ABSTRACT
Background: Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly. Methods: We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). Results: A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%. Conclusions: To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care;thus, AI only serves as complementary assistance for clinicians.
ABSTRACT
Background and Aim: The coronavirus disease 2019 pandemic has brought deteriorating physical and mental burdens to health care workers (HCWs) in Indonesia, mainly attributed to the lack of protection and screening among HCWs, patients' concealment of their travel and medical history, and perceived social stigma and discrimination. Hence, we deliver our perspectives and recommendations based on the current situation in Indonesia to enforce their safeties. We encourage stakeholders to implement a systematic approach by employing stringent prevention strategies, ensuring adequate personal protective equipment (PPE) provision and equitable PPE distribution, and routine HCWs screening to prevent nosocomial clusters, in addition to the provision of psychosocial support to HCWs by offering social aids and psychological sessions. Furthermore, social stigma and discrimination toward HCWs and patients should also be addressed and mitigated, thus preventing concealments of patients' history and alleviating emotional burdens. We believe that providing continuous support to HCWs would lead to key benefits in ensuring a winning battle against the COVID-19 pandemic. Relevance for Patients: HCWs are pivotal players in winning the battle against the COVID- 19 pandemic. Ensuring their safety and well-being will enable them to deliver better healthcare services, thus resulting in mutual benefit for themselves, the patients, and the nation's recovery.
ABSTRACT
BACKGROUND: There is mounting evidence related to the association between obesity and severity of COVID-19. However, the direct relationship of the increase in the severe COVID-19 risk factors, with an increase in body mass index (BMI), has not yet been evaluated. AIM: This meta-analysis aims to evaluate the dose-response relationship between body mass index (BMI) and poor outcome in patients with COVID-19. METHODS: A systematic literature search was conducted using PubMed, Europe PMC, ProQuest, and the Cochrane Central Database. The primary outcome was composite poor outcome composed of mortality and severity. The secondary outcomes were mortality and severity. RESULTS: A total of 34,390 patients from 12 studies were included in this meta-analysis. The meta-analysis demonstrated that obesity was associated with composite poor outcome (OR 1.73 [1.40, 2.14], P<0.001; I2: 55.6%), mortality (OR 1.55 [1.16, 2.06], P=0.003; I2: 74.4%), and severity (OR 1.90 [1.45, 2.48], P<0.001; I2: 5.2%) in patients with COVID-19. A pooled analysis of highest BMI versus reference BMI indicate that a higher BMI in the patients was associated with composite poor outcome (aOR 3.02 [1.82, 5.00], P<0.001; I2: 59.8%), mortality (aOR 2.85 [1.17, 6.92], P=0.002; I2: 79.7%), and severity (aOR 3.08 [1.78, 5.33], P<0.001; I2: 11.7%). The dose-response meta-analysis showed an increased risk of composite poor outcome by aOR of 1.052 [1.028, 1.077], P<0.001 for every 5kg/m2 increase in BMI (Pnon-linearity<0.001). The curve became steeper with increasing BMI. CONCLUSION: Dose-response meta-analysis demonstrated that increased BMI was associated with increased poor outcome in patients with COVID-19.