Objective To report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets. Design Systematic review and meta-analysis. Data sources Medline up to August 2019. Eligibility criteria for selecting studies and methods Randomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured. Results In 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I 2 =76%), with the top quartile of reported improvements ranging from 10% to 62%. In 30 trials reporting clinical endpoints, clinical decision support systems increased the proportion of patients achieving guideline based targets (eg, blood pressure or lipid control) by a median of 0.3% (interquartile range −0.7% to 1.9%). Two study characteristics (low baseline adherence and paediatric settings) were associated with significantly larger effects. Inclusion of these covariates in the multivariable meta-regression, however, did not reduce heterogeneity. Conclusions Most interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.
Objective: Assess the impact of allocation concealment and blinding on the results of trials addressing COVID-19 therapeutics. Data sources: World Health Organization (WHO) COVID-19 database and the Living Overview of the Evidence (L-OVE) COVID-19 platform by the Epistemonikos Foundation (up to February 4th 2022) Methods: We included trials that compared drug treatments, antiviral antibodies and cellular therapies with placebo or standard care. For the five most commonly reported outcomes, if sufficient data were available, we performed random-effects meta-regression comparing the results of trials with and without allocation concealment and trials in which both healthcare providers and patients were blinded with trials in which healthcare providers and/or patients were aware of the intervention. A ratio of odds ratios (ROR) > 1 or a difference in mean difference (DMD) > 0 indicates that trials without allocation concealment or open-label trials produced larger effects than trials with allocation concealment or blinded trials. Results: As of February 4th 2022, we have identified 488 trials addressing COVID-19 drug treatments and antiviral antibodies and cellular therapies. Of these, 436 trials reported on one or more of our outcomes of interest and were included in our analyses. We found that trials without allocation concealment probably overestimate mortality (ROR 1.14 [95% CI 0.92 to 1.41]), need for mechanical ventilation (ROR 1.26 [95% CI 0.97 to 1.64]), admission to hospital (ROR 1.93 [95% CI 0.83 to 4.48]), duration of hospitalization (DMD 1.94 [95% CI 0.86 to 3.02]), and duration of mechanical ventilation (DMD 2.64 [95% CI -0.90 to 6.18]), but results were imprecise. We did not find compelling evidence that double-blind and open-label trials produce consistently different results for mortality (ROR 1.00 [95% CI 0.87 to 1.15]), need for mechanical ventilation (ROR 1.03 [95% CI 0.84 to 1.26]), and duration of hospitalization (DMD 0.47 days [95% CI -0.38 to 1.32]). We found that open-label trials may overestimate the beneficial effects of interventions for hospitalizations (ROR 1.87 [95% CI 0.95 to 3.67] and duration of mechanical ventilation (DMD 1.02 days [95% CI -1.30 to 3.35]), but results were imprecise. Conclusion: We found compelling evidence that, compared to trials with allocation concealment, trials without allocation concealment may overestimate the beneficial effects of treatments. We did not find evidence that trials without blinding addressing COVID-19 interventions produce consistently different results from trials with blinding. Our results suggest that consideration of blinding status may not be sufficient to judge risk of bias due to imbalances in co-interventions. Evidence users may consider evidence of differences in co-interventions between trial arms when judging the trustworthiness of open-label trials. We suggest, however, evidence users to remain skeptical of trials without allocation concealment.
Objective To phenotype SLE based on symptom burden (disease damage, system involvement and patient reported outcomes), with a specific focus on objective and subjective cognitive function. Methods SLE patients aged 18–65 underwent objective cognitive assessment using the ACR Neuropsychological Battery (ACR-NB) and data was collected on demographic and clinical variables, disease burden/activity, health related quality of life (HRQoL), depression, anxiety, fatigue and perceived cognitive deficits. Similarity network fusion (SNF) was used to identify patient subtypes. Differences between the subtypes were evaluated using Kruskal-Wallis and chi-square tests. Results Of the 238 patients, 90% were female, mean age 41 ± 12 and disease duration 14 ± 10 years at the study visit. The SNF analysis defined two subtypes (A and B) with distinct patterns in objective and subjective cognitive function, disease burden/damage, HRQoL, anxiety and depression. Subtype A performed worst on all significantly different tests of objective cognitive function (p < 0.03) compared with subtype B. Subtype A also, had greater levels of subjective cognitive function (p < 0.001), disease burden/damage (p < 0.04), HRQoL (p < 0.001) and psychiatric measures (p < 0.001) compared with subtype B. Conclusion This study demonstrates the complexity of cognitive impairment (CI) in SLE and that individual, multi-factorial phenotypes exist. Those with greater disease burden, from SLE specific factors or other factors associated with chronic conditions, report poorer cognitive functioning and perform worse on objective cognitive measures. By exploring different ways of phenotyping SLE we may better define CI in SLE. Ultimately, this will aid our understanding of personalised CI trajectories and identification of appropriate treatments.
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