Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study
Background: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-even...
| Autores: | , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/351820 |
| Acceso en línea: | https://hdl.handle.net/2117/351820 https://dx.doi.org/10.1186/s12874-021-01340-8 |
| Access Level: | acceso abierto |
| Palabra clave: | Mortality Risk assessment COVID-19 (Disease) Cause-specifc hazard regression COVID-19 Prognosis Risk chart Mortalitat Avaluació del risc COVID-19 (Malaltia) Àrees temàtiques de la UPC::Ciències de la salut::Medicina Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | Background: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. // Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. // Results: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC¿=¿0.872 [CI 95%: 0.835–0.910]). // Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions. |
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