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

Journal Article (Systematic Review;Review;Journal Article)

Objectives

To 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.

Methods

We 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.

Results

A 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.

Conclusions

Information 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.

Full Text

Duke Authors

Cited Authors

  • Ohnuma, T; Uchino, S

Published Date

  • January 5, 2017

Published In

Volume / Issue

  • 12 / 1

Start / End Page

  • e0169341 -

PubMed ID

  • 28056039

Pubmed Central ID

  • 28056039

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

International Standard Serial Number (ISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0169341

Language

  • eng