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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients:
Zihang Zeng1,2,3, Yiming Ma1,2,3, Huihui Zeng1,2,3
1Department of Pulmonary and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Insights
Older age, hypertension, and elevated neutrophil, procalcitonin, prothrombin time, and D-dimer levels predict intensive care unit (ICU) transfer in COVID-19 patients. This helps in early triage and resource allocation.
Area of Science:
- Critical Care Medicine
- Infectious Diseases
- Epidemiology
Background:
- Coronavirus disease 2019 (COVID-19) poses a significant threat, necessitating identification of patients at high risk for intensive care unit (ICU) admission.
- Early prediction of ICU transfer is crucial for effective resource allocation and patient management.
Purpose of the Study:
- To investigate risk factors for ICU admission and transfer in hospitalized COVID-19 patients.
- To develop a predictive model for identifying patients requiring ICU transfer.
Main Methods:
- Retrospective, multicenter study of 461 adult COVID-19 patients.
- Cox proportional hazards regression model used to identify predictors of ICU transfer.
- Development of a nomogram based on initial laboratory data.
Main Results:
- Independent predictors for ICU transfer included older age (≥65 years), hypertension, elevated neutrophil count, procalcitonin, prothrombin time, and D-dimer levels.
- Lower lymphocyte count and albumin levels were associated with increased mortality.
- A predictive model was developed to identify high-risk patients for ICU transfer within 3 and 7 days.
Conclusions:
- The study identified key clinical and laboratory predictors for ICU transfer in COVID-19 patients.
- The developed nomogram aids in early identification of high-risk individuals, facilitating timely intervention.
- This predictive tool supports efficient allocation of critical care resources during the pandemic.
Abstract:
This retrospective, multicenter study investigated the risk factors associated with intensive care unit (ICU) admission and transfer in 461 adult patients with confirmed coronavirus disease 2019 (COVID-19) hospitalized from 22 January to 14 March 2020 in Hunan, China. Outcomes of ICU and non-ICU patients were compared, and a simple nomogram for predicting the probability of ICU transfer after hospital admission was developed based on initial laboratory data using a Cox proportional hazards regression model. Differences in laboratory indices were observed between patients admitted to the ICU and those who were not admitted. Several independent predictors of ICU transfer in COVID-19 patients were identified including older age (≥65 years) (hazard ratio [HR] = 4.02), hypertension (HR = 2.65), neutrophil count (HR = 1.11), procalcitonin level (HR = 3.67), prothrombin time (HR = 1.28), and D-dimer level (HR = 1.25). The lymphocyte count and albumin level were negatively associated with mortality (HR = 0.08 and 0.86, respectively). The developed model provides a means for identifying, at hospital admission, the subset of patients with COVID-19 who are at high risk of progression and would require transfer to the ICU within 3 and 7 days after hospitalization. This method of early patient triage allows a more effective allocation of limited medical resources.
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