CLLAT, a new model that predicts the risk of death in older adult patients with COVID-19 (Preprint)
Authors: Zhang Kemeng , Zhou Yun , He Ping 
Published: April 2022

BACKGROUND

COVID-19, characterized as a pandemic by the World Health Organization, is a significant threat to global public health [1]. Multiple studies have shown that most COVID-19 patients are asymptomatic or present with only mild pneumonia. However, some patients, especially elderly patients, are prone to severe or critically ill pneumonia and even death [2]. The morbidity and mortality rates of COVID-19 are closely associated with age and are much higher in the elderly, particularly 80 years and older, than in younger and middle-aged adults [3]. Therefore, an efficient mortality prediction model for elderly COVID-19 patients must be established.

CURB-65 and Pneumonia Severity Index (PSI) are accepted clinical prediction models for predicting mortality in patients hospitalized with community-acquired pneumonia (CAP), including SARS-CoV-2, which has emerged as a new pathogen for CAP. PSI and CURB-65 can predict in-hospital mortality in SARS-CoV-2 CAP patients with areas under the curves (AUCs) of 0.82 (95% CI: 0.78 - 0.86) and 0.79 (95% CI: 0.75 - 0.84), respectively[4]. While PSI appears to be a better predictive tool than CURB-65, its scoring scale is cumbersome and not conducive to identifying critically ill patients quickly. Therefore, a more efficient model that easily predicts the risk of death in elderly COVID-19 patients is needed, which makes early intervene in patients at high risk of death be possible.

OBJECTIVE

To establish a mortality risk prediction model by analyzing clinical data collected from elderly COVID-19 patients.

METHODS

Data from 495 elderly (≥65 years old) patients with COVID-19 were collected. 282 cases were selected randomly as the study cohort, and the remained 213 cases were used as the validation cohort. The study cohort was divided into survival group and death group. The gender, age, vital signs, comorbidities, clinical symptoms, medication, chest computed tomography (CT) score, and laboratory results between the two groups were compared using the Mann-Whitney or chi-square tests. Significant variables (P<0.05) from these two tests were included in a multivariate logistic regression model. Independent factors from the the multivariate logistic regression analysis (P<0.05) were included in the prediction model. The receiver operating characteristic (ROC) test was used to determine the area under the curve (AUC), specificity, and sensitivity of the new model in the study cohort. Furthermore, the AUCs, specificities, and sensitivities among the new prediction model, CURB65, and Pneumonia Severity Index (PSI) were compared using ROC test in the validation cohort.

RESULTS

43 (15.25%) elderly COVID-19 patients in the study cohort died during hospitalization. The predictive model (CLLAT) established on age, lymphocytes, lactate dehydrogenase (LDH), total bilirubin (TBIL), and treatment with corticosteroids exhibited an excellent predictive ability, with an AUC of 0.897 (95% CI, 0.844-0.949), a sensitivity of 90.50%, and a specificity of 73.20%. CLLAT’s predictive ability was competitively better than CURB65’s and PSI’s in the validation cohort, with corresponding AUCs of 0.913 (95% CI, 0.865-0.961), 0.687 (95% CI, 0.568-0.805), and 0.816 (95% CI 0.730-0.903), sensitivities of 88.50%, 65.40%, and 80.80%, and specificities of 80.60%, 65.10%, and 69.90%, respectively.

CONCLUSIONS

CLLAT can predict the risk of death in elderly COVID-19 patients with better predictive efficacy than CURB65 and PSI.