Efficacy and safety of ketamine-assisted electroconvulsive therapy in major depressive episode: a systematic review and network meta-analysis.

OBJECTIVE: To meta-analyze clinical efficacy and safety of ketamine compared with other anesthetic agents in the course of electroconvulsive therapy (ECT) in major depressive episode (MDE). METHODS: PubMed/MEDLINE, Cochrane Library, Embase, GoogleScholar, and US and European trial registries were searched from inception through May 23, 2023, with no language limits. We included RCTs with (1) a diagnosis of MDE; (2) ECT intervention with ketamine and/or other anesthetic agents; and (3) measures included: depressive symptoms, cognitive performance, remission or response rates, and serious adverse events. Network meta-analysis (NMA) was performed to compare ketamine and 7 other anesthetic agents. Hedges' g standardized mean differences (SMDs) were used for continuous measures, and relative risks (RRs) were used for other binary outcomes using random-effects models. RESULTS: Twenty-two studies were included in the systematic review. A total of 2322 patients from 17 RCTs were included in the NMA. The overall pooled SMD of ketamine, as compared with propofol as a reference group, was -2.21 (95% confidence interval [CI], -3.79 to -0.64) in depressive symptoms, indicating that ketamine had better antidepressant efficacy than propofol. In a sensitivity analysis, however, ketamine-treated patients had a worse outcome in cognitive performance than propofol-treated patients (SMD, -0.18; 95% CI, -0.28 to -0.09). No other statistically significant differences were found. CONCLUSIONS: Ketamine-assisted ECT is tolerable and may be efficacious in improving depressive symptoms, but a relative adverse impact on cognition may be an important clinical consideration. Anesthetic agents should be considered based on patient profiles and/or preferences to improve effectiveness and safety of ECT use.

Evidence Synthesis for Complex Interventions Using Meta-Regression Models.

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.

Vitamin D and Risk for Type 2 Diabetes in People With Prediabetes : A Systematic Review and Meta-analysis of Individual Participant Data From 3 Randomized Clinical Trials.

BACKGROUND: The role of vitamin D in people who are at risk for type 2 diabetes remains unclear. PURPOSE: To evaluate whether administration of vitamin D decreases risk for diabetes among people with prediabetes. DATA SOURCES: PubMed, Embase, and ClinicalTrials.gov from database inception through 9 December 2022. STUDY SELECTION: Eligible trials that were specifically designed and conducted to test the effects of oral vitamin D versus placebo on new-onset diabetes in adults with prediabetes. DATA EXTRACTION: The primary outcome was time to event for new-onset diabetes. Secondary outcomes were regression to normal glucose regulation and adverse events. Prespecified analyses (both unadjusted and adjusted for key baseline variables) were conducted according to the intention-to-treat principle. DATA SYNTHESIS: Three randomized trials were included, which tested cholecalciferol, 20 000 IU (500 mcg) weekly; cholecalciferol, 4000 IU (100 mcg) daily; or eldecalcitol, 0.75 mcg daily, versus matching placebos. Trials were at low risk of bias. Vitamin D reduced risk for diabetes by 15% (hazard ratio, 0.85 [95% CI, 0.75 to 0.96]) in adjusted analyses, with a 3-year absolute risk reduction of 3.3% (CI, 0.6% to 6.0%). The effect of vitamin D did not differ in prespecified subgroups. Among participants assigned to the vitamin D group who maintained an intratrial mean serum 25-hydroxyvitamin D level of at least 125 nmol/L (≥50 ng/mL) compared with 50 to 74 nmol/L (20 to 29 ng/mL) during follow-up, cholecalciferol reduced risk for diabetes by 76% (hazard ratio, 0.24 [CI, 0.16 to 0.36]), with a 3-year absolute risk reduction of 18.1% (CI, 11.7% to 24.6%). Vitamin D increased the likelihood of regression to normal glucose regulation by 30% (rate ratio, 1.30 [CI, 1.16 to 1.46]). There was no evidence of difference in the rate ratios for adverse events (kidney stones: 1.17 [CI, 0.69 to 1.99]; hypercalcemia: 2.34 [CI, 0.83 to 6.66]; hypercalciuria: 1.65 [CI, 0.83 to 3.28]; death: 0.85 [CI, 0.31 to 2.36]). LIMITATIONS: Studies of people with prediabetes do not apply to the general population. Trials may not have been powered for safety outcomes. CONCLUSION: In adults with prediabetes, vitamin D was effective in decreasing risk for diabetes. PRIMARY FUNDING SOURCE: None. (PROSPERO: CRD42020163522).

