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Development and external validation of a prognostic tool for COVID-19 critical disease.

ABSTRACT: Background: The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care.

Methods: This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia.

Results: Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21-88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27-88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease.

Conclusions and relevance: We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

SUBMITTER: Chow DS

PROVIDER: S-EPMC7725393 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and external validation of a prognostic tool for COVID-19 critical disease.

Chow Daniel S DS Glavis-Bloom Justin J Soun Jennifer E JE Weinberg Brent B Loveless Theresa Berens TB Xie Xiaohui X Mutasa Simukayi S Monuki Edwin E Park Jung In JI Bota Daniela D Wu Jie J Thompson Leslie L Boden-Albala Bernadette B Khan Saahir S Amin Alpesh N AN Chang Peter D PD

PloS one 20201209 12

<h4>Background</h4>The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medic ...[more]

PMID: 33296357

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Project description:ObjectivesExisting UK prognostic models for patients admitted to the 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 and 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 and 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 characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785).ConclusionsThe novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

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Project description:BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

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Dataset Information

Development and external validation of a prognostic tool for COVID-19 critical disease.

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Development and external validation of a prognostic tool for COVID-19 critical disease.

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