27 research outputs found
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
Efficacy of telemedicine for the management of cardiovascular disease: a systematic review and meta-analysis
BACKGROUND: Telemedicine has been increasingly integrated into chronic disease management through remote patient monitoring and consultation, particularly during the COVID-19 pandemic. We did a systematic review and meta-analysis of studies reporting effectiveness of telemedicine interventions for the management of patients with cardiovascular conditions. METHODS: In this systematic review and meta-analysis, we searched PubMed, Scopus, and Cochrane Library from database inception to Jan 18, 2021. We included randomised controlled trials and observational or cohort studies that evaluated the effects of a telemedicine intervention on cardiovascular outcomes for people either at risk (primary prevention) of cardiovascular disease or with established (secondary prevention) cardiovascular disease, and, for the meta-analysis, we included studies that evaluated the effects of a telemedicine intervention on cardiovascular outcomes and risk factors. We excluded studies if there was no clear telemedicine intervention described or if cardiovascular or risk factor outcomes were not clearly reported in relation to the intervention. Two reviewers independently assessed and extracted data from trials and observational and cohort studies using a standardised template. Our primary outcome was cardiovascular-related mortality. We evaluated study quality using Cochrane risk-of-bias and Newcastle-Ottawa scales. The systematic review and the meta-analysis protocol was registered with PROSPERO (CRD42021221010) and the Malaysian National Medical Research Register (NMRR-20–2471–57236). FINDINGS: 72 studies, including 127 869 participants, met eligibility criteria, with 34 studies included in meta-analysis (n=13 269 with 6620 [50%] receiving telemedicine). Combined remote monitoring and consultation for patients with heart failure was associated with a reduced risk of cardiovascular-related mortality (risk ratio [RR] 0·83 [95% CI 0·70 to 0·99]; p=0·036) and hospitalisation for a cardiovascular cause (0·71 [0·58 to 0·87]; p=0·0002), mostly in studies with short-term follow-up. There was no effect of telemedicine on all-cause hospitalisation (1·02 [0·94 to 1·10]; p=0·71) or mortality (0·90 [0·77 to 1·06]; p=0·23) in these groups, and no benefits were observed with remote consultation in isolation. Small reductions were observed for systolic blood pressure (mean difference –3·59 [95% CI –5·35 to –1·83] mm Hg; p<0·0001) by remote monitoring and consultation in secondary prevention populations. Small reductions were also observed in body-mass index (mean difference –0·38 [–0·66 to –0·11] kg/m(2); p=0·0064) by remote consultation in primary prevention settings. INTERPRETATION: Telemedicine including both remote disease monitoring and consultation might reduce short-term cardiovascular-related hospitalisation and mortality risk among patients with heart failure. Future research should evaluate the sustained effects of telemedicine interventions. FUNDING: The British Heart Foundation
Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model
The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, βt and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19
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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
Sustaining effective COVID-19 control in Malaysia through large-scale vaccination
Introduction: As of 3rd June 2021, Malaysia is experiencing a resurgence of COVID-19 cases. In response, the federal government has implemented various non-pharmaceutical interventions (NPIs) under a series of Movement Control Orders and, more recently, a vaccination campaign to regain epidemic control. In this study, we assessed the potential for the vaccination campaign to control the epidemic in Malaysia and four high-burden regions of interest, under various public health response scenarios.
Methods: A modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented.
