Construction and validation of a predictive nomogram for 28-day mortality in critically ill patients with toxic...

Toxic encephalopathy (TE) is a serious neurological complication among critically ill patients and is associated with considerable disability and mortality. Early risk prediction is essential for timely intervention and improved outcomes. However, evidence on prognostic modeling in TE remains limited. The primary objective of this study was to develop and validate a prognostic nomogram for predicting 28-day mortality in critically ill patients diagnosed with TE and to evaluate its clinical utility.

Adult patients diagnosed with TE were retrospectively selected from the MIMIC-IV database based on relevant International Classification of Diseases (ICD) codes. Baseline demographic, clinical, and laboratory variables were extracted, and candidate predictors were selected through univariate logistic regression analyses based on clinical relevance and statistical significance. Subsequently, a multivariable logistic regression model was developed and presented as a nomogram. The value of the predictive model was evaluated using the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA). Its potential clinical applicability was further assessed with a clinical impact curve (CIC).

A total of 3039 patients with TE were included, among whom 526 (17.3%) died within 28 days. The cohort was randomly divided into training and validation cohorts at a 7:3 ratio. The final model incorporated age (OR = 1.022, 95% CI: 1.013-1.032), heart rate (OR = 1.007, 95% CI: 1.001-1.013), hemoglobin (OR = 0.904, 95% CI: 0.852-0.958), albumin (OR = 0.677, 95% CI: 0.546-0.836), PaO₂ (OR = 0.998, 95% CI: 0.996-0.999), SOFA score (OR = 1.094, 95% CI: 1.043-1.148), APACHE II score (OR = 1.038, 95% CI: 1.011-1.066), and acute kidney injury (OR = 1.520, 95% CI: 1.132-2.501). The nomogram exhibited acceptable discriminative ability, with AUC values of 0.750 (95% CI: 0.725-0.776) and 0.757 (95% CI: 0.715-0.799) in the training and validation cohorts, respectively, along with satisfactory calibration. Furthermore, decision curve analysis (DCA) and clinical impact curve (CIC) results supported its clinical utility for individualized risk stratification.

Utilizing the MIMIC-IV database, we constructed and validated a prognostic nomogram to predict 28-day mortality in critically ill patients with TE. This tool may help clinicians identify high-risk individuals and optimize monitoring and supportive care.

Not applicable.