Perioperative albuminuria and clinical model to predict acute kidney injury in paediatric cardiac surgery.
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ABSTRACT: Background
AKI is an important complication post cardiac surgery in children. An early diagnosis can help in mitigating complications and allow for prognostication. Urinary albumin:creatinine ratio (ACR) as a biomarker can provide a cheaper and more accessible AKI risk assessment and prediction. There is a paucity of paediatric literature regarding its utility.Methods
This was a prospective observational study, enrolling all children aged 1 month to 18 years, who underwent cardiac surgery, with use of cardiopulmonary bypass. Cohort was divided into groups < 2 years and ≥ 2 years for analyses to account for differences in physiological albumin excretion with age.Results
Of 143 children enrolled in the study, 36 developed AKI. In both age groups, the post-operative ACR was higher than pre-operative ACR among patients with and without AKI. In the group aged ≥ 2 years, the highest first post-operative ACR tertile (> 75.8 mg/g) predicted post-operative AKI after adjusting for clinical variables (adjusted RR, 11.71; 1.85-16.59). In the group aged < 2 years, the highest first post-operative ACR tertile (> 141.3 mg/g) predicted post-operative AKI in unadjusted analysis but not after adjusting for clinical variables (RR, 2.78; 0.70-6.65). For AKI risk prediction, AUC (95% CI) was highest after combining clinical model and pre-operative ACR for groups aged < 2 years [0.805 (0.713-0.896)] and ≥ 2 years [0.872 (0.772-0.973)].Conclusions
This study provides evidence for use of albuminuria as a feasible biomarker in AKI prediction in children post cardiac surgery, especially when added to a clinical model. A higher resolution version of the Graphical abstract is available as Supplementary information.
SUBMITTER: Nautiyal A
PROVIDER: S-EPMC8451727 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceEffective treatment of acute kidney injury (AKI) is predicated on timely diagnosis; however, the lag in the increase in serum creatinine levels after kidney injury may delay therapy initiation.ObjectiveTo determine the derivation and validation of predictive models for AKI after cardiac surgery.Design, setting, and participantsMultivariable prediction models were derived based on a retrospective observational cohort of adult patients undergoing cardiac surgery between January 2000 and December 2019 from a US academic medical center (n = 58 526) and subsequently validated on an external cohort from 3 US community hospitals (n = 4734). The date of final follow-up was January 15, 2020.ExposuresPerioperative change in serum creatinine and postoperative blood urea nitrogen, serum sodium, potassium, bicarbonate, and albumin from the first metabolic panel after cardiac surgery.Main outcomes and measuresArea under the receiver-operating characteristic curve (AUC) and calibration measures for moderate to severe AKI, per Kidney Disease: Improving Global Outcomes (KDIGO), and AKI requiring dialysis prediction models within 72 hours and 14 days following surgery.ResultsIn a derivation cohort of 58 526 patients (median [IQR] age, 66 [56-74] years; 39 173 [67%] men; 51 503 [91%] White participants), the rates of moderate to severe AKI and AKIrequiring dialysis were 2674 (4.6%) and 868 (1.48%) within 72 hours and 3156 (5.4%) and 1018 (1.74%) within 14 days after surgery. The median (IQR) interval to first metabolic panel from conclusion of the surgical procedure was 10 (7-12) hours. In the derivation cohort, the metabolic panel-based models had excellent predictive discrimination for moderate to severe AKI within 72 hours (AUC, 0.876 [95% CI, 0.869-0.883]) and 14 days (AUC, 0.854 [95% CI, 0.850-0.861]) after the surgical procedure and for AKI requiring dialysis within 72 hours (AUC, 0.916 [95% CI, 0.907-0.926]) and 14 days (AUC, 0.900 [95% CI, 0.889-0.909]) after the surgical procedure. In the validation cohort of 4734 patients (median [IQR] age, 67 (60-74) years; 3361 [71%] men; 3977 [87%] White participants), the models for moderate to severe AKI after the surgical procedure showed AUCs of 0.860 (95% CI, 0.838-0.882) within 72 hours and 0.842 (95% CI, 0.820-0.865) within 14 days and the models for AKI requiring dialysis and 14 days had an AUC of 0.879 (95% CI, 0.840-0.918) within 72 hours and 0.873 (95% CI, 0.836-0.910) within 14 days after the surgical procedure. Calibration assessed by Spiegelhalter z test showed P >.05 indicating adequate calibration for both validation and derivation models.Conclusions and relevanceAmong patients undergoing cardiac surgery, a prediction model based on perioperative basic metabolic panel laboratory values demonstrated good predictive accuracy for moderate to severe acute kidney injury within 72 hours and 14 days after the surgical procedure. Further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.
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