Abstract
Objective:
The Life after Pediatric Sepsis Evaluation (LAPSE) investigation recently reported that one third of children who survive sepsis experience significant health-related quality of life (HRQL) impairment compared to baseline at one year after hospitalization. PERSEVERE is a multi-biomarker tool for estimating baseline risk of mortality among children with septic shock. We determined if the PERSEVERE biomarkers have predictive capacity for estimating the risk of hospital mortality and long-term HRQL morbidity among children with community-acquired septic shock.
Design:
Secondary analysis.
Setting:
Twelve academic pediatric intensive care units.
Patients:
A subset of LAPSE subjects (n = 173) with available blood samples.
Interventions:
None
Measurements and Main Results:
Three predefined outcomes from the LAPSE investigation were evaluated: all-cause hospital mortality (n = 173), and the composite outcome of mortality or Persistent, Serious Deterioration of HRQL (PSD-HRQL; > 25% below baseline) among surviving children at 1 month (n = 125) or 3 months (n = 117). PERSEVERE had an AUROC of 0.73 (95% C.I. 0.59 to 0.87; p = 0.002) for estimating the risk of hospital mortality and was independently associated with increased odds of hospital mortality. In multivariable analyses, PERSEVERE was not independently associated with increased odds of the composite outcome of mortality or deterioration of PSD-HRQL > 25% below baseline. A new decision tree utilizing the PERSEVERE biomarkers had an AUROC of 0.87 (95% C.I. 0.80 to 0.95) for estimating the risk of PSD-HRQL at 3 months among children who survived septic shock.
Conclusions:
PERSEVERE had modest performance for estimating hospital mortality in an external cohort of children with community acquired septic shock. The PERSEVERE biomarkers appear to have utility for estimating the risk of PSD-HRQL up to three months after surviving septic shock. These findings suggest an opportunity to develop a clinical tool for early assignment of risk for long term HRQL morbidity among children who survive septic shock.
Keywords: sepsis, morbidity, biomarkers, outcome, enrichment
INTRODUCTION
Pediatric sepsis represents a key public health burden—every 3–5 seconds someone in the world dies of sepsis (1). Most epidemiologic and descriptive studies have focused on sepsis-associated mortality, with much less attention given to longer term sepsis-associated morbidity experienced by children who survive sepsis. Recently, the Life after Pediatric Sepsis Evaluation (LAPSE) investigation prospectively evaluated long-term mortality and morbidity among children admitted to pediatric intensive care units (PICU) with septic shock, and reported critical illness risk factors associated with these adverse outcomes (2, 3). Significant health related quality of life (HRQL) impairment compared to baseline status persisted one year after hospitalization in a third of the LAPSE cohort.
The Pediatric Sepsis Biomarker Risk Model (PERSEVERE) is a multi-biomarker stratification tool for estimating a reliable baseline risk of mortality from pediatric septic shock (4–7). The PERSEVERE biomarkers are serum proteins measured during the first 24 hours following a septic shock diagnosis. They were originally identified using unbiased, discovery-oriented transcriptomic studies seeking to identify gene expression programs and pathways associated with poor outcome (8). PERSEVERE has been validated internally (4, 7, 9), but has not been tested in an external cohort, nor has it been further evaluated as a tool for estimating the risk of poor HRQL among children surviving septic shock.
The current study represents a secondary analysis of a subset of the LAPSE cohort for whom enrollment blood samples were obtained for measurement of the PERSEVERE biomarkers. This investigation included three broad goals. First, we tested the performance of PERSEVERE for estimating the risk of hospital mortality in the LAPSE cohort. Second, we examined the association between the PERSEVERE baseline mortality risk and the composite outcome of mortality or deterioration of Persistent, Serious Deterioration of HRQL below baseline (PSD-HRQL) among survivors of septic shock. Third, we utilized the PERSEVERE biomarkers to derive a new model to estimate the risk of PSD-HRQL at three months among children surviving septic shock.
METHODS
LAPSE Cohort
The LAPSE study protocol was approved by the Institutional Review Boards of the 12 participating institutions, either locally or through a central IRB, including approval for this secondary analysis involving the PERSEVERE biomarkers. Extensive details of the study protocol were previously published (2, 3). Briefly, after signed informed consent from a parent or legal guardian, children aged 1 month to 18 years with community-acquired septic shock were enrolled in this prospective longitudinal observational cohort outcome investigation to quantify sepsis-associated long-term mortality and morbidity among survivors.
