Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data
Early rehospitalization after discharge for an acute coronary syndrome, including acute myocardial infarction (AMI), is generally considered undesirable. The Centers for Medicare and Medicaid Services (CMS) base hospital financial incentives on risk-adjusted readmission rates after AMI, using claims...
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creator | McManus, David D., MD, ScM Saczynski, Jane S., PhD Lessard, Darleen, MS Waring, Molly E., PhD Allison, Jeroan, MD, MS Parish, David C., MD Goldberg, Robert J., PhD Ash, Arlene, PhD Kiefe, Catarina I., MD, PhD |
description | Early rehospitalization after discharge for an acute coronary syndrome, including acute myocardial infarction (AMI), is generally considered undesirable. The Centers for Medicare and Medicaid Services (CMS) base hospital financial incentives on risk-adjusted readmission rates after AMI, using claims data in its adjustment models. Little is known about the contribution to readmission risk of factors not captured by claims. For 804 consecutive patients >65 years discharged in 2011 to 2013 from 6 hospitals in Massachusetts and Georgia after an acute coronary syndrome, we compared a CMS-like readmission prediction model with an enhanced model incorporating additional clinical, psychosocial, and sociodemographic characteristics, after principal components analysis. Mean age was 73 years, 38% were women, 25% college educated, and 32% had a previous AMI; all-cause rehospitalization occurred within 30 days for 13%. In the enhanced model, previous coronary intervention (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.34 to 3.16; chronic kidney disease OR 1.89, 95% CI 1.15 to 3.10; low health literacy OR 1.75, 95% CI 1.14 to 2.69), lower serum sodium levels, and current nonsmoker status were positively associated with readmission. The discriminative ability of the enhanced versus the claims-based model was higher without evidence of overfitting. For example, for patients in the highest deciles of readmission likelihood, observed readmissions occurred in 24% for the claims-based model and 33% for the enhanced model. In conclusion, readmission may be influenced by measurable factors not in CMS′ claims-based models and not controllable by hospitals. Incorporating additional factors into risk-adjusted readmission models may improve their accuracy and validity for use as indicators of hospital quality. |
doi_str_mv | 10.1016/j.amjcard.2015.11.034 |
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The Centers for Medicare and Medicaid Services (CMS) base hospital financial incentives on risk-adjusted readmission rates after AMI, using claims data in its adjustment models. Little is known about the contribution to readmission risk of factors not captured by claims. For 804 consecutive patients >65 years discharged in 2011 to 2013 from 6 hospitals in Massachusetts and Georgia after an acute coronary syndrome, we compared a CMS-like readmission prediction model with an enhanced model incorporating additional clinical, psychosocial, and sociodemographic characteristics, after principal components analysis. Mean age was 73 years, 38% were women, 25% college educated, and 32% had a previous AMI; all-cause rehospitalization occurred within 30 days for 13%. In the enhanced model, previous coronary intervention (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.34 to 3.16; chronic kidney disease OR 1.89, 95% CI 1.15 to 3.10; low health literacy OR 1.75, 95% CI 1.14 to 2.69), lower serum sodium levels, and current nonsmoker status were positively associated with readmission. The discriminative ability of the enhanced versus the claims-based model was higher without evidence of overfitting. For example, for patients in the highest deciles of readmission likelihood, observed readmissions occurred in 24% for the claims-based model and 33% for the enhanced model. In conclusion, readmission may be influenced by measurable factors not in CMS′ claims-based models and not controllable by hospitals. Incorporating additional factors into risk-adjusted readmission models may improve their accuracy and validity for use as indicators of hospital quality.