Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.

Tetsu Ohnuma,Shigehiko Uchino,Emmanuel A Burdmann

doi:10.1371/journal.pone.0169341

Abstract

ObjectivesTo systematically review AKI outcome prediction models and their external validation studies, to describe the discrepancy of reported accuracy between the results of internal and external validations, and to identify variables frequently included in the prediction models.MethodsWe searched the MEDLINE and Web of Science electronic databases (until January 2016). Studies were eligible if they derived a model to predict mortality of AKI patients or externally validated at least one of the prediction models, and presented area under the receiver-operator characteristic curves (AUROC) to assess model discrimination. Studies were excluded if they described only results of logistic regression without reporting a scoring system, or if a prediction model was generated from a specific cohort.ResultsA total of 2204 potentially relevant articles were found and screened, of which 12 articles reporting original prediction models for hospital mortality in AKI patients and nine articles assessing external validation were selected. Among the 21 studies for AKI prediction models and their external validation, 12 were single-center (57%), and only three included more than 1,000 patients (14%). The definition of AKI was not uniform and none used recently published consensus criteria for AKI. Although good performance was reported in their internal validation, most of the prediction models had poor discrimination with an AUROC below 0.7 in the external validation studies. There were 10 common non-renal variables that were reported in more than three prediction models: mechanical ventilation, age, gender, hypotension, liver failure, oliguria, sepsis/septic shock, low albumin, consciousness and low platelet count.ConclusionsInformation in this systematic review should be useful for future prediction model derivation by providing potential candidate predictors, and for future external validation by listing up the published prediction models.

Highlights

Acute kidney injury (AKI) is a common complication among critically ill patients and their mortality is high [1,2,3,4]
Good performance was reported in their internal validation, most of the prediction models had poor discrimination with an area under the receiver-operator characteristic curves (AUROC) below 0.7 in the external validation studies
General severity scores for critically ill patients, e.g., Acute Physiology and Chronic Health Evaluation (APACHE) [5,6,7], Simplified Acute Physiology Score (SAPS) [8, 9], and Mortality Probability Model [10] have shown controversial results on the accuracy of predicting mortality in AKI patients [11,12,13], partly because those scores were generated from data that included only a few AKI patients

Summary

Introduction

Acute kidney injury (AKI) is a common complication among critically ill patients and their mortality is high [1,2,3,4]. Reliable AKI specific scoring systems are important to predict outcome of AKI patients and to provide severity stratification for clinical studies. General severity scores for critically ill patients, e.g., Acute Physiology and Chronic Health Evaluation (APACHE) [5,6,7], Simplified Acute Physiology Score (SAPS) [8, 9], and Mortality Probability Model [10] have shown controversial results on the accuracy of predicting mortality in AKI patients [11,12,13], partly because those scores were generated from data that included only a few AKI patients. Internal validation of these prediction models has shown good accuracy, the results of external validation studies for the models have been unsatisfactory [11, 25, 26]. There is neither consensus nor guideline recommending which prediction model to apply to clinical practice

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