Results: The national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the country's historical success in maintaining control throughout much of 2020
COVID-19 collaborative modelling for policy response in the Philippines, Malaysia and Vietnam
Mathematical models that capture COVID-19 dynamics have supported public health responses and policy development since the beginning of the pandemic, yet there is limited discourse to describe features of an optimal modelling platform to support policy decisions or how modellers and policy makers have engaged with each other. Here, we outline how we used a modelling software platform to support public health decision making for the COVID-19 response in the Western Pacific Region (WPR) countries of the Philippines, Malaysia and Viet Nam. This perspective describes an approach to support evidence-based public health decisions and policy, which may help inform other responses to similar outbreak events. The platform we describe formed the basis for one of the inaugural World Health Organization (WHO) Western Pacific (WPRO) Innovation Challenge awards, and was backed by collaboration between epidemiological modellers, those providing public health advice, and policy makers
Influence of Population Density for COVID-19 Spread in Malaysia: An Ecological Study
The rapid transmission of highly contagious infectious diseases within communities can yield potential hotspots or clusters across geographies. For COVID-19, the impact of population density on transmission models demonstrates mixed findings. This study aims to determine the correlations between population density, clusters, and COVID-19 incidence across districts and regions in Malaysia. This countrywide ecological study was conducted between 22 January 2021 and 4 February 2021 involving 51,476 active COVID-19 cases during Malaysia’s third wave of the pandemic, prior to the reimplementation of lockdowns. Population data from multiple sources was aggregated and spatial analytics were performed to visualize distributional choropleths of COVID-19 cases in relation to population density. Hierarchical cluster analysis was used to synthesize dendrograms to demarcate potential clusters against population density. Region-wise correlations and simple linear regression models were deduced to observe the strength of the correlations and the propagation effects of COVID-19 infections relative to population density. Distributional heats in choropleths and cluster analysis showed that districts with a high number of inhabitants and a high population density had a greater number of cases in proportion to the population in that area. The Central region had the strongest correlation between COVID-19 cases and population density (r = 0.912; 95% CI 0.911, 0.913; p < 0.001). The propagation effect and the spread of disease was greater in urbanized districts or cities. Population density is an important factor for the spread of COVID-19 in Malaysia
Scientific Abstract to Full Paper: Publication Rate over a 3-Year Period in a Malaysian Clinical Research Conference
Background: The publication rates of abstracts after they were presented at the National Conference for Clinical Research (NCCR), a scientific conference held in Malaysia, was determined to gauge the scientific value of the conference, whilst providing comparative information with other scientific conferences. Methods: All the abstracts that were presented at the NCCR from 2014 to 2016 were analysed. Keywords from the abstract title, along with the first, second, and last author’s name, were searched via PubMed, Google Scholar, and Scopus to determine publication status. Results: A total of 320 abstracts were analysed. Of those, 57 abstracts (17.8%) were published. Almost 70% of published abstracts appeared in open access journals that charge article processing fees. Early publications (≤18 months from the conference date) had higher median journal impact factors compared to later publications. Approximately 42% of the published abstracts had collaborations with the Institute for Clinical Research (ICR) or Clinical Research Centres (CRCs). An increasing number of authors in an abstract and having the first author from a research centre, reduced and increased the odds of publication, respectively. Conclusions: The NCCR publication rate is lower compared to the reported average in other scientific conferences abroad. More encouragement and support to publish should be provided to the presenting authors. Clinicians should also be encouraged to collaborate with research centres such as those from the ICR or CRCs to boost publication likelihoods
Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia
As COVID-19 dispersion occurs at different levels of gradients across geographies, the application of spatiotemporal science via computational methods can provide valuable insights to direct available resources and targeted interventions for transmission control. This ecological-correlation study evaluates the spatial dispersion of COVID-19 and its temporal relationships with crucial demographic and socioeconomic determinants in Malaysia, utilizing secondary data sources from public domains. By aggregating 51,476 real-time active COVID-19 case-data between 22 January 2021 and 4 February 2021 to district-level administrative units, the incidence, global and local Moran indexes were calculated. Spatial autoregressive models (SAR) complemented with geographical weighted regression (GWR) analyses were executed to determine potential demographic and socioeconomic indicators for COVID-19 spread in Malaysia. Highest active case counts were based in the Central, Southern and parts of East Malaysia regions of Malaysia. Countrywide global Moran index was 0.431 (p = 0.001), indicated a positive spatial autocorrelation of high standards within districts. The local Moran index identified spatial clusters of the main high–high patterns in the Central and Southern regions, and the main low–low clusters in the East Coast and East Malaysia regions. The GWR model, the best fit model, affirmed that COVID-19 spread in Malaysia was likely to be caused by population density (β coefficient weights = 0.269), followed by average household income per capita (β coefficient weights = 0.254) and GINI coefficient (β coefficient weights = 0.207). The current study concluded that the spread of COVID-19 was concentrated mostly in the Central and Southern regions of Malaysia. Population’s average household income per capita, GINI coefficient and population density were important indicators likely to cause the spread amongst communities
Myocarditis/pericarditis following vaccination with BNT162b2, CoronaVac, and ChAdOx1 among adolescent and adult in Malaysia
This study evaluates 21-day risk of myocarditis/pericarditis following COVID-19 vaccination among those aged 12 years and older in Malaysia. We used data from nationwide COVID-19 vaccine registry linked to hospital episode database to identify individuals vaccinated with BNT162b2, CoronaVac, or ChAdOx1 and hospitalised for myocarditis/pericarditis between 1 February 2021 and 28 February 2022. There were 87 myocarditis/pericarditis cases identified within 1–21 days after vaccination. Most cases were reported following BNT16262 vaccination (77.0%) with absolute risk of 0.33 cases/100,000 vaccinated persons or 1.73 per million doses administered. Highest risk was observed following second dose and in younger, male individuals. The risk of myocarditis/pericarditis following CoronaVac and ChAdOx1 were much lower compared to BNT162b2. The findings on higher risk observed among younger following mRNA vaccine were consistent with literature and important for targeted surveillance