A community approach to mortality prediction in sepsis via gene expression analysis

Timothy E Sweeney, Thanneer M Perumal, Ricardo Henao, Marshall Nichols, Judith A Howrylak, Augustine M Choi, Jesús F Bermejo-Martin, Raquel Almansa, Eduardo Tamayo, Emma E Davenport, Katie L Burnham, Charles J Hinds, Julian C Knight, Christopher W Woods, Stephen F Kingsmore, Geoffrey S Ginsburg, Hector R Wong, Grant P Parnell, Benjamin Tang, Lyle L Moldawer, Frederick E Moore, Larsson Omberg, Purvesh Khatri, Ephraim L Tsalik, Lara M Mangravite, Raymond J Langley, Timothy E Sweeney, Thanneer M Perumal, Ricardo Henao, Marshall Nichols, Judith A Howrylak, Augustine M Choi, Jesús F Bermejo-Martin, Raquel Almansa, Eduardo Tamayo, Emma E Davenport, Katie L Burnham, Charles J Hinds, Julian C Knight, Christopher W Woods, Stephen F Kingsmore, Geoffrey S Ginsburg, Hector R Wong, Grant P Parnell, Benjamin Tang, Lyle L Moldawer, Frederick E Moore, Larsson Omberg, Purvesh Khatri, Ephraim L Tsalik, Lara M Mangravite, Raymond J Langley

Abstract

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

Conflict of interest statement

The ‘Duke’ 18-gene score is the subject of a provisional patent filed by Duke University. The ‘Stanford’ 12-gene score is the subject of a provisional patent filed by Stanford University. T.E.S. and P.K. are co-founders of Inflammatix, Inc., which has a commercial interest in the ‘Stanford’ 12-gene score. The remaining authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
Overview of analysis: schema of our community-modeling-based approach to multi-cohort analysis. Three phases are shown, as described in the Methods section: (i) discovery, (ii) validation, and (iii) secondary validation (HAI cohorts)
Fig. 2
Fig. 2
Model performance of the four genomic mortality predictors as measured by (a) AUROC and (b) AUPRC. The three panels (top, middle, bottom) show boxplots of the performance across all Discovery, Validation, and HAI cohorts

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