{"doi":"10.1186/s13019-024-02665-3","title":"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)","abstract":"<jats:title>Abstract</jats:title><jats:sec>\n                <jats:title>Background</jats:title>\n                <jats:p>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.</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Methods</jats:title>\n                <jats:p>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 (<jats:italic>n</jats:italic> = 464) and a validation cohort (<jats:italic>n</jats:italic> = 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).</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Results</jats:title>\n                <jats:p>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.</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Conclusion</jats:title>\n                <jats:p>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.</jats:p>\n              </jats:sec>","journal":"Journal of Cardiothoracic Surgery","year":2024,"id":626325,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1620129,"name":"Zhican Liu","orcid":"0000-0002-5532-1632","position":1,"is_corresponding":false},{"id":1419152,"name":"Yunlong Zhu","orcid":"0000-0002-8460-2820","position":2,"is_corresponding":false},{"id":1060370,"name":"Mingxin Wu","orcid":"0000-0002-4622-9316","position":3,"is_corresponding":false},{"id":1620130,"name":"Haobo Huang","orcid":null,"position":4,"is_corresponding":false},{"id":455203,"name":"Wenbin Yang","orcid":"0000-0002-6485-8597","position":5,"is_corresponding":false},{"id":638782,"name":"Ke Peng","orcid":"0000-0003-0172-8725","position":6,"is_corresponding":false},{"id":1620131,"name":"Jianping Zeng","orcid":"0000-0002-4485-6164","position":7,"is_corresponding":false},{"id":196801,"name":"Lingling Zhang","orcid":"0009-0005-6902-3475","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"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)","abstract":"<jats:title>Abstract</jats:title><jats:sec>\n                <jats:title>Background</jats:title>\n                <jats:p>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.</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Methods</jats:title>\n                <jats:p>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 (<jats:italic>n</jats:italic> = 464) and a validation cohort (<jats:italic>n</jats:italic> = 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).</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Results</jats:title>\n                <jats:p>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.</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Conclusion</jats:title>\n                <jats:p>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.</jats:p>\n              </jats:sec>","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38555468","pmcid":"PMC10981323","openalex_id":"https://openalex.org/W4393344646","authors":[],"funders":[],"total_grants":0,"fwci":1.4819,"citation_percentile":0.83219706,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":2},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://cardiothoracicsurgery.biomedcentral.com/counter/pdf/10.1186/s13019-024-02665-3","host_type":"journal"},{"url":"https://cardiothoracicsurgery.biomedcentral.com/counter/pdf/10.1186/s13019-024-02665-3","host_type":"publisher"},{"url":"https://link.springer.com/content/pdf/10.1186/s13019-024-02665-3.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1186/s13019-024-02665-3/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1186/s13019-024-02665-3","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38555468","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10981323","host_type":"repository"},{"url":"https://doaj.org/article/63e2460bbe2c4aaeba5ccbb2f748e62c","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10981323/pdf/13019_2024_Article_2665.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC10981323","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC10981323?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Acute Myocardial Infarction Research","Sepsis Diagnosis and Treatment","Cardiac, Anesthesia and Surgical Outcomes","Humans","Prognosis","ST Elevation Myocardial Infarction","Patient Discharge","Retrospective Studies","Stroke Volume","Angiotensin Receptor Antagonists","Aftercare","Ventricular Function, Left","Angiotensin-Converting Enzyme Inhibitors","Percutaneous Coronary Intervention","Natriuretic Peptide, Brain"],"mesh_terms":["ST Elevation Myocardial Infarction","Aftercare","Angiotensin-Converting Enzyme Inhibitors","Humans","Patient Discharge","Prognosis","Retrospective Studies","Stroke Volume","Ventricular Function, Left","Natriuretic Peptide, Brain","Angiotensin Receptor Antagonists","Percutaneous Coronary Intervention"],"keywords":["Medicine","Internal medicine","Percutaneous coronary intervention","Myocardial infarction","Killip class","Cardiology","Receiver operating characteristic","Ejection fraction","Lasso (programming language)","Cohort","Heart failure","Predictive model","St-segment Elevation Myocardial Infarction (Stemi)","Least Absolute Shrinkage And Selection Operator (Lasso)","Decision Curve Analysis (Dca)","All-cause Mortality Risk"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T13:22:52.848504Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}