A Descriptive Review and Meta-Regression Study of Demographic and Study Context Factors in US Clinical Trials of Cognitive Behavioral Interventions for Alcohol or Other Drug Use.

Objectives. Cognitive-Behaviorally Based Interventions (CBIs) are evidence-based treatments for alcohol and other drug (AOD) use with potential variable effectiveness by population sub-groups. This study used evidence synthesis to examine treatment effect by demographic and study context factors in clinical trials of CBI for AOD. Methods. Studies were systematically identified, and their characteristics and outcome data were extracted and summarized. Standardized mean differences were calculated for within- and between-condition effects on substance use outcomes. Demographic and study context moderators were identified during data acquisition and several sensitivity analyses were conducted. Results. The sample included K = 29 trials and a total of 15 study-level moderators were examined. Information on participants' age, biological sex, and race were reported in at least 26 trials, but information on gender identity, sexual orientation, and ethnicity were reported infrequently or in non-inclusive ways. The mean between-condition effect size was small and moderately heterogenous (d = 0.158, 95% CI = 0.079, 0.238, I2 = 46%) and the mean within-condition effect size was large and showed high heterogeneity (dz = 1.147, 95% CI = 0.811, 1.482, - I2 = 96%). The specific drug targeted in the study and whether biological assay-based outcomes were used moderated between-condition CBI efficacy and the inclusion of co-occurring mental health conditions and study publication date moderated within-condition CBI effects. Conclusions. Results provide preliminary data on study context factors associated with effect estimates in United States based clinical trials of CBI for AOD.

Comparison of Low-Value Care Among Commercial and Medicaid Enrollees.

BACKGROUND: Low-value healthcare is costly and inefficient and may adversely affect patient outcomes. Despite increases in low-value service use, little is known about how the receipt of low-value care differs across payers. OBJECTIVE: To evaluate differences in the use of low-value care between patients with commercial versus Medicaid coverage. DESIGN: Retrospective observational analysis of the 2017 Rhode Island All-payer Claims Database, estimating the probability of receiving each of 14 low-value services between commercial and Medicaid enrollees, adjusting for patient sociodemographic and clinical characteristics. Ensemble machine learning minimized the possibility of model misspecification. PARTICIPANTS: Medicaid and commercial enrollees aged 18-64 with continuous coverage and an encounter at which they were at risk of receiving a low-value service. INTERVENTION: Enrollment in Medicaid or Commercial insurance. MAIN MEASURES: Use of one of 14 validated measures of low-value care. KEY RESULTS: Among 110,609 patients, Medicaid enrollees were younger, had more comorbidities, and were more likely to be female than commercial enrollees. Medicaid enrollees had higher rates of use for 7 low-value care measures, and those with commercial coverage had higher rates for 5 measures. Across all measures of low-value care, commercial enrollees received more (risk difference [RD] 6.8 percentage points; CI: 6.6 to 7.0) low-value services than their counterparts with Medicaid. Commercial enrollees were also more likely to receive low-value services typically performed in the emergency room (RD 11.4 percentage points; CI: 10.7 to 12.2) and services that were less expensive (RD 15.3 percentage points; CI 14.6 to 16.0). CONCLUSION: Differences in the provision of low-value care varied across measures, though average use was slightly higher among commercial than Medicaid enrollees. This difference was more pronounced for less expensive services indicating that financial incentives may not be the sole driver of low-value care.

Multi-state network meta-analysis of progression and survival data.

Multiple randomized controlled trials, each comparing a subset of competing interventions, can be synthesized by means of a network meta-analysis to estimate relative treatment effects between all interventions in the evidence base. Here we focus on estimating relative treatment effects for time-to-event outcomes. Cancer treatment effectiveness is frequently quantified by analyzing overall survival (OS) and progression-free survival (PFS). We introduce a method for the joint network meta-analysis of PFS and OS that is based on a time-inhomogeneous tri-state (stable, progression, and death) Markov model where time-varying transition rates and relative treatment effects are modeled with parametric survival functions or fractional polynomials. The data needed to run these analyses can be extracted directly from published survival curves. We demonstrate use by applying the methodology to a network of trials for the treatment of non-small-cell lung cancer. The proposed approach allows the joint synthesis of OS and PFS, relaxes the proportional hazards assumption, extends to a network of more than two treatments, and simplifies the parameterization of decision and cost-effectiveness analyses.

