Dataset Information

Development and Validation of a Predictive Model of Success in Bariatric Surgery.

ABSTRACT: PURPOSE:There are no criteria to establish priority for bariatric surgery candidates in the public health system in several countries. The aim of this study is to identify preoperative characteristics that allow predicting the success after bariatric surgery. MATERIALS AND METHODS:Four hundred and sixty-one patients submitted to Roux-en-Y gastric bypass were included. Success of the surgery was defined as the sum of five outcome variables, assessed at baseline and 12 months after the surgery: excess weight loss, use of continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) as a treatment for obstructive sleep apnea (OSA), daily number of antidiabetics, daily number of antihypertensive drugs, and all-cause mortality. Partial least squares (PLS) regression and multiple linear regression were performed to identify preoperative predictors. We performed a 90/10 split of the dataset in train and test sets and ran a leave-one-out cross-validation on the train set and the best PLS model was chosen based on goodness-of-fit criteria. RESULTS:The preoperative predictors of success after bariatric surgery included lower age, presence of non-alcoholic fatty liver disease and OSA, more years of CPAP/BiPAP use, negative history of cardiovascular disease, and lower number of antihypertensive drugs. The PLS model displayed a mean absolute percent error of 0.1121 in the test portion of the dataset, leading to accurate predictions of postoperative outcomes. CONCLUSION:This success index allows prioritizing patients with the best indication for the procedure and could be incorporated in the public health system as a support tool in the decision-making process.

SUBMITTER: Blume CA

PROVIDER: S-EPMC7666615 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Development and Validation of a Predictive Model of Success in Bariatric Surgery.

Blume Carina A CA Brust-Renck Priscila G PG Rocha Miriam K MK Leivas Gabriel G Neyeloff Jeruza L JL Anzanello Michel J MJ Fogliatto Flavio S FS Bahia Luciana R LR Telo Gabriela H GH Schaan Beatriz D BD

Obesity surgery 20201114 3

<h4>Purpose</h4>There are no criteria to establish priority for bariatric surgery candidates in the public health system in several countries. The aim of this study is to identify preoperative characteristics that allow predicting the success after bariatric surgery.<h4>Materials and methods</h4>Four hundred and sixty-one patients submitted to Roux-en-Y gastric bypass were included. Success of the surgery was defined as the sum of five outcome variables, assessed at baseline and 12 months after ...[more]

PMID: 33190175

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Project description:ObjectiveWe aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset.MethodsThis was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI.ResultA total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI.ConclusionsThe CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.

Project description:PurposeThe aim of this exploratory study was to investigate whether the degree of weight loss properly reflects improvements in cardiometabolic health among patients who underwent Roux-en-Y gastric bypass.MethodsIn this ancillary analysis from a clinical trial, patients were clustered into tertiles according to the magnitude of the percentage weight loss (1st tertile: "higher weight loss": -37.1 ± 5.8%; 2nd tertile: "moderate weight loss": -29.7 ± 1.4%; 3rd tertile: "lower weight loss": -24.2 ± 2.3%). Delta changes (9 months after surgery-baseline) in clustered cardiometabolic risk (i.e., blood pressure index, fasting glucose, high-density lipoprotein [HDL] and triglycerides [TG]), glycated hemoglobin (HbA1c), homeostasis model assessment (HOMA-IR), and C-reactive protein (CRP) were calculated.ResultsA total of 42 patients who had complete bodyweight data (age = 40 ± 8 year; BMI = 47.8 ± 7.1 kg/m2) were included. Surgery led to substantial weight loss (-37.9 ± 11.3 kg, P < 0,001), and clinically significant improvements in blood pressure index (-17.7 ± 8.2 mmHg, P < 0.001), fasting glucose (-36.6 ± 52.5 mg/dL, P < 0.001), HDL (9.4 ± 7.1 mg/dL, P < 0.001), TG (-35.8 ± 44.1 mg/dL P < 0,001), HbA1c (-1.2 ± 1.6%, P < 0.001), HOMA-IR (-4.7 ± 3.9 mg/dL, P < 0.001) and CRP (-8.5 ± 6.7 μg/mL P < 0.001). Comparisons across tertiles revealed no differences for cardiometabolic risk score, fasting glucose, HbAc1, HOMA-IR, blood pressure index, CRP, HDL, and TG (P > 0.05 for all). Individual variable analysis confirmed cardiometabolic improvements across the spectrum on weight-loss. There were no associations between weight loss and any dependent variable.ConclusionWeight loss following bariatric surgery does not correlate with improvements in cardiovascular risk factors. These findings suggest that weight loss alone may be insufficient to assess the cardiometabolic success of bariatric surgery, and the search for alternate proxies that better predict surgery success are needed.

Dataset Information

Development and Validation of a Predictive Model of Success in Bariatric Surgery.

Publications

Development and Validation of a Predictive Model of Success in Bariatric Surgery.

Similar Datasets

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