Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic...

Cardiogenic shock (CS) is a critical condition of end-organ hypoperfusion with high mortality. Fluctuations in blood glucose (BG) levels may exacerbate cardiovascular instability in critically ill patients. Time In Tight Range (TITR), defined as the percentage of time in the target BG range of 3.9-7.8 mmol/L (70-140 mg/dL), has become an increasingly important index of glycaemic status, but its impact on mortality in CS remains unclear. Interpretable machine learning (ML) models provide transparent, quantitative and visual insights into the prognostic importance of TITR, clarifying its pivotal role in outcome prediction and providing objective evidence to support individualised glucose management.

This study aimed to investigate the relationship between TITR and mortality in patients with CS, and to provide strong evidence for early intervention and personalised blood glucose management.

We conducted a retrospective multi-cohort study to examine the association between TITR and mortality in patients with CS. The relationship between TITR and in-hospital mortality was analysed using a restricted cubic spline (RCS) model, log-rank test, multivariable Cox and logistic regression analyses. ML models, including XGBoost, LightGBM, CatBoost, Gradient Boosting, Support Vector Machine (SVM), Neural Network and Naive Bayes, were developed to predict mortality and compared with traditional clinical scoring systems. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Sensitivity and subgroup analyses were used to reveal the robustness of the results.

RCS analysis revealed an inverse (L-shaped) association (p < 0.001) between TITR and in-hospital mortality in both the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU) cohorts. Kaplan-Meier survival analyses revealed the patients with TITR > 57% (High TITR group) had significantly lower in-hospital mortality than those with TITR ≤ 57% (Low TITR group) in both cohorts. The hazard ratios (HRs) (95% confidence interval [CI]) estimated by log-rank test were 1.72 (1.49, 1.99) and 1.49 (1.19, 1.87) in the MIMIC-IV and eICU cohorts, respectively (both p < 0.001). Sensitivity analyses yielded consistent results, confirming the robustness of the findings. In addition, analyses of ICU mortality, 28-day mortality (only available in the MIMIC-IV cohort), also demonstrated a consistent pattern with the primary outcome. Based on area under curve values (AUC), ML models, including CatBoost (AUC = 0.76; 95% CI: 0.73-0.80 in MIMIC-IV; 0.77; 95% CI: 0.72-0.83 in eICU), Gradient Boosting (AUC = 0.75; 95% CI: 0.72-0.79 and 0.74; 95% CI: 0.68-0.80) and XGBoost (AUC = 0.74; 95% CI: 0.70-0.77 and 0.76; 95% CI: 0.71-0.82), outperformed traditional scoring systems. Interpretability analysis via SHAP consistently highlighted TITR as the most influential factor in mortality prediction.

These findings underscore the critical role of TITR in outcome prediction and demonstrate both the superiority and interpretability of ML models for risk stratification and decision support. Achieving higher TITR is associated with improved outcomes, highlighting the importance of dynamic glucose control in this population.

The findings offer evidence-based guidance for ICU nursing interventions, highlighting TITR as a key modifiable factor for improving outcomes for patients with CS. The strong performance of ML models supports their clinical application for more accurate risk stratification, while identification of TITR as the primary mortality predictor-reinforced by transparent SHAP explanations-provides actionable insights to guide early, targeted interventions.