Reducing Inappropriate Proton Pump Inhibitors Use for Stress Ulcer Prophylaxis in Hospitalized Patients:Systematic Review of De-Implementation Studies

Background: A large proportion of proton pump inhibitor (PPI) prescriptions, including those for stress ulcer prophylaxis (SUP), are inappropriate. Our study purpose was to systematically review the effectiveness of de-implementation strategies aimed at reducing inappropriate PPI use for SUP in hospitalized, non-intensive care unit (non-ICU) patients. Methods: We searched MEDLINE and Embase databases (from inception to January 2020). Two authors independently screened references, performed data extraction, and critical appraisal. Randomized trials and comparative observational studies were eligible for inclusion. Criteria developed by the Cochrane Effective Practice and Organisation of Care (EPOC) group were used for critical appraisal. Besides the primary outcome (inappropriate PPI prescription or use), secondary outcomes included (adverse) pharmaceutical effects and healthcare use. Results: We included ten studies in this review. Most de-implementation strategies contained an educational component (meetings and/or materials), combined with either clinical guideline implementation (n = 5), audit feedback (n = 3), organizational culture (n = 4), or reminders (n = 1). One study evaluating the de-implementation strategy effectiveness showed a significant reduction (RR 0.14; 95% CI 0.03–0.55) of new inappropriate PPI prescriptions. Out of five studies evaluating the effectiveness of de-implementing inappropriate PPI use, four found a significant reduction (RR 0.21; 95% CI 0.18–0.26 to RR 0.76; 95% CI 0.68–0.86). No significant differences in the occurrence of pharmaceutical effects (n = 1) and in length of stay (n = 3) were observed. Adverse pharmaceutical effects were reported in two studies and five studies reported on PPI or total drug costs. No pooled effect estimates were calculated because of large statistical heterogeneity between studies. Discussion: All identified studies reported mainly educational interventions in combination with one or multiple other intervention strategies and all interventions were targeted at providers. Most studies found a small to moderate reduction of (inappropriate) PPI prescriptions or use

The methodological quality of 176,620 randomized controlled trials published between 1966 and 2018 reveals a positive trend but also an urgent need for improvement

Many randomized controlled trials (RCTs) are biased and difficult to reproduce due to methodological flaws and poor reporting. There is increasing attention for responsible research practices and implementation of reporting guidelines, but whether these efforts have improved the methodological quality of RCTs (e.g., lower risk of bias) is unknown. We, therefore, mapped risk-of-bias trends over time in RCT publications in relation to journal and author characteristics. Meta-information of 176,620 RCTs published between 1966 and 2018 was extracted. The risk-of-bias probability (random sequence generation, allocation concealment, blinding of patients/personnel, and blinding of outcome assessment) was assessed using a risk-of-bias machine learning tool. This tool was simultaneously validated using 63,327 human risk-of-bias assessments obtained from 17,394 RCTs evaluated in the Cochrane Database of Systematic Reviews (CDSR). Moreover, RCT registration and CONSORT Statement reporting were assessed using automated searches. Publication characteristics included the number of authors, journal impact factor (JIF), and medical discipline. The annual number of published RCTs substantially increased over 4 decades, accompanied by increases in authors (5.2 to 7.8) and institutions (2.9 to 4.8). The risk of bias remained present in most RCTs but decreased over time for allocation concealment (63% to 51%), random sequence generation (57% to 36%), and blinding of outcome assessment (58% to 52%). Trial registration (37% to 47%) and the use of the CONSORT Statement (1% to 20%) also rapidly increased. In journals with a higher impact factor (>10), the risk of bias was consistently lower with higher levels of RCT registration and the use of the CONSORT Statement. Automated risk-of-bias predictions had accuracies above 70% for allocation concealment (70.7%), random sequence generation (72.1%), and blinding of patients/personnel (79.8%), but not for blinding of outcome assessment (62.7%). In conclusion, the likelihood of bias in RCTs has generally decreased over the last decades. This optimistic trend may be driven by increased knowledge augmented by mandatory trial registration and more stringent reporting guidelines and journal requirements. Nevertheless, relatively high probabilities of bias remain, particularly in journals with lower impact factors. This emphasizes that further improvement of RCT registration, conduct, and reporting is still urgently needed

