Dataset Information

Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.

ABSTRACT:

Background

Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk.

Objective

(1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19.

Methods

A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, USA between March and July 2020 were included in the study. Seventy-five percent of patients were included in the training dataset (admitted March 1 to July 10). Through multivariable logistic regression, we developed a prediction model (probability score) for ICU transfer. Then, we validated the model by estimating its performance accuracy (area under the curve [AUC]) using data from the remaining 25% of patients (admitted July 11 to July 31).

Results

We included 2,014 and 671 patients in the training and validation datasets, respectively. Diabetes mellitus, coronary artery disease, chronic kidney disease, serum C-reactive protein, and serum lactate dehydrogenase were identified as significant risk factors for ICU transfer, and a prediction model was developed. The AUC was 0.752 for the training dataset and 0.769 for the validation dataset. We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/).

Conclusion

Our validated, simple, and accessible prediction model and web-based calculator for ICU transfer may be useful in assisting healthcare providers in identifying hospitalized patients with COVID-19 who are at high risk for clinical deterioration. Triage of such patients for early aggressive treatment can impact clinical outcomes for this potentially deadly disease.

SUBMITTER: Paranjape N

PROVIDER: S-EPMC7978341 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.

Paranjape Neha N Staples Lauren L LL Stradwick Christina Y CY Ray Herman Gene HG Saldanha Ian J IJ

PloS one 20210319 3

<h4>Background</h4>Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk.<h4>Objective</h4>(1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19.<h4>Methods</h4>A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, U ...[more]

PMID: 33740030

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Project description:ObjectiveTo investigate the clinical outcomes of patients with rheumatoid arthritis (RA) with COVID-19.MethodsThis retrospective study consisted of 361 patients with RA+ and 45,954 patients with RA- (March 2020 to August 2022) who tested positive for SARS-CoV-2 by polymerase-chain-reaction in the Montefiore Health System, which serves a large low-income, minority-predominant population in the Bronx and was an epicenter of the initial pandemic and subsequent surges. Primary outcomes were hospitalization, critical illness, and all-cause mortality associated with SARS-CoV-2 infection. Comparisons were made with and without adjustment for covariates, as well as with 1083 matched controls of patients with RA- and COVID-19.ResultsPatients with RA+ and COVID-19 were older (62.2 ± 23.5 vs. 45.5 ± 26.3; P < 0.001), were more likely females (83.1% vs. 55.8%; P < 0.001), were Black (35.5% vs. 30.3%; P < 0.05), and had higher rates of comorbidities (P < 0.05), hospitalization (52.4% vs. 32.5%; P < 0.005), critical illness (10.5% vs. 6.9%; P < 0.05), and mortality (11.1% vs. 6.2%; P < 0.01) compared with patients with RA- and COVID-19. Patients with RA+ with COVID-19 had higher odds of critical illness (adjusted odds ratio [aOR] = 1.46, 95% confidence interval [CI]: 1.09-1.93; P = 0.008) but no differences in hospitalization (aOR = 1.18 [95% CI: 0.93-1.49]; P = 0.16) and mortality (aOR = 1.34 [95% CI: 0.92-1.89]; P = 0.10) after adjusting for covariates. Odds ratio analysis identified age, hospitalization status, female sex, chronic kidney disease, chronic obstructive pulmonary disease, and Black race to be significant risk factors for COVID-19-related mortality. Pre-COVID-19 steroid and biologic therapy to treat RA were not significantly associated with worse outcomes (P > 0.05). Outcomes were not different between patients with RA+ and propensity-matched RA- controls (P > 0.05).ConclusionOur findings suggest that risk factors for adverse COVID-19 outcomes were not attributed to RA per se but rather age and preexisting medical conditions of patients with RA.

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Dataset Information

Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.

Background

Objective

Methods

Results

Conclusion

Publications

Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.

Similar Datasets

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