comparisons DP - 2016 06 AU - Normand ST AU - Ash AS AU - Fienberg SE AU - Stukel TA AU - Utts J AU - Louis TA PG - 392 DO - 10.1146/annurev-statistics-022513-115617 TA - Annual Review of Statistics and Its Application VI - 3 IP - 1 PMID- 27453458 OWN - NLM STAT- MEDLINE DA - 20160725 DCOM- 20170103 LR - 20170220 IS - 1091-4358 (Print) IS - 1099-176X (Linking) VI - 19 IP - 2 DP - 2016 Jun TI - Regional Variation in Physician Adoption of Antipsychotics: Impact on US Medicare Expenditures. PG - 69-78 AB - BACKGROUND: Regional variation in US Medicare prescription drug spending is driven by higher prescribing of costly brand-name drugs in some regions. This variation likely arises from differences in the speed of diffusion of newly-approved medications. Second-generation antipsychotics were widely adopted for treatment of severe mental illness and for several off-label uses. Rapid diffusion of new psychiatric drugs likely increases drug spending but its relationship to non-drug spending is unclear. The impact of antipsychotic diffusion on drug and medical spending is of great interest to public payers like Medicare, which finance a majority of mental health spending in the US. AIMS: We examine the association between physician adoption of new antipsychotics and antipsychotic spending and non-drug medical spending among disabled and elderly Medicare enrollees. METHODS: We linked physician-level data on antipsychotic prescribing from an all-payer dataset (IMS Health's XponentTM) to patient-level data from Medicare. Our physician sample included 16,932 US. psychiatrists and primary care providers with > 10 antipsychotic prescriptions per year from 1997-2011. We constructed a measure of physician adoption of 3 antipsychotics introduced during this period (quetiapine, ziprasidone and aripiprazole) by estimating a shared frailty model of the time to first prescription for each drug. We then assigned physicians to one of 306 U.S. hospital referral regions (HRRs) and measured the average propensity to adopt per region. Using 2010 data for a random sample of 1.6 million Medicare beneficiaries, we identified 138,680 antipsychotic users. A generalized linear model with gamma distribution and log link was used to estimate the effect of region-level adoption propensity on beneficiary-level antipsychotic spending and non-drug medical spending adjusting for patient demographic and socioeconomic characteristics, health status, eligibility category, and whether the antipsychotic was for an on- vs. off-label use. RESULTS: In our sample, mean patient age was 62 years, 42% were male, and 86% had low-income. Half of antipsychotic users in Medicare had an on-label indication. The weighted average propensity to adopt the three new antipsychotics varied four-fold across HRRs. For every one standard deviation increase in the propensity to adopt there was a 5% increase in antipsychotic spending after adjusting for covariates (adjusted ratio of spending 1.05, 95% CI 1.01-1.08, p = 0.005). Physician propensity to adopt new antipsychotics was not associated with non-drug medical spending (adjusted ratio 0.96, 95% CI 0.91-1.01, p < 0.117). DISCUSSION: These findings suggest wide regional variation in physicians' propensity to adopt new antipsychotic medications. While physician adoption of new antipsychotics was positively associated with antipsychotic expenditures, it was not associated with non-drug spending. Our analysis is limited to Medicare and may not generalize to other payers. Also, claims data do not allow for the measurement of health outcomes, which would be important to evaluate when calculating the value of rapid vs. slow technology adoption. FAU - Donohue, Julie M AU - Donohue JM AD - Department of Health Policy and Management, University of Pittsburgh, 130 DeSoto Street, A613, Pittsburgh, PA 15261, USA, jdonohue@pitt.edu. FAU - Normand, Sharon-Lise T AU - Normand SL FAU - Horvitz-Lennon, Marcela AU - Horvitz-Lennon M FAU - Men, Aiju AU - Men A FAU - Berndt, Ernst R AU - Berndt ER FAU - Huskamp, Haiden A AU - Huskamp HA LA - eng GR - R01 MH093359/MH/NIMH NIH HHS/United States PT - Journal Article PT - Research Support, N.I.H., Extramural PL - Italy TA - J Ment Health Policy Econ JT - The journal of mental health policy and economics JID - 9815374 RN - 0 (Antipsychotic Agents) SB - IM MH - Adult MH - Aged MH - Antipsychotic Agents/*therapeutic use MH - Female MH - Health Expenditures/*statistics & numerical data MH - Humans MH - Male MH - Medicare/*statistics & numerical data MH - Middle Aged MH - Practice Patterns, Physicians'/*statistics & numerical data MH - United States PMC - PMC5020418 MID - NIHMS814418 OID - NLM: NIHMS814418 OID - NLM: PMC5020418 EDAT- 2016/07/28 06:00 MHDA- 2017/01/04 06:00 CRDT- 2016/07/26 06:00 PHST- 2015/10/27 [received] PHST- 2016/05/09 [accepted] PST - ppublish SO - J Ment Health Policy Econ. 2016 Jun;19(2):69-78. PMID- 27199062 OWN - NLM STAT- In-Data-Review DA - 20160520 LR - 20170220 IS - 1558-3597 (Electronic) IS - 0735-1097 (Linking) VI - 67 IP - 20 DP - 2016 May 24 TI - Association of Guideline-Based Admission Treatments and Life Expectancy After Myocardial Infarction in Elderly Medicare Beneficiaries. PG - 2378-91 LID - 10.1016/j.jacc.2016.03.507 [doi] LID - S0735-1097(16)01693-4 [pii] AB - BACKGROUND: Guideline-based admission therapies for acute myocardial infarction (AMI) significantly improve 30-day survival, but little is known about their association with long-term outcomes. OBJECTIVES: This study evaluated the association of 5 AMI admission therapies (aspirin, beta-blockers, acute reperfusion therapy, door-to-balloon [D2B] time 90 min, and door-to-needle (D2N) times 65, its use among older ICD recipients is unknown. METHODS AND RESULTS: Medicare patients aged >65 matched to data in the National Cardiovascular Data Registry - ICD Registry from January 1, 2006 to March 31, 2010 were eligible for analysis (N=194 969). The proportion of ICD recipients enrolled in hospice, cumulative incidence of hospice admission, and factors associated with time to hospice enrollment were evaluated. Five years after device implantation, 50.9% of patients were either deceased or in hospice. Among decedents, 36.8% received hospice services. The cumulative incidence of hospice enrollment, accounting for the competing risk of death, was 4.7% (95% confidence interval [CI], 4.6%-4.8%) within 1 year and 21.3% (95% CI, 20.7%-21.8%) at 5 years. Factors most strongly associated with shorter time to hospice enrollment were older age (adjusted hazard ratio, 1.77; 95% CI, 1.73-1.81), class IV heart failure (versus class I; adjusted hazard ratio, 1.79; 95% CI, 1.66-1.94); ejection fraction <20 (adjusted hazard ratio, 1.57; 95% CI, 1.48-1.67), and greater hospice use among decedents in the patients' health referral region. CONCLUSIONS: More than one-third of older patients dying with ICDs receive hospice care. Five years after implantation, half of older ICD recipients are either dead or in hospice. Hospice providers should be prepared for ICD patients, whose clinical trajectories and broader palliative care needs require greater focus. CI - (c) 2016 The Authors. FAU - Kramer, Daniel B AU - Kramer DB AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). dkramer@bidmc.harvard.edu. FAU - Reynolds, Matthew R AU - Reynolds MR AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). FAU - Normand, Sharon-Lise AU - Normand SL AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). FAU - Parzynski, Craig S AU - Parzynski CS AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). FAU - Spertus, John A AU - Spertus JA AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). FAU - Mor, Vincent AU - Mor V AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). FAU - Mitchell, Susan L AU - Mitchell SL AD - From Hebrew SeniorLife Institute for Aging Research, Boston MA (D.B.K., S.L.M.); Smith Center for Outcomes Research in Cardiology, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston MA (D.B.K.); Harvard Medical School, Boston MA (D.B.K., S.L.M.); Harvard Clinical Research Institute, Boston MA (M.R.R.); Lahey Hospital & Medical Center, Burlington, MA (M.R.R.); Department of Health Care Policy, Harvard Medical School, Boston, MA (S.-L.N.); Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA (S.