What Is a Chronic Disease? A Contribution Based on the Secondary Use of 161 Million Discharge Records. Mellot, E., Balcaen, T., Calafiore, M., Bouzillé, G., Beuscart, J., Ficheur, G., & Chazard, E. Studies in Health Technology and Informatics, 264:263–267, August, 2019. doi abstract bibtex Several definitions of chronic diseases exist. The objective is to reuse a nationwide medical-administrative database (PMSI) to estimate the lifespan of diagnostic codes, hence the chronicity of the corresponding diseases. We analyzed 162 million inpatient stays from 2008 to 2014, and estimate the lifespan of every ICD-10 code for every patient, identified by a unique imprint. We calculated 200 indicators for different time and survival values, and selected the ones that maximized the area under the ROC curve (AUC) drawn by comparison against 4 chronic disease classifications: CCI, ALD, result from the analysis of ICD-10 labels, and a handmade list. The best indicator was the time to reach a survival of 4.5%. It enables to get the following AUC: 78.9% compared with CCI, 90.3% compared with ALD, 75.1% compared with labels analysis, and 91.5% compared with the handmade list. This indicator enables to classify 23,349 ICD-10 codes from "most chronic" to "most acute". The 100 most chronic codes are listed.
@article{mellot_what_2019,
title = {What {Is} a {Chronic} {Disease}? {A} {Contribution} {Based} on the {Secondary} {Use} of 161 {Million} {Discharge} {Records}},
volume = {264},
issn = {1879-8365},
shorttitle = {What {Is} a {Chronic} {Disease}?},
doi = {10.3233/SHTI190224},
abstract = {Several definitions of chronic diseases exist. The objective is to reuse a nationwide medical-administrative database (PMSI) to estimate the lifespan of diagnostic codes, hence the chronicity of the corresponding diseases. We analyzed 162 million inpatient stays from 2008 to 2014, and estimate the lifespan of every ICD-10 code for every patient, identified by a unique imprint. We calculated 200 indicators for different time and survival values, and selected the ones that maximized the area under the ROC curve (AUC) drawn by comparison against 4 chronic disease classifications: CCI, ALD, result from the analysis of ICD-10 labels, and a handmade list. The best indicator was the time to reach a survival of 4.5\%. It enables to get the following AUC: 78.9\% compared with CCI, 90.3\% compared with ALD, 75.1\% compared with labels analysis, and 91.5\% compared with the handmade list. This indicator enables to classify 23,349 ICD-10 codes from "most chronic" to "most acute". The 100 most chronic codes are listed.},
language = {eng},
journal = {Studies in Health Technology and Informatics},
author = {Mellot, Emeric and Balcaen, Thibaut and Calafiore, Matthieu and Bouzillé, Guillaume and Beuscart, Jean-Baptiste and Ficheur, Grégoire and Chazard, Emmanuel},
month = aug,
year = {2019},
pmid = {31437926},
keywords = {Big data, Chronic disease, Patient discharge},
pages = {263--267},
}
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