AimTo assess the effects of sodium‐glucoseco‐transporter‐2 (SGLT2) inhibitors on diabetic ketoacidosis (DKA) in patients with type 2 diabetes.Materials and MethodsWe searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to 13 June 2019 for randomized controlled trials (RCTs) that compared SGLT2 inhibitors with control in patients with type 2 diabetes. Paired reviewers independently screened citations, assessed the risk of bias and extracted data. Peto's method was used as the primary approach to pool the effect of SGLT2 inhibitors on DKA. Sensitivity analyses with the alternative effect measure (risk ratio) or pooling method (Mantel–Haenszel), the use of continuity correction of 0.5 for zero‐event trials or a generalized linear mixed model were conducted. Six preplanned subgroup analyses were performed to explore heterogeneity. The grading of recommendations assessment, development and evaluation (GRADE) approach was used to rate the quality of evidence.ResultsA total of 39 RCTs were included, involving 60 580 patients and 85 DKA events. SGLT2 inhibitors were statistically associated with an increased risk of DKA versus control (SGLT2 inhibitors: 62/34 961 [0.18%] vs. control: 23/25 211 [0.09%], Peto odds ratio [OR] 2.13, 95% confidence interval [CI] 1.38 to 3.27, I2 = 8%; RD 1.7 more events, 95% CI 0.6 more to 3.4 more events per 1000 over 5 years; high‐quality evidence). Sensitivity analyses showed similar results. The subgroup analyses by mean age (interaction P = 0 .02) and length of follow‐up (interaction P = 0 .03) showed a larger relative effect among older patients (aged ≥60 years) and those with longer use of SGLT2 inhibitors (>52 weeks).ConclusionsHigh‐quality evidence suggests that SGLT2 inhibitors may increase the risk of DKA in patients with type 2 diabetes. The apparent differences in treatment effects among patients of a different age or follow‐up were probable, suggesting the advisability of caution in patients with long‐term use of SGLT2 inhibitors or in older patients.
Background and Objective: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review.Study Design: Using the searched literature from a published systematic review, we trained and tested an NLP model that enables rapid title and abstract screening when updating a systematic review. The model was a light gradient boosting machine (LightGBM), an ensemble learning classifier which integrates four pretrained Bidirectional Encoder Representations from Transformers (BERT) models. We divided the searched citations into two sets (ie, training and test sets). The model was trained using the training set and assessed for screening performance using the test set. The searched citations, whose eligibility was determined by two independent reviewers, were treated as the reference standard.Results: The test set included 947 citations; our model included 340 citations, excluded 607 citations, and achieved 96% sensitivity, and 78% specificity. If the classifier assessment in the case study was accepted, reviewers would lose 8 of 180 eligible citations (4%), none of which were ultimately included in the systematic review after full-text consideration, while decreasing the workload by 64.1%.
Conclusion:NLP technology using the ensemble learning method may effectively assist in rapid literature screening when updating systematic reviews.
Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (
http://zhanglab.hzau.edu.cn/scgdrug
).
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