-L.N.); Center for Outcomes Research and Evaluation, Yale New Haven Hospital, Yale University, New Haven, CT (C.S.P.); Mid America Heart Institute, Kansas City, MO (J.A.S.); and Department of Health Services, Policy & Practice, Center for Gerontology and Health Care Research, Brown University School of Public Health, Providence, RI (V.M.). LA - eng GR - K23 AG045963/AG/NIA NIH HHS/United States GR - K24 AG033640/AG/NIA NIH HHS/United States GR - U01 FD004493/FD/FDA HHS/United States PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't DEP - 20160325 PL - United States TA - Circulation JT - Circulation JID - 0147763 SB - AIM SB - IM MH - Aged MH - Aged, 80 and over MH - Cohort Studies MH - Databases, Factual/*trends MH - Death, Sudden, Cardiac/epidemiology MH - Defibrillators, Implantable/adverse effects/*trends MH - Female MH - Follow-Up Studies MH - Hospices/*trends/*utilization MH - Humans MH - Male MH - Mortality/trends MH - *Registries PMC - PMC4872640 OID - NLM: PMC4872640 OTO - NOTNLM OT - defibrillators, implantable OT - health services research OT - heart failure OT - patient outcome assessment EDAT- 2016/03/27 06:00 MHDA- 2016/12/15 06:00 CRDT- 2016/03/27 06:00 PHST- 2015/12/01 [received] PHST- 2016/03/23 [accepted] AID - CIRCULATIONAHA.115.020677 [pii] AID - 10.1161/CIRCULATIONAHA.115.020677 [doi] PST - ppublish SO - Circulation. 2016 May 24;133(21):2030-7. doi: 10.1161/CIRCULATIONAHA.115.020677. Epub 2016 Mar 25. PMID- 27482121 OWN - NLM STAT- Publisher DA - 20160802 LR - 20170220 IS - 0003-1305 (Print) IS - 0003-1305 (Linking) VI - 70 IP - 1 DP - 2016 Mar 31 TI - The Central Role of Bayes' Theorem for Joint Estimation of Causal Effects and Propensity Scores. PG - 47-54 AB - Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of Bayes theorem, which presupposes a full probability model for the observed data that adheres to the likelihood principle. The goal of this paper is to explicate this fundamental feature of Bayesian estimation of causal effects with propensity scores in order to provide context for the existing literature and for future work on this important topic. FAU - Zigler, Corwin Matthew AU - Zigler CM AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health. LA - eng GR - P01 CA134294/CA/NCI NIH HHS/United States GR - R01 GM111339/GM/NIGMS NIH HHS/United States PT - Journal Article DEP - 20151214 PL - England TA - Am Stat JT - The American statistician JID - 0070454 PMC - PMC4962881 MID - NIHMS770928 OTO - NOTNLM OT - Bayesian estimation OT - Causal inference OT - Model feedback EDAT- 2016/08/03 06:00 MHDA- 2016/08/03 06:00 CRDT- 2016/08/03 06:00 PMCR- 2017/03/31 AID - 10.1080/00031305.2015.1111260 [doi] PST - ppublish SO - Am Stat. 2016 Mar 31;70(1):47-54. Epub 2015 Dec 14. PMID- 26831441 OWN - NLM STAT- MEDLINE DA - 20160202 DCOM- 20160530 LR - 20170202 IS - 1524-4539 (Electronic) IS - 0009-7322 (Linking) VI - 133 IP - 5 DP - 2016 Feb 02 TI - Letter by Wasfy et al Regarding Article, "Facility Level Variation in Hospitalization, Mortality, and Costs in the 30 Days After Percutaneous Coronary Intervention: Insights on Short-Term Healthcare Value From the Veterans Affairs Clinical Assessment, Reporting, and Tracking System (VA CART) Program". PG - e376 LID - 10.1161/CIRCULATIONAHA.115.017851 [doi] FAU - Wasfy, Jason H AU - Wasfy JH AD - Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA. FAU - Dominici, Francesca AU - Dominici F AD - Harvard T.H. Chan School of Public Health, Boston, MA. FAU - Yeh, Robert W AU - Yeh RW AD - The Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA. LA - eng GR - K23 HL118138/HL/NHLBI NIH HHS/United States GR - P01 CA134294/CA/NCI NIH HHS/United States GR - R01 GM111339/GM/NIGMS NIH HHS/United States PT - Comment PT - Letter PL - United States TA - Circulation JT - Circulation JID - 0147763 SB - AIM SB - IM CON - Circulation. 2015 Jul 14;132(2):101-8. PMID: 25951833 CIN - Circulation. 2016 Feb 2;133(5):e377. PMID: 26831442 MH - Female MH - Hospital Costs/*standards MH - *Hospitalization MH - Humans MH - Male MH - Patient Identification Systems/*standards MH - Percutaneous Coronary Intervention/*mortality MH - United States Department of Veterans Affairs/*standards MH - *Veterans PMC - PMC4827713 MID - NIHMS748250 OID - NLM: NIHMS748250 [Available on 02/02/17] OID - NLM: PMC4827713 [Available on 02/02/17] EDAT- 2016/02/03 06:00 MHDA- 2016/05/31 06:00 CRDT- 2016/02/03 06:00 AID - CIRCULATIONAHA.115.017851 [pii] AID - 10.1161/CIRCULATIONAHA.115.017851 [doi] PST - ppublish SO - Circulation. 2016 Feb 2;133(5):e376. doi: 10.1161/CIRCULATIONAHA.115.017851.