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The pediatric sepsis biomarker risk model

Hector R Wong et al. Crit Care. .

Abstract

Introduction: The 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. VSports手机版.

Methods: Twelve 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 V体育安卓版. 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. .

Results: The 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) V体育ios版. 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. .

Conclusions: The 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. VSports最新版本.

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Figures

Figure 1
Figure 1
Classification tree from the derivation cohort (n = 220). The classification tree consists of five biomarker-based decision rules and ten daughter nodes. The classification tree includes five of the twelve candidate stratification biomarkers: C-C chemokine ligand 3 (CCL3), heat shock protein 70 kDa 1B (HSPA1B), interleukin-8 (IL8), elastase 2 (ELA2), and lipocalin 2 (LCN2). Each node provides the total number of subjects in the node, the biomarker serum concentration-based decision rule, and the number of survivors and non-survivors with the respective rates. For consistency, the serum concentrations of all stratification biomarkers are provided in pg/ml. Terminal nodes 5, 8, and 9 are considered low-risk nodes, whereas terminal nodes 2, 4, 10 are considered high-risk terminal nodes. To calculate the diagnostic test characteristics, all subjects in the low-risk terminal nodes (n = 171) were classified as predicted survivors, whereas all subjects in the high-risk terminal nodes (n = 49) were classified as predicted non-survivors. The area under the curve (AUC) for the derivation cohort tree was 0.885.
Figure 2
Figure 2
Classification tree from the updated model based on the combined derivation and test cohorts (n = 355). The classification tree consists of six biomarker-based decision rules, one age-based decision rule, and fourteen daughter nodes. The classification tree includes five of the twelve candidate stratification biomarkers: C-C chemokine ligand 3 (CCL3), heat shock protein 70 kDa 1B (HSPA1B), interleukin-8 (IL8), granzyme B (GZMB), and matrix metalloproteinase-8 (MMP8). Each node provides the total number of subjects in the node, the biomarker serum concentration- or age-based decision rule, and the number of survivors and non-survivors with the respective rates. For consistency, the serum concentrations of all stratification biomarkers are provided in pg/ml. Terminal nodes 7, 11, and 14 are considered low-risk nodes, whereas terminal nodes 4, 8, 10, 12, and 13 are considered high-risk terminal nodes. To calculate the diagnostic test characteristics, all subjects in the low risk terminal nodes (n = 236) were classified as predicted survivors, whereas all subjects in the high risk terminal nodes (n = 119) were classified as predicted non-survivors. The area under the curve (AUC) for the re-calibrated decision tree was 0.883.
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