  Purpose. Elevations in initially obtained serum lactate levels are strong
predictors of mortality in critically ill patients. Identifying patients whose
serum lactate levels are more likely to increase can alert physicians to
intensify care and guide them in the frequency of tending the blood test. We
investigate whether machine learning models can predict subsequent serum
lactate changes.
  Methods. We investigated serum lactate change prediction using the MIMIC-III
and eICU-CRD datasets in internal as well as external validation of the eICU
cohort on the MIMIC-III cohort. Three subgroups were defined based on the
initial lactate levels: i) normal group (<2 mmol/L), ii) mild group (2-4
mmol/L), and iii) severe group (>4 mmol/L). Outcomes were defined based on
increase or decrease of serum lactate levels between the groups. We also
performed sensitivity analysis by defining the outcome as lactate change of
>10% and furthermore investigated the influence of the time interval between
subsequent lactate measurements on predictive performance.
  Results. The LSTM models were able to predict deterioration of serum lactate
values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762-0.771) for the
normal group, 0.77 (95% CI 0.768-0.772) for the mild group, and 0.85 (95% CI
0.840-0.851) for the severe group, with a slightly lower performance in the
external validation.
  Conclusion. The LSTM demonstrated good discrimination of patients who had
deterioration in serum lactate levels. Clinical studies are needed to evaluate
whether utilization of a clinical decision support tool based on these results
could positively impact decision-making and patient outcomes.
