Cardiovascular risk assessment tools: A scoping review

ObjectivesThe objective of this review was to describe cardiovascular risk (CVR) assessment methods and to identify evidence-based practice recommendations when dealing with population at risk of developing cardiovascular diseases.Review methods and data sourcesA literature review following the Arksey and O'Malley scoping review methodology was conducted. By using appropriate key terms, literature searches were conducted in PubMed, SciELO, Cochrane Library, Dialnet, ENFISPO, Medigraphic, ScienceDirect, Cuiden, and Lilacs databases. A complementary search on websites related to the area of interest was conducted. Articles published in English or Spanish in peer-review journals between 2010 and 2017. Critical appraisal for methodological quality was conducted. Data was extracted using ad-hoc tables and qualitatively synthesized.ResultsAfter eliminating duplicates, 55 325 records remained, and 1432 records were selected for screening. Out of these, 88 full-text articles were selected for eligibility criteria, and finally, 67 studies were selected for this review, and 25 studies were selected for evidence synthesis. In total, 23 CVR assessment tools have been identified, pioneered by the Framingham study. Qualitative findings were grouped into four thematic areas: assessment tools and scores, CVR indicators, comparative models, and evidence-based recommendations.ConclusionsIt is necessary to adapt the instruments to the epidemiological reality of the population. The most appropriate way to estimate CVR is to choose the assessment tool that best suits individual conditions, accompanied by a comprehensive assessment of the patient. More research is required to determine a single, adequate, and reliable tool.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).