To the best of our knowledge, this systematic review is the first to estimate the pooled prevalence of GDM among women in mainland China according to International Association of Diabetes and Pregnancy Study Groups criteria. The results of our systematic review suggest a high prevalence of GDM in mainland China, indicating that this country might have the largest number of GDM patients worldwide.

BackgroundMobile health apps for diabetes self-management have different functions. However, the efficacy and safety of each function are not well studied, and no classification is available for these functions.ObjectiveThe aims of this study were to (1) develop and validate a taxonomy of apps for diabetes self-management, (2) investigate the glycemic efficacy of mobile app-based interventions among adults with diabetes in a systematic review of randomized controlled trials (RCTs), and (3) explore the contribution of different function to the effectiveness of entire app-based interventions using the taxonomy.MethodsWe developed a 3-axis taxonomy with columns of clinical modules, rows of functional modules and cells of functions with risk assessments. This taxonomy was validated by reviewing and classifying commercially available diabetes apps. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Chinese Biomedical Literature Database, and ClinicalTrials.gov from January 2007 to May 2016. We included RCTs of adult outpatients with diabetes that compared using mobile app-based interventions with usual care alone. The mean differences (MDs) in hemoglobin A1c (HbA1c) concentrations and risk ratios of adverse events were pooled using a random-effects meta-analysis. After taxonomic classification, we performed exploratory subgroup analyses of the presence or absence of each module across the included app-based interventions.ResultsAcross 12 included trials involving 974 participants, using app-based interventions was associated with a clinically significant reduction of HbA1c (MD 0.48%, 95% CI 0.19%-0.78%) without excess adverse events. Larger HbA1c reductions were noted among patients with type 2 diabetes than those with type 1 diabetes (MD 0.67%, 95% CI 0.30%-1.03% vs MD 0.37%, 95% CI –0.12%-0.86%). Having a complication prevention module in app-based interventions was associated with a greater HbA1c reduction (with complication prevention: MD 1.31%, 95% CI 0.66%-1.96% vs without: MD 0.38%, 95% CI 0.09%-0.67%; intersubgroup P=.01), as was having a structured display (with structured display: MD 0.69%, 95% CI 0.32%-1.06% vs without: MD 0.69%, 95% CI –0.18%-0.53%; intersubgroup P=.03). However, having a clinical decision-making function was not associated with a larger HbA1c reduction (with clinical decision making: MD 0.19%, 95% CI –0.24%-0.63% vs without: MD 0.61%, 95% CI 0.27%-0.95%; intersubgroup P=.14).ConclusionsThe use of mobile app-based interventions yields a clinically significant HbA1c reduction among adult outpatients with diabetes, especially among those with type 2 diabetes. Our study suggests that the clinical decision-making function needs further improvement and evaluation before being added to apps.

Objective To assess the reporting, extent, and handling of loss to follow-up and its potential impact on the estimates of the effect of treatment in randomised controlled trials.Design Systematic review. We calculated the percentage of trials for which the relative risk would no longer be significant under a number of assumptions about the outcomes of participants lost to follow-up. Data sources Medline search of five top general medical journals, 2005-07.Eligibility criteria Randomised controlled trials that reported a significant binary primary patient important outcome. ResultsOf the 235 eligible reports identified, 31 (13%) did not report whether or not loss to follow-up occurred. In reports that did give the relevant information, the median percentage of participants lost to follow-up was 6% (interquartile range 2-14%). The method by which loss to follow-up was handled was unclear in 37 studies (19%); the most commonly used method was survival analysis (66, 35%). When we varied assumptions about loss to follow-up, results of 19% of trials were no longer significant if we assumed no participants lost to follow-up had the event of interest, 17% if we assumed that all participants lost to RESEARCHfollow-up had the event, and 58% if we assumed a worst case scenario (all participants lost to follow-up in the treatment group and none of those in the control group had the event). Under more plausible assumptions, in which the incidence of events in those lost to follow-up relative to those followed-up is higher in the intervention than control group, results of 0% to 33% trials were no longer significant. ConclusionPlausible assumptions regarding outcomes of patients lost to follow-up could change the interpretation of results of randomised controlled trials published in top medical journals. IntroductionLoss to follow-up in randomised controlled trials could bias results if the unavailability of data is associated with the likelihood of outcome events. For example, patients might fail to return for assessment because of deterioration in their medical condition, resulting in a higher frequency of adverse outcomes of interest associated with that condition. If the distribution of such patients differs between study arms, the prognostic balance created by randomisation will be disturbed. 1 2 Although analysis of patients for whom outcome data are available in the groups to which they are randomised will avoid bias as a result of factors such as non-adherence, it will not protect against potential bias associated with loss to follow-up. 3Although investigators strive to reduce the amount of missing data, in most instances they will fail to achieve complete follow-up.3-5 Indeed, 60-89% of randomised controlled trials have some missing outcome data.6-8 Interpretation of results is compromised when, as is often the case, investigators do not report strategies for handling such data.8 9 The most commonly reported strategy among trials that do report their approach is to restrict analyses to participants with ful...

You are a physician working at a regional trauma center. Your unit's committee, which is responsible for standardization of care, is considering using tranexamic acid to treat trauma patients arriving 3 hours after injury. Almost all the information on this topic is derived from a single, blinded trial that randomized trauma patients to tranexamic acid or placebo. The original publication reported that 99% of the enrolled patients were followed up and there was a reduction in all-cause mortality (relative risk [RR], 0.91; 95% CI. 0.85-0.97) with no apparent subgroup effect. A subsequent publication focused on an additional analysis addressing death from bleeding and reported a powerful subgroup effect with a large benefit for patients treated within 3 hours of injury and possible harm if treated 3 or more hours after injury. The committee's mandate is to decide whether tranexamic acid should not be given to patients 3 hours or more after injury. The credibility you place on the subgroup analysis will determine your decision. The Challenge of Subgroup AnalysisClinicians making treatment decisions use evidence applying most closely to the individual patient and treatment under consideration. To address this issue, clinical trialists and systematic review authors frequently conduct subgroup analyses to identify groups of patients (ie, sicker patients) who may respond differently to treatment than other groups (ie, less sick patients), or find more and less effective ways of administering treatment (eg, intravenous vs oral). 1,2 Although subgroup analyses may help individualize treatment, they may also mislead clinicians.For example, the Second International Study of Infarct Survival (ISIS-2) investigators reported an apparent subgroup effect: patients presenting with myocardial infarction born under the zodiac signs of Gemini or Libra did not experience the same reduction in vascular mortality attributable to aspirin that patients with other zodiac signs had (Table 1). 3 Despite statistical the findings' reaching significance (P = .003 for interaction), the investigators did not believe the subgroup effect-they reported the results to demonstrate the dangers of subgroup analysis. The eTable (in the Supplement) lists 19 examples in which other randomized clinical trial (RCT) authors have, when faced with biologically more plausible effects, claimed subgroup effects unsupported by subsequent evidence.Clinician scientists may underestimate the extent to which chance can create imbalances (see Box 1 for another illustration). In the situations we described, the investigators were either demonstrating (the ISIS-2 example) or being misled by (eTable in the Supplement) the play of chance. When treatment effects are similar across patient groups or across ways of administering treatments, subgroup analyses will sometimes reveal apparently compelling but actually spurious subgroup differences.The challenge for readers of the medical literature is to distinguish credible from less than credible reports of subgroup effects. Cl...