Morbidity was assessed primarily using developmentally appropriate measures of HRQL, namely the Pediatric Quality of Life Inventory (PedsQL™) (10–12) and the Stein Jessop Functional Status Scale (FSII-R) (13) at study entry (to reflect pre-illness status), study day 7 and 1, 3, 6, and 12 months following the initial admission for septic shock. The protocol used pre-specified criteria to dichotomize the presence of PSD-HRQL, at each study point, relative to pre-illness status.
Three predefined outcomes from the original LAPSE investigation were evaluated in the current analyses: all-cause hospital mortality, and the composite outcome of mortality or PSD-HRQL (> 25% below baseline) among surviving children at 1 or 3 months following admission to a PICU for a sepsis event. Blood samples were obtained for measurement of the PERSEVERE biomarkers in a subset of enrolled patients. These were obtained within 24 hours of enrollment, and 48 hours later (day 3 samples), consistent with the original derivation and subsequent internal validation of PERSEVERE (4, 6, 9). PERSEVERE data reflecting the day 1 samples were used for all analyses in the current study, unless otherwise specified. Obtaining blood samples was optional for each of the participating study sites. All blood samples were obtained from remnant blood in the clinical laboratories.
PERSEVERE Biomarkers and In-Hospital Mortality Risk Stratification
The PERSEVERE biomarkers include C-C chemokine ligand 3 (CCL3), interleukin 8 (IL8), heat shock protein 70 kDa 1B (HSPA1B), granzyme B (GZMB), and matrix metallopeptidase 8 (MMP8) (6). Serum concentrations of these biomarkers were measured using a multi-plex magnetic bead platform (MILLIPLEX™ MAP) designed for the PERSEVERE research program by the EMD Millipore Corporation (Billerica, MA). Biomarker concentrations were measured in a Luminex® 100/200 System (Luminex Corporation, Austin, TX), according to the manufacturers’ specifications. Assay performance data were previously published (6).
Each eligible LAPSE subject was classified according to the PERSEVERE decision tree using predefined biomarker-based decision rules and one age-based decision rule (Supplemental Figure 1). The baseline mortality probability for an individual subject reflects assignment to one of the eight terminal nodes of the PERSEVERE decision tree. These were the PERSEVERE-based mortality probabilities used for analyses.
Data Analysis
Performance of PERSEVERE for estimating the risk of hospital mortality was evaluated by constructing a receiver operating characteristic curve and calculating the 95% confidence intervals of the area under the curve. Diagnostic test characteristics, with 95% confidence intervals, were calculated by constructing a 2 × 2 contingency table, wherein the PERSEVERE mortality probability was dichotomized to reflect “predicted dead” or “predicted alive”, relative to the actual hospital mortality. Based on the PERSEVERE decision tree rules, subjects with a PERSEVERE mortality probability ≤ 0.025 were classified as “predicted alive”, whereas those with a PERSEVERE mortality probability ≥ 0.182 were classified as “predicted dead”, as previously reported (6, 7).
We used logistic regression to further measure the association between the PERSEVERE-based mortality probability (independent variable) and the three outcomes listed above (dependent variables). Because PERSEVERE reflects day 1 of a septic shock diagnosis, the other independent variables were selected based on this temporal limit. These included the Pediatric Risk of Mortality (PRISM) score, version IV (14), the maximum day 1 Pediatric Logistic Organ Dysfunction (PELOD) score, version 2 (15), and the maximum day 1 Vasoactive-Inotrope Score (VIS) (16). The independent variables were first tested in univariable analyses. Those with a significant association with the outcome of interest (p < 0.05) were subsequently included in the corresponding multivariable analyses.
