Development and validation of the COVID-19 severity index (CSI): a prognostic tool for early respiratory decompensation
Abstract
Objective
The goal of this study was to create a predictive model of early hospital respiratory decompensation among patients with COVID-19.
Design
Observational, retrospective cohort study.
Setting
Nine-hospital health system within the Northeastern United States.
Populations
Adult patients (≥ 18 years) admitted from the emergency department who tested positive for SARS-CoV-2 (COVID-19) up to 24 hours after initial presentation. Patients meeting criteria for respiratory critical illness within 4 hours of arrival were excluded.
Main outcome and performance measures
We used a composite endpoint of critical illness as defined by oxygen requirement (greater than 10 L/min by low-flow device, high-flow device, non-invasive, or invasive ventilation) or death within the first 24 hours of hospitalization. We developed models predicting our composite endpoint using patient demographic and clinical data available within the first four hours of arrival. Eight hospitals (n= 932) were used for model development and one hospital (n= 240) was held out for external validation. Area under receiver operating characteristic (AU-ROC), precision-recall curves (AU-PRC), and calibration metrics were used to compare predictive models to three illness scoring systems: Elixhauser comorbidity index, qSOFA, and CURB-65.
Results
During the study period from March 1, 2020 to April 27,2020, 1,792 patients were admitted with COVID-19. Six-hundred and twenty patients were excluded based on age or critical illness within the first 4 hours, yielding 1,172 patients in the final cohort. Of these patients, 144 (12.3%) met the composite endpoint within the first 24 hours. We first developed a bedside quick COVID-19 severity index (qCSI), a twelve-point scale using nasal cannula flow rate, respiratory rate, and minimum documented pulse oximetry. We then created a machine-learning gradient boosting model, the COVID-19 severity index (CSI), using twelve additional variables including inflammatory markers and liver chemistries. Both the qCSI (AU-ROC mean [95% CI]: 0.90 [0.85-0.96]) and CSI (AU-ROC: 0.91 [0.86-0.97]) outperformed the comparator models (qSOFA: 0.76 [0.69-0.85]; Elixhauser: 0.70 [0.62-0.80]; CURB-65: AU-ROC 0.66 [0.58-0.77]) on cross-validation and performed well on external validation (qCSI: 0.82, CSI: 0.76, CURB-65: 0.50, qSOFA: 0.59, Elixhauser: 0.61). We find that a qCSI score of 0-3 is associated with a less than 5% risk of critical respiratory illness, while a score of 9-12 is associated with a 57% risk of progression to critical illness.
Conclusions
A significant proportion of admitted COVID-19 patients decompensate within 24 hours of hospital presentation and these events are accurately predicted using bedside respiratory exam findings within a simple scoring system.
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