Machine learning models to predict success of endoscopic sleeve gastroplasty using total and excess weight loss percent achievement: a multicentre study.

BACKGROUND: The large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making. METHODS: ML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python's scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots. RESULTS: Multicenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data. Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results. CONCLUSIONS: Although preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.

Deep sedation versus orotracheal intubation for endoscopic sleeve gastroplasty (ESG): preliminary experience.

BACKGROUND: Endoscopic sleeve gastroplasty (ESG) is an emerging bariatric procedure currently performed under general anaesthesia with orotracheal intubation (OTI). Several studies have shown the feasibility of advanced endoscopic procedures under deep sedation (DS) without impacting patient outcomes or adverse event rates. Our goal was to perform an initial comparative analysis of ESG in DS with ESG under OTI. METHODS: A prospective institutional registry was reviewed for ESG patients between 12/2016 and 1/2021. Patients were stratified into OTI or DS cohorts, and the 1st 50 cases performed in each cohort were included for comparability. Univariate analysis was performed on demographics, intraoperative, and postoperative outcomes (up to 90 days). Multivariate analyses evaluated the relationship between anesthesia type, preclinical and clinical variables. RESULTS: Of the 50 DS patients, 21(42%) underwent primary and 29 (58%) revisional surgery. There was no significant differences in Mallampati score across groups. No DS patient required intubation. DS patients were younger (p = 0.006) and lower BMI (p = 0.002) than OTI. As expected, DS patients overall and in the primary subgroup had shorter operative time (p ≤ 0.001 and p = 0.003, respectively) and higher rates (84% DS vs. 20% OTI, p ≤ 0.001) of ambulatory procedures. There were no significant differences in the sutures used between groups (p = 0.616). DS patients required less postoperative opioids (p ≤ 0.001) and antiemetics (p = 0.006) than OTI. There were no significant differences in 3-month postoperative weight loss across cohorts. There was no rehospitalization in either group. In primary ESG cases, we found DS patients were more likely younger (p = 0.006), female (p = 0.001), and had a lower BMI (p = 0.0027). CONCLUSIONS: ESG under DS is safe and feasible in select patients. We found DS safely increased rates of outpatient care, reduced use of opioids and antiemetics, and provided the same results of postoperative weight loss. Patient selection for DS may be more clearer for durable weight loss.

Statistical models to preoperatively predict operative difficulty in laparoscopic cholecystectomy: A systematic review.

BACKGROUND: Laparoscopic cholecystectomy operative difficulty is highly variable and influences outcomes. This systematic review analyzes the performance and clinical value of statistical models to preoperatively predict laparoscopic cholecystectomy operative difficulty. METHODS: PRISMA guidelines were followed. PubMed, Embase, and the Cochrane Library were searched until June 2020. Primary studies developing or validating preoperative models predicting laparoscopic cholecystectomy operative difficulty in cohorts of >100 patients were included. Studies not reporting performance metrics or enough information for clinical implementation were excluded. Data were extracted according to CHARMS, and study quality was assessed using the PROBAST tool. RESULTS: In total, 2,654 articles were identified, and 22 met eligibility criteria. Eighteen were model development, whereas 4 were validation studies. Eighteen studies were at high risk of bias. However, 11 studies showed low concern for applicability. Identified models predict 9 definitions of laparoscopic cholecystectomy operative difficulty, the most common being conversion to open surgery and operating time. The most validated models predict an intraoperative difficulty scale and procedures >90 minutes with an area under the curve of >0.70 and >0.76, respectively. Commonly used predictors include demographic variables such as age and gender (9/18 models) and ultrasound findings such as gallbladder wall thickness (11/18). Clinical implementation was never studied. CONCLUSION: There is a longstanding interest in estimating laparoscopic cholecystectomy operative difficulty. Models to preoperatively predict laparoscopic cholecystectomy operative difficulty have generally good performance and seem applicable. However, an unambiguous definition of operative difficulty, validations, and clinical studies are needed to implement patients' stratification in laparoscopic cholecystectomy.