ABSTRACT
ObjectivesTo develop cross-validated prediction models for severe outcomes in COVID-19 using blood biomarker and demographic data; Demonstrate best practices for clinical data curation and statistical modelling decisions, with an emphasis on Bayesian methods. DesignRetrospective observational cohort study. SettingMulticentre across National Health Service (NHS) trusts in Southwest region, England, UK. ParticipantsHospitalised adult patients with a positive SARS-CoV 2 by PCR during the first wave (March - October 2020). 843 COVID-19 patients (mean age 71, 45% female, 32% died or needed ICU stay) split into training (n=590) and validation groups (n=253) along with observations on demographics, co-infections, and 30 laboratory blood biomarkers. Primary outcome measuresICU admission or death within 28-days of admission to hospital for COVID-19 or a positive PCR result if already admitted. ResultsPredictive regression models were fit to predict primary outcomes using demographic data and initial results from biomarker tests collected within 3 days of admission or testing positive if already admitted. Using all variables, a standard logistic regression yielded an internal validation median AUC of 0.7 (95% Interval [0.64,0.81]), and an external validation AUC of 0.67 [0.61, 0.71], a Bayesian logistic regression using a horseshoe prior yielded an internal validation median AUC of 0.78 [0.71, 0.85], and an external validation median AUC of 0.70 [0.68, 0.71]. Variable selection performed using Bayesian predictive projection determined a four variable model using Age, Urea, Prothrombin time and Neutrophil-Lymphocyte ratio, with a median AUC of 0.74 [0.67, 0.82], and external validation AUC of 0.70 [0.69, 0.71]. ConclusionsOur study reiterates the predictive value of previously identified biomarkers for COVID-19 severity assessment. Given the small data set, the full and reduced models have decent performance, but would require improved external validation for clinical application. The study highlights a variety of challenges present in complex medical data sets while maintaining best statistical practices with an emphasis on showcasing recent Bayesian methods.
ABSTRACT
IntroductionThe COVID-19 pandemic is impacting HIV care globally, with gaps in HIV treatment expected to increase HIV transmission and HIV-related mortality. We estimated how COVID-19-related disruptions could impact HIV transmission and mortality among men who have sex with men (MSM) in four cities in China. MethodsRegional data from China indicated that the number of MSM undergoing facility-based HIV testing reduced by 59% during the COVID-19 pandemic, alongside reductions in ART initiation (34%), numbers of sexual partners (62%) and consistency of condom use (25%). A deterministic mathematical model of HIV transmission and treatment among MSM in China was used to estimate the impact of these disruptions on the number of new HIV infections and HIV-related deaths. Disruption scenarios were assessed for their individual and combined impact over 1 and 5 years for a 3-, 4- or 6-month disruption period. ResultsOur China model predicted that new HIV infections and HIV-related deaths would be increased most by disruptions to viral suppression, with 25% reductions for a 3-month period increasing HIV infections by 5-14% over 1 year and deaths by 7-12%. Observed reductions in condom use increased HIV infections by 5-14% but had minimal impact (<1%) on deaths. Smaller impacts on infections and deaths (<3%) were seen for disruptions to facility testing and ART initiation, but reduced partner numbers resulted in 11-23% fewer infections and 0.4-1.0% fewer deaths. Longer disruption periods of 4 and 6 months amplified the impact of combined disruption scenarios. When all realistic disruptions were modelled simultaneously, an overall decrease in new HIV infections was always predicted over one year (3-17%), but not over 5 years (1% increase-4% decrease), while deaths mostly increased over one year (1-2%) and 5 years (1.2 increase - 0.3 decrease). ConclusionsThe overall impact of COVID-19 on new HIV infections and HIV-related deaths is dependent on the nature, scale and length of the various disruptions. Resources should be directed to ensuring levels of viral suppression and condom use are maintained to mitigate any adverse effects of COVID-19 related disruption on HIV transmission and control among MSM in China.
ABSTRACT
ObjectivesTo develop a regional model of COVID-19 dynamics, for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West of England (SW) as an example case. DesignOpen-source age-structured variant of a susceptible-exposed-infectious-recovered (SEIR) deterministic compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths. SettingSW at a time considered early in the pandemic, where National Health Service (NHS) authorities required evidence to guide localised planning and support decision-making. ParticipantsPublicly-available data on COVID-19 patients. Primary and secondary outcome measuresThe expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ("R") number over time. ResultsSW model projections indicate that, as of the 11th May 2020 (when lockdown measures were eased), 5,793 (95% credible interval, CrI, 2,003 - 12,051) individuals were still infectious (0.10% of the total SW England population, 95%CrI 0.04 - 0.22%), and a total of 189,048 (95%CrI 141,580 - 277,955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95%CrI 2.5 - 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on the 11th May 2020 was predicted to be 701 (95%CrI 169 - 1,543) and 110 (95%CrI 8 - 464) respectively. The R value in SW England was predicted to be 2.6 (95%CrI 2.0 - 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95%CrI 1.8 - 2.9) and lockdown/ school closures further reducing the R value to 0.6 (95CrI% 0.5 - 0.7). ConclusionsThe developed model has proved a valuable asset for local and regional healthcare services. The model will be used further in the SW as the pandemic evolves, and - as open source software - is portable to healthcare systems in other geographies. Future work/ applicationsO_LIOpen-source modelling tool available for wider use and re-use. C_LIO_LICustomisable to a number of granularities such as at the local, regional and national level. C_LIO_LISupports a more holistic understanding of intervention efficacy through estimating unobservable quantities, e.g. asymptomatic population. C_LIO_LIWhile not presented here, future use of the model could evaluate the effect of various interventions on transmission of COVID-19. C_LIO_LIFurther developments could consider the impact of bedded capacity in terms of resulting excess deaths. C_LI
