Children Comorbidity Score, a Simple Predictor for In-hospital Mortality: A Nationwide Inpatient Database Study in Japan.

IF 1.5 Q2 MEDICINE, GENERAL & INTERNAL
JMA journal Pub Date : 2025-04-28 Epub Date: 2025-04-04 DOI:10.31662/jmaj.2024-0333
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
{"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.

儿童共病评分,住院死亡率的简单预测指标:日本全国住院患者数据库研究
前言:利用日本全国住院患者数据库,我们旨在开发一种新的儿科患者共病评分,以预测住院死亡率——基于国际疾病分类第十版(ICD-10)代码的儿童共病评分(CCS)。方法:使用日本诊断程序组合数据库对2010年至2017年住院的儿科患者进行回顾性分析。80%的数据被用作训练集,我们将Lasso回归应用于一个包含56种候选共病类别的模型,以预测住院死亡率。我们采用Lasso回归中的1标准误差规则推导出一个简约模型,并强制进入12个儿科复杂慢性疾病(CCC)类别。因此,我们开发了CCS,这是一种基于整数的共病评分,使用具有非零系数的选定变量。其余20%的数据用作测试集,我们使用c统计、校准和决策曲线分析来评估CCS的预测性能,并将其与其他两个评分进行比较:使用ICD-10代码的基于CCS的评分和Charlson共病指数(CCI)。结果:在1,968,960例儿科患者中,我们观察到6,492例(0.33%)住院死亡率。采用Lasso回归进行变量选择,采用10个共病类别的整数型CCS,其鉴别能力(c统计量为0.720[95%可信区间(CI), 0.707-0.734])优于CCC(0.649[0.636-0.662])和CCI(0.544[0.533-0.555])。在所有年龄、性别和身体质量指数类别中,CCS的优越歧视是一致的。CCS具有良好的校正效果,校正斜率为1.027 (95% CI, 0.981 ~ 1.073)。决策曲线分析表明,与两种参考模型相比,CCS提供了最高的净效益。结论:基于icd -10的CCS在预测住院死亡率方面优于传统的合并症评分,可用于儿科住院患者的合并症评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信