A Novel Host-Proteome Signature for Distinguishing between Acute Bacterial and Viral Infections

<div><p>Bacterial and viral infections are often clinically indistinguishable, leading to inappropriate patient management and antibiotic misuse. Bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogens. Our aim was to identify novel viral-induced host proteins that can complement bacterial-induced proteins to increase diagnostic accuracy. Initially, we conducted a bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria. The best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. We further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96). The signature was superior to any of the individual proteins (P<0.001), as well as routinely used clinical parameters and their combinations (P<0.001). It remained robust across different physiological systems, times from symptom onset, and pathogens (AUCs 0.87-1.0). The accurate differential diagnosis provided by this novel combination of viral- and bacterial-induced proteins has the potential to improve management of patients with acute infections and reduce antibiotic misuse.</p></div

Signature performance is robust across different patient subgroups and outperforms lab parameters and protein biomarkers.

<p>(A) Signature AUCs in subgroups of the study cohort (bacterial and viral) are depicted. Square size is proportional to number of patients and error bars represent 95% CI. In the Pathogens analysis, each virus was compared to bacteria affecting the same physiological system, indicated in brackets. R-respiratory, C-central nervous system, G-gastrointestinal, U-urinary, K-skin, S-systemic (i.e. non-localized). Only pathogens detected in more than 5 patients are presented. PED—pediatric emergency departments, ED—emergency departments. For subgroup definitions see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120012#pone.0120012.t001" target="_blank">Table 1</a> legend. (B) Performance of clinical and lab parameters as well as the best performing pair (ANC and Lym %), triplet (ANC, Lym % and Pulse), and quadruplets (ANC, Lym %, Pulse, Mono %) of parameters, the values of which were combined using a logistic regression. Comparison was done on the entire study cohort (n = 653), apart from pulse (recorded in 292 bacterial and 326 viral patients), and respiratory rate (recorded in 292 bacterial and 326 viral patients). The signature performed significantly better (<i>P</i><10^–15) than the optimal quadruplet. (C) The signature performed significantly better (<i>P</i><10^–8) than biomarkers with a well-established role in the host response to infections. For each of the select biomarkers, analysis was performed in a subgroup of the study cohort (43≤n≤154 for each analysis, a convenience sample, n depended on the strength of the signal). Error bars represent 95% CI.</p

Signature measures of accuracy for diagnosing bacterial vs viral infections.

<p>Left: Performance estimates and their 95% CIs were obtained using a leave-10%-out cross-validation on all patients in the study cohort (n_Bacterial = 319, n_Viral = 334), Unanimous sub-cohort (n_Bacterial = 256, n_Viral = 271), and Microbiologically confirmed sub-cohort (n_Bacterial = 68, n_Viral = 173). Right: The analysis was repeated after filtering out patients with an equivocal immune response (study cohort [n_Bacterial = 290, n_Viral = 277, n_equivocal = 86], Unanimous [n_Bacterial = 233, n_Viral = 232, n_equivocal = 62] and Microbiologically confirmed [n_Bacterial = 64, n_Viral = 160, n_equivocal = 17]), which resembles the way clinicians are likely to use the signature. Additional measures of accuracy, including positive predictive value and negative predictive value, and their dependency on bacterial prevalence are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120012#pone.0120012.s005" target="_blank">S5 Data</a>.</p><p>Signature measures of accuracy for diagnosing bacterial vs viral infections.</p