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Peer-reviewedCC BY 4.0MedComm

Development and Validation of an Interpretable Machine‐Learning Model for Predicting Suboptimal Clinical Response After Sleeve Gastrectomy: A Multicenter Study

  • Yuntao Nie1
  • An Ping2,3
  • Tianshi Wang4
  • Haibo Tang5
  • Yishan Huang1
  • Zhe Wang1
  • Pengpeng Wang1
  • Siqi Wang1
  • Genzheng Liu1
  • Ximing Lin1
  • Shuman Ran1
  • Yan Liu6
11 more authors
  • Ming Zhu7
  • Jingwei Hu8
  • Chunhui Li9
  • Nianrong Zhang1
  • Baoyin Liu1
  • Lei Zhang10
  • Biao Zhou1
  • Ziru Tian11
  • Shaihong Zhu5
  • Yinlu Ding4
  • Hua Meng1
11 affiliations
  1. 1Department of General Surgery & Obesity and Metabolic Disease Center China‐Japan Friendship Hospital Beijing China
  2. 2School of Basic Medical Sciences Capital Medical University Beijing China
  3. 3Department of Education Beijing Luhe Hospital Capital Medical University Beijing China
  4. 4Department of General Surgery Cheeloo College of Medicine The Second Hospital of Shandong University, Shandong University Jinan Shandong China
  5. 5Department of General Surgery The Third Xiangya Hospital of Central South University Changsha Hunan China
  6. 6Department of Gastrointestinal Metabolic Surgery Zhengzhou Central Hospital, Zhengzhou University Zhengzhou Henan China
  7. 7Department of Gastrointestinal Surgery Jining No. 1 People's Hospital Jining Shandong China
  8. 8Department of Gastrointestinal Surgery The Second Affiliated Hospital of Wenzhou Medical University Wenzhou Zhejiang China
  9. 9Department of Endocrinology The Second Hospital of Hebei Medical University Shijiazhuang Hebei China
  10. 10Department of Oncology Sinopharm Tongmei General Hospital Datong Shanxi China
  11. 11National Cancer Center/National Clinical Research Center For Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College Beijing China
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Abstract

Suboptimal clinical response (SCR) after sleeve gastrectomy (SG) remains a major clinical challenge, yet reliable preoperative risk stratification is lacking. Therefore, we developed and externally validated an interpretable machine‐learning model for predicting SCR after SG using multicenter data from three tertiary hospitals in China. Overall, 1312 patients were included, comprising a derivation cohort (n = 1094) and two external validation cohorts (n = 84 and n = 134). SCR, defined as percentage of total weight loss < 25% at 1 year, occurred in 374 (34.2%), 34 (40.5%), and 45 (33.6%) patients, respectively. Among six machine‐learning algorithms, XGB achieved the best performance. The final model incorporated eight routinely available predictors. In internal validation, the model showed excellent discrimination, with an AUC of 0.821 (95% confidence interval [CI], 0.752–0.871), sensitivity of 0.803, and specificity of 0.714. External validation confirmed satisfactory discrimination (AUC 0.761; 95% CI, 0.688–0.823), good calibration, and clinical benefit. Notably, the XGB model significantly outperformed 10 clinicians across experience levels and remained superior in clinically challenging cases. SHAP analysis enhanced interpretability, with predictor importance ranked as BMI, age, fasting glucose, type 2 diabetes, vitamin B12, calcium, total cholesterol, and gamma‐glutamyl transferase. This interpretable XGB model may enable precise preoperative risk stratification and support personalized management for patients undergoing SG.

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Cite

BibTeXRIS
BibTeX
@article{nie2026development,
  title = {{Development and Validation of an Interpretable Machine‐Learning Model for Predicting Suboptimal Clinical Response After Sleeve Gastrectomy: A Multicenter Study}},
  author = {Yuntao Nie and An Ping and Tianshi Wang and Haibo Tang and Yishan Huang and Zhe Wang and Pengpeng Wang and Siqi Wang and Genzheng Liu and Ximing Lin and Shuman Ran and Yan Liu and Ming Zhu and Jingwei Hu and Chunhui Li and Nianrong Zhang and Baoyin Liu and Lei Zhang and Biao Zhou and Ziru Tian and Shaihong Zhu and Yinlu Ding and Hua Meng},
  journal = {MedComm},
  year = {2026},
  month = nov,
  doi = {10.1002/mco2.71110},
  url = {https://doi.org/10.1002/mco2.71110},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Development and Validation of an Interpretable Machine‐Learning Model for Predicting Suboptimal Clinical Response After Sleeve Gastrectomy: A Multicenter Study
AU  - Yuntao Nie
AU  - An Ping
AU  - Tianshi Wang
AU  - Haibo Tang
AU  - Yishan Huang
AU  - Zhe Wang
AU  - Pengpeng Wang
AU  - Siqi Wang
AU  - Genzheng Liu
AU  - Ximing Lin
AU  - Shuman Ran
AU  - Yan Liu
AU  - Ming Zhu
AU  - Jingwei Hu
AU  - Chunhui Li
AU  - Nianrong Zhang
AU  - Baoyin Liu
AU  - Lei Zhang
AU  - Biao Zhou
AU  - Ziru Tian
AU  - Shaihong Zhu
AU  - Yinlu Ding
AU  - Hua Meng
PY  - 2026
DA  - 2026/11/03
JO  - MedComm
DO  - 10.1002/mco2.71110
UR  - https://doi.org/10.1002/mco2.71110
AB  - Suboptimal clinical response (SCR) after sleeve gastrectomy (SG) remains a major clinical challenge, yet reliable preoperative risk stratification is lacking. Therefore, we developed and externally validated an interpretable machine‐learning model for predicting SCR after SG using multicenter data from three tertiary hospitals in China. Overall, 1312 patients were included, comprising a derivation cohort (n = 1094) and two external validation cohorts (n = 84 and n = 134). SCR, defined as percentage of total weight loss < 25% at 1 year, occurred in 374 (34.2%), 34 (40.5%), and 45 (33.6%) patients, respectively. Among six machine‐learning algorithms, XGB achieved the best performance. The final model incorporated eight routinely available predictors. In internal validation, the model showed excellent discrimination, with an AUC of 0.821 (95% confidence interval [CI], 0.752–0.871), sensitivity of 0.803, and specificity of 0.714. External validation confirmed satisfactory discrimination (AUC 0.761; 95% CI, 0.688–0.823), good calibration, and clinical benefit. Notably, the XGB model significantly outperformed 10 clinicians across experience levels and remained superior in clinically challenging cases. SHAP analysis enhanced interpretability, with predictor importance ranked as BMI, age, fasting glucose, type 2 diabetes, vitamin B12, calcium, total cholesterol, and gamma‐glutamyl transferase. This interpretable XGB model may enable precise preoperative risk stratification and support personalized management for patients undergoing SG.
SN  - 2688-2663
ER  -