Evidence map›Paper›PMID 42315960›Full record

ArticleEuropean journal of clinical nutrition2026

Comparative machine learning modeling of resting energy expenditure estimation in mechanically ventilated children after cardiac surgery.

Chenyu Li, Zhiyuan Zhu, Shilin Wang, Chao Li, Zhongyuan Lu, Qinnan Chen, Xu Wang

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European journal of clinical nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Chenyu Li *Department of Pediatric Intensive Care Unit, National Center for Cardiovascular Disease and Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0009-0002-0211-0441
Zhiyuan Zhu *Department of Pediatric Intensive Care Unit, National Center for Cardiovascular Disease and Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Shilin Wang *Department of Pediatric Intensive Care Unit, National Center for Cardiovascular Disease and Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Chao LiCapital Center for Children's Health, Capital Institute of Pediatrics, Capital Medical University, Beijing, China.
Zhongyuan LuDepartment of Pediatric Intensive Care Unit, National Center for Cardiovascular Disease and Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Qinnan ChenDepartment of Pediatric Intensive Care Unit, National Center for Cardiovascular Disease and Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Xu WangDepartment of Pediatric Intensive Care Unit, National Center for Cardiovascular Disease and Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. fwpicu@163.com.ORCID http://orcid.org/0000-0002-4887-3367

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveNutrition status is vital for children's recovery following cardiac surgery, with substantial inter-individual variability in metabolic demands. We aimed to develop machine learning (ML) models of postoperative resting energy expenditure (REE) by analysing the influential factors.

methodsWe retrospectively analyzed children who underwent indirect calorimetry (IC) for valid REE measurements within 4 to 24 h after cardiac surgery between January 2021 and December 2022 at our center. Nine ML algorithms were trained to predict REE. Bland-Altman analysis assessed agreement with measured REE, and SHAP was used for population-level interpretation and a representative patient case.

resultsA total of 278 mechanically ventilated children were analyzed. REE measured by IC ranged from 387 to 2642 kcal/d (715 [550, 964]). The root mean square error (RMSE) of the ML models ranged from 226 (95% CI: 184-267) to 281 (95% CI: 225-344) kcal/d, while the range for conventional equations was 249 (95% CI: 215-287) to 282 (95% CI: 252-315) kcal/d. Regularized linear models achieved the highest R

conclusionsThis study developed ML models to predict REE in children after cardiac surgery, with some models outperforming conventional equations. These findings highlight the potential of machine learning to optimize postoperative nutritional management by accurately capturing the non-linear metabolic response to surgical stress.

Indexed as

Basal MetabolismCardiac Surgical ProceduresEnergy MetabolismMachine LearningRespiration, ArtificialCalorimetry, IndirectChildChild, PreschoolFemaleHumansInfantMaleNutritional StatusPostoperative PeriodPredictive Learning ModelsRetrospective Studies

Identifiers

PMID42315960

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