Abstract

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.

Competing Interest Statement

Rebecca Ganetzky receives consulting fees from Minovia Therapeutics and is a paid advisor for Nurture Genomics. Stephen Master is a member of advisory boards for Indigo BioAutomation and Roche Diagnostics. The other authors have no conflicts of interest to disclose.

Funding Statement

Rebecca Ganetzky was supported by the National Institute of Diabetes Digestive and Kidney diseases (K08-DK113250) and the National Institute of General Medical Sciences (R35-GM151098). Ibrahim George-Sankoh was supported by the CHOP Mitochondrial Medicine Frontier program. The other authors received no additional funding.

Author Declarations

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IRB of the Children's Hospital of Philadelphia waived ethical approval for this work

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Data Availability

The data that support the findings of this study are available as part of the supplementary distribution file included with this manuscript.

AbbreviationsIEEMInborn Error of Energy MetabolismHIEHypoxic Ischemic EncephalopathyMASMeconium Aspiration SyndromePPHNPersistent Pulmonary Hypertension of the NewbornCHDCongenital Heart DiseaseROCReceiver Operator CharacteristicAUCArea Under the CurvePRPrecision Recall XGB: XGBoostRFRandom ForestIQRInterquartile rangeANOVAanalysis of varianceHSDhonestly significant difference