Table 2. Comparison of baseline characteristics in West Africa derivation and DRC validation cohorts.
| Derivation Cohort | Validation Cohort | ||
|---|---|---|---|
| Case-fatality rate (n, %) | 234 (40.4) | 22 (29.7) | |
| Continuous predictors (median, IQR) | |||
| Age | 10 (5–14) | 5 (1.5–14) | |
| Ct value | 25.1 (20.9–29.5) | 19.3 (17.6–26.1) | |
| Binary symptoms (n, %) a | |||
| Bleeding | 88 (15.1) | 17 (22.9) | |
| Diarrhea | 57 (9.8) | 40 (54.1) | |
| Respiratory distress | 9.7 (1.7) | 16 (21.6) | |
| Dysphagia | 19 (3.3) | 16 (21.6) | |
aCovariates presented are those included in the EPiC model.
Abbreviations: IQR: interquartile range; Ct: cycle threshold
We sought to improve model performance by recalibrating the intercept and slope of the calibration plot and adding a biomarker to the model that was only available in the DRC data. An analysis of peak laboratory test results measured within the first 48 hours after admission identified three variables each significantly (p <0.01) correlated with mortality: ALT (r = 0.57), AST (r = 0.56), and CK (r = 0.51). We omitted ALT because it is highly colinear with AST (Pearson correlation = 0.83. Despite limited availability of test results in the validation data (AST: n = 29; CK: n = 33), we used these new variables to build additional models. Models that incorporated an additional predictor outperformed the original EPiC model on the validation data, in which adding CK as a predictor produced an AUC of 0.87 (95% CI: 0.74–1) while adding AST gave an AUC of 0.90 (95% CI: 0.77–1). We also considered a third model with both AST and CK added as predictors, since the association between these two biomarkers was moderate (Pearson correlation = 0.52), suggesting that they contain some amount of mutually independent information that could be combined to improve the predictions. Indeed, the model with AST and CK yields a higher AUC of 0.95 (95% CI: 0.86–1). The confusion matrix for this model exhibits an almost perfect discriminative capability with only 1 misclassification in each outcome category (S5A and S5B Table). However, the sample size for this model was reduced further to n = 23, since it requires patients to have data for both biomarkers. The ROCs and calibration plots for these three models are shown in Fig 3.