A Validation Study Comparing Existing Prediction Models of Acute Kidney Injury in Patients with Acute Heart Failure: A Multi-Institution Database Study in Taiwan

Abstract Background:Acute kidney injury (AKI) is a common complication in hospitalized acute heart failure (AHF) patients and is associated with prolonged hospitalization, increased readmission rates, and mortality. The aim of this study was to externally validate existing prediction models of AKI in patients with AHF.Methods:A total of 10,364 patients hospitalized for acute heart failure (AHF) between 2008 and 2018 were extracted from the Chang Gung Research Database and analyzed. The primary outcome of interest was AKI, defined according to the KDIGO definition. We also extended the existing prediction models to predict AKI stage 3 and dialysis. The area under the receiver operating characteristic (AUC) curve was used to assess the discrimination performance of each prediction model. Results:Five existing prediction models were externally validated, with the AUCs for AKI prediction ranging from 0.543 to 0.73. These prediction models also performed well in serious AKI event prediction, with AUCs of 0.565–0.858 for predicting AKI stage 3 and AUCs of 0.539–0.845 for predicting dialysis within 7 days. Among the five models, the Forman risk score and the prediction model reported by Wang et al. showed the most favorable discrimination and calibration performance. The Forman risk score had AUCs for discriminating AKI, AKI stage 3, and dialysis within 7 days of 0.696, 0.829, and 0.817, respectively. The Wang et al. model had AUCs for discriminating AKI, AKI stage 3, and dialysis within 7 days of 0.73, 0.858, and 0.845, respectively. Conclusion:The Forman risk score and the Wang et al. prediction model are simple and accurate tools for predicting AKI and serious AKI events in patients with AHF. They can aid clinicians in evaluating the risk of AKI in these patients and in planning and initiating adequate disease management in a timely manner.

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A validation study comparing existing prediction models of acute kidney injury in patients with acute heart failure

Scientific Reports ◽

10.1038/s41598-021-90756-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tao Han Lee ◽

Pei-Chun Fan ◽

Jia-Jin Chen ◽

Victor Chien‐Chia Wu ◽

Cheng-Chia Lee ◽

...

Keyword(s):

Heart Failure ◽

Acute Kidney Injury ◽

Prediction Model ◽

Acute Heart Failure ◽

Risk Score ◽

Operating Characteristic ◽

Prediction Models ◽

Discrimination Performance ◽

Kidney Injury ◽

Research Database

AbstractAcute kidney injury (AKI) is a common complication in acute heart failure (AHF) and is associated with prolonged hospitalization and increased mortality. The aim of this study was to externally validate existing prediction models of AKI in patients with AHF. Data for 10,364 patients hospitalized for acute heart failure between 2008 and 2018 were extracted from the Chang Gung Research Database and analysed. The primary outcome of interest was AKI, defined according to the KDIGO definition. The area under the receiver operating characteristic (AUC) curve was used to assess the discrimination performance of each prediction model. Five existing prediction models were externally validated, and the Forman risk score and the prediction model reported by Wang et al. showed the most favourable discrimination and calibration performance. The Forman risk score had AUCs for discriminating AKI, AKI stage 3, and dialysis within 7 days of 0.696, 0.829, and 0.817, respectively. The Wang et al. model had AUCs for discriminating AKI, AKI stage 3, and dialysis within 7 days of 0.73, 0.858, and 0.845, respectively. The Forman risk score and the Wang et al. prediction model are simple and accurate tools for predicting AKI in patients with AHF.

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A simple nomogram to predict contrast-induced acute kidney injury in patients with congestive heart failure undergoing coronary angiography

European Heart Journal ◽

10.1093/ehjci/ehaa946.2554 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

L Lei ◽

Y He ◽

Z Guo ◽

B Liu ◽

J Liu ◽

...

Keyword(s):

Heart Failure ◽

Congestive Heart Failure ◽

Acute Kidney Injury ◽

Coronary Angiography ◽

Prediction Models ◽

Validation Cohort ◽

Kidney Injury ◽

Class Ii ◽

Functional Class ◽

Killip Class

Abstract Background Patients with congestive heart failure (CHF) are vulnerable to contrast-induced acute kidney injury (CI-AKI), but few prediction models are currently available. Objectives We aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography. Methods A total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48–72 hours following the procedure. Predictors for the nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer–Lemeshow test and 1000 bootstrap samples. Results The incidence of CI-AKI was 9.06% (n=170) in the total sample, 8.64% (n=109) in the development cohort and 9.92% (n=61) in the validation cohort (p=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs 0.75, p=0.061), as well as a well-fitted calibration curve. Conclusions The present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed. Figure 1 Funding Acknowledgement Type of funding source: None

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Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

10.21203/rs.3.rs-948336/v1 ◽

2021 ◽

Author(s):

Xuecheng Zhang ◽

Kehua Zhou ◽

Jingjing Zhang ◽

Ying Chen ◽

Hengheng Dai ◽

...

Keyword(s):

Heart Failure ◽

Systematic Review ◽

Prediction Model ◽

Acute Heart Failure ◽

Risk Prediction ◽

Methodological Quality ◽

Critical Appraisal ◽

Prediction Models ◽

Cochrane Library ◽

Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

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Abstract 13032: Resolution of Congestion Before Coronary Angiography in Acute Heart Failure is Associated With Reduced Risk of Contrast-induced Acute Kidney Injury

Circulation ◽

10.1161/circ.132.suppl_3.13032 ◽

2015 ◽

Vol 132 (suppl_3) ◽

Author(s):

Jonghanne Park ◽

Jin Joo Park ◽

Young-Jin Cho ◽

Yeon-Yee Yoon ◽

Il-Young Oh ◽

...

Keyword(s):

Heart Failure ◽

Acute Kidney Injury ◽

Body Weight ◽

Coronary Angiography ◽

Acute Heart Failure ◽

Kidney Injury ◽

Renal Perfusion ◽

Volume Status ◽

Absolute Deviation ◽

Reduced Risk

Objectives: We investigated the risk factors for contrast-induced acute kidney injury (CIAKI) after coronary angiography (CAG) in patients with acute heart failure (AHF), especially with regard to the volume status. Background: Heart failure is a well-known risk factor for CIAKI after CAG. In HF patients, renal perfusion decreases with systemic congestion. Thus, the standard prevention strategy with isotonic solution infusion may be inappropriate while decongestion may be beneficiary in AHF patients undergoing CAG. Deviation from dry body weight suggests imbalanced volume status. Methods: A total of 199 AHF patients who underwent CAG were eligible for the analysis. Absolute deviation of body weight (

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