Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

Abstract

Abstract Purpose Serum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients as suggested by The Surviving Sepsis Campaign serum lactate levels should be assessed and early lactate clearance-directed therapy is associated with decreased mortality. Monitoring a patient's vital parameters and repeatedly done blood analysis may have deleterious effects on the patient and brings an economical burden. Machine learning algorithms and trend analysis are gaining importance to overcome these unwanted facts. In this context, we aimed to investigate if an artificial intelligence approach can predict lactate trends from non-invasive clinical variables of patients with sepsis. Methods In this retrospective study, adult patients with sepsis from the MIMIC-IV dataset who had at least two serum lactate measurements recorded within the first 6 hours of sepsis diagnosis and who also has an ICU length of stay ≥ 24 hours are evaluated and ≥1mmol/l change is considered as a trend indicator. For prediction of lactate trend Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers are evaluated. Results LMT algorithm outperformed other classifiers (AUC= 0.832). J48 decision tree performed worse when predicting constant lactate trend. LMT algorithm with 4 features (heart rate, oxygen saturation, lactate value before sepsis diagnosis, and time interval variables) achieved 0.821 in terms of AUC. Conclusion We can say that machine learning models that employ logistic regression architectures, i.e. LMT algorithm achieved good results in lactate trend prediction tasks can be effectively used to assess the state of the patient whether it is stable or improving.