What are Effective Strategies to Reduce Low-Value Care? An Analysis of 121 Randomized Deimplementation Studies

Background: Low-value care is healthcare leading to no or little clinical benefit for the patient. The best (combinations of) interventions to reduce low-value care are unclear. Purpose: To provide an overview of randomized controlled trials (RCTs) evaluating deimplementation strategies, to quantify the effectiveness and describe different combinations of strategies. Methods: Analysis of 121 RCTs (1990-2019) evaluating a strategy to reduce low-value care, identified by a systematic review. Deimplementation strategies were described and associations between strategy characteristics and effectiveness explored. Results: Of 109 trials comparing deimplementation to usual care, 75 (69%) reported a significant reduction of low-value healthcare practices. Seventy-three trials included in a quantitative analysis showed a median relative reduction of 17% (IQR 7%-42%). The effectiveness of deimplementation strategies was not associated with the number and types of interventions applied. Conclusions and Implications: Most deimplementation strategies achieved a considerable reduction of low-value care. We found no signs that a particular type or number of interventions works best for deimplementation. Future deimplementation studies should map relevant contextual factors, such as the workplace culture or economic factors. Interventions should be tailored to these factors and provide details regarding sustainability of the effect.</p

Prediction models for diagnosis and prognosis of covid-19: : systematic review and critical appraisal

Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity. Funding: LW, BVC, LH, and MDV acknowledge specific funding for this work from Internal Funds KU Leuven, KOOR, and the COVID-19 Fund. LW is a postdoctoral fellow of Research Foundation-Flanders (FWO) and receives support from ZonMw (grant 10430012010001). BVC received support from FWO (grant G0B4716N) and Internal Funds KU Leuven (grant C24/15/037). TPAD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050). VMTdJ was supported by the European Union Horizon 2020 Research and Innovation Programme under ReCoDID grant agreement 825746. KGMM and JAAD acknowledge financial support from Cochrane Collaboration (SMF 2018). KIES is funded by the National Institute for Health Research (NIHR) School for Primary Care Research. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant C49297/A27294). JM was supported by the Cancer Research UK (programme grant C49297/A27294). PD was supported by the NIHR Biomedical Research Centre, Oxford. MOH is supported by the National Heart, Lung, and Blood Institute of the United States National Institutes of Health (grant R00 HL141678). ICCvDH and BCTvB received funding from Euregio Meuse-Rhine (grant Covid Data Platform (coDaP) interref EMR187). The funders played no role in study design, data collection, data analysis, data interpretation, or reporting.Peer reviewedPublisher PD

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Maximizing research value: adequate reporting and effective (de-)implementation strategies

