BACKGROUND AND OBJECTIVES: Continuous glucose monitoring (CGM) could be a valuable instrument for measurement of glucose concentration in preterm neonate. We undertook a systematic review and meta-analysis to compare the diagnostic accuracy of CGM devices to intermittent blood glucose evaluation methods for the detection of hypoglycaemic or hypoglycaemic events in preterm infants. DATA SOURCES: A structured electronic database search was performed for studies that assessed the accuracy of CGM against any intermittent blood glucose testing methods in detecting episodes of altered glycaemia in preterm infants. No restrictions were used. Three review authors screened records and included studies. DATA EXTRACTION: Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. From individual patient data (IPD), sensitivity and specificity were determined using predefined thresholds. The mean absolute relative difference (MARD) of the studied CGM devices was assessed and if those satisfied the accuracy requirements (EN ISO 15197). IPD datasets were meta-analysed using a logistic mixed-effects model. A bivariate model was used to estimate the summary receiver operating characteristic curve (ROC) curve and extract the area under the curve (AUC). The overall level of certainty of the evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. RESULTS: Among 4481 records, 11 were included. IPD datasets were obtained for five studies. Only two of the studies showed an MARD lower than 10%, with none of the five CGM devices studied satisfying the European Union (EU) ISO 15197 requirements. Pooled sensitivity and specificity of CGM devices for hypoglycaemia were 0.39 and 0.99, whereas for hyperglycaemia were 0.87 and 0.99, respectively. The AUC was 0.70 and 0.86, respectively. The certainty of the evidence was considered as low to moderate. Limitations primarily related to the lack of representative population, reference standard and CGM device. CONCLUSIONS: ...
OBJECTIVE: To assess barriers and facilitators to de-implementation. DESIGN: A qualitative evidence synthesis with a framework analysis. DATA SOURCES: Medline, Embase, Cochrane Library and Rx for Change databases until September 2018 were searched. ELIGIBILITY CRITERIA: We included studies that primarily focused on identifying factors influencing de-implementation or the continuation of low-value care, and studies describing influencing factors related to the effect of a de-implementation strategy. DATA EXTRACTION AND SYNTHESIS: The factors were classified on five levels: individual provider, individual patient, social context, organisational context, economic/political context. RESULTS: We identified 333 factors in 81 articles. Factors related to the individual provider (n=131; 74% barriers, 17% facilitators, 9% both barrier/facilitator) were associated with their attitude (n=72; 55%), knowledge/skills (n=43; 33%), behaviour (n=11; 8%) and provider characteristics (n=5; 4%). Individual patient factors (n=58; 72% barriers, 9% facilitators, 19% both barrier/facilitator) were mainly related to knowledge (n=33; 56%) and attitude (n=13; 22%). Factors related to the social context (n=46; 41% barriers, 48% facilitators, 11% both barrier/facilitator) included mainly professional teams (n=23; 50%) and professional development (n=12; 26%). Frequent factors in the organisational context (n=67; 67% barriers, 25% facilitators, 8% both barrier/facilitator) were available resources (n=28; 41%) and organisational structures and work routines (n=24; 36%). Under the category of economic and political context (n=31; 71% barriers, 13% facilitators, 16% both barrier/facilitator), financial incentives were most common (n=27; 87%). CONCLUSIONS: This study provides in-depth insight into the factors within the different (sub)categories that are important in reducing low-value care. This can be used to identify barriers and facilitators in low-value care practices or to stimulate development of strategies that need further refinement. ...
