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Mapping Metabolism: Monitoring Lactate Dehydrogenase Activity Directly in Tissue
Published on: June 21, 2018
The role of changes in lactate dehydrogenase (LDH) levels in predicting COVID-19 severity and mortality: A biomarker
Seyed Hassan Saadat1, Behzad Einollahi1, Hadi Norouzi2
1Nephrology and Urology Research Center, Clinical Sciences Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Insights
Dynamic changes in lactate dehydrogenase (LDH) predict COVID-19 severity. Both significant LDH reductions and elevations indicate higher risks for mortality and ICU admission in COVID-19 patients.
Area of Science:
- Biomarkers in infectious diseases
- Clinical chemistry and diagnostics
- COVID-19 research
Background:
- Lactate dehydrogenase (LDH) is a potential COVID-19 severity biomarker.
- The predictive value of dynamic LDH changes (ΔLDH) for COVID-19 outcomes is not well-established.
Purpose of the Study:
- To evaluate the predictive capability of ΔLDH for clinical outcomes in hospitalized COVID-19 patients.
- To investigate the relationship between ΔLDH and COVID-19 severity, including mortality and ICU admission.
Main Methods:
- Retrospective cohort study of 5635 adult COVID-19 patients.
- Analysis of serial lactate dehydrogenase (LDH) measurements and calculation of ΔLDH.
- Stratification by ΔLDH quartiles, multivariable logistic regression, and ROC analysis for outcome prediction.
Main Results:
- A U-shaped relationship was observed between ΔLDH and adverse outcomes.
- Both significant LDH reductions (Q1) and elevations (Q4) were associated with increased mortality and ICU admission risk.
- ΔLDH demonstrated excellent discrimination for mortality (AUC=0.78) with an optimal cutoff of 181.5 U/L.
Conclusions:
- Dynamic changes in LDH (ΔLDH) are a powerful, independent predictor of COVID-19 severity.
- A novel U-shaped risk pattern for COVID-19 outcomes associated with ΔLDH was identified.
- Serial LDH monitoring and the 181.5 U/L threshold can aid in COVID-19 risk stratification and management.
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.
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