ABSTRACT
BackgroundImmunocompromised patients may be at higher risk of mortality if hospitalised with COVID-19 compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death, and how this risk changed over the pandemic. MethodsWe included patients >=19yrs with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK. We defined immunocompromise as: immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination and co-morbidities. We used Bayesian logistic regression to explore mortality over time. FindingsBetween 17/01/2020 and 28/02/2022 we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. 29% (n=6,499) of immunocompromised and 21% (n=28,608) of immunocompetent patients died in hospital. The odds of in-hospital mortality were elevated for immunocompromised patients (adjOR 1.44, 95% CI 1.39-1.50, p<0.001). As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50-69yrs was 88% for men and 83% for women, and for those >80yrs was 99% for men, and 98% for women. ConclusionsImmunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses and monoclonal antibodies should be considered for this group. FundingNational Institute for Health Research; Medical Research Council; Chief Scientist Office, Scotland.
ABSTRACT
BackgroundSARS-CoV-2 have been shown to be associated with more severe disease and death in cancer patient. A systematic review and meta-analysis was conducted to determine the risk by age, tumour type and treatment of infection with SARS-CoV-2 in cancer patients. MethodsSystematic review by searching PubMed, Web of Science, and Scopus for articles published in English up to June 14, 2021 of SARS-CoV-2 infection in >10 patients with malignant disease. Outcomes included factors in patients with malignant disease that may predict a poor outcome from COVID-19 compared to patients without malignant disease, including patient demographics, tumour subtype and cancer treatments. A meta-analysis was performed using random effects model. Results81 studies were included, totalling 61,532 cancer patients. Haematological malignancies comprised 22.1% (9,672 of 43,676) of cases. Relative risk (RR) of mortality when age and sex matched was 1.69 (95% CI, 1.46-1.95; p<0.001; I2=51%). RR of mortality, versus non-cancer patients, was associated with decreasing age (exp(b)0.96; 95% CI, 0.922-0.994; p=0.028) but not male sex (exp(b)1.89; 95% CI, 0.222-6.366; p=0.83). RR of mortality in those with haematological malignancies versus non-cancer control was 1.81 (95% CI, 1.53-2.95; I2=0.0%). Compared to other cancers, increased risk of death was seen for lung (RR 1.68, 95% CI, 1.45-1.94; p<0.001), genitourinary (RR 1.11; 95% CI, 1.00-1.24; p=0.059) and haematological malignancies (RR 1.42; 95% CI, 1.31-1.54; p<0.001). Breast (RR 0.51; 95% CI, 0.36-0.71; p<0.001) and gynaecological cancers (RR 0.76; 95% CI, 0.62-0.93; p=0.009) had lower risk of death. Receipt of chemotherapy had greatest overall pooled mortality risk of 30% (95% CI, 25-36%; I2=86.97%) and endocrine therapy the lowest at 11% (95% CI, 6-16%; I2=70.7%). ConclusionsCancer patients, particularly younger cancer patients, appear at increased risk of mortality from COVID-19 compared to non-cancer patients. Differences in outcomes were seen based on tumour types and treatment. Highlights- To our knowledge this is the largest review and meta-analysis of COVID-19 in cancer patients with insights into tumour types and therapies. - In unadjusted analysis cancer doubles the risk of COVID-19 related mortality. This decreased when adjusted for age and sex. - Younger cancer patients have the highest risk of mortality when compared to non-cancer COVID-19 patient of a similar age. - Patients with lung, genitourinary and haematological malignancies are at increased risk of mortality, breast and gynaecological cancers are at lower risk. - Patients on chemotherapy have the highest pooled mortality risk with those on endocrine therapy the lowest.
ABSTRACT
BackgroundCancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical. ObjectiveTo identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET) MethodData was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission ([≥]24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool. ResultsTraining and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died. Conclusions and RelevanceCORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.
ABSTRACT
IntroductionVery little is known about possible clinical sequelae that may persist after resolution of the acute Coronavirus Disease 2019 (COVID-19). A recent longitudinal cohort from Italy including 143 patients recovered after hospitalisation with COVID-19 reported that 87% had at least one ongoing symptom at 60 day follow-up. Early indications suggest that patients with COVID-19 may need even more psychological support than typical ICU patients. The assessment of risk factors for longer term consequences requires a longitudinal study linked to data on pre-existing conditions and care received during the acute phase of illness. Methods and analysisThis is an international open-access prospective, observational multi-site study. It will enrol patients following a diagnosis of COVID-19. Tier 1 is developed for following up patients day 28 post-discharge, additionally at 3 to 6 months intervals. This module can be used to identify sub-sets of patients experiencing specific symptomatology or syndromes for further follow up. A Tier 2 module will be developed for in-clinic, in-depth follow up. The primary aim is to characterise physical consequences in patients post-COVID-19. Secondary aim includes estimating the frequency of and risk factors for post-COVID-19 medical sequalae, psychosocial consequences and post-COVID-19 mortality. A subset of patients will have sampling to characterize longer term antibody, innate and cell-mediated immune responses to SARS-CoV-2. Ethics and disseminationThis collaborative, open-access study aims to characterize the frequency of and risk factors for long-term consequences and characterise the immune response over time in patients following a diagnosis of COVID-19 and facilitate standardized and longitudinal data collection globally. The outcomes of this study will inform strategies to prevent long term consequences; inform clinical management, direct rehabilitation, and inform public health management to reduce overall morbidity and improve outcomes of COVID-19. Article summaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIAs an international prospective, observational study we provide open-access standardised tools that can be adapted by any site interested in following up patients with COVID-19, for independent or combined analysis, to forward knowledge into short and long term consequences of COVID-19. C_LIO_LIThis study aims to inform strategies to prevent longer term sequalae; inform clinical management, rehabilitation, and public health management strategies to reduce morbidity and improve outcomes. C_LIO_LIThe protocol will be used for a sub-set of patients, already included in the existing cohort of more than 85,973 individuals hospitalized with confirmed COVID-19 infection across 42 countries (as of 20 July 2020), using the ISARIC/WHO standardized Core- or RAPID Case Report Forms (CRFs). C_LIO_LIThe data will be linked with data on pre-existing comorbidities, presentation, clinical care and treatments documented in the existing cohort already documented using the ISARIC/WHO standardized Core- or RAPID CRFs. C_LIO_LIThe data collection tool is developed to facilitate wide dissemination and uptake, by enabling patient self-assessment, however, follow up of patients requires consent and resources, which might limit the uptake and bias the data towards countries /sites with capacity to follow up patients over time. C_LI
