Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital
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Le résumé fourni par la source
Abstract Background Cancer 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. Objective To 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) Method Data 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. Results Training 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 Relevance CORONET, 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital
- Date Crossref
- 03/12/2020
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Manchester Cancer Research UK Manchester Institute Cancer Biomarker Centre pays non établi dans la noticeUniversité ou école supérieure
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The Christie NHS Foundation Trust pays non établi dans la noticeOrganisme public
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Cancer Research UK Manchester Institute pays non établi dans la noticeStructure de recherche
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University Hospitals of Morecambe Bay NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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Weston Park Cancer Centre pays non établi dans la noticeÉtablissement de santé
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University of Dundee pays non établi dans la noticeUniversité ou école supérieure
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University of Liverpool pays non établi dans la noticeUniversité ou école supérieure
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Clatterbridge Cancer Centre NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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University Hospital Southampton NHS Foundation Trust Cancer Sciences Academic Unit pays non établi dans la noticeÉtablissement de santé
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University of Southampton pays non établi dans la noticeUniversité ou école supérieure
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University of Leicester Leicester Cancer Research Centre pays non établi dans la noticeUniversité ou école supérieure
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University Hospitals Plymouth NHS Trust pays non établi dans la noticeÉtablissement de santé
Cancer Research UK Manchester Institute Cancer Biomarker Centre — University of Manchester, The Christie NHS Foundation Trust et Cancer Research UK Manchester Institute, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.