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes.

BACKGROUND: There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations. OBJECTIVES: To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care. SEARCH METHODS: We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section. SELECTION CRITERIA: We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy. DATA COLLECTION AND ANALYSIS: We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors.  Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available. MAIN RESULTS: We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted.  Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three.  Combinations of the three most effective QI strategies were estimated to lead to the below effects:  - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%;  - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg;  - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other). AUTHORS' CONCLUSIONS: There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.

Active learning for efficiently training emulators of computationally expensive mathematical models.

An emulator is a fast-to-evaluate statistical approximation of a detailed mathematical model (simulator). When used in lieu of simulators, emulators can expedite tasks that require many repeated evaluations, such as sensitivity analyses, policy optimization, model calibration, and value-of-information analyses. Emulators are developed using the output of simulators at specific input values (design points). Developing an emulator that closely approximates the simulator can require many design points, which becomes computationally expensive. We describe a self-terminating active learning algorithm to efficiently develop emulators tailored to a specific emulation task, and compare it with algorithms that optimize geometric criteria (random latin hypercube sampling and maximum projection designs) and other active learning algorithms (treed Gaussian Processes that optimize typical active learning criteria). We compared the algorithms' root mean square error (RMSE) and maximum absolute deviation from the simulator (MAX) for seven benchmark functions and in a prostate cancer screening model. In the empirical analyses, in simulators with greatly varying smoothness over the input domain, active learning algorithms resulted in emulators with smaller RMSE and MAX for the same number of design points. In all other cases, all algorithms performed comparably. The proposed algorithm attained satisfactory performance in all analyses, had smaller variability than the treed Gaussian Processes, and, on average, had similar or better performance as the treed Gaussian Processes in six out of seven benchmark functions and in the prostate cancer model.

Pharmacologic and Nonpharmacologic Treatments for Urinary Incontinence in Women: A Systematic Review and Network Meta-analysis of Clinical Outcomes.

Background: Urinary incontinence (UI), a common malady in women, most often is classified as stress, urgency, or mixed. Purpose: To compare the effectiveness of pharmacologic and nonpharmacologic interventions to improve or cure stress, urgency, or mixed UI in nonpregnant women. Data Sources: MEDLINE, Cochrane Central Register of Controlled Trials (Wiley), Cochrane Database of Systematic Reviews (Wiley), EMBASE (Elsevier), CINAHL (EBSCO), and PsycINFO (American Psychological Association) from inception through 10 August 2018. Study Selection: 84 randomized trials that evaluated 14 categories of interventions and reported categorical cure or improvement outcomes. Data Extraction: 1 researcher extracted study characteristics, results, and study-level risk of bias, with verification by another independent researcher. The research team collaborated to assess strength of evidence (SoE) across studies. Data Synthesis: 84 studies reported cure or improvement outcomes (32 in stress UI, 16 in urgency UI, 4 in mixed UI, and 32 in any or unspecified UI type). The most commonly evaluated active intervention types included behavioral therapies, anticholinergics, and neuromodulation. Network meta-analysis showed that all interventions, except hormones and periurethral bulking agents (variable SoE), were more effective than no treatment in achieving at least 1 favorable UI outcome. Among treatments used specifically for stress UI, behavioral therapy was more effective than either α-agonists or hormones in achieving cure or improvement (moderate SoE); α-agonists were more effective than hormones in achieving improvement (moderate SoE); and neuromodulation was more effective than no treatment for cure, improvement, and satisfaction (high SoE). Among treatments used specifically for urgency UI, behavioral therapy was statistically significantly more effective than anticholinergics in achieving cure or improvement (high SoE), both neuromodulation and onabotulinum toxin A (BTX) were more effective than no treatment (high SoE), and BTX may have been more effective than neuromodulation in achieving cure (low SoE). Limitation: Scarce direct (head-to-head trial) evidence and population heterogeneity based on UI type, UI severity, and history of prior treatment. Conclusion: Most nonpharmacologic and pharmacologic interventions are more likely than no treatment to improve UI outcomes. Behavioral therapy, alone or in combination with other interventions, is generally more effective than pharmacologic therapies alone in treating both stress and urgency UI. Primary Funding Source: Patient-Centered Outcomes Research Institute. (PROSPERO: CRD42017069903).

