Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study.

BACKGROUND: The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging. OBJECTIVES: To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction. SETTING: The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453. METHODS: We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2. RESULTS: This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making. CONCLUSION: This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.

Development and validation of a predictive model of the hospital cost associated with bariatric surgery.

Hospitals are facing difficulties in predicting, evaluating, and managing cost-affecting parameters in patient treatments. Inaccurate cost prediction leads to a deficit in operational revenue. This study aims to determine the ability of Machine Learning (ML) algorithms to predict the cost of care in bariatric and metabolic surgery and develop a predictive tool for improved cost analysis. 602 patients who underwent bariatric and metabolic surgery at Wetzikon hospital from 2013 to 2019 were included in the study. Multiple variables including patient factors, surgical factors, and post-operative complications were tested using a number of predictive modeling strategies. The study was registered under Req 2022-00659 and approved by an institutional review board. The cost was defined as the sum of all costs incurred during the hospital stay, expressed in CHF (Swiss Francs). The data was preprocessed and split into a training set (80%) and a test set (20%) to build and validate models. The final model was selected based on the mean absolute percentage error (MAPE). The Random Forest model was found to be the most accurate in predicting the overall cost of bariatric surgery with a mean absolute percentage error of 12.7. The study provides evidence that the Random Forest model could be used by hospitals to help with financial calculations and cost-efficient operation. However, further research is needed to improve its accuracy. This study serves as a proof of principle for an efficient ML-based prediction tool to be tested on multi-center data in future phases of the study.

Prasugrel increases the need for platelet transfusions and surgical reexploration rates compared with clopidogrel in coronary artery bypass surgery.

BACKGROUND: We hypothesized that patients treated with prasugrel compared with those treated with clopidogrel before coronary artery bypass graft (CABG) surgery are at increased risk of hemorrhagic complications needing transfusions and are more likely to need surgical reexploration. PATIENTS AND METHODS: Preoperative and postoperative clinical data were collected prospectively on 143 consecutive patients treated preoperatively with prasugrel (n = 59) or clopidogrel (n = 84) before undergoing isolated CABG at our institution from January 2011 to October 2012. RESULTS: Patients receiving prasugrel showed a slightly higher additive EuroSCORE I compared with those treated with clopidogrel (7 vs. 5, p = 0.001), and more often underwent urgent/emergent CABG (47 vs. 27%, p = 0.01). Patients treated with prasugrel were more likely than those treated with clopidogrel to need perioperative platelet transfusions (25 vs. 12%, p = 0.04). The numbers of transfusions of red blood cells, fresh-frozen plasma, or fibrinogen were high in both groups but showed no statistical differences. Patients treated with prasugrel, however, more often needed surgical reexploration for bleeding complications (8 vs. 1%, p = 0.03). Logistic regression revealed that preoperative therapy with prasugrel (RR 2.9, p = 0.01) and urgent/emergent surgery (RR 2.2, p = 0.04) were predictors for the composite endpoint "need for perioperative platelet transfusion and/or surgical reexploration." CONCLUSION: Pretreatment with prasugrel, compared with clopidogrel, in patients undergoing isolated CABG is associated with an increased need for platelet transfusions and a higher risk of surgical reexploration for bleeding complications.

The trabecular architecture of the superior articular process of the lumbar spine (L2-S1).

The role of the facet joint in low back pain has gained public attention lately. The objective of our study was to investigate whether there is any difference in the adaptation of the cancellous bone in the superior articular process depending on the specific stress condition in different levels of the spine. Therefore, the trabecular structure of the superior articular processes of L2 and S1 of 15 cadavers (aged 63-100 years) were studied using muCT (micro-computer tomography). Each sample was divided into five sections, each of which containing 20% of the slices. The following structure parameters were compared between L2 and S1 as well as within each process; bone-volume-fraction (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), structure-model-index (SMI) and degree of anisotropy (DA). Statistically significant differences were observed between L2 and S1 for the BV/TV, SMI, Tb.Th and Tb.N in superior 2 sections. BV/TV, Tb.Th and Tb.N were higher in S1 than in L2. The SMI is lower, and even negative in S1 compared to L2, showing a more plate-like structure. Within the articular process all structure parameters show a similar distribution in L2 and S1. BV/TV, Tb.N and DA decreased from cranial to caudal while Tb.Th was highest in the most cranial and caudal sections, with the lowest value in the middle. The SMI, on the other hand, increased from cranial to caudal displaying more rod-like structures. These results can be explained by the different stress the processes of the different spinal levels are exposed to as well as the specific motion patterns of the facet joint. The processes of the os sacrum are exposed to a higher axial and ventral load due to their location and the lumbosacral flexion. In addition the upper sections of each process experience higher stress peaks than the lower ones. Therefore, this study shows the material distribution within the cancellous bone adapts to these specific stress conditions the facet joints are exposed to.