A novel machine learning approach to identify social risk factors associated with textbook outcomes after surgery

International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery

Artificial intelligence (AI) is transforming the landscape of medicine, including surgical science and practice. The evolution of AI from rule-based systems to advanced machine learning and deep learning algorithms has opened new avenues for its application in metabolic and bariatric surgery (MBS). AI has the potential to enhance various aspects of MBS, including education and training, decision-making, procedure planning, cost and time efficiency, optimization of surgical techniques, outcome and complication prediction, patient education, and access to care. However, concerns persist regarding the reliability of AI-generated decisions and associated ethical considerations. This study aims to establish a consensus on the role of AI in MBS using a modified Delphi method. A panel of 68 leading metabolic and bariatric surgeons from 35 countries participated in this consensus-building process, providing expert insights into the integration of AI in MBS. Of the 28 statements evaluated, a consensus of at least 70% was achieved for all, with 25 statements reaching consensus in the first round and the remaining three in the second round. Experts agreed that AI has the potential to enhance the evaluation of surgical skills in MBS by providing objective, detailed assessments, enabling personalized feedback, and accelerating the learning curve. Most experts also recognized AI's role in identifying qualified candidates for MBS referrals, helping patient and procedure selection, and addressing specific clinical questions. However, concerns were raised about the potential overreliance on AI-generated recommendations. The consensus emphasized the need for ethical guidelines governing AI use and the inclusion of AI's role in decision-making within the patient consent process. Furthermore, the results suggest that AI education should become an essential component of future surgical training. Advancements in AI-driven robotics and AI-integrated genomic applications were also identified as promising developments that could significantly shape the future of MBS.

Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis

BACKGROUND

Machine learning (ML) approaches have become increasingly popular in predicting surgical outcomes. However, it is unknown whether they are superior to traditional statistical methods such as logistic regression (LR). This study aimed to perform a systematic review and meta-analysis to compare the performance of ML vs LR models in predicting postoperative outcomes for patients undergoing gastrointestinal (GI) surgery.

METHODS

A systematic search of Embase, MEDLINE, Cochrane, Web of Science, and Google Scholar was performed through December 2022. The primary outcome was the discriminatory performance of ML vs LR models as measured by the area under the receiver operating characteristic curve (AUC). A meta-analysis was then performed using a random effects model.

RESULTS

A total of 62 LR models and 143 ML models were included across 38 studies. On average, the best-performing ML models had a significantly higher AUC than the LR models (ΔAUC, 0.07; 95% CI, 0.04-0.09; P < .001). Similarly, on average, the best-performing ML models had a significantly higher logit (AUC) than the LR models (Δlogit [AUC], 0.41; 95% CI, 0.23-0.58; P < .001). Approximately half of studies (44%) were found to have a low risk of bias. Upon a subset analysis of only low-risk studies, the difference in logit (AUC) remained significant (ML vs LR, Δlogit [AUC], 0.40; 95% CI, 0.14-0.66; P = .009).

CONCLUSION

We found a significant improvement in discriminatory ability when using ML over LR algorithms in predicting postoperative outcomes for patients undergoing GI surgery. Subsequent efforts should establish standardized protocols for both developing and reporting studies using ML models and explore the practical implementation of these models.

Textbook Oncological Outcome in European GASTRODATA

OBJECTIVE

To assess the rate of textbook outcome (TO) and textbook oncological outcome (TOO) in the European population based on the GASTRODATA registry.

BACKGROUND

TO is a composite parameter assessing surgical quality and strongly correlates with improved overall survival. Following the standard of treatment for locally advanced gastric cancer, TOO was proposed as a quality and optimal multimodal treatment parameter.

METHODS

TO was achieved when all the following criteria were met: no intraoperative complications, radical resection according to the surgeon, pR0 resection, retrieval of at least 15 lymph nodes, no severe postoperative complications, no reintervention, no admission to the intensive care unit, no prolonged length of stay, no postoperative mortality and no hospital readmission. TOO was defined as TO with the addition of perioperative chemotherapy compliance.

RESULTS

Of the 2558 patients, 1700 were included in the analysis. TO was achieved in 1164 (68.5%) patients. The use of neoadjuvant chemotherapy [odds ratio (OR) = 1.33, 95% CI: 1.04-1.70] and D2 or D2+ lymphadenectomy (OR = 1.55, 95% CI: 1.15-2.10) had a positive impact on TO achievement. Older age (OR = 0.73, 95% CI: 0.54-0.94), pT3/4 (OR = 0.79, 95% CI: 0.63-0.99), ASA 3/4 (OR = 0.68, 95% CI: 0.54-0.86) and total gastrectomy (OR = 0.56, 95% CI: 0.45-0.70), had a negative impact on TO achievement. TOO was achieved in 388 (22.8%) patients. Older age (OR = 0.37, 95% CI: 0.27-0.53), pT3 or pT4 (OR = 0.52, 95% CI: 0.39-0.69), and ASA 3 or 4 (OR = 0.58, 95% CI: 0.43-0.79) had a negative impact on TOO achievement.

CONCLUSIONS

Despite successively improved surgical outcomes, stage-appropriate chemotherapy in adherence to the current guidelines for multimodal treatment of gastric cancer remains poor. Further implementation of oncologic quality metrics should include greater emphasis on perioperative chemotherapy and adequate lymphadenectomy.