The Problem With Mechanistic Risk of Bias Assessments in Evidence Synthesis of Observational Studies and a Practical Alternative: Assessing the Impact of Specific Sources of Potential Bias.

The trustworthiness of individual studies is routinely characterized in systemic reviews by evaluating risk of bias, often by mechanistically applying standardized algorithms. However, such instruments prioritize the repeatability of the process over a more thoughtful and informative but necessarily somewhat more subjective approach. In mechanistic risk of bias assessments, the focus is on determining whether specific biases are present, but these assessments do not provide insights into the direction, magnitude, and relative importance of individual biases. In such assessments, all potential biases are naively treated as equally important threats to validity and equally important across all research topics, potentially leading to inappropriate conclusions about the overall strength of the available evidence. Instead, risk of bias assessments be should focused on identifying a few of the most likely influential sources of bias, based on methodologic and subject matter expertise, classifying each specific study on the basis of on how effectively it has addressed each potential bias, and determining whether results differ across studies in relation to susceptibility to each hypothesized source of bias. This approach provides insight into the potential impact of each specific bias, identifies a subset of studies likely to best approximate the causal effect, and suggests design features needed to improve future research.

Adverse Events Associated with Nonsurgical Treatments for Urinary Incontinence in Women: a Systematic Review.

BACKGROUND: Urinary incontinence (UI) is a common malady in women. Numerous nonsurgical treatments are available, each associated with risk of adverse events (AEs). METHODS: We systematically reviewed nonsurgical interventions for urgency, stress, or mixed UI in women, focusing on AEs. We searched MEDLINE®, Cochrane Central Trials Registry, Cochrane Database of Systematic Reviews, and Embase® through December 4, 2017. We included comparative studies and single-group studies with at least 50 women. Abstracts were screened independently in duplicate. One researcher extracted study characteristics and results with verification by another independent researcher. When at least four studies of a given intervention reported the same AE, we conducted random effects model meta-analyses of proportions. We also assessed the strength of evidence. RESULTS: There is low strength of evidence that AEs are rare with behavioral therapies and neuromodulation, and that periurethral bulking agents may result in erosion and increase the risk of voiding dysfunction. High strength of evidence finds that anticholinergics and alpha agonists are associated with high rates of dry mouth and constitutional effects such as fatigue and gastrointestinal complaints. Onabotulinum toxin A (BTX) is also associated with increased risk of urinary tract infections (UTIs) and voiding dysfunction (moderate strength of evidence). DISCUSSION: Behavioral therapies and neuromodulation have low risk of AEs. Anticholinergics and alpha agonists have high rates of dry mouth and constitutional effects. BTX is associated with UTIs and voiding dysfunction. Periurethral bulking agents are associated with erosion and voiding dysfunction. These AEs should be considered when selecting appropriate UI treatment options. AE reporting is inconsistent and AE rates across studies tended to vary widely. Trials should report AEs more consistently.

Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis.

Tests for disease often produce a continuous measure, such as the concentration of some biomarker in a blood sample. In clinical practice, a threshold C is selected such that results, say, greater than C are declared positive and those less than C negative. Measures of test accuracy such as sensitivity and specificity depend crucially on C, and the optimal value of this threshold is usually a key question for clinical practice. Standard methods for meta-analysis of test accuracy (i) do not provide summary estimates of accuracy at each threshold, precluding selection of the optimal threshold, and furthermore, (ii) do not make use of all available data. We describe a multinomial meta-analysis model that can take any number of pairs of sensitivity and specificity from each study and explicitly quantifies how accuracy depends on C. Our model assumes that some prespecified or Box-Cox transformation of test results in the diseased and disease-free populations has a logistic distribution. The Box-Cox transformation parameter can be estimated from the data, allowing for a flexible range of underlying distributions. We parameterise in terms of the means and scale parameters of the two logistic distributions. In addition to credible intervals for the pooled sensitivity and specificity across all thresholds, we produce prediction intervals, allowing for between-study heterogeneity in all parameters. We demonstrate the model using two case study meta-analyses, examining the accuracy of tests for acute heart failure and preeclampsia. We show how the model can be extended to explore reasons for heterogeneity using study-level covariates.

Psychometric Properties of Functional, Ambulatory, and Quality of Life Instruments in Lower Limb Amputees: A Systematic Review.

