Anna Noel-Storr

The impact of general and regional anesthesia on the incidence of post-operative cognitive dysfunction and post-operative delirium: a systematic review with meta-analysis.

Post-operative cognitive complications such as delirium have been consistently associated with poor short and long term outcomes, and the role of anesthesia, particularly the role of general versus regional anesthesia, remains unclear. The objective of this systematic review with meta-analysis was to compare the influence of general, regional, or a combination of anesthesia on the development of Post-Operative Cognitive Dysfunction (POCD) and Post-Operative Delirium (POD). Standard bibliographic databases were searched and complimented by hand searching of original and review article references. Included studies were randomized controlled trials comparing general to regional (spinal, epidural, or intravenous block) or a combination of these in a cohort who were pre-operatively cognitively normal and had an average age exceeding fifty. Where POD was the principle outcome, studies must have employed the DSM or ICD criteria. Where POCD was the principal outcome, this was defined as any objective cognitive impairment. Twenty one studies were considered suitable for inclusion. There was no effect of anesthesia type on the odds ratio of developing POD (0.88, 0.51-1.51 with 95% confidence) however general anesthesia was marginally non-significantly associated with POCD (odds ratio of 1.34, 0.93-1.95 with 95% confidence). There was no evidence of publication bias. In conclusion, it appears that general anesthesia, compared to others, may increase the risk of developing POCD; however this has not been shown for POD. Possible reasons for this finding have been explored. This data would advocate for the use of regional anesthesia wherever possible especially in people otherwise vulnerable to developing cognitive symptoms.

Reporting standards for studies of diagnostic test accuracy in dementia: The STARDdem Initiative

Objective: To provide guidance on standards for reporting studies of diagnostic test accuracy for dementia disorders. Methods: An international consensus process on reporting standards in dementia and cognitive impairment (STARDdem) was established, focusing on studies presenting data from which sensitivity and specificity were reported or could be derived. A working group led the initiative through 4 rounds of consensus work, using a modified Delphi process and culminating in a face-to-face consensus meeting in October 2012. The aim of this process was to agree on how best to supplement the generic standards of the STARD statement to enhance their utility and encourage their use in dementia research. Results: More than 200 comments were received during the wider consultation rounds. The areas at most risk of inadequate reporting were identified and a set of dementia-specific recommendations to supplement the STARD guidance were developed, including better reporting of patient selection, the reference standard used, avoidance of circularity, and reporting of test-retest reliability. Conclusion: STARDdem is an implementation of the STARD statement in which the original checklist is elaborated and supplemented with guidance pertinent to studies of cognitive disorders. Its adoption is expected to increase transparency, enable more effective evaluation of diagnostic tests in Alzheimer disease and dementia, contribute to greater adherence to methodologic standards, and advance the development of Alzheimer biomarkers.

Systematic review of the body of evidence for the use of biomarkers in the diagnosis of dementia.

Although recent diagnostic criteria for Alzheimers disease propose the use of biomarkers, validation of these biomarkers by diagnostic test accuracy studies is a necessary first step, followed by the synthesis of the evidence from these studies in systematic reviews and meta‐analyses. The quality of the resulting evidence depends on the number and size of the primary studies, their quality, and the adequacy of their reporting. This systematic review assesses the weight and quality of the evidence available from primary diagnostic test accuracy studies.

Cost‐effectiveness of noninvasive liver fibrosis tests for treatment decisions in patients with chronic hepatitis C

The cost‐effectiveness of noninvasive tests (NITs) as alternatives to liver biopsy is unknown. We compared the cost‐effectiveness of using NITs to inform treatment decisions in adult patients with chronic hepatitis C (CHC). We conducted a systematic review and meta‐analysis to calculate the diagnostic accuracy of various NITs using a bivariate random‐effects model. We constructed a probabilistic decision analytical model to estimate health care costs and outcomes (quality‐adjusted life‐years; QALYs) using data from the meta‐analysis, literature, and national UK data. We compared the cost‐effectiveness of four treatment strategies: testing with NITs and treating patients with fibrosis stage ≥F2; testing with liver biopsy and treating patients with ≥F2; treat none; and treat all irrespective of fibrosis. We compared all NITs and tested the cost‐effectiveness using current triple therapy with boceprevir or telaprevir, but also modeled new, more‐potent antivirals. Treating all patients without any previous NIT was the most effective strategy and had an incremental cost‐effectiveness ratio (ICER) of £9,204 per additional QALY gained. The exploratory analysis of currently licensed sofosbuvir treatment regimens found that treat all was cost‐effective, compared to using an NIT to decide on treatment, with an ICER of £16,028 per QALY gained. The exploratory analysis to assess the possible effect on results of new treatments, found that if SVR rates increased to >90% for genotypes 1‐4, the incremental treatment cost threshold for the “treat all” strategy to remain the most cost‐effective strategy would be £37,500. Above this threshold, the most cost‐effective option would be noninvasive testing with magnetic resonance elastography (ICER = £9,189). Conclusions: Treating all adult patients with CHC, irrespective of fibrosis stage, is the most cost‐effective strategy with currently available drugs in developed countries. (Hepatology 2014;60:832–843)

Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

Abstract Objectives Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. Methods We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Results Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%–99% recall) with substantially less effort (we observed a reduction of around 60%–80%) than relying on manual screening alone. Conclusions Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks.

