The role of changes in lactate dehydrogenase (LDH) levels in predicting COVID-19 severity and mortality: A biomarker analysis

BACKGROUND: Lactate dehydrogenase (LDH) has emerged as a potential biomarker for COVID-19 severity, but the diagnostic value of its dynamic changes (ΔLDH) remains unclear. This study aimed to determine the predictive value of ΔLDH for clinical outcomes in hospitalized patients with COVID-19. METHODS: We performed a retrospective cohort study of 5635 adults with confirmed COVID-19, analyzing LDH measurements. The patients were stratified by ΔLDH quartiles (Q1-Q4). Multivariable logistic regression was used to assess the associations with mortality and ICU admission, while ROC analysis was used to determine the predictive performance. RESULTS: A U-shaped relationship was observed, with both extreme reductions (Q1: ΔLDH ≤ -196 U/L) and elevations (Q4: ΔLDH ≥ 108 U/L) predicting adverse outcomes. Q4 patients had a higher risk of mortality (aOR = 6.72, 95 % CI: 5.31-8.51) and a higher risk of ICU admission (aOR = 6.63, 95 % CI: 5.26-8.36) compared to Q1. ΔLDH revealed excellent discrimination for mortality (AUC = 0.78) with an optimal cutoff at 181.5 U/L (sensitivity = 66.7 %, specificity = 88.9 %). CONCLUSION: ΔLDH is a powerful, independent predictor of COVID-19 severity, revealing a novel U-shaped risk pattern. The 181.5 U/L threshold offers clinically actionable guidance for risk stratification. These findings support the incorporation of serial LDH monitoring into COVID-19 management protocols.