Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

ObjectivesExisting UK prognostic models for patients admitted to hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score. DesignCandidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection. SettingModel development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. ParticipantsPatients with COVID-19 admitted to UHB January-August 2020 were included. Main outcome measuresDeath and ITU admission within 28 days of admission. Results1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786). ConclusionsThe novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patients probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated. Article SummaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIWe developed novel prognostic models predicting mortality and ITU admission within 28 days of admission for patients hospitalised with COVID-19, using a large routinely collected dataset gathered at admission with a wide range of possible predictors (demographic variables, symptoms, physiological measures, imaging, laboratory test results). C_LIO_LIThese novel models showed good discrimination and calibration in both derivation and external validation cohorts, and outperformed the existing ISARIC model and 4C score in the derivation dataset. We found that addition of comorbidities to the set of candidate predictors included in model derivation did not improve model performance. C_LIO_LIIf integrated into hospital electronic medical records systems, the model algorithms will provide a predicted probability of mortality or ITU admission for each patient based on their individual data at, or close to, the time of admission, which will support clinicians decision making with regard to appropriate patient care pathways and triage. This information might also assist clinicians in explaining complex prognostic assessments and decisions to patients and their relatives. C_LIO_LIA limitation of the study was that in the external validation cohort we were unable to examine all of the predictors included in the original full UHB model due to only a reduced set of candidate predictors being available in CovidCollab. Nevertheless, the reduced model performed well and the results suggest it may be applicable in a wide range of datasets where only a reduced set of predictor variables is available. C_LIO_LIFurthermore, it was not possible to carry out stratified analysis by ethnicity as the UHB dataset contained too few patients in most of the strata, and no ethnicity data was available in the CovidCollab dataset. C_LI

Individual factors underlie temperature variation in sickness and in health: influence of age, BMI and genetic factors in a multi-cohort study

IntroductionAgeing affects immune function resulting in aberrant fever response to infection. We assess the effects of biological variables on basal temperature and temperature in COVID-19 infection, proposing an updated temperature threshold for older adults. MethodsParticipants: O_LIUnaffected twin volunteers: 1089 adult TwinsUK participants. C_LIO_LILondon hospitalised COVID-19+: 520 adults with emergency admission. C_LIO_LIBirmingham hospitalised COVID-19+: 757 adults with emergency admission. C_LIO_LICommunity-based COVID-19+: 3972 adults self-reporting a positive test using the COVID Symptom Study mobile application. C_LI AnalysisHeritability assessed using saturated and univariate ACE models; Linear mixed-effect and multivariable linear regression analysing associations between temperature, age, sex and BMI; multivariable logistic regression analysing associations between fever ([≥]37.8{degrees}C) and age; receiver operating characteristic (ROC) analysis to identify temperature threshold for adults [≥] 65 years. ResultsAmong unaffected volunteers, lower BMI (p=0.001), and older age (p<0.001) associated with lower basal temperature. Basal temperature showed a heritability of 47% (95% Confidence Interval 18-57%). In COVID-19+ participants, increasing age associated with lower temperatures in cohorts (c) and (d) (p<0.001). For each additional year of age, participants were 1% less likely to demonstrate a fever (OR 0.99; p<0.001). Combining healthy and COVID-19+ participants, a temperature of 37.4{degrees}C in adults [≥]65 years had similar sensitivity and specificity to 37.8{degrees}C in adults <65 years for discriminating fever in COVID-19. ConclusionsAgeing affects temperature in health and acute infection. Significant heritability indicates biological factors contribute to temperature regulation. Our observations indicate a lower threshold (37.4{degrees}C) should be considered for assessing fever in older adults. Key PointsO_LIOlder adults, particularly those with lower BMI, have a lower basal temperature and a lower temperature in response to infection C_LIO_LIBasal temperature is heritable, suggesting biological factors underlying temperature regulation C_LIO_LIOur findings support a lower temperature threshold of 37.4{degrees}C for identifying possible COVID-19 infection in older adults C_LIO_LIThis has implications for case detection, surveillance and isolation and could be incorporated into observation assessment C_LI

Delirium is a presenting symptom of COVID-19 in frail, older adults: a cohort study of 322 hospitalised and 535 community-based older adults

BackgroundFrailty, increased vulnerability to physiological stressors, is associated with adverse outcomes. COVID-19 exhibits a more severe disease course in older, co-morbid adults. Awareness of atypical presentations is critical to facilitate early identification. ObjectiveTo assess how frailty affects presenting COVID-19 symptoms in older adults. DesignObservational cohort study of hospitalised older patients and self-report data for community-based older adults. SettingAdmissions to St Thomas Hospital, London with laboratory-confirmed COVID-19. Community-based data for 535 older adults using the COVID Symptom Study mobile application. SubjectsHospital cohort: patients aged 65 and over (n=322); unscheduled hospital admission between March 1st, 2020-May 5th, 2020; COVID-19 confirmed by RT-PCR of nasopharyngeal swab. Community-based cohort: participants aged 65 and over enrolled in the COVID Symptom Study (n=535); reported test-positive for COVID-19 from March 24th (application launch)-May 8th, 2020. MethodsMultivariate logistic regression analysis performed on age-matched samples from hospital and community-based cohorts to ascertain association of frailty with symptoms of confirmed COVID-19. ResultsHospital cohort: significantly higher prevalence of delirium in the frail sample, with no difference in fever or cough. Community-based cohort :significantly higher prevalence of probable delirium in frailer, older adults, and fatigue and shortness of breath. ConclusionsThis is the first study demonstrating higher prevalence of delirium as a COVID-19 symptom in older adults with frailty compared to other older adults. This emphasises need for systematic frailty assessment and screening for delirium in acutely ill older patients in hospital and community settings. Clinicians should suspect COVID-19 in frail adults with delirium.