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Embolic stroke (ES) is characterized by high morbidity and mortality. Its mortality predictors remain unclear. The present study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: Medical Information Mart for Intensive Care (MIMIC)-IV for training and internal validation, and eICU Collaborative Research Database (eICU-CRD) for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the receiver operating characteristic (AUROC).We adopted recursive feature elimination (RFE) for feature selection.We assessed model performance using three disease-severity scoring systems as benchmarks. Of the 1566 and 207 ES patients enrolled in the two databases, there were 173 (15.70%), 73 (15.57%), and 36 (17.39%) hospital mortality in the training, internal validation, and external validation cohort, respectively. The random forest (RF) model had the largest AUROC (0.806) in the internal validation phase and was chosen as the best model. The AUROC of the RF compact (RF-COM) model containing the top six features identified by RFE was 0.795. In the external validation phase, the AUROC of the RF model was 0.838, and the RF-COM model was 0.830, outperforming other models. Our findings suggest that the RF model was the best model and the top six predictors of ES hospital mortality were Glasgow Coma Scale, white blood cell, blood urea nitrogen, bicarbonate, age, and mechanical ventilation. [ABSTRACT FROM AUTHOR]

Objectives. Acupoint catgut embedding therapy has shown effectiveness in treating functional constipation; however, relevant, high-quality clinical evidence is scarce. This study aimed to systematically assess the effectiveness and safety of acupoint catgut embedding in treating poststroke constipation. Methods. Correlative randomized controlled trials were identified through a comprehensive literature search of PubMed, Cochrane Library/Cochrane Central Register of Controlled Trials, Web of Science, Embase, China National Knowledge Internet, Chinese Biomedical Literature Database, Wanfang, and VIP databases from inception until February 2022. Meta-analysis was performed using RevMan 5.3 software. Results. Fifteen trials involving 1084 patients were identified. The meta-analysis revealed that the acupoint catgut embedding group was significantly superior to the non-catgut embedding group with regard to the efficacy rate (RR = 1.27, 95% CI (1.19, 1.37), P < 0.05), the first defecation time (MD = −3.08, 95% CI (−4.53, −1.63), P < 0.05), the defecation sensation score (MD = −0.44, 95% CI (−0.61, −0.26), P < 0.05), the degree of difficulty in defecation (MD = −0.73, 95% CI (−1.10, −0.37), P < 0.05), the PAC-QOL scale score (MD = −10.06, 95% CI (−13.47, −6.64), P < 0.05), and the symptom integral (MD = −3.15, 95% CI (−3.60, −2.71), P < 0.05). However, there was no significant difference in the stool property score (MD = 0.06, 95% CI (−0.39, 0.50), P > 0.05) as well as the incidence of adverse reactions (RD = 0.01, 95% CI (−0.01, 0.03), P > 0.05) between the two groups. Conclusions. The results showed that acupoint catgut embedding is probably an effective and safe acupuncture treatment strategy for poststroke constipation. Nevertheless, more rigorously designed, standardized, large-sample, and multicenter randomized controlled designs are warranted to further verify the findings of this study. [ABSTRACT FROM AUTHOR]