Classification and Regression Tree (CART) Modeling
CART analysis (Salford Predictive Modeler, v8.2; Minitab Inc., State College, PA) was used to derive a model estimating the risk of the dichotomous outcome variable, PSD-HRQL at three months following admission to the PICU for the septic shock event, among those who survived to hospital discharge. The candidate predictor variables considered in the modeling procedures included the day 1 and day 3 PERSEVERE biomarker concentrations, age, maximum day 1 PELOD score, and day 1 platelet count. None of the candidate predictor variables were weighted. Terminal nodes that did not improve classification and terminal nodes that contained <5% of the subjects in the root node were pruned. Performance of the tree was evaluated by calculating the area under the receiver operating characteristic curve and calculating diagnostic test characteristics. The tree was further evaluated using a 10-fold cross validation procedure and calculating a summary area under the receiver operating characteristic curve.
RESULTS
Study Cohort
Among the 389 subjects enrolled in the original LAPSE cohort, 173 (44%) had blood samples available for measurement of the PERSEVERE biomarkers. Supplemental Table 1 compares the clinical and demographic characteristics of this subgroup to that of the remaining LAPSE subjects without PERSEVERE data. The subjects with PERSEVERE data tended to be older and were less medically complex at baseline, relative to those without PERSEVERE data. No other differences were noted.
Performance of PERSEVERE in the LAPSE Subgroup
The 173 LAPSE subjects with PERSEVERE data were classified according to the predefined rules of the PERSEVERE decision tree to test the ability of PERSEVERE to estimate the risk of hospital mortality (Supplemental Figure 1). PERSEVERE risk estimation generated an area under the receiver operating characteristic curve of 0.73 (95% C.I. 0.59 to 0.87; p = 0.002) for distinguishing between hospital survivors and non-survivors in the LAPSE cohort. Table 1 summarizes the diagnostic test characteristics of PERSEVERE for predicting hospital mortality.
Table 1:
Diagnostic test characteristics of PERSEVERE for predicting hospital mortality in the LAPSE cohort (n = 173).
| Variable | Value | 95% C.I. |
|---|---|---|
| Area under the curve | 0.73 | 0.59 to 0.87 |
| True Positives, n | 13 | -- |
| True Negatives, n | 106 | -- |
| False Positives, n | 49 | -- |
| False Negatives, n | 5 | -- |
| Sensitivity | 72% | 46 to 89 |
| Specificity | 68% | 60 to 75 |
| Positive Predictive Value | 21% | 12 to 34 |
| Negative Predictive Value | 95% | 89 to 98 |
| (+) Likelihood Ratio | 2.3 | 1.6 to 3.3 |
| (−) Likelihood Ratio | 0.4 | 0.2 to 0.9 |
Association of PERSEVERE with Outcome
The association between the PERSEVERE baseline mortality risk and outcomes was further assessed using logistic regression. Three separate outcomes were evaluated: in-hospital mortality (n = 173) and the composite outcome of mortality or PSD-HRQL assessed at 1 month (n = 125) or 3 months (n = 117). The other independent variables considered in the logistic regression procedures were PRISM, maximum PELOD on day 1, and maximal VIS on day 1 (Table 2).
Table 2:
Univariable and multivariable analyses of candidate variables associated with outcome.
| Univariable Analysis | Multivariable Analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| In-Hospital Mortality | PERSEVERE1 | 173 | 1.9 | 1.3 – 2.7 | <0.001 | 1.7 | 2.5 – 4.1 | 0.010 | |
| PRISM | 1.1 | 1.0 – 1.1 | 0.021 | 1.0 | 1.0 – 1.1 | 0.638 | |||
| PELOD | 1.2 | 1.1 – 1.4 | <0.001 | 1.2 | 1.0 – 1.4 | 0.053 | |||
| VIS | 1.0 | 1.0 – 1.0 | 0.050 | 1.0 | 1.0 – 1.0 | 0.748 | |||
| Mortality or PSD-HRQL2 At 1 Month | PERSEVERE1 | 125 | 1.4 | 1.0 – 1.9 | 0.039 | 1.2 | 0.9 – 1.7 | 0.194 | |
| PRISM | 1.0 | 1.0 – 1.1 | 0.389 | -- | -- | - | |||
| PELOD | 1.1 | 1.0 – 1.2 | 0.030 | 1.1 | 1.0 – 1.2 | 0.143 | |||
| VIS | 1.0 | 1.0 – 1.0 | 0.762 | -- | -- | -- | |||
| Mortality or PSD-HRQL2At 3 Months | PERSEVERE1 | 117 | 1.6 | 1.1 – 2.2 | 0.005 | 1.4 | 1.0 – 2.0 | 0.051 | |
| PRISM | 1.0 | 1.0 – 1.1 | 0.242 | -- | -- | -- | |||
| PELOD | 1.2 | 1.1 – 1.3 | 0.002 | 1.1 | 1.0 – 1.3 | 0.016 | |||
| VIS | 1.0 | 1.0 – 1.0 | 0.121 | -- | -- | -- | |||
The raw PERSEVERE mortality probability was transformed by a factor of 10 for the logistic regression analyses, such that the PERSEVERE-related odds ratios reflect a 0.1 increase in the PERSEVERE mortality probability.
