ArticleCritical care explorations2023
A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients.
Article in Critical care explorations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Assessment of Electronic Clinical Monitoring Systems in the Pediatric Intensive Care Unit: Prospective Concordance Study.JMIR formative research · 2026Article
- Perioperative Factors Associated with 2-Year Neurodevelopmental Outcome in Neonates Undergoing Congenital Cardiac Surgery with Cardiopulmonary Bypass: A Single-Center Cohort Study.Pediatric cardiology · 2026Article
- Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.Pediatric cardiology · 2026Review
- EHR Sampling Interval Bias Detection and Burden of Blood Pressure Excursions: Implications for Clinical Decision Support and Model Validity in Pediatric ECMO.Information (Basel) · 2026Article
- The Acidemia Index: A Near Real-Time Risk Analytics Algorithm is Associated With Measured Acidemia in Pediatric Critical Care Patients, a Multi-Center Validation Study.Critical care explorations · 2025Observational
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12 authors.
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Abstract
backgroundPostoperative pediatric congenital heart patients are predisposed to develop low-cardiac output syndrome. Serum lactate (lactic acid [LA]) is a well-defined marker of inadequate systemic oxygen delivery.
objectivesWe hypothesized that a near real-time risk index calculated by a noninvasive predictive analytics algorithm predicts elevated LA in pediatric patients admitted to a cardiac ICU (CICU). DERIVATION COHORT: Ten tertiary CICUs in the United States and Pakistan. VALIDATION COHORT: Retrospective observational study performed to validate a hyperlactatemia (HLA) index using T3 platform data (Etiometry, Boston, MA) from pediatric patients less than or equal to 12 years of age admitted to CICU ( PREDICTION MODEL: Physiologic algorithm used to calculate an HLA index that incorporates physiologic data from patients in a CICU. The algorithm uses Bayes' theorem to interpret newly acquired data in a near real-time manner given its own previous assessment of the physiologic state of the patient.
resultsA total of 58,168 LA measurements were obtained from 3,496 patients included in a validation dataset. HLA was defined as LA level greater than 4 mmol/L. Using receiver operating characteristic analysis and a complete dataset, the HLA index predicted HLA with high sensitivity and specificity (area under the curve 0.95). As the index value increased, the likelihood of having higher LA increased (
conclusionsThese results validate the capacity of the HLA index. This novel index can provide a noninvasive prediction of elevated LA. The HLA index showed strong positive association with elevated LA levels, potentially providing bedside clinicians with an early, noninvasive warning of impaired cardiac output and oxygen delivery. Prospective studies are required to analyze the effect of this index on clinical decision-making and outcomes in pediatric population.
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