Daily prediction of next-day disease severity in critically ill patients: A prospective cohort study

Predictive models for intensive care unit (ICU) patients mainly focus on mortality, but short-term disease severity is more relevant for day-to-day decision-making.

The aim of this study was to develop and validate a daily prediction model for next-day disease severity in ICU patients.

Data from the Simple Intensive Care Studies-II prospective cohort study of acutely admitted critically ill adults, including data collected during the first 7 days of admission such as Sequential Organ Failure Assessment (SOFA) score-related measurements, were used to fit a mixed-effects logistic regression model for next-day deterioration. Deterioration was defined as a decline in the total (≥2) and organ-specific (≥1) SOFA scores. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis.

A total of 1009 patients were included. The final predictive model for overall next-day deterioration included six predictors (the total SOFA score on the current day, the minimum value of arterial pH, Glasgow Coma Scale score, mean arterial blood pressure, mechanical ventilation, and its effect differing between the first and subsequent ICU days). The model achieved an AUC of 0.74 (95% confidence interval: 0.70-0.78). In the decision curve analysis, within probability thresholds of 0.2-0.5, the model showed a higher net benefit than did strategies of treating all patients or treating no patients. Organ-specific prediction models for next-day deterioration in respiration, cardiovascular, and renal function showed slightly better performance than the overall model (AUCs: 0.79, 0.79, and 0.81, respectively).

Daily prediction models can predict next-day disease severity in overall, respiration, cardiovascular, and renal function amongst ICU patients. They offer clinical benefits within a range of probability thresholds and could support decision-making for ICU physicians.