PSD-HRQL, persistent, serious deterioration of health-related quality of life > 25% below baseline.
Among the 173 LAPSE subjects with PERSEVERE data, 18 (10%) died during hospitalization. All four independent variables were associated with hospital mortality in univariable analyses. When the four independent variables were considered in a multivariable analysis, only PERSEVERE was independently associated with hospital mortality. PELOD was marginally associated with hospital mortality in the multivariable analysis.
Among the 125 LAPSE subjects with PERSEVERE data and HRQL data at one month, 49 (39%) also had the composite outcome of mortality or PSD-HRQL. PERSEVERE and PELOD were associated with this composite adverse outcome in univariable analyses. However, when both variables were considered in a multivariable analysis, neither were independently associated.
Among the 117 LAPSE subjects with PERSEVERE data and HRQL data at 3 months, 37 (32%) also had the composite outcome of mortality or PSD-HRQL. PERSEVERE and PELOD were associated with this composite outcome in univariable analyses. However, when both variables were considered in a multivariable analysis, only PELOD was independently associated with this composite outcome. PERSEVERE was marginally associated with this composite outcome in the multivariable analysis.
To determine if hospital mortality was the primary factor influencing the association between PERSEVERE and the composite adverse outcomes, the univariable analyses were repeated after excluding subjects who did not survive to hospital discharge. Among 109 subjects who survived to hospital discharge, 33 (30%) had PSD-HRQL at 1 month. PERSEVERE was not associated with this measure of HRQL morbidity in this subgroup of subjects who survived to hospital discharge (O.R. 1.1; 95% C.I. 0.7 to 1.5; p = 0.766). Among 99 subjects who survived to hospital discharge, 19 (19%) had PSD-HRQL at 3 months. PERSEVERE was not associated with this measure of HRQL morbidity at 3 months in this subgroup who survived to hospital discharge (O.R. 1.2; 95% C.I. 0.8 to 1.8; p = 0.375).
Estimating the Risk of Poor HRQL Outcome Among Survivors of Sepsis
Since the association between PERSEVERE and the composite poor outcomes was primarily influenced by hospital mortality, a new decision tree was derived to estimate the risk of PSD-HRQL at 3 months among septic shock survivors. Figure 1 shows the derived decision tree, which includes the day 1 CCL3 concentration, the day 1 IL8 concentration, and age. None of the other candidate predictor variables added to predictive capacity. The decision tree consists of three low risk terminal nodes (TN1, TN3, and TN4, probability of poor outcome 0.000 to 0.059) and two high risk terminal nodes (TN2 and TN5; probability of poor outcome 0.320 to 0.692). The decision tree had an area under the receiver operating characteristic curve of 0.87 (95% C.I. 0.80 to 0.95). Ten-fold cross validation of the decision tree yielded a summary area under the receiving operator characteristic curve of 0.74. Table 3 summarizes the diagnostic test characteristics of the decision tree for predicting PSD-HRQL at 3 months among children who survive septic shock.
Figure 1. The derived decision tree for estimating the risk of PSD-HRQL at 3 months, among subjects who survived to hospital discharge.
The primary outcome is dichotomized as “bad” or “good” to reflect whether the study subject had PSD-HRQL deterioration at 3 months after enrollment. The root node contains all subjects (n = 99) and provides the number of subjects with good or bad outcome, and the respective rates. Subsequent to the root node, subjects are allocated to daughter nodes according to decision rules reflecting either a biomarker concentration (pg/mL) or age. Each daughter node provides the decision rule used to generate the respective daughter nodes, and the number of subjects with good or bad outcome, and the respective rates, which correspond to the respective probabilities of good or bad outcome. The decision tree contains two CCL3-based decision rules, one IL8-based decision rule, and one age-based decision rule. The assignment of risk probability for good or bad outcome is based on allocation to one of the five terminal nodes (TN). Subjects allocated to TN1, TN3, and TN4 have a low probability of poor outcome (0.000 to 0.059), whereas subjects allocated to TN2 and TN5 have a higher probability of poor outcome (0.320 to 0.692). These probabilities are used to calculate the area under the receiver operating characteristic curve and the diagnostic test characteristics.
