Cao JY, Zhang LX, Zhou XJ. Development and validation of a nomogram model for predicting the risk of pre-hospital delay in patients with acute myocardial infarction. World J Cardiol 2024; 16(2): 80-91 [PMID: 38456069 DOI: 10.4330/wjc.v16.i2.80]
Corresponding Author of This Article
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. caojiaoyu@126.com
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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: https://creativecommons.org/Licenses/by-nc/4.0/
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. caojiaoyu@126.com
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 Processing time: 133 Days and 9.7 Hours
Abstract
BACKGROUND
Acute myocardial infarction (AMI) is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium. Timely medical contact is critical for successful AMI treatment, and delays increase the risk of death for patients. Pre-hospital delay time (PDT) is a significant challenge for reducing treatment times, as identifying high-risk patients with AMI remains difficult. This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care, ultimately reducing PDT and improving treatment outcomes.
AIM
To construct a nomogram model for forecasting pre-hospital delay (PHD) likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.
METHODS
A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022. The study included 252 patients, with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio. Independent risk factors influencing PHD were identified in the development group, leading to the establishment of a nomogram model for predicting PHD in patients with AMI. The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.
RESULTS
Independent risk factors for PHD in patients with AMI included living alone, hyperlipidemia, age, diabetes mellitus, and digestive system diseases (P < 0.05). A nomogram model incorporating these five predictors accurately predicted PHD occurrence. The receiver operating characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787 (95% confidence interval: 0.716–0.858) and 0.770 (95% confidence interval: 0.660-0.879) in the development and validation groups, respectively, demonstrating the model's good discriminatory ability. The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts (P > 0.05), indicating satisfactory model calibration.
CONCLUSION
The nomogram model, developed with independent risk factors, accurately forecasts PHD likelihood in AMI individuals, enabling efficient identification of PHD risk in these patients.
Core Tip: The study developed a nomogram model to predict pre-hospital delay (PHD) in acute myocardial infarction (AMI) patients. Independent risk factors for PHD were identified, and a nomogram was constructed using these predictors. The model showed good discriminatory ability and satisfactory calibration. This nomogram can effectively identify PHD risk in AMI patients.
