老年2型糖尿病合并脑梗死患者长期住院时间预测模型的建立与验证

Development and validation of a predictive model for prolonged length of stay in elderly type 2 diabetes mellitus patients combined with cerebral infarction.

作者信息

Tang Mingshan, Zhao Yan, Xiao Jing, Jiang Side, Tan Juntao, Xu Qian, Pan Chengde, Wang Jie

机构信息

Department of Neurology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.

Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.

出版信息

Front Neurol. 2024 Aug 1;15:1405096. doi: 10.3389/fneur.2024.1405096. eCollection 2024.

DOI:10.3389/fneur.2024.1405096

PMID:39148703

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11325865/

Abstract

BACKGROUND

This study aimed to identify the predictive factors for prolonged length of stay (LOS) in elderly type 2 diabetes mellitus (T2DM) patients suffering from cerebral infarction (CI) and construct a predictive model to effectively utilize hospital resources.

METHODS

Clinical data were retrospectively collected from T2DM patients suffering from CI aged ≥65 years who were admitted to five tertiary hospitals in Southwest China. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were conducted to identify the independent predictors of prolonged LOS. A nomogram was constructed to visualize the model. The discrimination, calibration, and clinical practicality of the model were evaluated according to the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

RESULTS

A total of 13,361 patients were included, comprising 6,023, 2,582, and 4,756 patients in the training, internal validation, and external validation sets, respectively. The results revealed that the ACCI score, OP, PI, analgesics use, antibiotics use, psychotropic drug use, insurance type, and ALB were independent predictors for prolonged LOS. The eight-predictor LASSO logistic regression displayed high prediction ability, with an AUROC of 0.725 (95% confidence interval [CI]: 0.710-0.739), a sensitivity of 0.662 (95% CI: 0.639-0.686), and a specificity of 0.675 (95% CI: 0.661-0.689). The calibration curve (bootstraps = 1,000) showed good calibration. In addition, the DCA and CIC also indicated good clinical practicality. An operation interface on a web page (https://xxmyyz.shinyapps.io/prolonged_los1/) was also established to facilitate clinical use.

CONCLUSION

The developed model can predict the risk of prolonged LOS in elderly T2DM patients diagnosed with CI, enabling clinicians to optimize bed management.

摘要

背景

本研究旨在确定老年2型糖尿病（T2DM）合并脑梗死（CI）患者住院时间延长的预测因素，并构建预测模型以有效利用医院资源。

方法

回顾性收集中国西南地区5家三级医院收治的年龄≥65岁的T2DM合并CI患者的临床资料。采用最小绝对收缩和选择算子（LASSO）回归模型和多变量逻辑回归分析确定住院时间延长的独立预测因素。构建列线图以直观展示该模型。根据受试者工作特征曲线下面积（AUROC）、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）对模型的区分度、校准度和临床实用性进行评估。

结果

共纳入13361例患者，其中训练集、内部验证集和外部验证集分别有6023例、2582例和4756例患者。结果显示，急性脑梗死（ACCI）评分、手术、外周置入中心静脉导管（PICC）、使用镇痛药、使用抗生素、使用精神药物、保险类型和白蛋白（ALB）是住院时间延长的独立预测因素。八因素LASSO逻辑回归显示出较高的预测能力，AUROC为0.725（95%置信区间[CI]：0.710 - 0.739），灵敏度为0.662（95%CI：0.639 - 0.686），特异度为0.675（95%CI：0.661 - 0.689）。校准曲线（自抽样次数 = 1000）显示校准良好。此外，DCA和CIC也表明该模型具有良好的临床实用性。还建立了一个网页操作界面（https://xxmyyz.shinyapps.io/prolonged_los1/）以方便临床使用。