Table 3:
Diagnostic test characteristics of newly derived model to estimate the risk of PSD-HRQL at 3 months (n = 99).
| Variable | Value | 95% C.I. |
|---|---|---|
| Area under the curve (AUC) | 0.87 | 0.80 to 0.95 |
| 10-fold cross validation AUC | 0.74 | -- |
| True Positives, n | 17 | -- |
| True Negatives, n | 59 | -- |
| False Positives, n | 21 | -- |
| False Negatives, n | 2 | -- |
| Sensitivity | 89% | 65 to 98 |
| Specificity | 74% | 63 to 83 |
| Positive Predictive Value | 45% | 29 to 62 |
| Negative Predictive Value | 97% | 88 to 99 |
| (+) Likelihood Ratio | 3.4 | 2.3 to 5.1 |
| (−) Likelihood Ratio | 0.1 | 0.04 to 5.3 |
DISCUSSION
This study represents the first external validation of PERSEVERE. External validation is critical for assessing generalizability and reliability of stratification models. The LAPSE cohort reflects patients with community acquired septic shock, whereas the original derivation and testing of PERSEVERE involved children who acquired septic shock in both the outpatient and inpatient settings, and who were enrolled with no exclusion criteria other than failure to obtain informed consent. In this context, PERSEVERE had modest performance in the LAPSE cohort. We note that there were five subjects who did not survive to hospital discharge but who were classified by PERSEVERE as having a low risk of mortality. Among these five false negative subjects, four were allocated to terminal node 2 of the PERSEVERE decision tree (Supplemental Table 1). This is a low risk terminal node with a baseline mortality risk estimate of 0.011. A majority of LAPSE subjects (59%) were allocated to terminal node 2, yielding an overall mortality rate of 0.039 among subjects allocated to terminal node 2. This actual mortality rate is not is not overly discordant with the PERSEVERE-estimated mortality risk of 0.011.
When we considered PERSEVERE, PRISM, PELOD, and VIS in a multivariable logistic regression, PERSEVERE was the only variable independently associated with hospital mortality, suggesting that PERSEVERE might provide mortality risk information beyond that provided by well-established clinical and physiological parameters. We note, however, that these results are in contrast to previous studies showing that PELOD and PRISM performed well among children with sepsis (17, 18). Whether these discrepancies reflect our small sample size, the fact that LAPSE enrollment was limited to community-acquired septic shock, or other unknown variables requires further exploration.
This study also assessed the association between the PERSEVERE baseline mortality risk and PSD-HRQL, as originally measured and defined in the LAPSE investigation (2, 3). The PERSEVERE baseline mortality risk was associated with poor outcome, defined as the composite outcome of mortality or PSD-HRQL. However, this association was no longer apparent when subjects who did not survive to hospital discharge were excluded, suggesting that PERSEVERE per se has limited utility for estimating the risk of long-term HRQL morbidity among children surviving sepsis.
It nonetheless stands to reason that the biological pathways associated with increased risk of mortality from pediatric septic shock, as reflected by the PERSEVERE biomarkers, might also be associated with increased risk of long-term HRQL morbidity. We tested this possibility by deriving a new model to estimate the risk of PSD-HRQL at 3 months following admission for the sepsis event. The new model had excellent test characteristics and demonstrated acceptable performance upon cross validation.
Among the various candidate predictor variables considered, the newly derived model included the day 1 concentrations of CCL3 and IL8, and age, indicating that these variables have the strongest association with increased risk of poor, long-term HRQL. These same variables contribute to the original PERSEVERE decision tree (4–6). CCL3 concentrations inform the first level decision rule of both PERSEVERE and the current model for estimating the risk of sepsis-associated long term HRQL morbidity. Collectively, these similarities suggest biological commonalities in the pathways for both mortality and poor HRQL outcome among children with community acquired septic shock.
