Evaluation of viral load in patients with Ebola virus disease in Liberia: a retrospective observational study

Background Viral load in patients with Ebola virus disease affects case fatality rate and is an important parameter used for diagnostic cutoffs, stratification in randomised controlled trials, and epidemiological studies. However, viral load in Ebola virus disease is currently estimated using numerous different assays and protocols that were not developed or validated for this purpose. Here, our aim was to conduct a laboratory-based re-evaluation of the viral loads of a large cohort of Liberian patients with Ebola virus disease and analyse these data in the broader context of the west Africa epidemic. Methods In this retrospective observational study, whole blood samples from patients at the Eternal Love Winning Africa Ebola treatment unit (Monrovia, Liberia) were re-extracted with an optimised protocol and analysed by droplet digital PCR (ddPCR) using a novel semi-strand specific assay to measure viral load. To allow for more direct comparisons, the ddPCR viral loads were also back-calculated to cycle threshold (C-t) values. The new viral load data were then compared with the C-t values from the original diagnostic quantitative RT-PCR (qRT-PCR) testing to identify differing trends and discrepancies. Findings Between Aug 28 and Dec 18, 2014, 727 whole blood samples from 528 individuals were collected. 463 (64%) were first-draw samples and 409 (56%) were from patients positive for Ebola virus (EBOV), species Zaire ebolavirus. Of the 307 first-draw EBOV-positive samples, 127 (41%) were from survivors and 180 (59%) were from non-survivors; 155 (50%) were women, 145 (47%) were men, and seven (2%) were not recorded, and the mean age was 29.3 (SD 15.0) years for women and 31.8 (SD 14.8) years for men. Survivors had significantly lower mean viral loads at presentation than non-survivors in both the reanalysed dataset (5.61 [95% CI 5.34-5.87] vs 7.19 [6.99-7.38] log 10 EBOV RNA copies per mL; p<0.0001) and diagnostic dataset (C-t value 28.72 [27.97-29.47] vs 26.26 [25.72-26.81]; p<0.0001). However, the prognostic capacity of viral load increased with the reanalysed dataset (odds ratio [OR] of death 8.06 [95% CI 4.81-13.53], p<0.0001 for viral loads above 6.71 log 10 EBOV RNA copies per mL vs OR of death 2.02 [1.27-3.20], p=0.0028 for C-t values below 27.37). Diagnostic qRT-PCR significantly (p<0.0001) underestimated viral load in both survivors and non-survivors (difference in diagnostic C-t value minus laboratory C-t value of 1.79 [95% CI 1.16-2.43] for survivors and 5.15 [4.43-5.87] for non-survivors). Six samples that were reported negative by diagnostic testing were found to be positive upon reanalysis and had high viral loads. Interpretation Inaccurate viral load estimation from diagnostic C-t values is probably multifactorial; however, unaddressed PCR inhibition from tissue damage in patients with fulminant Ebola virus disease could largely account for the discrepancies observed in our study. Testing protocols for Ebola virus disease require further standardisation and validation to produce accurate viral load estimates, minimise false negatives, and allow for reliable epidemiological investigation. Copyright (c) 2022 The Author(s). Published by Elsevier Ltd.