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Machine Learning Improves the Predictive Utility of Lactic Acid in Hospitalized Infants
Insights
Machine learning accurately predicts inborn errors of energy metabolism (IEEM) in infants with hyperlactatemia. This approach improves diagnosis over lactate levels alone, aiding critical prognostication.
Area of Science:
- Biomedical Informatics
- Neonatology
- Metabolic Disorders
Background:
- Hyperlactatemia is a frequent condition in hospitalized infants.
- Identifying the cause of hyperlactatemia is crucial for appropriate treatment and prognosis.
- Inborn errors of energy metabolism (IEEM) are a critical differential diagnosis in neonatal hyperlactatemia.
Purpose of the Study:
- To develop and validate a machine learning model for predicting IEEM in hospitalized infants with hyperlactatemia.
- To compare the diagnostic utility of machine learning models with lactate levels alone.
Main Methods:
- Retrospective cohort study of infants aged 0-90 days with lactate levels >= 5 mmol/L.
- Machine learning models (Random Forest, XGBoost) were trained and validated using clinical and laboratory data, including plasma amino acid and acylcarnitine levels.
- Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC).
Main Results:
- Machine learning models achieved an AUC-ROC of 0.81 in predicting IEEM, significantly outperforming lactate alone (AUC-ROC 0.56).
- Infants with IEEM had significantly higher lactate levels (median 12.6 mmol/L) compared to other causes.
- Mortality was high overall (30%) and particularly elevated in infants with IEEM (51%).
Conclusions:
- Machine learning demonstrates high diagnostic utility for identifying IEEM in neonatal hyperlactatemia.
- This approach facilitates computer-aided interpretation of complex data, enabling faster and more accurate diagnoses.
- Early and accurate diagnosis of IEEM is vital for improving outcomes in infants with hyperlactatemia.
Background And Objectives:
Hyperlactatemia is common in hospitalized infants. Machine learning was applied to clinical and laboratory characteristics in hospitalized infants with hyperlactatemia to identify predictors of inborn error of energy metabolism (IEEM).
Methods:
Retrospective cohort study of hospitalized infants aged 0-90 days (2012-2020) with a lactate ≥ 5 mmol/L. Final diagnosis was discretized to IEEM, cardiac, hypoxia, infectious and other. Random forest and XGBoost models were tuned and compared using cross-validation, and a final model was evaluated on an independent test set to determine ability to predict IEEM.
Results:
Among 1000 infants, median lactate was 8 mmol/L. The overall mortality rate was 30%, (N=291) and was 51% (N=21) among infants with an IEEM (N=41). Lactate was significantly higher in infants with an IEEM (12.6 mmol/L; IQR: 5-27 mmol/L). Machine learning analysis including plasma amino acid and acylcarnitine values yielded an area under the ROC curve (AUC-ROC) of 0.81 in a held-out test set, and was significantly better than lactate alone in a comparable population (AUC-ROC 0.81 vs. 0.56, p=0.027).
Conclusions:
Rapid diagnosis of IEEM vs. other causes is essential for neonatal hyperlactatemia prognostication. Machine learning has high diagnostic utility, serving as a framework for computer-aided interpretation of complex diagnostic data.
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