A Review of Current Heart Failure Apps

Background: Heart disease is the second leading cause of death in Canada, with tremendous economic impacts on the healthcare system. Currently, there are several smartphone based heart failure (HF) apps available for patients. These apps provide information to patients regarding HF, and how to monitor and manage their condition. This review describes the current literature on HF apps, and describes the features offered by these apps. Methods and Results: Peer-reviewed literature was searched and revealed only a limited number of studies (8) related to HF apps, including HeartMapp, SUPPORT-HF and CardioManager.  A Google-based grey literature search was conducted, and Google Play and the Apple Store were also searched to identify additional HF-related apps. These searches revealed several other HF-related apps (total 11), the features of which are described in the current review. Conclusion: This review will help healthcare providers select apps for themselves and recommend HF apps to their patients that provide the most suitable disease and management information and monitoring capability. The insight will also help software developers design apps in the future that will provide better support to patients with HF and help the healthcare providers monitor their condition better

7. Prevalence of CAD in asymptomatic type II diabetics, using MPI as screening tool. Single center cross sectional study from KSA

Clinical research. Presentation Type: Oral presentation. Introduction: In patients with type 2 diabetes coronary artery disease (CAD) is a major cause of mortality and morbidity. Knowing the elevated risk of cardiovascular events and high prevalence of silent myocardial ischemia, screening asymptomatic diabetic patients-yet controversial-is appealing. The aim of the study is to measure the prevalence of silent ischemia in asymptomatic type-II diabetic patient with at least one or more of the given risk factors i.e. Hypertension, Dyslipidemia, Smoking, obesity and F/H of CAD. Myocardial perfusion Imaging is a sensitive test to look for myocardial ischemia. Methodology: This is a single center cross sectional study, approved by the institutional review board of the hospital. The study subjects were type-II diabetes of >5 years duration, asymptomatic, having one or more of the risk factors; The subjects were screened for CAD using myocardial perfusion imaging (MPI). Further intervention or treatment was left to primary physician in case of positive results. Results: A total of 137 patients-after obtaining an informed consent-underwent MPI. There were no complications during the tests. All of the patients tolerated the test well. ECGs were obtained. Two independent reviewers (blinded to each other’s findings) reviewed tests. A test was considered ”Positive” only if both reviewers results matched (in distribution, severity and size). Of 137 cohort, 21(15%) showed perfusion defects consistent with significant myocardial ischemia in a specific coronary artery distribution. Average sum stress score (SSS) was 5 (range 4–8, mode 4). Of the whole group, patients with higher HbA1C had the positive MPI. Results of positive patients were relayed to their primary physicians. Conclusion: Despite higher rate of diabetes in Saudi Arabia, asymptomatic Diabetics have a lower than expected incidence of active CAD. There would be a need to test this notion further. This would require more studies to confirm our findings in Saudi population

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc