Lactate Dehydrogenase to Albumin ratio as a Predictive Factor of COVID-19 Patients' Outcome; a Cross-sectional Study

Abstract

Introduction: Despite the increasing vaccination coverage, COVID-19 is still a concern. With the limited health care capacity, early risk stratification is crucial to identify patients who should be prioritized for optimal management. The present study investigates whether on-admission lactate dehydrogenase to albumin ratio (LAR) can be used to predict COVID-19 outcomes. Methods: This retrospective cross-sectional study evaluated hospitalized COVID-19 patients in an academic referral center in Iran from May 2020 to October 2020. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the value of LAR in the prediction ofmortality. The Yuden index was used to find the optimal cut-off of LAR to distinguish severity. Patients were classified into three groups (LAR tertiles), first: LAR<101.46, second: 101.46 <= LAR< 148.78, and third group: LAR >= 148.78. Logistic regression analysis was used to identify the association between tertiles of LAR, as well as the relationship between each one-unit increase in LAR with mortality and ICU admission in three models, based on potential confounding variables. Results: A total of 477 patients were included. Among all patients, 100 patients (21) died, and 121 patients (25.4) were admitted to intensive care unit (ICU). In the third group, the risk of mortality and ICU admission increased 7.78 times (OR=7.78, CI: 3.95-15.26; p <0.0001) and 4.49 times (OR=4.49, CI: 2.01-9.04; p <0.0001), respectively, compared to the first group. The AUC of LAR for prediction of mortality was 0.768 (95 CI 0.69- 0.81). LAR >= 136, with the sensitivity and specificity of 72 (95CI: 62.1-80.5) and 70 (95CI: 64.9-74.4), respectively, was the optimal cut-off value for predicting mortality. Conclusion: High LAR was associated with higher odds of COVID-19 mortality, ICU admission, and length of hospitalization. On-admission LAR levels might help health care workers identify critical patients early on.