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Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model.

ABSTRACT: 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.

SUBMITTER: Wong HR

PROVIDER: S-EPMC7720682 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model.

Wong Hector R HR Caldwell J Timothy JT Cvijanovich Natalie Z NZ Weiss Scott L SL Fitzgerald Julie C JC Bigham Michael T MT Jain Parag N PN Schwarz Adam A Lutfi Riad R Nowak Jeffrey J Allen Geoffrey L GL Thomas Neal J NJ Grunwell Jocelyn R JR Baines Torrey T Quasney Michael M Haileselassie Bereketeab B Lindsell Christopher J CJ

Science translational medicine 20191101 518

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 ...[more]

PMID: 31723040

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Project description:IntroductionThe intrinsic heterogeneity of clinical septic shock is a major challenge. For clinical trials, individual patient management, and quality improvement efforts, it is unclear which patients are least likely to survive and thus benefit from alternative treatment approaches. A robust risk stratification tool would greatly aid decision-making. The objective of our study was to derive and test a multi-biomarker-based risk model to predict outcome in pediatric septic shock.MethodsTwelve candidate serum protein stratification biomarkers were identified from previous genome-wide expression profiling. To derive the risk stratification tool, biomarkers were measured in serum samples from 220 unselected children with septic shock, obtained during the first 24 hours of admission to the intensive care unit. Classification and Regression Tree (CART) analysis was used to generate a decision tree to predict 28-day all-cause mortality based on both biomarkers and clinical variables. The derived tree was subsequently tested in an independent cohort of 135 children with septic shock.ResultsThe derived decision tree included five biomarkers. In the derivation cohort, sensitivity for mortality was 91% (95% CI 70 - 98), specificity was 86% (80 - 90), positive predictive value was 43% (29 - 58), and negative predictive value was 99% (95 - 100). When applied to the test cohort, sensitivity was 89% (64 - 98) and specificity was 64% (55 - 73). In an updated model including all 355 subjects in the combined derivation and test cohorts, sensitivity for mortality was 93% (79 - 98), specificity was 74% (69 - 79), positive predictive value was 32% (24 - 41), and negative predictive value was 99% (96 - 100). False positive subjects in the updated model had greater illness severity compared to the true negative subjects, as measured by persistence of organ failure, length of stay, and intensive care unit free days.ConclusionsThe pediatric sepsis biomarker risk model (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl) reliably identifies children at risk of death and greater illness severity from pediatric septic shock. PERSEVERE has the potential to substantially enhance clinical decision making, to adjust for risk in clinical trials, and to serve as a septic shock-specific quality metric.

Project description:Objective: To study warning signs of serious infections in febrile children presenting to PED, ascertain their risk of having sepsis, and evaluate their management. Design: Prospective observational study. Setting: A single pediatric emergency department (PED). Participants: Febrile children, aged 1 month-16 years, with >= 1 warning signs of sepsis. Interventions and Main outcome measures: Clinical characteristics, including different thresholds for tachycardia and tachypnoea, and their association with (1) delivery of pediatric sepsis 6 (PS6) interventions, (2) final diagnosis of invasive bacterial infection (IBI), (3) the risk for pediatric intensive care unit (PICU) admission, and (4) death. Results: Forty-one percent of 5,156 febrile children had warning signs of sepsis. 1,606 (34%) children had tachypnoea and 1,907 (39%) children had tachycardia when using APLS threshold values. Using the NICE sepsis guidelines thresholds resulted in 1,512 (32%) children having tachypnoea (kappa 0.56) and 2,769 (57%) children having tachycardia (kappa 0.66). Of 1,628 PED visits spanning 1,551 disease episodes, six children (0.4%) had IBI, with one death (0.06%), corresponding with 256 children requiring escalation of care according to sepsis guideline recommendations for each child with IBI. There were five additional PICU admissions (0.4%). 121 (7%) had intravenous antibiotics in PED; 39 children (2%) had an intravenous fluid bolus, inotrope drugs were started in one child. 440 children (27%) were reviewed by a senior clinician. In 4/11 children with IBI or PICU admission or death, PS6 interventions were delivered within 60 min after arriving. 1,062 (65%) visits had no PS6 interventions. Diagnostic performance of vital signs or sepsis criteria for predicting serious illness yielded a large proportion of false positives. Lactataemia was not associated with giving iv fluid boluses (p = 0.19) or presence of serious bacterial infections (p = 0.128). Conclusion: Many febrile children (41%) present with warning signs for sepsis, with only few of them undergoing investigations or treatment for true sepsis. Children with positive isolates in blood or CSF culture presented in a heterogeneous manner, with varying levels of urgency and severity of illness. Delivery of sepsis care can be improved in only a minority of children with IBI or admitted to PICU.

