Discovery and Validation of Prognostic Biomarker Models to Guide Triage among Adult Dengue Patients at Early Infection

abstract: ...Background Dengue results in a significant public health burden in endemic regions. The World Health Organization (WHO) recommended the use of warning signs (WS) to stratify patients at risk of severe dengue disease in 2009. However, WS is limited in stratifying adult dengue patients at early infection (Day 13 post fever), who require close monitoring in hospitals to prevent severe dengue. The aim of this study is to identify and validate prognostic models, built with differentially expressed biomarkers, that enable the early identification of those with early dengue infection that require close clinical monitoring. Methods RNA microarray and protein assays were performed to identify differentially expressed biomarkers of severity among 92 adult dengue patients recruited at early infection from years 20052008. This comprised 47 cases who developed WS after first presentation and required hospitalization (WS+Hosp), as well as 45 controls who did not develop WS after first presentation and did not require hospitalization (Non-WS+Non-Hosp). Independent validation was conducted with 80 adult dengue patients recruited from years 20092012. Prognostic models were developed based on forward stepwise and backward elimination estimation, using multiple logistic regressions. Prognostic power was estimated by the area under the receiver operating characteristic curve (AUC). Results The WS+Hosp group had significantly higher viral load (P<0.001), lower platelet (P<0.001) and lymphocytes counts (P = 0.004) at early infection compared to the Non-WS+Non-Hosp group. From the RNA microarray and protein assays, the top single RNA and protein prognostic models at early infection were CCL8 RNA (AUC:0.73) and IP-10 protein (AUC:0.74), respectively. The model with CCL8 , VPS13C RNA, uPAR protein, and with CCL8 , VPS13C RNA and platelets were the best biomarker models for stratifying adult dengue patients at early infection, with sensitivity and specificity up to 83% and 84%, respectively. These results were tested in the independent validation group, showing sensitivity and specificity up to 96% and 54.6%, respectively. Conclusions At early infection, adult dengue patients who later presented WS and require hospitalization have significantly different pathophysiology compared with patients who consistently presented no WS and / or require no hospitalization. The molecular prognostic models developed and validated here based on these pathophysiology differences, could offer earlier and complementary indicators to the clinical WHO 2009 WS guide, in order to triage adult dengue patients at early infection.

...+Hosp group had significantly higher viral load (P<0.001), lower platelet (P<0.001) and lymphocytes counts (P = 0.004) at early infection compared to the Non-WS+Non-Hosp group. From the RNA microarray and protein assays, the top single RNA and protein prognostic models at early infection were CCL8 RNA (AUC:0.73) and IP-10 protein (AUC:0.74), respectively. The model with CCL8 , VPS13C RNA, uPAR protein, and with CCL8 , VPS13C RNA and platelets were the best biomarker models for stratifying adult

... models at early infection The IP-10 protein (Model 1; AUC = 0.74) and CCL8 RNA (Model 3; AUC = 0.73) biomarkers were the top single biomarker models for protein and RNA, respectively ( Table 2 ). Among the multiple biomarker models developed (and shown in Table 2 ), Model 13 ( CCL8 , VPS13C RNA, and uPAR protein) and Model 14 ( CCL8 , VPS13C RNA, and Platelets Level) had the greatest AUC of about 0.90 and 0.88, respectively, and both were more parsimonious than Model 12 ( HIST14HE , VPS13C RNAs, and

... 88.89 68.75 52.63 N.A 24 Mucosal Bleeding 0.5000 0 100 - 52.33 N.A Internal Validation using Fluidigm for CCL8 , VPS13C 13 CCL8 , VPS13C , uPAR 0.8420 77.27 61.90 68.00 72.22 0.34 14 CCL8 , VPS13C , PLT 0.8682 69.57 83.87 76.19 78.79 0.74 11 IP10, CCL8 0.7451 41.18 88.89 70 70.59 0.83 Sen- Sensitivity; Spe- Specificity; PPV- Positive predictive value; NPV- Negative predictive value; N.A.- Not applicable. Sen, Spe, PPV and NPV are based on probability cutoff of 0.5. ^ Forward Stepwise Estimation from

... mucosal bleeding) were not as effective as Model 13 and 14 ( Table 2 ).Even though the multiple biomarker marker Model 21(platelets, viral Ct level, and CCL8 RNA) had AUC of0.87, it does not fit the dataset well (Goodness-of-fit test P<0.05). In order to evaluate the microarray results for CCL8 , VPS13C RNAs, real-time polymerase chain reaction technology (Fluidigm) was selected to validate the CCL8 , VPS13C RNA levels. Using Fluidigm as an internal validation with the same biological samples, Model

... 16.4 34.3 90.0 0.5 48.0 67.3 40.0 74.0 0.8 8.0 98.2 66.7 70.1 13 CCL8 , VPS13C , uPAR 0.2 96.0 49.1 46.2 96.4 0.5 64.0 76.4 55.2 82.4 0.8 40.0 90.9 66.7 76.9 14 CCL8 , VPS13C , Platelets 0.2 96.0 54.6 49.0 96.8 0.5 60.0 78.2 55.6 81.1 0.8 32.0 94.6 72.7 75.4 Discussion The diverse clinical spectrum of dengue disease presentations is still a challenge for health care workers in dengue endemic regions, especially to identify patients early that will later require clinical interventions to prevent

... biomarker is a pro-inflammatory chemokine [ 35 ], which has been highly associated as a biomarker to predict severity of several inflammatory diseases including infectious diseases, immune dysfunction and tumor development [ 36 ]. In our best prognostic analysis, Models 13 & 14, we also utilized uPAR protein and VPS13C RNA. Soluble uPAR is a versatile signaling proteinase receptor [ 37 ] that had been suggested as a biomarker to predict survival of HIV-1 infection [ 38 ] and to discriminate primary

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Gene: VPS13C - vacuolar protein sorting 13 homolog C (S. cerevisiae) - (NCBI)

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