Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Cardiol. Feb 26, 2024; 16(2): 80-91
Published online Feb 26, 2024. doi: 10.4330/wjc.v16.i2.80
Development and validation of a nomogram model for predicting the risk of pre-hospital delay in patients with acute myocardial infarction
Jiao-Yu Cao, Li-Xiang Zhang, Xiao-Juan Zhou
Jiao-Yu Cao, Li-Xiang Zhang, Xiao-Juan Zhou, Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
Co-first authors: Jiao-Yu Cao and Li-Xiang Zhang.
Author contributions: Cao JY and Zhang LX contributed equally to this work; Cao JY and Zhou XJ designed the research study; Cao JY and Zhang LX performed the research; Zhang LX contributed analytic tools; Cao JY and Zhang LX analyzed the data and wrote the manuscript; All authors have read and approve the final manuscript.
Institutional review board statement: This study obtained ethical approval from the Medical Ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China, under the approval ID: 2023-RE-124.
Informed consent statement: Due to the retrospective nature of the study, the necessity for informed consent from the study participants was exempted by the Medical Ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China.
Conflict-of-interest statement: All authors declare that they have no conflicts of interest.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Jiao-Yu Cao, MMed, Chief Nurse, Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, No. 1 Swan Lake Road, Hefei 230001, Anhui Province, China.
Received: October 10, 2023
Peer-review started: October 10, 2023
First decision: December 18, 2023
Revised: January 2, 2024
Accepted: February 2, 2024
Article in press: February 2, 2024
Published online: February 26, 2024
Research background

Acute myocardial infarction (AMI), a lethal heart condition, results from coronary artery blockages that cause myocardial ischemia and necrosis. Treatment delays heighten death risks, making prompt medical response critical. This study focuses on reducing pre-hospital delays by identifying high-risk AMI patients, developing a risk prediction model, and implementing tailored strategies for timely care.

Research motivation

The timely management of AMI is crucial for improving patient outcomes, yet pre-hospital delay time (PDT) poses a significant challenge, leading to increased morbidity and mortality rates. This research is motivated by the need to understand the determinants of PDT in AMI patients and develop a robust risk prediction model. By identifying high-risk individuals and implementing targeted strategies to reduce PDT, this study aims to enhance the delivery of prompt and effective care. Its significance lies in addressing a critical knowledge gap in cardiovascular medicine and offering practical solutions to optimize AMI treatment outcomes for future research in this field.

Research objectives

The main objective is to investigate determinants of pre-hospital delay (PHD) in AMI patients and construct a risk prediction model. Realizing these objectives has significant implications for future research in this field, allowing refinement of models, development of evidence-based guidelines, and optimization of AMI treatment strategies for improved patient outcomes.

Research methods

This retrospective cohort study investigated determinants of PHD in AMI patients and developed a risk prediction model. Data on 26 indicators were collected from AMI patients admitted to a tertiary hospital in Anhui Province, China. Statistical analysis involved logistic regression, nomogram modeling, receiver operating characteristic curve analysis, calibration tests, and decision curve analysis. The study contributes to advancing AMI management research.

Research results

This study identified risk factors for post-hospital discharge in acute myocardial infarction patients. Living alone, hyperlipidemia, age, diabetes, and digestive system diseases were significant predictors. A nomogram model accurately predicted the risk of post-hospital discharge. This model can help healthcare professionals identify high-risk patients and provide targeted interventions, but further validation is needed in larger populations.

Research conclusions

This study concludes that the newly developed nomogram model, incorporating independent risk factors, accurately predicts the likelihood of post-hospital discharge in acute myocardial infarction patients. This model offers a valuable tool for efficiently identifying individuals at risk of post-hospital discharge, providing potential benefits for targeted interventions and improved patient outcomes in clinical practice.

Research perspectives

Future research should address limitations of the retrospective design, limited sample size, and subset of predictors. Large-scale, multi-center studies with comprehensive data are needed to enhance generalizability. Exploring additional risk factors and refining predictive models can improve accuracy for forecasting post-hospital discharge outcomes in acute myocardial infarction patients, benefiting clinical decision-making.