# Establishing a Predictive Model for the Occurrence of CI-AKI After PCI in Patients With Coronary Heart Disease Based on Serum-Derived Biomarkers

## TL;DR

This study develops a model to predict contrast-induced kidney injury after heart procedures using blood markers, helping identify high-risk patients early.

## Contribution

The novel contribution is a predictive model for CI-AKI based on serum-derived biomarkers with validated clinical applicability.

## Key findings

- Neutrophil count, low-density lipoprotein, and PLR are independent risk factors for CI-AKI.
- The model showed good accuracy with ROC areas of 0.73 and 0.71 in training and validation groups.
- The model's predictions align with actual outcomes and provide clinical benefit within specific risk thresholds.

## Abstract

Objective: To identify risk factors for contrast-induced acute kidney injury (CI-AKI) post-PCI in coronary heart disease (CHD) patients, analyze novel inflammatory markers, and develop a predictive model.

Methods: CHD patients admitted to Northern Jiangsu People's Hospital in Yangzhou, Jiangsu Province, China, from January 1, 2019, to December 31, 2022, were selected, and a total of 628 patients were included in this study by collecting the general information, past history, and relevant laboratory test results of all patients and excluding those with imperfect relevant medical records, including 142 cases in the CI-AKI group and 486 cases in the non-CI-AKI group. According to the ratio of 7:3, they were randomly divided into a training group (n = 439) and a validation group (n = 189). Independent risk factors for the occurrence of postoperative CI-AKI were screened by unifactorial and multifactorial logistic regression analyses in the training group, a clinical prediction model was established, and the prediction efficiency and applicability of the prediction model were analyzed by ROC curves, DCA curves, and H–L curves in the two groups.

Results: Regression analysis suggested that neutrophil count, low-density lipoprotein, and PLR were independent risk factors for CI-AKI (p < 0.05); a model for predicting CI-AKI was established based on the above indexes, and the areas under the ROC curves of the model in the training and validation groups were 0.73 (0.67–0.78) and 0.71 (0.62–0.79), respectively; the H–L curve suggests that the predicted situation of the model is consistent with the actual occurrence, and the DCA curve suggests that patients in the training group and the validation group will have the greatest clinical benefit when the thresholds for the occurrence of postoperatively induced acute kidney injury are 0.26–0.82 and 0.30–0.97, respectively.

Conclusion: This CI-AKI prediction model demonstrates good accuracy and clinical applicability, aiding early high-risk patient identification and intervention.

Trial Registration: Chinese Registry of Clinical Trials: ChiCTR2500099751