Abstract
BackgroundEarly identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values.MethodsPatients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer–Lemeshow Goodness-of-fit test, and calibration curve analysis.ResultsOut of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902–0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899–0.947). Hosmer–Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703).ConclusionWe developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.
Highlights
Identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources
The healthcare facilities with a low capacity of ICU beds have difficulties in the follow-up of patients. These facilities can prefer to transfer the patients with high risk for the development of ICU need to the further healthcare facilities with high capacity of ICU beds, while they can follow-up the patients with low risk for ICU need in their hospital wards
We found that comorbid diseases were more frequent in severe cases requiring ICU follow up than the patients without ICU need, comorbidities were not identified as optimal predictors during the development process of the nomogram
Summary
Identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. Early detection of patients whose illness will progress helps the physician to decide whether the patient should be followed up in the hospital or outpatient clinic or if there is a need for transferring to a referral center. We aimed to construct and validate a nomogram and a web-based calculation tool that incorporated demographic, clinical characteristics, and initial laboratory results at admission to hospital for predicting the development of severe illness that will require ICU follow up
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