Scholarlink

Objective <p>Nutrition 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.</p> Methods <p>We 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.</p> Results <p>A 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 <i>R</i><sup>2</sup> of 0.64 (95% CI: 0.49–0.76). The top 10 most important variables associated with REE in the optimal model are weight, age, height, preoperative serum albumin, preoperative CK-MB, vasoactive inotropic score (VIS), CPB time, postoperative LVEDD Z-score, gender, and postoperative NT-proBNP.</p> Conclusions <p>This 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年6月18日

查看全文
  • Chenyu Li,
  • Zhiyuan Zhu,
  • Shilin Wang,
  • Chao Li,
  • Zhongyuan Lu,
  • Qinnan Chen,
  • Xu Wang

摘要

Objective

Nutrition 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.

Methods

We 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.

Results

A 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 R2 of 0.64 (95% CI: 0.49–0.76). The top 10 most important variables associated with REE in the optimal model are weight, age, height, preoperative serum albumin, preoperative CK-MB, vasoactive inotropic score (VIS), CPB time, postoperative LVEDD Z-score, gender, and postoperative NT-proBNP.

Conclusions

This 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.