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A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients

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 noninva...

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Autores principales: Asfari, Ahmed, Wolovits, Joshua, Gazit, Avihu Z., Abbas, Qalab, Macfadyen, Andrew J., Cooper, David S., Futterman, Craig, Penk, Jamie S., Kelly, Robert B., Salvin, Joshua W., Borasino, Santiago, Zaccagni, Hayden J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695536/
http://dx.doi.org/10.1097/CCE.0000000000001013
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author Asfari, Ahmed
Wolovits, Joshua
Gazit, Avihu Z.
Abbas, Qalab
Macfadyen, Andrew J.
Cooper, David S.
Futterman, Craig
Penk, Jamie S.
Kelly, Robert B.
Salvin, Joshua W.
Borasino, Santiago
Zaccagni, Hayden J
author_facet Asfari, Ahmed
Wolovits, Joshua
Gazit, Avihu Z.
Abbas, Qalab
Macfadyen, Andrew J.
Cooper, David S.
Futterman, Craig
Penk, Jamie S.
Kelly, Robert B.
Salvin, Joshua W.
Borasino, Santiago
Zaccagni, Hayden J
author_sort Asfari, Ahmed
collection PubMed
description 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.
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spelling pubmed-106955362023-12-05 A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients Asfari, Ahmed Wolovits, Joshua Gazit, Avihu Z. Abbas, Qalab Macfadyen, Andrew J. Cooper, David S. Futterman, Craig Penk, Jamie S. Kelly, Robert B. Salvin, Joshua W. Borasino, Santiago Zaccagni, Hayden J Crit Care Explor Predictive Modeling Report 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. Lippincott Williams & Wilkins 2023-12-01 /pmc/articles/PMC10695536/ http://dx.doi.org/10.1097/CCE.0000000000001013 Text en Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Predictive Modeling Report
Asfari, Ahmed
Wolovits, Joshua
Gazit, Avihu Z.
Abbas, Qalab
Macfadyen, Andrew J.
Cooper, David S.
Futterman, Craig
Penk, Jamie S.
Kelly, Robert B.
Salvin, Joshua W.
Borasino, Santiago
Zaccagni, Hayden J
A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients
title A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients
title_full A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients
title_fullStr A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients
title_full_unstemmed A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients
title_short A Near Real-Time Risk Analytics Algorithm Predicts Elevated Lactate Levels in Pediatric Cardiac Critical Care Patients
title_sort near real-time risk analytics algorithm predicts elevated lactate levels in pediatric cardiac critical care patients
topic Predictive Modeling Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695536/
http://dx.doi.org/10.1097/CCE.0000000000001013
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