Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
| Autor: | Marja Bonarius, Anton Schreuder, Pedro J Freire Jorge, Martijn F. Boomsma, Wytze S de Boer, Eefje Jong, Steven Schalekamp, Rogier A van Dijk, Cornelia M. Schaefer-Prokop, Gerada Judith M Herder, Oscar A Groot, Merel Huisman |
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| Rok vydání: | 2021 |
| Předmět: |
Male
2019-20 coronavirus outbreak Pediatrics medicine.medical_specialty Coronavirus disease 2019 (COVID-19) Hospitalized patients Critical Illness MEDLINE 030218 nuclear medicine & medical imaging Cohort Studies 03 medical and health sciences 0302 clinical medicine All institutes and research themes of the Radboud University Medical Center Medicine Humans Radiology Nuclear Medicine and imaging Medical history Original Research Aged Retrospective Studies Aged 80 and over business.industry COVID-19 Retrospective cohort study Middle Aged Models Theoretical Prognosis Hospitalization 030220 oncology & carcinogenesis Critical illness Female Radiography Thoracic business Cohort study Rare cancers Radboud Institute for Health Sciences [Radboudumc 9] |
| Zdroj: | Radiology, 298, E46-E54 Radiology Radiology, 298, 1, pp. E46-E54 |
| ISSN: | 0033-8419 |
| Popis: | Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, while the capacity of intensive care units (ICUs) is a limiting factor during the peak of the pandemic and generally dependent on a country’s clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Material and Methods In this retrospective study including patients from 7th March 2020 to 24th April 2020, a consecutive cohort of hospitalized patients with RT-PCR-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (i.e. death and/or ICU admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, CXR and laboratory findings. Distribution and severity of lung involvement was visually assessed using an 8-point scale (chest radiography score). Internal validation was performed using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (69 ±12 years, 237 male) of whom 168 (47%) developed critical illness. The final risk model’s variables included gender, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease and chest radiography score at hospital presentation. The AUC of the model was 0.77 (95% CI: 0.72-0.81, P < .001). A risk calculator was derived for individual risk assessment; Dutch COVID-19 risk model (see Appendix E2). At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness of which 59 (83%) would be true-positives. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of ICU beds/facilities. |
| Databáze: | OpenAIRE |
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