Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation

医学 重症监护室 共病 子群分析 接收机工作特性 急诊医学 危重病 重症监护医学 内科学 病危 置信区间
作者
Xiaoli Liu,Pan Hu,Wesley Yeung,Zhongheng Zhang,Vanda Ho,Chao Liu,Clark DuMontier,Patrick Thoral,Zhi Mao,Desen Cao,Roger G. Mark,Zhengbo Zhang,Mengling Feng,Deyu Li,Leo Anthony Celi
出处
期刊:The Lancet Digital Health [Elsevier BV]
卷期号:5 (10): e657-e667 被引量:26
标识
DOI:10.1016/s2589-7500(23)00128-0
摘要

BackgroundComorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias.MethodsIn this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs.FindingsUsing the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851–0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829–0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812–0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869–0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96–1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model.InterpretationThe ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed.FundingNational Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
月牙儿完成签到,获得积分10
1秒前
loong发布了新的文献求助10
2秒前
贰鸟应助shuaishuyi采纳,获得10
5秒前
6秒前
酷波er应助loong采纳,获得10
7秒前
搜集达人应助肖珂采纳,获得10
8秒前
jinjinshan完成签到,获得积分10
9秒前
典雅的觅儿完成签到,获得积分10
9秒前
大喵发布了新的文献求助10
10秒前
10秒前
莉莉娅完成签到 ,获得积分10
12秒前
一丁雨完成签到,获得积分10
13秒前
13秒前
ll完成签到,获得积分10
14秒前
自由的梦露完成签到,获得积分10
14秒前
绿泡泡发布了新的文献求助10
15秒前
莉莉娅关注了科研通微信公众号
15秒前
15秒前
16秒前
J.关闭了J.文献求助
16秒前
TT木木发布了新的文献求助10
19秒前
孤独的大灰狼完成签到 ,获得积分10
20秒前
酷波er应助乂贰ZERO叁采纳,获得10
20秒前
22秒前
24秒前
TTTaT完成签到,获得积分10
24秒前
在水一方应助泥嚎采纳,获得10
24秒前
悟空应助开心岩采纳,获得50
25秒前
小龙完成签到,获得积分10
25秒前
mutongchen完成签到,获得积分10
25秒前
然大宝发布了新的文献求助10
26秒前
26秒前
麦子发布了新的文献求助10
27秒前
27秒前
27秒前
yufei完成签到,获得积分20
28秒前
wenbaka完成签到 ,获得积分10
28秒前
J.关闭了J.文献求助
30秒前
Jasper应助绿泡泡采纳,获得10
33秒前
WRZ完成签到,获得积分10
33秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989797
求助须知:如何正确求助?哪些是违规求助? 3531910
关于积分的说明 11255394
捐赠科研通 3270563
什么是DOI,文献DOI怎么找? 1805008
邀请新用户注册赠送积分活动 882157
科研通“疑难数据库(出版商)”最低求助积分说明 809190