Unknown

Dataset Information

0

Prognostic and predictive value of endothelial dysfunction biomarkers in sepsis-associated acute kidney injury: risk-stratified analysis from a prospective observational cohort of pediatric septic shock.


ABSTRACT:

Background

Sepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to identify those at highest risk of SA-AKI.

Methods

Secondary analyses of prospective observational cohort of pediatric septic shock. Primary outcome of interest was the presence of ≥ Stage II KDIGO SA-AKI on day 3 based on serum creatinine (D3 SA-AKI SCr). Biomarkers including those prospectively validated to predict pediatric sepsis mortality (PERSEVERE-II) were measured in Day 1 (D1) serum. Multivariable regression was used to test the independent association between endothelial markers and D3 SA-AKI SCr. We conducted risk-stratified analyses and developed prediction models using Classification and Regression Tree (CART), to estimate risk of D3 SA-AKI among prespecified subgroups based on PERSEVERE-II risk.

Results

A total of 414 patients were included in the derivation cohort. Patients with D3 SA-AKI SCr had worse clinical outcomes including 28-day mortality and need for CRRT. Serum soluble thrombomodulin (sTM), Angiopoietin-2 (Angpt-2), and Tie-2 were independently associated with D3 SA-AKI SCr. Further, Tie-2 and Angpt-2/Tie-2 ratios were influenced by the interaction between D3 SA-AKI SCr and risk strata. Logistic regression demonstrated models predictive of D3 SA-AKI risk performed optimally among patients with high- or intermediate-PERSEVERE-II risk strata. A 6 terminal node CART model restricted to this subgroup of patients had an area under the receiver operating characteristic curve (AUROC) 0.90 and 0.77 upon tenfold cross-validation in the derivation cohort to distinguish those with and without D3 SA-AKI SCr and high specificity. The newly derived model performed modestly in a unique set of patients (n = 224), 84 of whom were deemed high- or intermediate-PERSEVERE-II risk, to distinguish those patients with high versus low risk of D3 SA-AKI SCr.

Conclusions

Endothelial dysfunction biomarkers are independently associated with risk of severe SA-AKI. Pending validation, incorporation of endothelial biomarkers may facilitate prognostic and predictive enrichment for selection of therapeutics in future clinical trials among critically ill children.

SUBMITTER: Atreya MR 

PROVIDER: S-EPMC10318688 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

Prognostic and predictive value of endothelial dysfunction biomarkers in sepsis-associated acute kidney injury: risk-stratified analysis from a prospective observational cohort of pediatric septic shock.

Atreya Mihir R MR   Cvijanovich Natalie Z NZ   Fitzgerald Julie C JC   Weiss Scott L SL   Bigham Michael T MT   Jain Parag N PN   Schwarz Adam J AJ   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   Alder Matthew N MN   Goldstein Stuart L SL   Stanski Natalja L NL  

Critical care (London, England) 20230703 1


<h4>Background</h4>Sepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to id  ...[more]

Similar Datasets

| S-EPMC4738818 | biostudies-other
| S-EPMC4227847 | biostudies-literature
| S-EPMC7965651 | biostudies-literature
| S-EPMC10900966 | biostudies-literature
2007-06-14 | GSE8121 | GEO
| S-EPMC7260414 | biostudies-literature
| S-EPMC5538252 | biostudies-other
| S-EPMC10300988 | biostudies-literature
| S-EPMC5201138 | biostudies-literature
| S-EPMC3947064 | biostudies-literature