The term evidence-based medicine (EBM) refers to the process of integrating research findings (evidence) with clinical expertise and patient preferences in making decisions for individual patients. EBM requires useful research reports. Furthermore, additional activities are usually needed to ensure the uptake of research evidence in routine clinical practice. This thesis aims to explore and improve the methods to report healthcare research and implement research findings, which are both essential components to facilitate EBM. The first part of the thesis focuses on prediction model studies and the TRIPOD reporting guideline. It describes an evaluation of the reporting of this study type and provides a systematic method for such an evaluation. We found that, in general, prediction models were poorly reported and that information essential for their identification, use in individual risk prediction, or for external validation often was not detailed enough. Following these findings, we developed additional guidance for reporting prediction model studies in abstracts. TRIPOD for Abstracts is a checklist of 12 items that is applicable to all types of prediction model studies (including development, external validation, added value and model updating studies), regardless the clinical domain or the statistical approach used. Furthermore, we assessed the endorsement of TRIPOD and other reporting guidelines by medical journals and conducted an online survey among editors to reveal potential barriers and facilitators to the implementation of reporting guidelines The second part of the thesis addresses the implementation of evidence recommending to abandon the routine use of a specific healthcare practice of low-value, so called de-implementation. It presents an evidence synthesis regarding factors influencing de-implementation. For a large part these factors were related to the individual healthcare provider, and rather to attitude than to knowledge or behaviour, but also factors related to the patient, social context, organizational context and economical/political context were identified. Although future research should investigate this more specifically, it seems that patient-provider interaction, the fear of consequences of withholding a test or treatment, and financial incentives are more important factors in de-implementation than in implementation. Through a systematic review of the literature we found that many de-implementation strategies achieve a considerable reduction of low-value care, especially those applying a multifaceted strategy, including reminders, audit and feedback, or patient-targeted interventions. Details regarding sustainability of effect and impact on health outcomes were often not evaluated. This is, however, essential information for interpretation and application of findings with regard to rolling out successful de-implementation strategies at a larger scale. Both concepts explored in this thesis show that it requires a combination of quantitative and qualitative research to collect all the necessary information to design and evaluate effective strategies to promote the uptake of research findings in clinical practice. Only then we can let patients benefit from the available evidence and maximize the value of our research

Maximizing research value: adequate reporting and effective (de-)implementation strategies

The term evidence-based medicine (EBM) refers to the process of integrating research findings (evidence) with clinical expertise and patient preferences in making decisions for individual patients. EBM requires useful research reports. Furthermore, additional activities are usually needed to ensure the uptake of research evidence in routine clinical practice. This thesis aims to explore and improve the methods to report healthcare research and implement research findings, which are both essential components to facilitate EBM. The first part of the thesis focuses on prediction model studies and the TRIPOD reporting guideline. It describes an evaluation of the reporting of this study type and provides a systematic method for such an evaluation. We found that, in general, prediction models were poorly reported and that information essential for their identification, use in individual risk prediction, or for external validation often was not detailed enough. Following these findings, we developed additional guidance for reporting prediction model studies in abstracts. TRIPOD for Abstracts is a checklist of 12 items that is applicable to all types of prediction model studies (including development, external validation, added value and model updating studies), regardless the clinical domain or the statistical approach used. Furthermore, we assessed the endorsement of TRIPOD and other reporting guidelines by medical journals and conducted an online survey among editors to reveal potential barriers and facilitators to the implementation of reporting guidelines The second part of the thesis addresses the implementation of evidence recommending to abandon the routine use of a specific healthcare practice of low-value, so called de-implementation. It presents an evidence synthesis regarding factors influencing de-implementation. For a large part these factors were related to the individual healthcare provider, and rather to attitude than to knowledge or behaviour, but also factors related to the patient, social context, organizational context and economical/political context were identified. Although future research should investigate this more specifically, it seems that patient-provider interaction, the fear of consequences of withholding a test or treatment, and financial incentives are more important factors in de-implementation than in implementation. Through a systematic review of the literature we found that many de-implementation strategies achieve a considerable reduction of low-value care, especially those applying a multifaceted strategy, including reminders, audit and feedback, or patient-targeted interventions. Details regarding sustainability of effect and impact on health outcomes were often not evaluated. This is, however, essential information for interpretation and application of findings with regard to rolling out successful de-implementation strategies at a larger scale. Both concepts explored in this thesis show that it requires a combination of quantitative and qualitative research to collect all the necessary information to design and evaluate effective strategies to promote the uptake of research findings in clinical practice. Only then we can let patients benefit from the available evidence and maximize the value of our research

Julius Symposium 2017 - Heus, P

To quantitatively assess the impact of de-implementation on low-value care, we reviewed studies that expressed low-value care as the proportion inappropriate care (rather than total volume of care) both before and after de-implementation