Telemedicine-Based Management of Oral Anticoagulation Therapy:Systematic Review and Meta-analysis

BACKGROUND: Oral anticoagulation is the cornerstone treatment of several diseases. Its management is often challenging, and different telemedicine strategies have been implemented to support it. OBJECTIVE: The aim of the study is to systematically review the evidence on the impact of telemedicine-based oral anticoagulation management compared to usual care on thromboembolic and bleeding events. METHODS: Randomized controlled trials were searched in 5 databases from inception to September 2021. Two independent reviewers performed study selection and data extraction. Total thromboembolic events, major bleeding, mortality, and time in therapeutic range were assessed. Results were pooled using random effect models. RESULTS: In total, 25 randomized controlled trials were included (n=25,746 patients) and classified as moderate to high risk of bias by the Cochrane tool. Telemedicine resulted in lower rates of thromboembolic events, though not statistically significant (n=13 studies, relative risk [RR] 0.75, 95% CI 0.53-1.07; I2=42%), comparable rates of major bleeding (n=11 studies, RR 0.94, 95% CI 0.82-1.07; I2=0%) and mortality (n=12 studies, RR 0.96, 95% CI 0.78-1.20; I2=11%), and an improved time in therapeutic range (n=16 studies, mean difference 3.38, 95% CI 1.12-5.65; I2=90%). In the subgroup of the multitasking intervention, telemedicine resulted in an important reduction of thromboembolic events (RR 0.20, 95% CI 0.08-0.48). CONCLUSIONS: Telemedicine-based oral anticoagulation management resulted in similar rates of major bleeding and mortality, a trend for fewer thromboembolic events, and better anticoagulation quality compared to standard care. Given the potential benefits of telemedicine-based care, such as greater access to remote populations or people with ambulatory restrictions, these findings may encourage further implementation of eHealth strategies for anticoagulation management, particularly as part of multifaceted interventions for integrated care of chronic diseases. Meanwhile, researchers should develop higher-quality evidence focusing on hard clinical outcomes, cost-effectiveness, and quality of life. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020159208; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=159208.</p

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data.

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard

Effect of probiotic and synbiotic formulations on anthropometrics and adiponectin in overweight and obese participants: A systematic review and meta-analysis of randomized controlled trials

Accumulating evidence suggests obesity and its complication are linked to gut microbiota and probiotics can affect the metabolic functions of humans. The goal of this study was to systematically review the effect of probiotic and synbiotic formulations on body mass index (BMI), total body fat, waist circumstance (WC), Waist–hip ratio (WHR), and adiponectin in overweight and obese Participants in randomized trials (RCTs). A comprehensive search performed in PubMed/MEDLINE, Cochrane and SCOPUS by two researchers, independently without language or release date restrictions up to 15th October 2019. PRISMA guidelines followed to perform this meta-analysis. The inclusion criteria were: 1) RCT design, 2) intervention by pro or synbiotic, 3) Anthropometrics and/or adiponectin levels as outcome. DerSimonian and Laird random effect model used to combine results of included studies. Thirty-two studies contained 2105 participants (n = 28–200) were analyzed in this meta-analysis. Average length of intervention in included studies was 10.18 weeks and ranged from 3 to 12 weeks. Combined results showed significant reduction in BMI (WMD: −0.25 kg/m2; 95% CI −0.33, −0.17; I2 = 96%), total body fat (WMD: −0.75%; 95% CI −0.90, −0.61; I2 = 63%), WC (WMD: −0.99 cm; 95% CI −1.33, −0.66; I2 = 92%), and WHR (WMD: −0.01; 95% CI −0.02, 0.01; I2 = 15%) in probiotic group compared to placebo. There was no significant effect on adiponectin levels by probiotic intervention (WMD: −0.01 μg/ml; 95% CI −0.33, 0.32; I2 = 90%). Furthermore, meta-regression showed significant relation between duration of intervention and reduction of BMI (coef = −0.1533, p < 0.001) and WC (coef = −0.7131, p < 0.001). The combined results showed reduction in BMI, body fat, WC, and WHR in overweight and obese patients by supplementation with probiotics or synbiotics

Barriers and facilitators to utilizing digital health technologies by healthcare professionals

Abstract Digital technologies change the healthcare environment, with several studies suggesting barriers and facilitators to using digital interventions by healthcare professionals (HPs). We consolidated the evidence from existing systematic reviews mentioning barriers and facilitators for the use of digital health technologies by HP. Electronic searches were performed in five databases (Cochrane Database of Systematic Reviews, Embase®, Epistemonikos, MEDLINE®, and Scopus) from inception to March 2023. We included reviews that reported barriers or facilitators factors to use technology solutions among HP. We performed data abstraction, methodological assessment, and certainty of the evidence appraisal by at least two authors. Overall, we included 108 reviews involving physicians, pharmacists, and nurses were included. High-quality evidence suggested that infrastructure and technical barriers (Relative Frequency Occurrence [RFO] 6.4% [95% CI 2.9–14.1]), psychological and personal issues (RFO 5.3% [95% CI 2.2–12.7]), and concerns of increasing working hours or workload (RFO 3.9% [95% CI 1.5–10.1]) were common concerns reported by HPs. Likewise, high-quality evidence supports that training/educational programs, multisector incentives, and the perception of technology effectiveness facilitate the adoption of digital technologies by HPs (RFO 3.8% [95% CI 1.8–7.9]). Our findings showed that infrastructure and technical issues, psychological barriers, and workload-related concerns are relevant barriers to comprehensively and holistically adopting digital health technologies by HPs. Conversely, deploying training, evaluating HP’s perception of usefulness and willingness to use, and multi-stakeholders incentives are vital enablers to enhance the HP adoption of digital interventions