Short-Term Changes in Weight, Body Composition, and Metabolic Biomarkers After Laparoscopic Sleeve Gastrectomy in Patients with Obesity: A Comparative Prospective Study.

PURPOSE: To investigate the changes in weight, body composition, and metabolic biomarkers in patients with obesity after laparoscopic sleeve gastrectomy (LSG) and compare those changes between patients with and without metabolic syndrome (MS). MATERIALS AND METHODS: This retrospective longitudinal study included 76 patients who underwent LSG, among whom 32 had complete 1-year postoperative body composition and metabolic biomarkers. Body composition was measured by quantitative CT. Weight changes were compared between the MS and non-MS groups at 1-, 3-, 6-, and 12-month post-LSG in all patients; changes in body compositions and metabolic biomarkers from one day pre-LSG to 12-month post-LSG were also compared in those 32 patients. RESULTS: MS occurred in 46% (35/76) of all patients and 44% (14/32) of patients with complete follow-up data. Excess weight loss was lower in the MS group than that in the non-MS group at 1-, 3-, 6-, and 12-month post-LSG; the 12-month difference was significant (MS vs. non-MS: 0.91 ± 0.22 vs. 1.07 ± 0.42, P = 0.04). The greatest rate of visceral fat area (VFA) change occurred 12-month post-LSG in both the non-MS [0.62(0.55,0.7)] and MS [0.6(0.51,0.63)] groups. The most significant reduction in ectopic fat occurred in liver fat (LF) [non-MS, 0.45(0.22,0.58); MS, 0.39(0.23,0.58)]. CONCLUSION: LGS significantly improves weight, body composition, and metabolic biomarkers in populations with obesity, regardless of whether they have MS. Among the body composition, VFA and LF were the most significantly improved body composition measurements.

Nomogram Based on Body Composition and Prognostic Nutritional Index Predicts Survival After Curative Resection of Gastric Cancer.

RATIONALE AND OBJECTIVES: This study aimed to identify independent prognostic factors for gastric cancer (GC) patients after curative resection using quantitative computed tomography (QCT) combined with prognostic nutritional index (PNI), and to develop a nomogram prediction model for individualized prognosis. MATERIALS AND METHODS: This study retrospectively analyzed 119 patients with GC who underwent curative resection from January 2016 to March 2018. The patients' preoperative clinical pathological data were recorded, and all patients underwent QCT scans before and after curative resection to obtain QCT parameters: bone mineral density (BMD), skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA) and CT fat fraction (CTFF), then relative rate of change in each parameter (ΔBMD, ΔSMA, ΔVFA, ΔSFA, ΔCTFF) was calculated after time normalization. Multivariate Cox proportional hazards was used to establish a nomogram model that based on independent prognostic factors. The concordance index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve and clinical decision curve were used to evaluate the predictive performance and clinical benefit of the nomogram model． RESULTS: This study found that ΔCTFF, ΔVFA, ΔBMD and PNI are independent prognostic factors for overall survival (OS) (hazard ratio: 1.034, 0.895, 0.976, 2.951, respectively, all p < 0.05). The established nomogram model could predict the area under the ROC curve of OS at 1, 3 and 5 years as 0.816, 0.815 and 0.881, respectively. The C-index was 0.743 (95% CI, 0.684-0.801), and the decision curve analysis showed that this model has good clinical net benefit. CONCLUSION: The nomogram model based on body composition and PNI is reliable in predicting the individualized survival of underwent curative resection for GC patients.

Machine Learning Using Presentation CT Perfusion Imaging for Predicting Clinical Outcomes in Patients With Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND. Prediction of outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) is challenging using current clinical predictors. OBJECTIVE. The purpose of our study was to evaluate the utility of machine learning (ML) models incorporating presentation clinical and CT perfusion imaging (CTP) data in predicting delayed cerebral ischemia (DCI) and poor functional outcome in patients with aSAH. METHODS. This study entailed retrospective analysis of data from 242 patients (mean age, 60.9 ± 11.8 [SD] years; 165 women, 77 men) with aSAH who, as part of a prospective trial, underwent CTP followed by standardized evaluation for DCI during initial hospitalization and poor 3-month functional outcome (i.e., modified Rankin scale score ≥ 4). Patients were randomly divided into training (n = 194) and test (n = 48) sets. Five ML models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], and category boosting [CatBoost]) were developed for predicting outcomes using presentation clinical and CTP data. The least absolute shrinkage and selection operator method was used for feature selection. Ten-fold cross-validation was performed in the training set. Traditional clinical models were developed using stepwise LR analysis of clinical, but not CTP, data. RESULTS. Qualitative CTP analysis was identified as the most impactful feature for both outcomes. In the test set, the traditional clinical model, KNN, LR, SVM, RF, and CatBoost showed AUC for predicting DCI of 0.771, 0.812, 0.824, 0.908, 0.930, and 0.949, respectively, and AUC for predicting poor 3-month functional outcome of 0.863, 0.858, 0.879, 0.908, 0.926, and 0.958. CatBoost was selected as the optimal model. In the test set, AUC was higher for CatBoost than for the traditional clinical model for predicting DCI (p = .004) and poor 3-month functional outcome (p = .04). In the test set, sensitivity and specificity for predicting DCI were 92.3% and 60.0% for the traditional clinical model versus 92.3% and 85.7% for CatBoost, and sensitivity and specificity for predicting poor 3-month functional outcome were 100.0% and 65.8% for the traditional clinical model versus 90.0% and 94.7% for CatBoost. A web-based prediction tool based on CatBoost was created. CONCLUSION. ML models incorporating presentation clinical and CTP data outperformed traditional clinical models in predicting DCI and poor 3-month functional outcome. CLINICAL IMPACT. ML models may help guide early management of patients with aSAH.