# Nomogram Model to Predict Acute Kidney Injury in Hospitalized Patients with Heart Failure

## TL;DR

This study developed a nomogram model to predict acute kidney injury in hospitalized heart failure patients using factors like age, pneumonia, D-dimer, and albumin.

## Contribution

A new nomogram model was created and validated for predicting AKI in heart failure patients.

## Key findings

- The incidence of AKI in the cohort was 19%.
- Age, pneumonia, D-dimer, and albumin were identified as independent predictors of AKI.
- The model showed moderate discriminability with AUCs of 0.760 and 0.744 in training and test sets.

## Abstract

Acute kidney injury (AKI) is a common complication
of acute heart failure (HF) that can prolong hospitalization time and worsen the
prognosis. The objectives of this research were to ascertain independent risk
factors of AKI in hospitalized HF patients and validate a nomogram risk
prediction model established using those factors.

Finally, 967 patients hospitalized for HF were included. Patients were randomly
assigned to the training set (n = 677) or test set (n = 290). Least absolute
shrinkage and selection operator (LASSO) regression was performed for variable
selection, and multivariate logistic regression analysis was used to search for
independent predictors of AKI in hospitalized HF patients. A nomogram prediction
model was then developed based on the final identified predictors. The
performance of the nomogram was assessed in terms of discriminability, as
determined by the area under the receiver operating characteristic (ROC) curve
(AUC), and predictive accuracy, as determined by calibration plots.

The incidence of AKI in our cohort was 19%. After
initial LASSO variable selection, multivariate logistic regression revealed that
age, pneumonia, D-dimer, and albumin were independently associated with AKI in
hospitalized HF patients. The nomogram prediction model based on these
independent predictors had AUCs of 0.760 and 0.744 in the training and test sets,
respectively. The calibration plots indicate a strong concordance between the
estimated AKI probabilities and the observed probabilities.

A nomogram prediction model based on pneumonia, age, D-dimer, and albumin can
help clinicians predict the risk of AKI in HF patients with moderate
discriminability.