OBJECTIVE: Summarize the psychometric properties of functional, ambulatory, and quality of life instruments among adult lower limb amputees, highlighting evidence deemed generalizable to the United States Medicare population. DATA SOURCES: Six databases and existing systematic reviews through October 30, 2017. Searches included terms for lower limb amputation or prostheses and outcome measures in humans, without language restriction. STUDY SELECTION: We included peer-reviewed studies of at least 20 adults (≥18 years) with lower limb amputation. Eligible studies reported on psychometric properties of functional, ambulatory, or quality of life instruments. Fifty-three of 425 retrieved articles (12%) met criteria. DATA EXTRACTION: Study characteristics and psychometric property data (validity, reliability, responsiveness, minimum detectable change, minimal important difference, or floor or ceiling effect) were extracted into a customized form based on standardized criteria. All extracted data were confirmed by 2 experts in systematic review and rehabilitation outcome measurement. Instruments were categorized regarding having been validated and found reliable. Other reported psychometric properties were recorded. Studies were also assessed for applicability to the Medicare population based on age and amputation etiology (dysvascular). DATA SYNTHESIS: Fifty-six studies (in 53 articles) reported psychometric properties of 50 instruments. There is evidence for both validity and reliability for 30 instruments, 17 of which have evidence that was deemed generalizable to the Medicare population. Most of the remaining instruments have evidence of either validity or reliability, but not both. Twelve instruments have been assessed specifically among lower limb amputees prior to prosthesis prescription. Thirteen instruments have been assessed regarding their predictive properties for future outcomes. CONCLUSIONS: Numerous instruments assessing ambulation, function, quality of life, and other patient-centered outcomes have evidence of validity and reliability for adults with lower limb amputations. Researchers and clinicians should use validated, reliable instruments when feasible. Many existing and new instruments require validation for use with lower limb amputees.

Treatments of Primary Basal Cell Carcinoma of the Skin: A Systematic Review and Network Meta-analysis.

Background: Most interventions for basal cell carcinoma (BCC) have not been compared in head-to-head randomized trials. Purpose: To evaluate the comparative effectiveness and safety of treatments of primary BCC in adults. Data Sources: English-language searches of MEDLINE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Embase from inception to May 2018; reference lists of guidelines and systematic reviews; and a search of ClinicalTrials.gov in August 2016. Study Selection: Comparative studies of treatments currently used in adults with primary BCC. Data Extraction: One investigator extracted data on recurrence, histologic clearance, clinical clearance, cosmetic outcomes, quality of life, and mortality, and a second reviewer verified extractions. Several investigators evaluated risk of bias for each study. Data Synthesis: Forty randomized trials and 5 nonrandomized studies compared 18 interventions in 9 categories. Relative intervention effects and mean outcome frequencies were estimated using frequentist network meta-analyses. Estimated recurrence rates were similar for excision (3.8% [95% CI, 1.5% to 9.5%]), Mohs surgery (3.8% [CI, 0.7% to 18.2%]), curettage and diathermy (6.9% [CI, 0.9% to 36.6%]), and external-beam radiation (3.5% [CI, 0.7% to 16.8%]). Recurrence rates were higher for cryotherapy (22.3% [CI, 10.2% to 42.0%]), curettage and cryotherapy (19.9% [CI, 4.6% to 56.1%]), 5-fluorouracil (18.8% [CI, 10.1% to 32.5%]), imiquimod (14.1% [CI, 5.4% to 32.4%]), and photodynamic therapy using methyl-aminolevulinic acid (18.8% [CI, 10.1% to 32.5%]) or aminolevulinic acid (16.6% [CI, 7.5% to 32.8%]). The proportion of patients reporting good or better cosmetic outcomes was better for photodynamic therapy using methyl-aminolevulinic acid (93.8% [CI, 79.2% to 98.3%]) or aminolevulinic acid (95.8% [CI, 84.2% to 99.0%]) than for excision (77.8% [CI, 44.8% to 93.8%]) or cryotherapy (51.1% [CI, 15.8% to 85.4%]). Data on quality of life and mortality were too sparse for quantitative synthesis. Limitation: Data are sparse, and effect estimates are imprecise and informed by indirect comparisons. Conclusion: Surgical treatments and external-beam radiation have low recurrence rates for the treatment of low-risk BCC, but substantial uncertainty exists about their comparative effectiveness versus other treatments. Gaps remain regarding high-risk BCC subtypes and important outcomes, including costs. Primary Funding Source: Agency for Healthcare Research and Quality. (PROSPERO: CRD42016043353).

Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study.

OBJECTIVE: Assessing risks of bias in randomized controlled trials (RCTs) is an important but laborious task when conducting systematic reviews. RobotReviewer (RR), an open-source machine learning (ML) system, semi-automates bias assessments. We conducted a user study of RobotReviewer, evaluating time saved and usability of the tool. MATERIALS AND METHODS: Systematic reviewers applied the Cochrane Risk of Bias tool to four randomly selected RCT articles. Reviewers judged: whether an RCT was at low, or high/unclear risk of bias for each bias domain in the Cochrane tool (Version 1); and highlighted article text justifying their decision. For a random two of the four articles, the process was semi-automated: users were provided with ML-suggested bias judgments and text highlights. Participants could amend the suggestions if necessary. We measured time taken for the task, ML suggestions, usability via the System Usability Scale (SUS) and collected qualitative feedback. RESULTS: For 41 volunteers, semi-automation was quicker than manual assessment (mean 755 vs. 824 s; relative time 0.75, 95% CI 0.62-0.92). Reviewers accepted 301/328 (91%) of the ML Risk of Bias (RoB) judgments, and 202/328 (62%) of text highlights without change. Overall, ML suggested text highlights had a recall of 0.90 (SD 0.14) and precision of 0.87 (SD 0.21) with respect to the users' final versions. Reviewers assigned the system a mean 77.7 SUS score, corresponding to a rating between "good" and "excellent". CONCLUSIONS: Semi-automation (where humans validate machine learning suggestions) can improve the efficiency of evidence synthesis. Our system was rated highly usable, and expedited bias assessment of RCTs.

Real-Time Emergency Department Electronic Notifications Regarding High-Risk Patients: A Systematic Review.

Background: To systematically review evidence on the feasibility and efficacy of real-time electronic notifications about patients at high risk of emergency department (ED) recidivism. Methods: Eight electronic databases were searched for empirical studies of real-time ED-based electronic tools, identifying adult patients at high risk of frequent utilization. Study selection and data extraction were performed independently by two reviewers. Qualitative data synthesis and assessment of strength of evidence were conducted through consensus discussion. Results: Of 2,256 records found through the search, 210 were duplicates, 2,004 were excluded based on abstract review, and 31 were excluded after full text review. The final sample consisted of 10 studies described in 11 articles describing the effect of real-time ED-based electronic notifications for high-risk patients. Three were randomized controlled trials (RCTs). All notifications were based on prespecified markers of risk. Seven studies integrated complex care plans into the electronic health record. Effect on ED use and length of stay (LOS) was mixed: nine studies reported decreased ED use, although results were statistically significant in only three studies; for LOS, one study reported a statistically significant reduction. Impact on cost and financial metrics was promising, with three (of three studies reporting this metric) showing improved organizational financial metrics. Three RCTs reported a reduction in opioid prescriptions. Conclusions: Real-time electronic notifications of ED providers regarding patients at high risk of ED recidivism are feasible. They may help reduce resource utilization and costs. Large knowledge gaps remain regarding patient- and provider-centered outcomes.

Propensity Score-Based Methods in Comparative Effectiveness Research on Coronary Artery Disease.

This review examines the conduct and reporting of observational studies using propensity score-based methods to compare coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or medical therapy for patients with coronary artery disease. A systematic selection process identified 48 studies: 20 addressing CABG versus PCI; 21 addressing bare-metal stents versus drug-eluting stents; 5 addressing CABG versus medical therapy; 1 addressing PCI versus medical therapy; and 1 addressing drug-eluting stents versus balloon angioplasty. Of 32 studies reporting information on variable selection, 7 relied exclusively on statistical criteria for the association of covariates with treatment, and 5 used such criteria to determine whether product or nonlinear terms should be included in the propensity score model. Twenty-five (52%) studies reported assessing covariate balance using the estimated propensity score, but only 1 described modifications to the propensity score model based on this assessment. The over 400 variables used in the 48 propensity score models were classified into 12 categories and 60 subcategories; only 17 subcategories were represented in at least half of the propensity score models. Overall, reporting of propensity score-based methods in observational studies comparing CABG, PCI, and medical therapy was incomplete; when adequately described, the methods used were often inconsistent with current methodological standards.

Recommendations for the Conduct and Reporting of Modeling and Simulation Studies in Health Technology Assessment.

