Lactate clearance in infants undergoing surgery for congenital heart disease

Desplanque, Laetitia; Hamaide-Defrocourt, Florent; Berkia, I.; Tourneur, Tiffany; Albinni, Souha; Bojan, Mirela

doi:10.1111/aor.13402

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Increased lactate load by packed red blood cell transfusions and decreased lactate clearance may all result in hyperlactatemia. 32 In children and adolescents, benign lactic acidosis is common without haemodynamic disturbance. 33 These post-operative behaviours of lactate must be investigated in future studies.…”

Section: Discussionmentioning

confidence: 99%

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

et al. 2022

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Background: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight post-operative children, median age 4 months (2.9–11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33–197), mean lactate level at admission of 22.3 mg/dL (range 6.3–71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance. Results: The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw. Conclusions: The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.

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Section: Discussionmentioning

confidence: 99%

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

et al. 2022

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“…4 Lactate clearance rather than lactate concentration has been correlated to anaerobic metabolism and adequacy of perfusion during cardiopulmonary by-pass. 11 This behavior raised the question if lactate over time could help explain NIV failure. We investigated blood lactate indices at different times.…”

Section: Discussionmentioning

confidence: 99%

“…As severe anemia and blood transfusions could increase lactate indices, we registered hematocrit at baseline. 11 Subjects contributed to lactate data for 48 hours or less if one failed to NIV earlier or if NIV was stopped. Blood lactate data corresponding to the beginning of NIV (time 0), 6 hours later (time 6), 24 hours later (time 24), and 48 hours later (time 48) were recorded.…”

Section: Methodsmentioning

confidence: 99%

Arterial Blood Lactate in Children Primarily Submitted to Noninvasive Ventilation: A Time-Dependent ROC Analysis

Luz

Silva

2020

Journal of Child Science

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The purpose of this study was to evaluate blood lactate measurements and failure to noninvasive ventilation (NIV) in children admitted to a pediatric intensive care unit. This was a retrospective observational single-center study performed between June 2016 and June 2017. Dynamic lactate indices and failure to NIV in 63 children < 18 years and > 1 month old were examined; we considered blood lactate analyses at time 0, 6, 24, and 48 hours. NIV failure group had a higher pediatric risk of mortality (PRISM) score. Lactate indices decreased over time in the success group (p < 0.001). The best area under the curve observed was 69%, at 48 hours. Considering all measurements, the area under the curve for time-dependent receiver-operating characteristic curve was 58.6%. This study demonstrated blood lactate indices evolution over time in children submitted to NIV.

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“…Laetitia Desplanque et al of the Bichat Claude Bernard University Hospital, Paris, France evaluated the clearance of lactate in infants undergoing surgery with cardiopulmonary bypass (CPB). A retrospective cohort of 27 infants <1 year of age with repeated lactate measurements during CPB, and a known lactate concentration in the packed red blood cells (PRBCs) used during CPB.…”

Section: Cardiopulmonary Support and Membrane Oxygenationmentioning

confidence: 99%

Artificial Organs 2019: A year in review

Malchesky¹

2020

Artificial Organs

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In this Editor’s Review, articles published in 2019 are organized by category and summarized. These provide a brief reflection of the research and progress in artificial organs intended to advance and better human life while providing insight for continued application of these technologies and methods. Artificial Organs continues in the original mission of its founders “to foster communications in the field of artificial organs on an international level.” Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. Peer‐reviewed Special Issues this year included contributions from the 14th International Conference on Pediatric Mechanical Circulatory Support Systems and Pediatric Cardiopulmonary Perfusion edited by Dr Akif Undar, and the 26th Congress of the International Society for Mechanical Circulatory Support edited by Dr Minoru Ono and Dr Francesco Moscato. Additionally, important editorials highlighted the need for sustainability in hemodialysis, challenges and opportunities in mechanical circulatory support, progress in artificial pancreas development, historical perspectives on ventilators and dialysis, tissue engineering for cardiac support, and regional updates from India and China. Our Pioneer Series continues to highlight the many researchers who created this field of study. This year we debuted a new series entitled “Recent Progress in Artificial Organs” prepared by Vakhtang Tchantchaleishvili and Elizabeth Maynes of Thomas Jefferson University, Philadelphia, PA, USA. This series highlights recent advances and new developments in the field. We take this time also to express our gratitude to our authors for contributing their work to this journal. We offer our very special thanks to our reviewers who give so generously of their time and expertise to review, critique, and especially provide meaningful suggestions to the author’s work. Without our dedicated expert reviewers, the quality expected from such a journal would not be possible. We also express our special thanks to our Publisher, John Wiley & Sons, for their expert attention and support in the production Artificial Organs. We look forward to reporting further advances in the coming years.

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Lactate clearance in infants undergoing surgery for congenital heart disease

Cited by 7 publications

References 27 publications

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

Arterial Blood Lactate in Children Primarily Submitted to Noninvasive Ventilation: A Time-Dependent ROC Analysis

Artificial Organs 2019: A year in review

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