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

作者全名："Tang, Mingshan; Zhao, Yan; Xiao, Jing; Jiang, Side; Tan, Juntao; Xu, Qian; Pan, Chengde; Wang, Jie"

作者地址："[Tang, Mingshan; Zhao, Yan; Xiao, Jing; Jiang, Side; Pan, Chengde; Wang, Jie] Chongqing Med Univ, Affiliated Banan Hosp, Dept Neurol, Chongqing, Peoples R China; [Tan, Juntao] Chongqing Med Univ, Operat Management Off, Affiliated Banan Hosp, Chongqing, Peoples R China; [Xu, Qian] Chongqing Med Univ, Lib, Chongqing, Peoples R China"

通信作者："Pan, CD (通讯作者)，Chongqing Med Univ, Affiliated Banan Hosp, Dept Neurol, Chongqing, Peoples R China."

来源：FRONTIERS IN NEUROLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001290818300001

JCR分区：Q2

影响因子：3.4

年份：2024

卷号：15

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：type 2 diabetes mellitus; cerebral infarction; length of stay; prediction model; nomogram

摘要："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."

基金机构：Chongqing Health Commission and Science and Technology Bureau [2024QNXM017]; Scientific and Technological Research Program of Chongqing Municipal Education Commission [KJQN202300454]; Banan District Science and Technology Bureau of Chongqing Municipality [BNWJ202300106]; Natural Science Foundation of Chongqing [cstc2020jcyj-msxmX1039]

基金资助正文："The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by Projects of Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (grant number 2024QNXM017), Scientific and Technological Research Program of Chongqing Municipal Education Commission (grant number KJQN202300454), Banan District Science and Technology Bureau of Chongqing Municipality (grant number BNWJ202300106) and the Natural Science Foundation of Chongqing (grant number cstc2020jcyj-msxmX1039)."