Models and simulations are valuable tools for addressing the uncertainty, tradeoffs, and heterogeneous preferences that complicate research questions in health technology assessment. This article presents recommendations for the conduct and reporting of modeling and simulation studies based on a systematic review of published recommendation statements, a survey of Web sites of international health technology assessment organizations, and input from experts and other stakeholders. The recommendations apply to mathematical models that represent structural relationships among model components and integrate information from multiple sources; they address model identification, estimation, verification, and validation, as well as the conduct of sensitivity, stability, and uncertainty analyses. They are organized into model conceptualization and structure, data, model assessment and consistency, and interpreting and reporting results. They should contribute to increased use and better conduct and reporting of modeling and simulation studies in health technology assessment.

Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine.

IMPORTANCE: Since publication of the report by the Panel on Cost-Effectiveness in Health and Medicine in 1996, researchers have advanced the methods of cost-effectiveness analysis, and policy makers have experimented with its application. The need to deliver health care efficiently and the importance of using analytic techniques to understand the clinical and economic consequences of strategies to improve health have increased in recent years. OBJECTIVE: To review the state of the field and provide recommendations to improve the quality of cost-effectiveness analyses. The intended audiences include researchers, government policy makers, public health officials, health care administrators, payers, businesses, clinicians, patients, and consumers. DESIGN: In 2012, the Second Panel on Cost-Effectiveness in Health and Medicine was formed and included 2 co-chairs, 13 members, and 3 additional members of a leadership group. These members were selected on the basis of their experience in the field to provide broad expertise in the design, conduct, and use of cost-effectiveness analyses. Over the next 3.5 years, the panel developed recommendations by consensus. These recommendations were then reviewed by invited external reviewers and through a public posting process. FINDINGS: The concept of a "reference case" and a set of standard methodological practices that all cost-effectiveness analyses should follow to improve quality and comparability are recommended. All cost-effectiveness analyses should report 2 reference case analyses: one based on a health care sector perspective and another based on a societal perspective. The use of an "impact inventory," which is a structured table that contains consequences (both inside and outside the formal health care sector), intended to clarify the scope and boundaries of the 2 reference case analyses is also recommended. This special communication reviews these recommendations and others concerning the estimation of the consequences of interventions, the valuation of health outcomes, and the reporting of cost-effectiveness analyses. CONCLUSIONS AND RELEVANCE: The Second Panel reviewed the current status of the field of cost-effectiveness analysis and developed a new set of recommendations. Major changes include the recommendation to perform analyses from 2 reference case perspectives and to provide an impact inventory to clarify included consequences.

Do statins impair cognition? A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: In 2012, the United States Food and Drug Administration (FDA) issued a warning regarding potential adverse effects of HMG-CoA reductase inhibitors (statins) on cognition, based on the Adverse Events Reporting System and a review of the medical literature. We aimed to synthesize randomized clinical trial (RCTs) evidence on the association between statin therapy and cognitive outcomes. METHODS: We searched MEDLINE, EMBASE, and Cochrane CENTRAL through December 2012, and reviewed published systematic reviews of statin treatment. We sought RCTs that compared statin treatment versus placebo or standard care, and reported at least one cognitive outcome (frequency of adverse cognitive events or measurements using standard neuropsychological cognitive test scores). Studies reporting sufficient information to calculate effect sizes were included in meta-analyses. Standardized and unstandardized mean differences were calculated for continuous outcomes for global cognition and for pre-specified cognitive domains. The main outcome was change in cognition measured by neuropsychological tests; an outcome of secondary interest was the frequency of adverse cognitive events observed during follow-up. RESULTS: We identified 25 RCTs (all placebo-controlled) reporting cognitive outcomes in 46,836 subjects, of which 23 RCTs reported cognitive test results in 29,012 participants. Adverse cognitive outcomes attributable to statins were rarely reported in trials involving cognitively normal or impaired subjects. Furthermore, meta-analysis of cognitive test data (14 studies; 27,643 participants) failed to show significant adverse effects of statins on all tests of cognition in either cognitively normal subjects (standardized mean difference 0.01, 95% confidence interval, CI, -0.01 to 0.03, p = 0.42) or Alzheimer's disease subjects (standardized mean difference -0.05, 95% CI -0.19 to 0.10, p = 0.38). CONCLUSIONS: Statin therapy was not associated with cognitive impairment in RCTs. These results raise questions regarding the continued merit of the FDA warning about potential adverse effects of statins on cognition.