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

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

Mingshan Tang #  1 , Yan Zhao #  1 , Jing Xiao  1 , Side Jiang  1 , Juntao Tan  2 , Qian Xu  3 , Chengde Pan  1 , Jie Wang  1
Affiliations

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

Mingshan Tang et al. Front Neurol. .

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.

Keywords: cerebral infarction; length of stay; nomogram; prediction model; type 2 diabetes mellitus.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Features selection by LASSO. (A) LASSO coefficient profile (y-axis) of the 21 features. The upper x-axis represents the average number of predictors and the lower x-axis is the log (λ). (B) Tenfold cross-validation for tuning parameter selection in the LASSO model.
Figure 2
Figure 2
Forest plot showing the results of multivariable analysis.
Figure 3
Figure 3
Nomogram predicting prolonged LOS in elderly T2DM patients complicated with CI.
Figure 4
Figure 4
AUROC of the model in the training set.
Figure 5
Figure 5
The calibration curve of the model in the training set.
Figure 6
Figure 6
DCA of the model in the training set.
Figure 7
Figure 7
CIC of the model in the training set.
Figure 8
Figure 8
An example of a model predicting prolonged LOS in elderly T2DM patients complicated with CI via a link.
See this image and copyright information in PMC

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