Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

Within the UK, COVID-19 has contributed towards over 103,000 deaths. Multiple risk factors for COVID-19 have been identified including various demographics, co-morbidities, biochemical parameters, and physical assessment findings. However, using this vast data to improve clinical care has proven challenging. The main aim of this paper is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, to aid risk-stratification and earlier clinical decision-making. Anonymized data regarding 44 independent predictor variables of 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-controlled analysis. Primary outcomes included inpatient mortality, level of ventilatory support and oxygen therapy required, and duration of inpatient treatment. Secondary pulmonary embolism was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were created using Bayesian Networks, and cross-validated. The developed Bayesian machine learning models were able to predict, using feature selected risk factors, the probability of inpatient mortality (F1 score 83.7%, PPV 82%, NPV 67.9%); level of ventilatory support required (F1 score varies from 55.8% "High-flow Oxygen level" to 71.5% "ITU-Admission level"); duration of inpatient treatment (varies from 46.7% for greater less than or equal 2 days but<3 days" to 69.8% "less than or equal 1 day"); and risk of pulmonary embolism sequelae (F1 score 85.8%, PPV of 83.7%, and NPV of 80.9% ). Overall, our findings demonstrate reliable, multivariable predictive models for 4 outcomes, that utilize readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as clinical decision-making tools.

This raises the challenge of how to sustain services in the next wave of infection, and how to increase earlier diagnosis more rapidly than the current planned rollout of Lung Health Checks, taking into account the changes in behaviour of patients and clinicians alike during this pandemic.

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Introduction: ST-elevation myocardial infarction (STEMI) is a leading cause of morbidity and mortality worldwide.  Present study is designed to gather data in our population about the prevalence of metabolic syndrome (MetS) in STEMI. The gathered data will help in better understanding of association of STEMI and metabolic syndrome. This will also help the physicians to devise preventive strategies based on life style modifications to limit further progression of this syndrome particularly in individuals who are at higher risk for CAD. Objectives: To determine the frequency of metabolic syndrome in patients presented with ST segment elevated myocardial infarction. Study design: Cross sectional study. Duration of Study: 6 months (01-08-2018 to 31-01-2019) Settings: Department of Cardiology, Pakistan Institute of Medical Sciences Islamabad Subjects and Methods: A total of two hundred and fifty-seven (n=257) patients of both gender between age 18-75 years who were diagnosed cases of ST segment elevated myocardial infarction (STEMI) were enrolled in this study. Fasting blood sugar, Serum TG, HDL, blood pressure and waist circumference was estimated and frequency of metabolic syndrome was evaluated. Results: Metabolic syndrome was found present in 37.4% (n=96/257) patients as per our operational definition. Frequency of metabolic syndrome was significantly higher in males as compared to females (P=0.048). No significant difference noted in the frequency of metabolic syndrome when data was stratified for different age groups (P=0.717). Conclusions: Metabolic syndrome was observed to the highly prevalent among patients presented with ST segment elevated myocardial infarction and was significantly higher in males.