</description><identifier>ISSN: 0002-9149</identifier><identifier>EISSN: 1879-1913</identifier><identifier>DOI: 10.1016/j.amjcard.2015.11.034</identifier><identifier>PMID: 26718235</identifier><identifier>CODEN: AJCDAG</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Acute Coronary Syndrome - epidemiology ; Acute Coronary Syndrome - therapy ; Acute coronary syndromes ; Aged ; Cardiovascular ; Disease Management ; Female ; Follow-Up Studies ; Heart attacks ; Hospitalization ; Humans ; Incidence ; Insurance Claim Review - statistics & numerical data ; Male ; Medical records ; Medicare - statistics & numerical data ; Patient Discharge - statistics & numerical data ; Patient Readmission - statistics & numerical data ; Reproducibility of Results ; Retrospective Studies ; Risk Factors ; Time Factors ; United States - epidemiology</subject><ispartof>The American journal of cardiology, 2016-02, Vol.117 (4), p.501-507</ispartof><rights>Elsevier Inc.</rights><rights>2016 Elsevier Inc.</rights><rights>Copyright © 2016 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Feb 15, 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c620t-cb2448aedf01bdcf31df1140ac438f9a447f1ecbc777ed931e6eeee7b86300c43</citedby><cites>FETCH-LOGICAL-c620t-cb2448aedf01bdcf31df1140ac438f9a447f1ecbc777ed931e6eeee7b86300c43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1763003416?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26718235$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McManus, David D., MD, ScM</creatorcontrib><creatorcontrib>Saczynski, Jane S., PhD</creatorcontrib><creatorcontrib>Lessard, Darleen, MS</creatorcontrib><creatorcontrib>Waring, Molly E., PhD</creatorcontrib><creatorcontrib>Allison, Jeroan, MD, MS</creatorcontrib><creatorcontrib>Parish, David C., MD</creatorcontrib><creatorcontrib>Goldberg, Robert J., PhD</creatorcontrib><creatorcontrib>Ash, Arlene, PhD</creatorcontrib><creatorcontrib>Kiefe, Catarina I., MD, PhD</creatorcontrib><creatorcontrib>TRACE-CORE Investigators</creatorcontrib><title>Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data</title><title>The American journal of cardiology</title><addtitle>Am J Cardiol</addtitle><description>Early rehospitalization after discharge for an acute coronary syndrome, including acute myocardial infarction (AMI), is generally considered undesirable. The Centers for Medicare and Medicaid Services (CMS) base hospital financial incentives on risk-adjusted readmission rates after AMI, using claims data in its adjustment models. Little is known about the contribution to readmission risk of factors not captured by claims. For 804 consecutive patients >65 years discharged in 2011 to 2013 from 6 hospitals in Massachusetts and Georgia after an acute coronary syndrome, we compared a CMS-like readmission prediction model with an enhanced model incorporating additional clinical, psychosocial, and sociodemographic characteristics, after principal components analysis. Mean age was 73 years, 38% were women, 25% college educated, and 32% had a previous AMI; all-cause rehospitalization occurred within 30 days for 13%. In the enhanced model, previous coronary intervention (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.34 to 3.16; chronic kidney disease OR 1.89, 95% CI 1.15 to 3.10; low health literacy OR 1.75, 95% CI 1.14 to 2.69), lower serum sodium levels, and current nonsmoker status were positively associated with readmission. The discriminative ability of the enhanced versus the claims-based model was higher without evidence of overfitting. For example, for patients in the highest deciles of readmission likelihood, observed readmissions occurred in 24% for the claims-based model and 33% for the enhanced model. In conclusion, readmission may be influenced by measurable factors not in CMS′ claims-based models and not controllable by hospitals. Incorporating additional factors into risk-adjusted readmission models may improve their accuracy and validity for use as indicators of hospital quality.</description><subject>Acute Coronary Syndrome - epidemiology</subject><subject>Acute Coronary Syndrome - therapy</subject><subject>Acute coronary syndromes</subject><subject>Aged</subject><subject>Cardiovascular</subject><subject>Disease Management</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Heart attacks</subject><subject>Hospitalization</subject><subject>Humans</subject><subject>Incidence</subject><subject>Insurance Claim Review - statistics & numerical data</subject><subject>Male</subject><subject>Medical records</subject><subject>Medicare - statistics & numerical data</subject><subject>Patient Discharge - statistics & numerical data</subject><subject>Patient Readmission - statistics & numerical data</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Time Factors</subject><subject>United States - 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epidemiology</topic><topic>Acute Coronary Syndrome - therapy</topic><topic>Acute coronary syndromes</topic><topic>Aged</topic><topic>Cardiovascular</topic><topic>Disease Management</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Heart