{"title":"Children Comorbidity Score, a Simple Predictor for In-hospital Mortality: A Nationwide Inpatient Database Study in Japan.","authors":"Kayo Ikeda Kurakawa, Akira Okada, Takaaki Konishi, Nobuaki Michihata, Miho Ishimaru, Hiroki Matsui, Kiyohide Fushimi, Hideo Yasunaga, Toshimasa Yamauchi, Masaomi Nangaku, Takashi Kadowaki, Satoko Yamaguchi","doi":"10.31662/jmaj.2024-0333","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Utilizing a nationwide inpatient database in Japan, we aimed to develop a novel comorbidity score for pediatric patients to predict in-hospital mortality-the Children Comorbidity Score (CCS)-based on the International Classification of Diseases, 10th Revision (ICD-10) codes.</p><p><strong>Methods: </strong>We retrospectively analyzed pediatric patients hospitalized between 2010 and 2017 using the Japanese Diagnosis Procedure Combination database. Eighty percent of the data was used as a training set, where we applied Lasso regression to a model with 56 candidate comorbidity categories to predict in-hospital mortality. We employed the 1-standard-error rule in Lasso regression to derive a parsimonious model and forced the entry of 12 categories of pediatric Complex Chronic Conditions (CCC). Thus, we developed the CCS, an integer-based comorbidity score using the selected variables with nonzero coefficients. The remaining 20% of the data was used as the test set, where we evaluated the CCS's predictive performance using C-statistics, calibration, and decision curve analysis, comparing it with two other scores: a CCC-based score using ICD-10 codes and the Charlson Comorbidity Index (CCI).</p><p><strong>Results: </strong>Among 1,968,960 pediatric patients, we observed 6,492 (0.33%) in-hospital mortalities. The developed integer-based CCS, utilizing 10 comorbidity categories via variable selection by Lasso regression, had better discrimination ability (C-statistics, 0.720 [95% confidence intervals (CI), 0.707-0.734]) than the CCC (0.649 [0.636-0.662]) and CCI (0.544 [0.533-0.555]). The superior discrimination of the CCS was consistent across all age categories, sexes, and body mass index categories. The CCS showed good calibration, with a calibration slope of 1.027 (95% CI, 0.981-1.073). Decision curve analysis indicated that the CCS provided the highest net benefit compared to either of the reference models.</p><p><strong>Conclusions: </strong>The ICD-10-based CCS outperformed conventional comorbidity scores in predicting in-hospital mortality and would be useful in comorbidity assessment among pediatric inpatients.</p>","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 2","pages":"568-579"},"PeriodicalIF":1.5000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095624/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMA journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31662/jmaj.2024-0333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/4 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Utilizing a nationwide inpatient database in Japan, we aimed to develop a novel comorbidity score for pediatric patients to predict in-hospital mortality-the Children Comorbidity Score (CCS)-based on the International Classification of Diseases, 10th Revision (ICD-10) codes.
Methods: We retrospectively analyzed pediatric patients hospitalized between 2010 and 2017 using the Japanese Diagnosis Procedure Combination database. Eighty percent of the data was used as a training set, where we applied Lasso regression to a model with 56 candidate comorbidity categories to predict in-hospital mortality. We employed the 1-standard-error rule in Lasso regression to derive a parsimonious model and forced the entry of 12 categories of pediatric Complex Chronic Conditions (CCC). Thus, we developed the CCS, an integer-based comorbidity score using the selected variables with nonzero coefficients. The remaining 20% of the data was used as the test set, where we evaluated the CCS's predictive performance using C-statistics, calibration, and decision curve analysis, comparing it with two other scores: a CCC-based score using ICD-10 codes and the Charlson Comorbidity Index (CCI).
Results: Among 1,968,960 pediatric patients, we observed 6,492 (0.33%) in-hospital mortalities. The developed integer-based CCS, utilizing 10 comorbidity categories via variable selection by Lasso regression, had better discrimination ability (C-statistics, 0.720 [95% confidence intervals (CI), 0.707-0.734]) than the CCC (0.649 [0.636-0.662]) and CCI (0.544 [0.533-0.555]). The superior discrimination of the CCS was consistent across all age categories, sexes, and body mass index categories. The CCS showed good calibration, with a calibration slope of 1.027 (95% CI, 0.981-1.073). Decision curve analysis indicated that the CCS provided the highest net benefit compared to either of the reference models.
Conclusions: The ICD-10-based CCS outperformed conventional comorbidity scores in predicting in-hospital mortality and would be useful in comorbidity assessment among pediatric inpatients.