TY - JOUR
T1 - Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model
AU - Wong, Hector R.
AU - Caldwell, J. Timothy
AU - Cvijanovich, Natalie Z.
AU - Weiss, Scott L.
AU - Fitzgerald, Julie C.
AU - Bigham, Michael T.
AU - Jain, Parag N.
AU - Schwarz, Adam
AU - Lutfi, Riad
AU - Nowak, Jeffrey
AU - Allen, Geoffrey L.
AU - Thomas, Neal J.
AU - Grunwell, Jocelyn R.
AU - Baines, Torrey
AU - Quasney, Michael
AU - Haileselassie, Bereketeab
AU - Lindsell, Christopher J.
N1 - Publisher Copyright:
© 2019 The Authors.
PY - 2019/11/13
Y1 - 2019/11/13
N2 - Sepsis remains a major public health problem with no major therapeutic advances over the last several decades. The clinical and biological heterogeneity of sepsis have limited success of potential new therapies. Accordingly, there is considerable interest in developing a precision medicine approach to inform more rational development, testing, and targeting of new therapies. We previously developed the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate mortality risk and proposed its use as a prognostic enrichment tool in sepsis clinical trials; prognostic enrichment selects patients based on mortality risk independent of treatment. Here, we show that PERSEVERE has excellent performance in a diverse cohort of children with septic shock with potential for use as a predictive enrichment strategy; predictive enrichment selects patients based on likely response to treatment. We demonstrate that the PERSEVERE biomarkers are reliably associated with mortality in mice challenged with experimental sepsis, thus providing an opportunity to test precision medicine strategies in the preclinical setting. Using this model, we tested two clinically feasible therapeutic strategies, guided by the PERSEVERE-based enrichment, and found that mice identified as high risk for mortality had a greater bacterial burden and could be rescued by higher doses of antibiotics. The association between higher pathogen burden and higher mortality risk was corroborated among critically ill children with septic shock. This bedside to bench to bedside approach provides proof of principle for PERSEVERE-guided application of precision medicine in sepsis.
AB - Sepsis remains a major public health problem with no major therapeutic advances over the last several decades. The clinical and biological heterogeneity of sepsis have limited success of potential new therapies. Accordingly, there is considerable interest in developing a precision medicine approach to inform more rational development, testing, and targeting of new therapies. We previously developed the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate mortality risk and proposed its use as a prognostic enrichment tool in sepsis clinical trials; prognostic enrichment selects patients based on mortality risk independent of treatment. Here, we show that PERSEVERE has excellent performance in a diverse cohort of children with septic shock with potential for use as a predictive enrichment strategy; predictive enrichment selects patients based on likely response to treatment. We demonstrate that the PERSEVERE biomarkers are reliably associated with mortality in mice challenged with experimental sepsis, thus providing an opportunity to test precision medicine strategies in the preclinical setting. Using this model, we tested two clinically feasible therapeutic strategies, guided by the PERSEVERE-based enrichment, and found that mice identified as high risk for mortality had a greater bacterial burden and could be rescued by higher doses of antibiotics. The association between higher pathogen burden and higher mortality risk was corroborated among critically ill children with septic shock. This bedside to bench to bedside approach provides proof of principle for PERSEVERE-guided application of precision medicine in sepsis.
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U2 - 10.1126/scitranslmed.aax9000
DO - 10.1126/scitranslmed.aax9000
M3 - Article
C2 - 31723040
AN - SCOPUS:85074959277
SN - 1946-6234
VL - 11
JO - Science Translational Medicine
JF - Science Translational Medicine
IS - 518
M1 - eaax9000
ER -