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. 2024 Feb 29;24(1):86.
doi: 10.1186/s12871-024-02467-z.

Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database

Jincun Shi  1 , Fujin Chen  1 , Kaihui Zheng  1 , Tong Su  1 , Xiaobo Wang  1 , Jianhua Wu  1 , Bukao Ni  1 , Yujie Pan  2
Affiliations

Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database

Jincun Shi et al. BMC Anesthesiol. .

Abstract

Background: The duration of hospitalization, especially in the intensive care unit (ICU), for patients with diabetic ketoacidosis (DKA) is influenced by patient prognosis and treatment costs. Reducing ICU length of stay (LOS) in patients with DKA is crucial for optimising healthcare resources utilization. This study aimed to establish a nomogram prediction model to identify the risk factors influencing prolonged LOS in ICU-managed patients with DKA, which will serve as a basis for clinical treatment, healthcare safety, and quality management research.

Methods: In this single-centre retrospective cohort study, we performed a retrospective analysis using relevant data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Clinical data from 669 patients with DKA requiring ICU treatment were included. Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. Subsequently, the selected variables were subjected to a multifactorial logistic regression analysis to determine independent risk factors for prolonged ICU LOS in patients with DKA. A nomogram prediction model was constructed based on the identified predictors. The multivariate variables included in this nomogram prediction model were the Oxford acute severity of illness score (OASIS), Glasgow coma scale (GCS), acute kidney injury (AKI) stage, vasoactive agents, and myocardial infarction.

Results: The prediction model had a high predictive efficacy, with an area under the curve value of 0.870 (95% confidence interval [CI], 0.831-0.908) in the training cohort and 0.858 (95% CI, 0.799-0.916) in the validation cohort. A highly accurate predictive model was depicted in both cohorts using the Hosmer-Lemeshow (H-L) test and calibration plots.

Conclusion: The nomogram prediction model proposed in this study has a high clinical application value for predicting prolonged ICU LOS in patients with DKA. This model can help clinicians identify patients with DKA at risk of prolonged ICU LOS, thereby enhancing prompt intervention and improving prognosis.

Keywords: Diabetic ketoacidosis; Intensive care unit; Length of stay; MIMIC-IV database; Nomogram prediction model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The flowchart of the study
Fig. 2
Fig. 2
Variable selection by the LASSO binary logistic regression model. A. The process of selecting the most suitable λ (0.071) in the LASSO model by means of 10-fold cross-validation. B. Six variables with nonzero coefficients were selected by deriving the optimal lambda
Fig. 3
Fig. 3
Nomogram for predicting prolonged ICU LOS in patients with DKA
Fig. 4
Fig. 4
ROC curve of the nomogram prediction model. (A) training cohort, (B) validation cohort
Fig. 5
Fig. 5
Calibration curve of the nomogram prediction model. (A) training cohort, (B) validation cohort
Fig. 6
Fig. 6
Decision curve analysis of the nomogram prediction model. (A) training cohort, (B) validation cohort
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