PURPOSE: Heart failure (HF) is a common, serious, and still poorly known illness, which might benefit from studies in claims databases. However, to provide reliable estimates, HF patients must be adequately identified. This validation study aimed to estimate the diagnostic accuracy of the International Classification of Diseases, Tenth Revision (ICD-10) codes I50.x, heart failure, in the French hospital discharge diagnoses database. METHODS: This study was performed in two university hospitals, comparing recorded discharge diagnoses and electronic health records (EHRs). Patients with discharge ICD-10 codes 150.x were randomly selected. Their EHRs were reviewed to classify HF diagnosis as definite, potential, or miscoded based on the European Society of Cardiology diagnostic criteria, from which the codes' positive predictive value (PPV) was computed. To estimate sensitivity, patients with an EHR HF diagnosis were identified, and the presence of the I50.x codes was sought for in the hospital discharge database. RESULTS: Two hundred possible cases of HF were selected from the hospital discharge database, and 229 patients with an HF diagnosis were identified from the EHR. The PPV of I50.x codes was 60.5% (95% CI, 53.7%-67.3%) for definite HF and 88.0% (95% CI, 83.5%-92.5%) for definite/potential HF. The sensitivity of I50.x codes was 64.2% (95% CI, 58.0%-70.4%). PPV results were similar in both hospitals; sensitivity depended on the source of EHR: Departments of cardiology had a higher sensitivity than had nonspecialized wards. CONCLUSIONS: Diagnosis codes I50.x in discharge summary databases accurately identify patients with HF but fail to capture some of them.
PURPOSE:Heart failure (HF) is a common, serious, and still poorly known illness, which might benefit from studies in claims databases. However, to provide reliable estimates, HF patients must be adequately identified. This validation study aimed to estimate the diagnostic accuracy of the International Classification of Diseases, Tenth Revision (ICD-10) codes I50.x, heart failure, in the French hospital discharge diagnoses database. METHODS: This study was performed in two university hospitals, comparing recorded discharge diagnoses and electronic health records (EHRs). Patients with discharge ICD-10 codes 150.x were randomly selected. Their EHRs were reviewed to classify HF diagnosis as definite, potential, or miscoded based on the European Society of Cardiology diagnostic criteria, from which the codes' positive predictive value (PPV) was computed. To estimate sensitivity, patients with an EHR HF diagnosis were identified, and the presence of the I50.x codes was sought for in the hospital discharge database. RESULTS: Two hundred possible cases of HF were selected from the hospital discharge database, and 229 patients with an HF diagnosis were identified from the EHR. The PPV of I50.x codes was 60.5% (95% CI, 53.7%-67.3%) for definite HF and 88.0% (95% CI, 83.5%-92.5%) for definite/potential HF. The sensitivity of I50.x codes was 64.2% (95% CI, 58.0%-70.4%). PPV results were similar in both hospitals; sensitivity depended on the source of EHR: Departments of cardiology had a higher sensitivity than had nonspecialized wards. CONCLUSIONS: Diagnosis codes I50.x in discharge summary databases accurately identify patients with HF but fail to capture some of them.
Authors: Manuel Méndez-Bailón; Noel Lorenzo-Villalba; Rodrigo Jiménez-García; Valentin Hernández-Barrera; Jose María de Miguel-Yanes; Javier de Miguel-Diez; Nuria Muñoz-Rivas; Emmanuel Andrès; Ana Lopez-de-Andrés Journal: J Clin Med Date: 2022-02-16 Impact factor: 4.241
Authors: Harun Kundi; Rishi K Wadhera; Jordan B Strom; Linda R Valsdottir; Changyu Shen; Dhruv S Kazi; Robert W Yeh Journal: JAMA Cardiol Date: 2019-11-01 Impact factor: 14.676
Authors: Nicolas H Thurin; Pauline Bosco-Levy; Patrick Blin; Magali Rouyer; Jérémy Jové; Stéphanie Lamarque; Séverine Lignot; Régis Lassalle; Abdelilah Abouelfath; Emmanuelle Bignon; Pauline Diez; Marine Gross-Goupil; Michel Soulié; Mathieu Roumiguié; Sylvestre Le Moulec; Marc Debouverie; Bruno Brochet; Francis Guillemin; Céline Louapre; Elisabeth Maillart; Olivier Heinzlef; Nicholas Moore; Cécile Droz-Perroteau Journal: BMC Med Res Methodol Date: 2021-05-01 Impact factor: 4.615
Authors: Héctor Bueno; Clara Goñi; Rafael Salguero-Bodes; Beatriz Palacios; Lourdes Vicent; Guillermo Moreno; Nicolás Rosillo; Luis Varela; Margarita Capel; Juan Delgado; Fernando Arribas; Manuel Del Oro; Carmen Ortega; Jose L Bernal Journal: Front Cardiovasc Med Date: 2022-03-17