attacks</topic><topic>Hospitalization</topic><topic>Humans</topic><topic>Incidence</topic><topic>Insurance Claim Review - statistics & numerical data</topic><topic>Male</topic><topic>Medical records</topic><topic>Medicare - statistics & numerical data</topic><topic>Patient Discharge - statistics & numerical data</topic><topic>Patient Readmission - statistics & numerical data</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Time Factors</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McManus, David D., MD, ScM</creatorcontrib><creatorcontrib>Saczynski, Jane S., PhD</creatorcontrib><creatorcontrib>Lessard, Darleen, MS</creatorcontrib><creatorcontrib>Waring, Molly E., PhD</creatorcontrib><creatorcontrib>Allison, Jeroan, MD, MS</creatorcontrib><creatorcontrib>Parish, David C., MD</creatorcontrib><creatorcontrib>Goldberg, Robert J., PhD</creatorcontrib><creatorcontrib>Ash, Arlene, PhD</creatorcontrib><creatorcontrib>Kiefe, Catarina I., MD, PhD</creatorcontrib><creatorcontrib>TRACE-CORE Investigators</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing and Allied Health Source</collection><collection>Physical Education Index</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Research Library</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The American journal of cardiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McManus, David D., MD, ScM</au><au>Saczynski, Jane S., PhD</au><au>Lessard, Darleen, MS</au><au>Waring, Molly E., PhD</au><au>Allison, Jeroan, MD, MS</au><au>Parish, David C., MD</au><au>Goldberg, Robert J., PhD</au><au>Ash, Arlene, PhD</au><au>Kiefe, Catarina I., MD, PhD</au><aucorp>TRACE-CORE Investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data</atitle><jtitle>The American journal of cardiology</jtitle><addtitle>Am J Cardiol</addtitle><date>2016-02-15</date><risdate>2016</risdate><volume>117</volume><issue>4</issue><spage>501</spage><epage>507</epage><pages>501-507</pages><issn>0002-9149</issn><eissn>1879-1913</eissn><coden>AJCDAG</coden><abstract>Early rehospitalization after discharge for an acute coronary syndrome, including acute myocardial infarction (AMI), is generally considered undesirable. The Centers for Medicare and Medicaid Services (CMS) base hospital financial incentives on risk-adjusted readmission rates after AMI, using claims data in its adjustment models. Little is known about the contribution to readmission risk of factors not captured by claims. For 804 consecutive patients >65 years discharged in 2011 to 2013 from 6 hospitals in Massachusetts and Georgia after an acute coronary syndrome, we compared a CMS-like readmission prediction model with an enhanced model incorporating additional clinical, psychosocial, and sociodemographic characteristics, after principal components analysis. Mean age was 73 years, 38% were women, 25% college educated, and 32% had a previous AMI; all-cause rehospitalization occurred within 30 days for 13%. In the enhanced model, previous coronary intervention (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.34 to 3.16; chronic kidney disease OR 1.89, 95% CI 1.15 to 3.10; low health literacy OR 1.75, 95% CI 1.14 to 2.69), lower serum sodium levels, and current nonsmoker status were positively associated with readmission. The discriminative ability of the enhanced versus the claims-based model was higher without evidence of overfitting. For example, for patients in the highest deciles of readmission likelihood, observed readmissions occurred in 24% for the claims-based model and 33% for the enhanced model. In conclusion, readmission may be influenced by measurable factors not in CMS′ claims-based models and not controllable by hospitals. Incorporating additional factors into risk-adjusted readmission models may improve their accuracy and validity for use as indicators of hospital quality.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>26718235</pmid><doi>10.1016/j.amjcard.2015.11.034</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Acute Coronary Syndrome - epidemiology Acute Coronary Syndrome - therapy Acute coronary syndromes Aged Cardiovascular Disease Management Female Follow-Up Studies Heart attacks Hospitalization Humans Incidence Insurance Claim Review - statistics & numerical data Male Medical records Medicare - statistics & numerical data Patient Discharge - statistics & numerical data Patient Readmission - statistics & numerical data Reproducibility of Results Retrospective Studies Risk Factors Time Factors United States - epidemiology |
title | Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data |
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