Accurately Predicting Cardiovascular Risk—and Acting on It
Authors: Dhruv S. Kazi, Kirsten Bibbins-Domingo
Journal: Annals of Internal Medicine (2019)

Editorials7 January 2020Accurately Predicting Cardiovascular Risk—and Acting on ItDhruv S. Kazi, MD, MSc, MS and Kirsten Bibbins-Domingo, PhD, MD, MASDhruv S. Kazi, MD, MSc, MSRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, and Harvard Medical School Boston, Massachusetts (D.S.K.)Search for more papers by this author and Kirsten Bibbins-Domingo, PhD, MD, MASUniversity of California, San Francisco San Francisco, California (K.B.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M19-3662 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail “Prediction is hazardous,” goes the adage, “especially about the future.” Yet, identification of patients' risk for future adverse events is a mainstay of cardiology and, more broadly, a goal of health services research. Models help clinicians and others predict clinical outcomes (for example, 6-month survival after an index hospitalization) or health care costs (for example, total spending in the first year after a stroke), examine risk over varying time horizons, and evaluate risk in subpopulations of interest. Prediction models can help inform patients about expected prognosis as they make clinical decisions or help clinicians target preventive interventions toward persons at ...References1. Dodson JA, Hajduk A, Geda M, et al. Predicting 6-month mortality for older adults hospitalized with acute myocardial infarction. A cohort study. Ann Intern Med. 2020;172:12-21. doi:10.7326/M19-0974 LinkGoogle Scholar2. Kundi H, Wadhera RK, Strom JB, et al. Association of frailty with 30-day outcomes for acute myocardial infarction, heart failure, and pneumonia among elderly adults. JAMA Cardiol. 2019. [PMID: 31553402] doi:10.1001/jamacardio.2019.3511 CrossrefMedlineGoogle Scholar3. Kundi H, Valsdottir LR, Popma JJ, et al. Impact of a claims-based frailty indicator on the prediction of long-term mortality after transcatheter aortic valve replacement in Medicare beneficiaries. Circ Cardiovasc Qual Outcomes. 2018;11:e005048. [PMID: 30354574] doi:10.1161/CIRCOUTCOMES.118.005048 CrossrefMedlineGoogle Scholar4. The National Academies of Sciences, Engineering, and Medicine. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health. Washington, DC: National Academies Pr; 2019. Google Scholar Author, Article, and Disclosure InformationAffiliations: Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, and Harvard Medical School Boston, Massachusetts (D.S.K.)University of California, San Francisco San Francisco, California (K.B.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-3662.Corresponding Author: Kirsten Bibbins-Domingo, PhD, MD, MAS, Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Box #0560, San Francisco, CA 94158; e-mail, Kirsten.[email protected]edu.Current Author Addresses: Dr. Kazi: Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, 375 Longwood Avenue, 4th Floor, Boston, MA 02215.Dr. Bibbins-Domingo: Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Box #0560, San Francisco, CA 94158.This article was published at Annals.org on 10 December 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPredicting 6-Month Mortality for Older Adults Hospitalized With Acute Myocardial Infarction John A. Dodson , Alexandra M. Hajduk , Mary Geda , Harlan M. Krumholz , Terrence E. Murphy , Sui Tsang , Mary E. Tinetti , Michael G. Nanna , Richard McNamara , Thomas M. Gill , and Sarwat I. Chaudhry Metrics Cited byRussian Society for the Prevention of Noncommunicable Diseases (ROPNIZ). Alimentary-dependent risk factors for chronic non-communicable diseases and eating habits: dietary correction within the framework of preventive counseling. Methodological GuidelinesUse of Machine Learning Models to Predict Death After Acute Myocardial Infarction 7 January 2020Volume 172, Issue 1Page: 61-62KeywordsDecision makingElderlyElectronic medical recordsForecastingFrailtyHearingHospitalizationsMortalityMyocardial infarctionVision ePublished: 10 December 2019 Issue Published: 7 January 2020 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...