Project description:OBJECTIVES:The original Pediatric Sepsis Biomarker Risk Model and revised (Pediatric Sepsis Biomarker Risk Model-II) biomarker-based risk prediction models have demonstrated utility for estimating baseline 28-day mortality risk in pediatric sepsis. Given the paucity of prediction tools in pediatric acute respiratory distress syndrome, and given the overlapping pathophysiology between sepsis and acute respiratory distress syndrome, we tested the utility of Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II for mortality prediction in a cohort of pediatric acute respiratory distress syndrome, with an a priori plan to revise the model if these existing models performed poorly. DESIGN:Prospective observational cohort study. SETTING:University affiliated PICU. PATIENTS:Mechanically ventilated children with acute respiratory distress syndrome. INTERVENTIONS:Blood collection within 24 hours of acute respiratory distress syndrome onset and biomarker measurements. MEASUREMENTS AND MAIN RESULTS:In 152 children with acute respiratory distress syndrome, Pediatric Sepsis Biomarker Risk Model performed poorly and Pediatric Sepsis Biomarker Risk Model-II performed modestly (areas under receiver operating characteristic curve of 0.61 and 0.76, respectively). Therefore, we randomly selected 80% of the cohort (n = 122) to rederive a risk prediction model for pediatric acute respiratory distress syndrome. We used classification and regression tree methodology, considering the Pediatric Sepsis Biomarker Risk Model biomarkers in addition to variables relevant to acute respiratory distress syndrome. The final model was comprised of three biomarkers and age, and more accurately estimated baseline mortality risk (area under receiver operating characteristic curve 0.85, p < 0.001 and p = 0.053 compared with Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II, respectively). The model was tested in the remaining 20% of subjects (n = 30) and demonstrated similar test characteristics. CONCLUSIONS:A validated, biomarker-based risk stratification tool designed for pediatric sepsis was adapted for use in pediatric acute respiratory distress syndrome. The newly derived Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model demonstrates good test characteristics internally and requires external validation in a larger cohort. Tools such as Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model have the potential to provide improved risk stratification and prognostic enrichment for future trials in pediatric acute respiratory distress syndrome.

Project description:Objective New-onset atrial fibrillation (NOAF) is a common complication and one of the primary causes of increased mortality in critically ill adults. Since early assessment of the risk of developing NOAF is difficult, it is critical to establish predictive tools to identify the risk of NOAF. Methods We retrospectively enrolled 1,568 septic patients treated at Wuhan Union Hospital (Wuhan, China) as a training cohort. For external validation of the model, 924 patients with sepsis were recruited as a validation cohort at the First Affiliated Hospital of Xinjiang Medical University (Urumqi, China). Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses were used to screen predictors. The area under the ROC curve (AUC), calibration curve, and decision curve were used to assess the value of the predictive model in NOAF. Results A total of 2,492 patients with sepsis (1,592 (63.88%) male; mean [SD] age, 59.47 [16.42] years) were enrolled in this study. Age (OR: 1.022, 1.009–1.035), international normalized ratio (OR: 1.837, 1.270–2.656), fibrinogen (OR: 1.535, 1.232–1.914), C-reaction protein (OR: 1.011, 1.008–1.014), sequential organ failure assessment score (OR: 1.306, 1.247–1.368), congestive heart failure (OR: 1.714, 1.126–2.608), and dopamine use (OR: 1.876, 1.227–2.874) were used as risk variables to develop the nomogram model. The AUCs of the nomogram model were 0.861 (95% CI, 0.830–0.892) and 0.845 (95% CI, 0.804–0.886) in the internal and external validation, respectively. The clinical prediction model showed excellent calibration and higher net clinical benefit. Moreover, the predictive performance of the model correlated with the severity of sepsis, with higher predictive performance for patients in septic shock than for other patients. Conclusion The nomogram model can be used as a reliable and simple predictive tool for the early identification of NOAF in patients with sepsis, which will provide practical information for individualized treatment decisions.

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Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model.

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Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model.

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