The challenges for developing prognostic prediction models for acute kidney injury in hospitalized children: A systematic review.

Abstract

Importance: Acute kidney injury (AKI) is common in hospitalized children which could rapidly progress into chronic kidney disease if not timely diagnosed. Prognostic prediction models for AKI were established to identify AKI early and improve children's prognosis.

Objective: To appraise prognostic prediction models for pediatric AKI.

Methods: Four English and four Chinese databases were systematically searched from January 1, 2010, to June 6, 2022. Articles describing prognostic prediction models for pediatric AKI were included. The data extraction was based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The risk of bias (ROB) was assessed according to the Prediction model Risk of Bias Assessment Tool guideline. The quantitative synthesis of the models was not performed due to the lack of methods regarding the meta-analysis of prediction models.

Results: Eight studies with 16 models were included. There were significant deficiencies in reporting and all models were considered at high ROB. The area under the receiver operating characteristic curve to predict AKI ranged from 0.69 to 0.95. However, only about one-third of models have completed internal or external validation. The calibration was provided only in four models. Three models allowed easy bedside calculation or electronic automation, and two models were evaluated for their impacts on clinical practice.

Interpretation: Besides the modeling algorithm, the challenges for developing prediction models for pediatric AKI reflected by the reporting deficiencies included ways of handling baseline serum creatinine and age-dependent blood biochemical indexes. Moreover, few prediction models for pediatric AKI were performed for external validation, let alone the transformation in clinical practice. Further investigation should focus on the combination of prediction models and electronic automatic alerts.