Use of Viremia to Evaluate the Baseline Case Fatality Ratio of Ebola Virus Disease and Inform Treatment Studies: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The case fatality ratio (CFR) of Ebola virus disease (EVD) can vary over time and space for reasons that are not fully understood. This makes it difficult to define the baseline CFRs needed to evaluate treatments in the absence of randomized controls. Here, we investigate whether viremia in EVD patients may be used to evaluate baseline EVD CFRs. METHODS AND FINDINGS: We analyzed the laboratory and epidemiological records of patients with EVD confirmed by reverse transcription PCR hospitalized in the Conakry area, Guinea, between 1 March 2014 and 28 February 2015. We used viremia and other variables to model the CFR. Data for 699 EVD patients were analyzed. In the week following symptom onset, mean viremia remained stable, and the CFR increased with viremia, V, from 21% (95% CI 16%-27%) for low viremia (V < 104.4 copies/ml) to 53% (95% CI 44%-61%) for intermediate viremia (104.4 ≤ V < 105.2 copies/ml) and 81% (95% CI 75%-87%) for high viremia (V ≥ 105.2 copies/ml). Compared to adults (15-44 y old [y.o.]), the CFR was larger in young children (0-4 y.o.) (odds ratio [OR]: 2.44; 95% CI 1.02-5.86) and older adults (≥ 45 y.o.) (OR: 2.84; 95% CI 1.81-4.46) but lower in children (5-14 y.o.) (OR: 0.46; 95% CI 0.24-0.86). An order of magnitude increase in mean viremia in cases after July 2014 compared to those before coincided with a 14% increase in the CFR. Our findings come from a large hospital-based study in Conakry and may not be generalizable to settings with different case profiles, such as with individuals who never sought care. CONCLUSIONS: Viremia in EVD patients was a strong predictor of death that partly explained variations in CFR in the study population. This study provides baseline CFRs by viremia group, which allow appropriate adjustment when estimating efficacy in treatment studies. In randomized controlled trials, stratifying analysis on viremia groups could reduce sample size requirements by 25%. We hypothesize that monitoring the viremia of hospitalized patients may inform the ability of surveillance systems to detect EVD patients from the different severity strata.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".