NDLI: Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI)

Content Provider	Springer Nature : BioMed Central
Author	Zhang, Lingling Liu, Zhican Zhu, Yunlong Wu, Mingxin Huang, Haobo Yang, Wenbin Peng, Ke Zeng, Jianping
Abstract	Background Accurately predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains a complex and critical challenge. The primary objective of this study was to develop and validate a robust risk prediction model to assess the 12-month and 24-month mortality risk in STEMI patients after hospital discharge. Methods A retrospective study was conducted on 664 STEMI patients who underwent PPCI at Xiangtan Central Hospital Chest Pain Center between 2020 and 2022. The dataset was randomly divided into a training cohort (n = 464) and a validation cohort (n = 200) using a 7:3 ratio. The primary outcome was all-cause mortality following hospital discharge. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify the optimal predictive variables. Based on these variables, a regression model was constructed to determine the significant predictors of mortality. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results The prognostic model was developed based on the LASSO regression results and further validated using the independent validation cohort. LASSO regression identified five important predictors: age, Killip classification, B-type natriuretic peptide precursor (NTpro-BNP), left ventricular ejection fraction (LVEF), and the usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEI/ARB/ARNI). The Harrell's concordance index (C-index) for the training and validation cohorts were 0.863 (95% CI: 0.792–0.934) and 0.888 (95% CI: 0.821–0.955), respectively. The area under the curve (AUC) for the training cohort at 12 months and 24 months was 0.785 (95% CI: 0.771–0.948) and 0.812 (95% CI: 0.772–0.940), respectively, while the corresponding values for the validation cohort were 0.864 (95% CI: 0.604–0.965) and 0.845 (95% CI: 0.705–0.951). These results confirm the stability and predictive accuracy of our model, demonstrating its reliable discriminative ability for post-discharge all-cause mortality risk. DCA analysis exhibited favorable net benefit of the nomogram. Conclusion The developed nomogram shows potential as a tool for predicting post-discharge mortality in STEMI patients undergoing PPCI. However, its full utility awaits confirmation through broader external and temporal validation.
Related Links	https://cardiothoracicsurgery.biomedcentral.com/counter/pdf/10.1186/s13019-024-02665-3.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	17498090
DOI	10.1186/s13019-024-02665-3
Journal	Journal of Cardiothoracic Surgery
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2024-03-30
Access Restriction	Open
Subject Keyword	Cardiac Surgery Thoracic Surgery ST-segment elevation myocardial infarction (STEMI) All-cause mortality risk Predictive model Least Absolute Shrinkage and Selection Operator (LASSO) Decision Curve Analysis (DCA)
Content Type	Text
Resource Type	Article
Subject	Surgery Cardiology and Cardiovascular Medicine Pulmonary and Respiratory Medicine
Journal Impact Factor	1.5/2023
5-Year Journal Impact Factor	1.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction.

Predicting 30-Day Mortality Using ST-Segment Elevation Resolution in ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention: An Indian Scenario.

Machine learning prediction of no reflow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.

Development and Validation of Risk Nomogram Model Predicting Coronary Microvascular Obstruction in Patients with ST-Segment Elevation Myocardial Infarction (STEMI) Undergoing Primary Percutaneous Catheterization.

Impact of maintenance dose of eptifibatide in patients with ST-segment elevation myocardial infarction who underwent primary percutaneous coronary intervention.

A nomogram for predicting the risk of no-reflow after primary percutaneous coronary intervention in elderly patients with ST-segment elevation myocardial infarction.

STEMI or non-STEMI: that is the question

Long-term follow-up in patients with ST-segment elevation myocardial infarction who underwent primary percutaneous coronary intervention

Trends in Short‐ and Long‐Term ST‐Segment–Elevation Myocardial Infarction Prognosis Over 3 Decades: A Mediterranean Population‐Based ST‐Segment–Elevation Myocardial Infarction Registry

Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI)

Similar Documents

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction.

Predicting 30-Day Mortality Using ST-Segment Elevation Resolution in ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention: An Indian Scenario.

Machine learning prediction of no reflow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.

Development and Validation of Risk Nomogram Model Predicting Coronary Microvascular Obstruction in Patients with ST-Segment Elevation Myocardial Infarction (STEMI) Undergoing Primary Percutaneous Catheterization.

Impact of maintenance dose of eptifibatide in patients with ST-segment elevation myocardial infarction who underwent primary percutaneous coronary intervention.

A nomogram for predicting the risk of no-reflow after primary percutaneous coronary intervention in elderly patients with ST-segment elevation myocardial infarction.

STEMI or non-STEMI: that is the question

Long-term follow-up in patients with ST-segment elevation myocardial infarction who underwent primary percutaneous coronary intervention

Trends in Short‐ and Long‐Term ST‐Segment–Elevation Myocardial Infarction Prognosis Over 3 Decades: A Mediterranean Population‐Based ST‐Segment–Elevation Myocardial Infarction Registry

Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI)