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Author: search.filters.author.SIMANCAS RACINES, DANIEL ALEJANDROSubject: search.filters.subject.meta-analysis

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    Effects of Preoperative Exercise Interventions in Patients Undergoing Metabolic and Bariatric Surgery: A Systematic Review and Meta-Analysis
    (MDPI AG, 2025-09-01)
    SIMANCAS RACINES, DANIEL ALEJANDRO
    ;
    PARISE VASCO, JUAN MARCOS
    ;
    ANGAMARCA IGUAGO, JAIME WLADIMIR
    ;
    Ashley Carolina Cuzco Macias
    ;
    Carlos Soria
    Background: Obesity affects over one billion people globally. Bariatric surgery is the most effective long-term intervention for severe obesity. However, postoperative outcomes can vary considerably, with such factors as baseline fitness and cardiorespiratory reserve influencing surgical outcomes. This systematic review aimed to evaluate the effects of preoperative exercise or physical activity, compared to standard care or no intervention, on preoperative fitness parameters and perioperative surgical outcomes in adults with obesity undergoing metabolic and bariatric surgery. Methods: A systematic review was conducted in accordance with the recommendations of the Cochrane Handbook and the PRISMA guidelines. Randomized controlled trials, non-randomized controlled trials, and cohort studies with control groups evaluating preoperative exercise interventions were included. Two independent reviewers conducted study selection, data extraction, and risk of bias assessment using Cochrane tools. Meta-analyses were performed using random effects models, with standardized mean differences calculated for continuous outcomes. Evidence certainty was assessed using the GRADE approach. Results: A total of 15 studies, including 1378 participants, were identified for qualitative synthesis, with 12 contributing data for quantitative meta-analysis. Preoperative exercise interventions significantly improved six-minute walk test distance (SMD 2.01; 95% CI: 0.51 to 3.50; p = 0.009) and VO2 peak (SMD 1.02; 95% CI: 0.52 to 1.51; p < 0.0001). BMI reduction was significant (SMD −0.96; 95% CI: −1.75 to −0.16; p = 0.02), while weight change was not statistically significant (SMD −0.81; 95% CI: −1.72 to 0.09; p = 0.08). One study reported a reduction in hospital length of stay of 0.64 days (95% CI: −0.86 to −0.42; p < 0.00001). Evidence certainty was rated as very low to low across all outcomes. Conclusions: Preoperative exercise interventions have been shown to significantly improve cardiorespiratory fitness in bariatric surgery candidates, with large effect sizes for functional capacity measures. Despite the low certainty of the evidence, these findings suggest that supervised exercise programs should be incorporated into the preoperative care of bariatric surgery patients.
    Scopus© Citations 1
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    Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis
    (Frontiers Media SA, 2025-01-17)
    Feras Al-Obeidat
    ;
    Wael Hafez
    ;
    Asrar Rashid
    ;
    Mahir Khalil Jallo
    ;
    Munier Gador
    Background: Leukemia is the 11th most prevalent type of cancer worldwide, with acute myeloid leukemia (AML) being the most frequent malignant blood malignancy in adults. Microscopic blood tests are the most common methods for identifying leukemia subtypes. An automated optical image-processing system using artificial intelligence (AI) has recently been applied to facilitate clinical decision-making. Aim: To evaluate the performance of all AI-based approaches for the detection and diagnosis of acute myeloid leukemia (AML). Methods: Medical databases including PubMed, Web of Science, and Scopus were searched until December 2023. We used the “metafor” and “metagen” libraries in R to analyze the different models used in the studies. Accuracy and sensitivity were the primary outcome measures. Results: Ten studies were included in our review and meta-analysis, conducted between 2016 and 2023. Most deep-learning models have been utilized, including convolutional neural networks (CNNs). The common- and random-effects models had accuracies of 1.0000 [0.9999; 1.0001] and 0.9557 [0.9312, and 0.9802], respectively. The common and random effects models had high sensitivity values of 1.0000 and 0.8581, respectively, indicating that the machine learning models in this study can accurately detect true-positive leukemia cases. Studies have shown substantial variations in accuracy and sensitivity, as shown by the Q values and I2 statistics. Conclusion: Our systematic review and meta-analysis found an overall high accuracy and sensitivity of AI models in correctly identifying true-positive AML cases.
    Scopus© Citations 12