Prediction of postoperative complications in patients after bariatric surgery with using the artificial intelligence

The OBJECTIVE was to develop, validate, and implement a clinical and laboratory model based on machine learning (ML) for the early identification and timely prevention of postoperative complications in patients who have undergone bariatric surgery.
METHODS AND MATERIALS. A retrospective analysis was performed on clinical data from 2,011 patients who underwent bariatric procedures. The study included demographic characteristics, clinical symptoms, laboratory findings, imaging results, and details of the postoperative course. A logistic regression model was constructed using automatic class weighting to correct for class imbalance and improve predictive accuracy. The model’s performance was evaluated using key metrics: AUC, accuracy, sensitivity, specificity, and F1-score.
RESULTS. The developed model demonstrated high predictive performance (AUC=0.975, accuracy=94.8 %, sensitivity=93.1 %). The most significant predictors of postoperative complications were elevated C-reactive protein levels, tachycardia, fever, symptom severity, and inflammatory laboratory markers.
CONCLUSION. The proposed machine learning model shows substantial potential for integration into clinical practice and may serve as a foundation for developing intelligent systems for early complication warning, automated monitoring, and clinical decision support.

1. Sheikhtaheri A., Orooji A., Pazouki A. et al. A Clinical decision support system for predicting the early complications of one-anastomosis gastric bypass surgery. Obes Surg. 2019;29:2276–86. https://doi.org/10.1007/s11695-019-03849-w

2. Cao Y., Fang X., Ottosson J. et al. A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery. J Clin Med. 2019;8: 668. https://doi.org/10.3390/jcm8050668

3. Yang C., Kessler M., Taebi N. et al. Remote follow-up with a mobile application is equal to traditional outpatient follow-up after bariatric surgery: the BELLA pilot trial. Obes Surg. 2023;33:1702–9. https://doi.org/10.1007/s11695-023-06587-2

4. Lopez-Arevalo I., Aldana-Bobadilla E., Molina-Villegas A. et al. A memory-efficient encoding method for processing mixed-type data on machine learning. Entropy (Basel). 2020;22:1391. https://doi.org/10.3390/e22121391

5. Wang S., Dai Y., Shen J. et al. Research on expansion and classification of imbalanced data based on SMOTE algorithm. Sci Rep. 2021;11:24039. https://doi.org/10.1038/s41598-021-03430-5

6. Razzaghi T., Safro I., Ewing J. et al. Predictive models for bariatric surgery risks with imbalanced medical datasets. Ann Oper Res. 2019;280:1–18. https://doi.org/10.1007/s10479-019-03156-8

7. Migenda N., Möller R., Schenck W. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLoS ONE. 2021;16: e0248896. https://doi.org/10.1371/journal.pone.0248896

8. Hassan A.M., Rajesh A., Asaad M. et al. Artificial intelligence and machine learning in prediction of surgical complications: current state, applications, and implications. Am Surg. 2023;89:25–30. https://doi.org/10.1177/00031348221101488

9. Stam W.T., Goedknegt L.K., Ingwersen E.W. et al. The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: a systematic review. Surgery. 2022;171:1014–21 https://doi.org/10.1016/j.surg.2021.10.002

10. Mierzwa A.S., Mocanu V., Marcil G. et al. Characterizing timing of postoperative complications following elective Roux-en-Y gastric bypass and sleeve gastrectomy. Obes Surg. 2021;31:4492–501. https://doi.org/10.1007/s11695-021-05638-w

11. Zucchini N., Capozzella E., Giuffrè M. et al. Advanced non-linear modeling and explainable artificial intelligence techniques for predicting 30-day complications in bariatric surgery: a single-center study. OBES SURG. 2024;34:3627–38. https://doi.org/10.1007/s11695-024-07501-0

12. Groene P., Eisenlohr J., Zeuzem C. et al. Postoperative nausea and vomiting in bariatric surgery in comparison to non-bariatric gastric surgery. Wideochir Inne Tech Maloinwazyjne. 2019;14:90–5. https://doi.org/10.5114/wiitm.2018.77629

13. Suh S., Helm M.,Kindel T.L. et al. The impact of nausea on post-operative outcomes in bariatric surgery patients. Surg Endosc. 2020;34:3085–91. https://doi.org/10.1007/s00464-019-07058-5