Predicting 30-Day Mortality for Patients With Acute Heart Failure in the Emergency Department
Authors: Saeid Safiri, Jamal Hallajzadeh, Erfan Ayubi
Journal: Annals of Internal Medicine (2018)

Letters19 June 2018Predicting 30-Day Mortality for Patients With Acute Heart Failure in the Emergency DepartmentSaeid Safiri, PhD, Jamal Hallajzadeh, PhD, and Erfan Ayubi, PhDSaeid Safiri, PhDMaragheh University of Medical Sciences, Maragheh, Iran (S.S., J.H.)Search for more papers by this author, Jamal Hallajzadeh, PhDMaragheh University of Medical Sciences, Maragheh, Iran (S.S., J.H.)Search for more papers by this author, and Erfan Ayubi, PhDShahid Beheshti University of Medical Sciences, Tehran, Iran (E.A.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L18-0095 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Miró and colleagues (1) aimed to predict 30-day mortality for patients with acute heart failure in the emergency department. They identified 13 independent risk factors and combined these into an overall score, the Multiple Estimation of risk based on the Emergency department Spanish Score In patients with AHF (MEESSI-AHF). They found that this score was an excellent predictor of 30-day mortality. Although their investigation has been valuable, some methodological issues need to be addressed.First, scientists usually use combined outcomes, such as all-cause mortality, to improve the power of statistical tests. However, the authors mostly neglect the ...References1. Miró Ò, Rossello X, Gil V, Martín-Sánchez FJ, Llorens P, Herrero-Puente P, et al; ICA-SEMES Research Group. Predicting 30-day mortality for patients with acute heart failure in the emergency department: a cohort study. Ann Intern Med. 2017;167:698-705. [PMID: 28973663]. doi:10.7326/M16-2726 LinkGoogle Scholar2. Richardson DB, Hamra GB, MacLehose RF, Cole SR, Chu H. Hierarchical regression for analyses of multiple outcomes. Am J Epidemiol. 2015;182:459-67. [PMID: 26232395] doi:10.1093/aje/kwv047 CrossrefMedlineGoogle Scholar3. Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;359:j4587. [PMID: 29038130] doi:10.1136/bmj.j4587 CrossrefMedlineGoogle Scholar4. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837-45. [PMID: 3203132] CrossrefMedlineGoogle Scholar5. Jewell NP. Statistics for Epidemiology. Boca Raton: CRC Pr; 2003. Google Scholar Author, Article, and Disclosure InformationAffiliations: Maragheh University of Medical Sciences, Maragheh, Iran (S.S., J.H.)Shahid Beheshti University of Medical Sciences, Tehran, Iran (E.A.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L18-0095. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPredicting 30-Day Mortality for Patients With Acute Heart Failure in the Emergency Department Òscar Miró , Xavier Rossello , Víctor Gil , Francisco Javier Martín-Sánchez , Pere Llorens , Pablo Herrero-Puente , Javier Jacob , Héctor Bueno , Stuart J. Pocock , and Predicting 30-Day Mortality for Patients With Acute Heart Failure in the Emergency Department Óscar Miró and Stuart J. Pocock Metrics 19 June 2018Volume 168, Issue 12Page: 898-899KeywordsDisclosureEmergency departmentEpidemiologyHeart failureMedical risk factorsMortalityPotassiumScientistsStatistical methodsSystolic pressure ePublished: 19 June 2018 Issue Published: 19 June 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...