Background: Blood lactate is a key biomarker of tissue hypoperfusion and metabolic distress, reflecting the balance between oxygen delivery and cellular metabolism. Early identification of rising lactate levels is critical for timely intervention in critically ill patients, yet existing predictive tools are static and limited to specific ICU subgroups. Methods: We developed and externally validated two machine-learning models that provide hourly, real-time predictions of hyperlactatemia (lactate >2 mmol/L) within 6- and 12-hour horizons in general ICU populations. This retrospective, multi-cohort study used AmsterdamUMCdb and MIMIC-III for model development and internal validation, and eICU and HiRID for external validation. Adult ICU stays lasting ≥24 hours with lactate measurements ≤12 hours apart were included. Models based on routinely collected vital signs and laboratory data were trained using XGBoost and assessed for discrimination, calibration, decision-curve utility, and subgroup fairness. Results: The development dataset included 13,573 ICU stays and 577,414 hourly samples. The 6-hour model achieved AUROC/AUPR of 0.87/0.384 (AmsterdamUMCdb) and 0.814/0.401 (MIMIC-III) internally, and 0.772/0.295 (eICU) and 0.823/0.246 (HiRID) externally. The 12-hour model showed consistent performance (AUROC ≥0.76 across all cohorts). Calibration and decision-curve analyses confirmed robust generalization and net clinical benefit without recalibration. Conclusions: These validated, continuously operating models enable real-time, near-future lactate risk assessment throughout the ICU stay, supporting earlier recognition of tissue hypoperfusion and metabolic shock in unselected critically ill populations.
Zappalá et al. (2026) studied this question.