A near real-time risk analytics algorithm predicts elevated lactate levels in pediatric cardiac critical care patients

Authors

Ahmed Asfari, University of Alabama at Birmingham, United States
Joshua Wolovits, UT Southwestern Medical Center, United States
Avihu Z. Gazit, Washington University, United States
Qalab Abbas, Aga Khan UniversityFollow
Andrew J. Macfadyen, University of Nebraska Medical Center, United States
David S. Cooper, University of Cincinnati College of Medicine, Ohio
Craig Futterman, George Washington University, United States
Jamie S. Penk, Northwestern University, United States
Robert B. Kelly, Children's Hospital of Orange County, United States
Joshua W. Salvin, Harvard Medical School, United States

Document Type

Report

Department

Paediatrics and Child Health

Abstract

Background: Postoperative 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.
Objectives: We 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 (n = 3,496) from January 1, 2018, to December 31, 2020. Patients lacking required data for module or LA measurements were excluded.
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.
Results: A 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 (p < 0.01). In the validation dataset, the relative risk of having LA greater than 4 mmol/L when the HLA index is less than 1 is 0.07 (95% CI: 0.06-0.08), and the relative risk of having LA less than 4 mmol/L when the HLA index greater than 99 is 0.13 (95% CI, 0.12-0.14).
Conclusions: These 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.

Comments

Pagination is not provided by the author/publisher.

Publication (Name of Journal)

Critical Care Explorations

Recommended Citation

Asfari, A., Wolovits, J., Gazit, A. Z., Abbas, Q., Macfadyen, A. J., Cooper, D. S., Futterman, C., Penk, J. S., Kelly, R. B., Salvin, J. W. (2023). A near real-time risk analytics algorithm predicts elevated lactate levels in pediatric cardiac critical care patients. Critical Care Explorations, 5(12), e1013.
Available at: https://ecommons.aku.edu/pakistan_fhs_mc_women_childhealth_paediatr/1439