Background: Changing population demographics have led to an increasing number of functionally dependent older people who require care and medical treatment. In many countries, government policy aims to shift resources into the community from institutional care settings with the expectation that this will reduce costs and improve the quality of care compared. Objectives: To assess the effects of long-term home or foster home care versus institutional care for functionally dependent older people. Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Library, MEDLINE, Embase, CINAHL, and two trials registers to November 2015. Selection criteria: We included randomised and non-randomised trials, controlled before-after studies and interrupted time series studies complying with the EPOC study design criteria and comparing the effects of long-term home care versus institutional care for functionally dependent older people. Data collection and analysis: Two reviewers independently extracted data and assessed the risk of bias of each included study. We reported the results narratively, as the substantial heterogeneity across studies meant that meta-analysis was not appropriate. Main results: We included 10 studies involving 16,377 participants, all of which were conducted in high income countries. Included studies compared community-based care with institutional care (care homes). The sample size ranged from 98 to 11,803 (median N = 204). There was substantial heterogeneity in the healthcare context, interventions studied, and outcomes assessed. One study was a randomised trial (N = 112); other included studies used designs that had potential for bias, particularly due lack of randomisation, baseline imbalances, and non-blinded outcome assessment. Most studies did not select (or exclude) participants for any specific disease state, with the exception of one study that only included patients if they had a stroke. All studies had methodological limitations, so readers should interpret results with caution. It is uncertain whether long-term home care compared to nursing home care decreases mortality risk (2 studies, N = 314, very-low certainty evidence). Estimates ranged from a nearly three-fold increased risk of mortality in the homecare group (risk ratio (RR) 2.89, 95% confidence interval (CI) 1.57 to 5.32) to a 62% relative reduction (RR 0.38, 95% CI 0.17 to 0.61). We did not pool data due to the high degree of heterogeneity (I2 = 94%). It is uncertain whether the intervention has a beneficial effect on physical function, as the certainty of evidence is very low (5 studies, N = 1295). Two studies reported that participants who received long-term home care had improved activities of daily living compared to those in a nursing home, whereas a third study reported that all participants performed equally on physical function. It is uncertain whether long-term home care improves happiness compared to nursing home care (RR 1.97, 95% CI 1.27 to 3.04) or general satisfaction because the certainty of evidence was very low (2 studies, N = 114). The extent to which long-term home care was associated to more or fewer adverse health outcomes than nursing home care was not reported. It is uncertain whether long-term home care compared to nursing home care decreases the risk of hospital admission (very low-certainty evidence, N = 14,853). RR estimates ranged from 2.75 (95% CI 2.59 to 2.92), showing an increased risk for those receiving care at home, to 0.82 (95% CI 0.72 to 0.93), showing a slightly reduced risk for the same group. We did not pool data due to the high degree of heterogeneity (I2 = 99%). Authors' conclusions: There are insufficient high-quality published data to support any particular model of care for functionally dependent older people. Community-based care was not consistently beneficial across all the included studies; there were some data suggesting that community-based care may be associated with improved quality of life and physical function compared to institutional care. However, community alternatives to institutional care may be associated with increased risk of hospitalisation. Future studies should assess healthcare utilisation, perform economic analysis, and consider caregiver burden.
BACKGROUND: Changing population demographics have led to an increasing number of functionally dependent older people who require care and medical treatment. In many countries, government policy aims to shift resources into the community from institutional care settings with the expectation that this will reduce costs and improve the quality of care compared. OBJECTIVES: To assess the effects of long‐term home or foster home care versus institutional care for functionally dependent older people. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Library, MEDLINE, Embase, CINAHL, and two trials registers to November 2015. SELECTION CRITERIA: We included randomised and non‐randomised trials, controlled before‐after studies and interrupted time series studies complying with the EPOC study design criteria and comparing the effects of long‐term home care versus institutional care for functionally dependent older people. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and assessed the risk of bias of each included study. We reported the results narratively, as the substantial heterogeneity across studies meant that meta‐analysis was not appropriate. MAIN RESULTS: We included 10 studies involving 16,377 participants, all of which were conducted in high income countries. Included studies compared community‐based care with institutional care (care homes). The sample size ranged from 98 to 11,803 (median N = 204). There was substantial heterogeneity in the healthcare context, interventions studied, and outcomes assessed. One study was a randomised trial (N = 112); other included studies used designs that had potential for bias, particularly due lack of randomisation, baseline imbalances, and non‐blinded outcome assessment. Most studies did not select (or exclude) participants for any specific disease state, with the exception of one study that only included patients if they had a stroke. All studies had methodological limitations, so readers ...
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 reviewed ; Publisher PDF