AD-8 for diagnosis of dementia across a variety of healthcare settings

This is a protocol for a Cochrane Review (Diagnostic test accuracy). The objectives are as follows: To determine the diagnostic accuracy of the informant-based questionnaire AD-8, in detection of all-cause (undifferentiated) dementia in adults. We will present data for each healthcare setting where AD-8 may be employed (community; primary care; secondary care). AD-8 for diagnosis of dementia across a variety of healthcare settings (Protocol) Copyright

Associations with publication and assessing publication bias in dementia diagnostic test accuracy studies

Biomarkers are of increasing interest in dementia research. Studies describing favourable accuracy of various dementia tests have influenced research, guidelines and diagnostic criteria. Publication bias is known to compromise reports on efficacy of therapeutic interventions. Traditional methods of quantifying publication bias are not suited to reviews of diagnostic tests. We aimed to describe rates and predictors of publication of dementia test accuracy studies presented at scientific meetings.

Cost-effectiveness of noninvasive liver fibrosis tests for treatment decisions in patients with chronic hepatitis B in the UK: systematic review and economic evaluation.

We compared the cost‐effectiveness of various noninvasive tests (NITs) in patients with chronic hepatitis B and elevated transaminases and/or viral load who would normally undergo liver biopsy to inform treatment decisions. We searched various databases until April 2012. We conducted a systematic review and meta‐analysis to calculate the diagnostic accuracy of various NITs using a bivariate random‐effects model. We constructed a probabilistic decision analytical model to estimate health care costs and outcomes quality‐adjusted‐life‐years (QALYs) using data from the meta‐analysis, literature, and national UK data. We compared the cost‐effectiveness of four decision‐making strategies: testing with NITs and treating patients with fibrosis stage ≥F2, testing with liver biopsy and treating patients with ≥F2, treat none (watchful waiting) and treat all irrespective of fibrosis. Treating all patients without prior fibrosis assessment had an incremental cost‐effectiveness ratio (ICER) of £28 137 per additional QALY gained for HBeAg‐negative patients. For HBeAg‐positive patients, using Fibroscan was the most cost‐effective option with an ICER of £23 345. The base case results remained robust in the majority of sensitivity analyses, but were sensitive to changes in the ≥F2 prevalence and the benefit of treatment in patients with F0–F1. For HBeAg‐negative patients, strategies excluding NITs were the most cost‐effective: treating all patients regardless of fibrosis level if the high cost‐effectiveness threshold of £30 000 is accepted; watchful waiting if not. For HBeAg‐positive patients, using Fibroscan to identify and treat those with ≥F2 was the most cost‐effective option.

Machine Learning for Identifying Randomized Controlled Trials: an evaluation and practitioner’s guide

Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this work, we evaluate ML models for RCT classification (support vector machines, convolutional neural networks, and ensemble approaches). We trained and optimized support vector machine and convolutional neural network models on the titles and abstracts of the Cochrane Crowd RCT set. We evaluated the models on an external dataset (Clinical Hedges), allowing direct comparison with traditional database search filters. We estimated area under receiver operating characteristics (AUROC) using the Clinical Hedges dataset. We demonstrate that ML approaches better discriminate between RCTs and non‐RCTs than widely used traditional database search filters at all sensitivity levels; our best‐performing model also achieved the best results to date for ML in this task (AUROC 0.987, 95% CI, 0.984‐0.989). We provide practical guidance on the role of ML in (1) systematic reviews (high‐sensitivity strategies) and (2) rapid reviews and clinical question answering (high‐precision strategies) together with recommended probability cutoffs for each use case. Finally, we provide open‐source software to enable these approaches to be used in practice.

Cognitive tests to help diagnose dementia in symptomatic people in primary care and the community

What brief cognitive test should a busy GP use when trying to assess someone who might have dementia? The menu of choices is long; one review found 11 options.1 The Cochrane Dementia and Cognitive Improvement Group (CDCIG) is conducting a series of reviews to evaluate the evidence of a range of tests for diagnosing dementia. To date, reviews have been published addressing the accuracy of two tests in primary care: the Informant Questionnaire for Cognitive Disorders in the Elderly (IQCODE) and the Mini Mental State Examination [MMSE]. Reviewers found only one study that investigated the use of the IQCODE in primary care,2 and six that investigated the use of the MMSE.3 A review of the Montreal Cognitive Assessment [MoCA] found no studies that evaluated the accuracy of the test in primary care.4 Reviews are underway for the Mini-Cog and AD-8 tests (see http://dta.cochrane.org/reviews-and-protocols-crg). The IQCODE is a structured informant questionnaire; 26-item and 16-item versions exist and scores range from 1 (no impairment) to 5 (more impairment).4 In the one study that investigated the use of the IQCODE at a threshold of 3.2 in primary care the sensitivity was 100% and specificity 76%, whereas at a threshold of 3.7 the sensitivity was 75% and specificity 98%.2 The MMSE is one of the oldest and therefore …