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.

ASSOCIATION OF SMOKING AND SEVERITY OF COVID-19 INFECTION AMONG 5,889 PATIENTS IN MALAYSIA: A MULTI-CENTER OBSERVATIONAL STUDY.

OBJECTIVE: This study aims to investigate the association between smoking and the severity of COVID-19 infection during the initial wave of this pandemic in Malaysia. METHODS: This is a multi-center observational study using secondary hospital data collected retrospectively from February 1, 2020, until May 30, 2020. Clinical records of all real-time polymerase chain reaction (RT-PCR)-confirmed COVID-19 cases with smoking status, co-morbidities, clinical features, and disease management were retrieved. Severity was assessed by the presence of complications and outcomes of COVID-19 infection. Logistic regression was used to determine the association between COVID-19 disease severity and smoking status. RESULTS: A total of 5,889 COVID-19 cases were included in the analysis. Ever smokers had a higher risk of having COVID-19 complications, such as acute respiratory distress syndrome (odds ratio [OR] 1.69; 95% confidence interval [CI] 1.09-2.55), renal injury (OR 1.55; 95% CI 1.10-2.14), and acute liver injury (OR 1.33; 95% CI 1.01-1.74), compared with never smokers. However, in terms of disease outcomes, there were no differences between the two groups. CONCLUSION: Although no significant association was found in terms of disease outcomes, smoking is associated with a higher risk of having complications owing to COVID-19 infection.

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients.

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome. Funding: Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford's COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix.

Clinical characteristics and risk factors for severe COVID-19 infections in Malaysia: A nationwide observational study.

BACKGROUND: COVID-19 emerged as a major public health outbreak in late 2019. Malaysia reported its first imported case on 25th January 2020, and adopted a policy of extensive contact tracing and hospitalising of all cases. We describe the clinical characteristics of COVID-19 cases nationwide and determine the risk factors associated with disease severity. METHOD: Clinical records of all RT-PCR confirmed COVID-19 cases aged ≥12 years admitted to 18 designated hospitals in Malaysia between 1st February and 30th May 2020 with complete outcomes were retrieved. Epidemiological history, co-morbidities, clinical features, investigations, management and complications were captured using REDCap database. Variables were compared between mild and severe diseases. Univariate and multivariate regression were used to identify determinants for disease severity. FINDINGS: The sample comprised of 5889 cases (median age 34 years, male 71.7%). Majority were mild (92%), and 3.3% required intensive care, with 80% admitted within the first five days. Older age (≥51 years), underlying chronic kidney disease and chronic pulmonary disease, fever, cough, diarrhoea, breathlessness, tachypnoea, abnormal chest radiographs and high serum CRP (≥5 mg/dL) on admission were significant determinants for severity (p<0.05). The case fatality rate was 1.2%, and the three commonest complications were liver injuries (6.7%), kidney injuries (4%), and acute respiratory distress syndrome (2.3%). INTERPRETATIONS: Lower case fatality rate was possibly contributed by young cases with mild diseases and early hospitalisation. Abnormal chest radiographic findings in elderly with tachypnoea require close monitoring in the first five days to detect early deterioration.

Recruitment and participation of a survey in a public-private primary care setting: experience from the QUALICOPC Malaysia.

AIM: The purpose of this paper is to describe the recruitment strategies, the response rates and the reasons for non-response of Malaysian public and private primary care doctors in an international survey on the quality, cost and equity in primary care. BACKGROUND: Low research participation by primary care doctors, especially those working in the private sector, is a challenge to quality benchmarking. METHODS: Primary care doctors were sampled through multi-stage sampling. The first stage-sampling unit was the primary care clinics, which were randomly sampled from five states in Malaysia to reflect their proportions in two strata - sector (public/private) and location (urban/rural). Strategies through endorsement, personalised invitation, face-to-face interview and non-monetary incentives were used to recruit public and private doctors. Data collection was carried out by fieldworkers through structured questionnaires. FINDINGS: A total of 221 public and 239 private doctors participated in the study. Among the public doctors, 99.5% response rates were obtained. Among the private doctors, a 32.8% response rate was obtained. Totally, 30% of private clinics were uncontactable by telephone, and when these were excluded, the overall response rate is 46.8%. The response rate of the private clinics across the states ranges from 31.5% to 34.0%. A total of 167 answered the non-respondent questionnaire. Among the non-respondents, 77.4 % were male and 22.6% female (P = 0.011). There were 33.6% of doctors older than 65 years (P = 0.003) and 15.9% were from the state of Sarawak (P = 0.016) when compared to non-respondents. Reason for non-participation included being too busy (51.8%), not interested (32.9%), not having enough patients (9.1%) and did not find it beneficial (7.9%). Our study demonstrated the feasibility of obtaining favourable response rate in a survey involving doctors from public and private primary care settings.