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| http://dx.doi.org/10.1016/j.pathol.2020.08.010 | DOI Listing |
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients.
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South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature.
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Background: Pulmonary embolism (PE) is a life-threatening condition with high morbidity and mortality. The diagnosis of PE is challenging due to nonspecific symptoms, making reliable diagnostic tools essential. This study addresses the clinical impact of interassay variability in D-dimer measurements on the utilization and diagnostic yield of computed tomography pulmonary angiography (CTPA).
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In the pursuit of personalized medicine, there is a growing demand for computational models with parameters that are easily obtainable to accelerate the development of potential solutions. Blood tests, owing to their affordability, accessibility, and routine use in healthcare, offer valuable biomarkers for assessing hemostatic balance in thrombotic and bleeding disorders. Incorporating these biomarkers into computational models of blood coagulation is crucial for creating patient-specific models, which allow for the analysis of the influence of these biomarkers on clot formation.
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Venous thromboembolism (VTE), consisting of both deep vein thrombosis (DVT) and pulmonary embolism (PE), is an extremely common condition both in the United States and worldwide. Not only is the diagnosis associated with significant morbidity and mortality for patients but it also imposes a deleterious financial burden on the US healthcare system. Diagnosis may be challenging due to variability in clinical presentation and requires a sequential workup including assessment of clinical pretest probability for VTE, D-dimer testing, and imaging.
View Article and Find Full Text PDFThe impact of new-onset atrial fibrillation in the setting of acute coronary syndrome.
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Approximately 10 % of patients who have suffered from myocardial infarction develop new-onset atrial fibrillation (AF). Coronary artery disease implicating atrial branches has been associated with AF. The following variables have been associated with new-onset AF in the setting of acute coronary syndrome: older age, history of hypertension, history of angina, history of stroke, chronic renal failure, body mass index, no statin use, worse nutritional status, worse Killip class, admission heart rate ≥ 85 bpm, complete atrioventricular block, Glasgow prognostic score, Syntax score, CHEST score > 3, PRECISE-DAPT score ≥ 25, left ventricular ejection fraction ≤40 %, increased left atrial diameter, E/E' ratio > 12, epicardial fat tissue thickness, and thrombolysis in myocardial infarction flow < 3.
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