ArticleJournal of clinical medicine2021
Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 15 citations in OpenAlex.
- Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Elevated Leukocyte Glucose Index (LGI) Is Associated with Diabetic Ketoacidosis (DKA) Severity and Presence of Microvascular Complications.Medicina (Kaunas, Lithuania) · 2025Observational
- Risk factors for failed extubation within 7 days in elderly critically ill patients based on respiratory mechanics and clinical indicators: a retrospective cohort study.Frontiers in medicine · 2025Article
- Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.Frontiers in medicine · 2025Article
- Association between hypomagnesemia and serum lactate levels in patients with sepsis: a retrospective observational study.Journal of anesthesia, analgesia and critical care · 2024Article
- Exploring the Potential of Chatbots in Critical Care Nephrology.Medicines (Basel, Switzerland) · 2023Review
- Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury.Journal of clinical medicine · 2022Article
- Proposal to Consider Chemical/Physical Microenvironment as a New Therapeutic Off-Target Approach.Pharmaceutics · 2022Article
- Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation.Diagnostics (Basel, Switzerland) · 2022Article
- Development and validation of a machine-learning model for prediction of hypoxemia after extubation in intensive care units.Annals of translational medicine · 2022Article
- Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.Journal of personalized medicine · 2021Article
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Authors and funding
16 authors at 10 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU.
methodsWe used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset.
resultsOf 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69).
conclusionThe ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.
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