Parag Goyal1, Budhaditya Bose2, Ruth Masterson Creber2, Udhay Krishnan3, Mei Yang4, Joanne Brady4, Jyotishman Pathak2. 1. Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY; Division of General Internal Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York. Electronic address: pag9051@med.cornell.edu. 2. Department of Population Health Sciences, Weill Cornell Medicine, New York, New York. 3. Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY. 4. Merck & Co., Kenilworth, NJ, USA.
Abstract
BACKGROUND: There is interest in leveraging the electronic medical records (EMRs) to improve knowledge and understanding of patients' characteristics and outcomes of patients with ambulatory heart failure (HF). However, the diagnostic performance of International Classification of Diseases (ICD) -10 diagnosis codes from the EMRs for patients with HF and with reduced or preserved ejection fraction (HFrEF or HFpEF) in the ambulatory setting are unknown. METHODS: We examined a cohort of patients aged ≥ 18 with at least 1 outpatient encounter for HF between January 2016 and June 2018 and an echocardiogram conducted within 180 days of the outpatient encounter for HF. We defined HFrEF encounters as those with ICD-10 codes of I50.2x (systolic heart failure); and we defined HFpEF encounters as those with ICD-10 codes of I50.3x (diastolic heart failure). The referent definitions of HFrEF and HFpEF were based on echocardiograms conducted within 180 days of the ambulatory encounter for HF RESULTS: We examined 68,952 encounters of 14,796 unique patients with HF. The diagnostic performance parameters for HFrEF (based on ICD-10 I50.2x only) depended on LVEF cutoff, with a sensitivity ranging from 68%-72%, specificity 63%-68%, positive predictive value 47%-63%, and negative predictive value 73%-84%. The diagnostic performance parameters for HFpEF depended on left ventricular ejection fraction cut-off, with sensitivity ranging from 34%-39%, specificity 92%-94%, positive predictive value 86%-93%, and negative predictive value 39%-54%. CONCLUSIONS: ICD-10 coding abstracted from the EMR for HFrEF vs HFpEF in the ambulatory setting had suboptimal diagnostic performance and, thus, should not be used alone to examine HFrEF and HFpEF in the ambulatory setting.
BACKGROUND: There is interest in leveraging the electronic medical records (EMRs) to improve knowledge and understanding of patients' characteristics and outcomes of patients with ambulatory heart failure (HF). However, the diagnostic performance of International Classification of Diseases (ICD) -10 diagnosis codes from the EMRs for patients with HF and with reduced or preserved ejection fraction (HFrEF or HFpEF) in the ambulatory setting are unknown. METHODS: We examined a cohort of patients aged ≥ 18 with at least 1 outpatient encounter for HF between January 2016 and June 2018 and an echocardiogram conducted within 180 days of the outpatient encounter for HF. We defined HFrEF encounters as those with ICD-10 codes of I50.2x (systolic heart failure); and we defined HFpEF encounters as those with ICD-10 codes of I50.3x (diastolic heart failure). The referent definitions of HFrEF and HFpEF were based on echocardiograms conducted within 180 days of the ambulatory encounter for HF RESULTS: We examined 68,952 encounters of 14,796 unique patients with HF. The diagnostic performance parameters for HFrEF (based on ICD-10 I50.2x only) depended on LVEF cutoff, with a sensitivity ranging from 68%-72%, specificity 63%-68%, positive predictive value 47%-63%, and negative predictive value 73%-84%. The diagnostic performance parameters for HFpEF depended on left ventricular ejection fraction cut-off, with sensitivity ranging from 34%-39%, specificity 92%-94%, positive predictive value 86%-93%, and negative predictive value 39%-54%. CONCLUSIONS: ICD-10 coding abstracted from the EMR for HFrEF vs HFpEF in the ambulatory setting had suboptimal diagnostic performance and, thus, should not be used alone to examine HFrEF and HFpEF in the ambulatory setting.
Authors: Gianluigi Savarese; Ola Vedin; Domenico D'Amario; Alicia Uijl; Ulf Dahlström; Giuseppe Rosano; Carolyn S P Lam; Lars H Lund Journal: JACC Heart Fail Date: 2019-03-06 Impact factor: 12.035
Authors: Benjamin A Steinberg; Xin Zhao; Paul A Heidenreich; Eric D Peterson; Deepak L Bhatt; Christopher P Cannon; Adrian F Hernandez; Gregg C Fonarow Journal: Circulation Date: 2012-05-21 Impact factor: 29.690
Authors: Arnold J Greenspon; Jasmine D Patel; Edmund Lau; Jorge A Ochoa; Daniel R Frisch; Reginald T Ho; Behzad B Pavri; Steven M Kurtz Journal: J Am Coll Cardiol Date: 2012-09-19 Impact factor: 24.094
Authors: Héctor Bueno; Joseph S Ross; Yun Wang; Jersey Chen; María T Vidán; Sharon-Lise T Normand; Jeptha P Curtis; Elizabeth E Drye; Judith H Lichtman; Patricia S Keenan; Mikhail Kosiborod; Harlan M Krumholz Journal: JAMA Date: 2010-06-02 Impact factor: 56.272
Authors: David T Saxon; Peter J Kennel; Heidi M Guyer; Parag Goyal; Scott L Hummel; Matthew C Konerman Journal: Mayo Clin Proc Date: 2020-04 Impact factor: 7.616
Authors: Parag Goyal; Zaid I Almarzooq; Evelyn M Horn; Maria G Karas; Irina Sobol; Rajesh V Swaminathan; Dmitriy N Feldman; Robert M Minutello; Harsimran S Singh; Geoffrey W Bergman; S Chiu Wong; Luke K Kim Journal: Am J Med Date: 2016-06 Impact factor: 4.965
Authors: Parag Goyal; Zaid I Almarzooq; Jim Cheung; Hooman Kamel; Udhay Krishnan; Dmitriy N Feldman; Evelyn M Horn; Luke K Kim Journal: Int J Cardiol Date: 2018-09-01 Impact factor: 4.164
Authors: Rainu Kaushal; George Hripcsak; Deborah D Ascheim; Toby Bloom; Thomas R Campion; Arthur L Caplan; Brian P Currie; Thomas Check; Emme Levin Deland; Marc N Gourevitch; Raffaella Hart; Carol R Horowitz; Isaac Kastenbaum; Arthur Aaron Levin; Alexander F H Low; Paul Meissner; Parsa Mirhaji; Harold A Pincus; Charles Scaglione; Donna Shelley; Jonathan N Tobin Journal: J Am Med Inform Assoc Date: 2014-05-12 Impact factor: 4.497