We note the limitations of this study, including that it is a secondary analysis. Due to the availability of blood samples, less than 50% of the original LAPSE cohort was included in the analysis. The resulting small sample size is at risk for generating unreliable estimates of the diagnostic test characteristics. This also raises the possibility of selection bias, although the differences between LAPSE participants with and without PERSEVERE blood samples were minimal (Supplemental Table 1). In addition, although the newly derived model performed well upon cross validation, it nonetheless requires prospective testing in an independent cohort that also includes a broader population of patients not limited to those with community acquired septic shock. Finally, we were unable to analyze HRQL outcomes at one year after surviving sepsis due to a limited sample size.
In summary, PERSEVERE had modest performance for estimating hospital mortality in an external cohort of children with community acquired septic shock. The PERSEVERE biomarkers, measured early during the acute phase of septic shock, appear to have utility for estimating the risk of PSD-HRQL up to three months after surviving sepsis, albeit with a different decision tree than the PERSEVERE mortality prediction model. This ancillary investigation suggests an opportunity to develop a clinical tool for early assignment of HRQL morbidity risk, and therefore enable more efficient and targeted implementation of interventions to mitigate the risk of HRQL morbidity and to target rehabilitation among children surviving septic shock.
Supplementary Material
Supplemental Figure 1: Classification of the LAPSE cohort according to the PERSEVERE decision tree. The primary outcome is hospital mortality. The root node contains all subjects (n = 173) and provides the number of subjects who were dead or alive at hospital discharge, and the respective rates. Subsequent to the root node, subjects are allocated to daughter nodes according to the predefined PERSEVERE decision rules reflecting either a biomarker concentration (pg/mL) or age. Each daughter node provides the decision rule used to generate the respective daughter nodes, and the number of subjects who were dead or alive at hospital discharge, and the respective rates. The assignment of baseline mortality probability is based on allocation to one of eight terminal nodes (TN). Each terminal nodes provides the probability (“prob.”) of hospital mortality or survival, based on the previously published PERSEVERE model, and the actual rates of death or survival among subjects allocated to that terminal node.
Funding:
R01HD073362 (JJZ) and R35GM126943 (HRW)
Copyright form disclosure:
Drs. Wong, Reeder, Berg, Meert, Hall, Mourani, Sorenson, and Varni’s institutions received funding from the National Institutes of Health (NIH). Dr. Wong disclosed that he and Cincinnati Children’s Research Foundation hold United States patents for the PERSEVERE biomarkers reported in this manuscript. Drs. Wong, Reeder, Banks, Berg, Meert, Hall, McQuillen, Mourani, Chima, Sorenson, Varni, and Zimmerman received support for article research from the NIH. Drs. Banks, McQuillen, and Zimmerman’s institutions received funding from the National Institutes of Child Health and Human Development. Drs. Banks and Sorenson disclosed government work. Dr. Hall received funding from La Jolla Pharmaceuticals (consulting). Dr. McGalliard disclosed work for hire; work was performed as part of Seattle Children’s Research Institute biostatistics and programming core. Dr. Zimmerman’s institution received funding from Immunexpress, Elsevier Publishing, and the Society of Critical Care Medicine.
Footnotes
Disclosure: The Cincinnati Children’s Research Foundation and Dr. Wong hold United States patents for the PERSEVERE biomarkers reported in this manuscript.
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Associated Data
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Supplementary Materials
Supplemental Figure 1: Classification of the LAPSE cohort according to the PERSEVERE decision tree. The primary outcome is hospital mortality. The root node contains all subjects (n = 173) and provides the number of subjects who were dead or alive at hospital discharge, and the respective rates. Subsequent to the root node, subjects are allocated to daughter nodes according to the predefined PERSEVERE decision rules reflecting either a biomarker concentration (pg/mL) or age. Each daughter node provides the decision rule used to generate the respective daughter nodes, and the number of subjects who were dead or alive at hospital discharge, and the respective rates. The assignment of baseline mortality probability is based on allocation to one of eight terminal nodes (TN). Each terminal nodes provides the probability (“prob.”) of hospital mortality or survival, based on the previously published PERSEVERE model, and the actual rates of death or survival among subjects allocated to that terminal node.