Development and Validation of a Risk Score for Prediction of Acute Kidney Injury in Patients With Acute Decompensated Heart Failure: A Prospective Cohort Study in China

Overview

Journal J Am Heart Assoc

Specialty Cardiology & Vascular Diseases

Date 2016 Nov 18

PMID 27852590

Citations 15

Authors

Li Zhi Zhou

Xiao Bing Yang

Ying Guan

Xing Xu

Ming T Tan

Fan Fan Hou

Ping Yan Chen

Affiliations

Soon will be listed here.

Abstract

Background: Although several risk factors for acute kidney injury (AKI) have been identified, early detection of AKI in acute decompensated heart failure patients remains a challenge. The aim of this study was to develop and validate a risk score for early prediction of AKI in acute decompensated heart failure patients.

Methods And Results: A total of 676 consecutive acute decompensated heart failure participants were prospectively enrolled from 6 regional central hospitals. Data from 507 participants were analyzed. Participants from 4 of the 6 hospitals (n=321) were used to develop a risk score and conduct internal validation. External validation of the developed risk score was conducted in participants from the other 2 hospitals (n=186). Sequential logistic regression was used to develop and validate the risk score. The c statistic and calibration plot were used to assess the discrimination and calibration of the proposed risk score. The overall occurrence of AKI was 33.1% (168/507). The risk score, ranging from 0 to 55, demonstrated good discriminative power with an optimism-corrected c statistic of 0.859. Similar results were obtained from external validation with c statistic of 0.847 (95% CI 0.819-0.927). The risk score had good calibration with no apparent over- or under-prediction observed from calibration plots.

Conclusions: The novel risk score is a simple and accurate tool that can help clinicians assess the risk of AKI in acute decompensated heart failure patients, which in turn helps them plan and initiate the most appropriate disease management for patients in time.

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