{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:43Z","timestamp":1760146303092,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Suzhou Hybiome Biomedical Engineering Co., Ltd."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: This study demonstrates differences in the distribution of multiple cardiovascular biomarkers between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) patients. Diagnostic machine learning predictive models measured at the time of admission and 1\/2 h post-admission, achieving competitive diagnostic predictive results. Objective: This study aims to explore the diagnostic value of changes in high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with suspected NSTEMI. Methods: A total of 267 patients presented with chest pain, requiring confirmation of acute coronary syndrome (ACS) subtypes (NSTEMI vs. UA). Hs-cTnI and other cardiac markers, such as creatine kinase-MB (CK-MB) and Myoglobin (Myo), were analyzed. Machine learning techniques were employed to assess the application of hs-cTnI level changes in the clinical diagnosis of NSTEMI. Results: Levels of CK-MB, Myo, hs-cTnI measured at admission, hs-cTnI measured 1\u20132 h after admission, and NT-proBNP in NSTEMI patients were significantly higher than those in UA patients (p &lt; 0.001). There was a positive correlation between hs-cTnI and CK-MB, as well as Myo (R = 0.72, R = 0.51, R = 0.60). The optimal diagnostic model, Hybiome_1\/2h, demonstrated an F1-Score of 0.74, an AUROC of 0.96, and an AP of 0.89. Conclusions: This study confirms the significant value of hs-cTnI as a sensitive marker of myocardial injury in the diagnosis of NSTEMI. Continuous monitoring of hs-cTnI levels enhances the accuracy of distinguishing NSTEMI from UA. The models indicate that the Hybiome hs-cTnI assays perform comparably well to the Beckman assays in predicting NSTEMI. Moreover, incorporating hs-cTnI measurements taken 1\u20132 h post-admission significantly enhances the model\u2019s effectiveness.<\/jats:p>","DOI":"10.3390\/diagnostics14202322","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T06:46:52Z","timestamp":1729234012000},"page":"2322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Diagnostic Model for Early Stage NSTEMI: Using hs-cTnI 1\/2h Changes and Multiple Cardiovascular Biomarkers"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8308-177X","authenticated-orcid":false,"given":"Junyi","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilin","family":"Ge","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3059-5068","authenticated-orcid":false,"given":"Jiashu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1001\/jamacardio.2016.2049","article-title":"Updates on Acute Coronary Syndrome: A Review","volume":"1","author":"Eisen","year":"2016","journal-title":"JAMA Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.cpcardiol.2017.04.006","article-title":"Non-ST Elevation Acute Coronary Syndromes: A Comprehensive Review","volume":"42","author":"Ifedili","year":"2017","journal-title":"Curr. Probl. Cardiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"311","DOI":"10.4329\/wjr.v14.i8.311","article-title":"Triple Rule-out Computed Tomography Angiography: Evaluation of Acute Chest Pain in COVID-19 Patients in the Emergency Department","volume":"14","author":"Bahadir","year":"2022","journal-title":"World J. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1001\/jama.2022.0358","article-title":"Diagnosis and Treatment of Acute Coronary Syndromes","volume":"327","author":"Bhatt","year":"2022","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101","DOI":"10.7326\/M21-1499","article-title":"Performance of the European Society of Cardiology 0\/1-Hour, 0\/2-Hour, and 0\/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction: An International Collaborative Meta-Analysis","volume":"175","author":"Chiang","year":"2022","journal-title":"Ann. Intern. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1080\/10408363.2017.1285268","article-title":"High-Sensitivity Cardiac Troponin Assays: From Improved Analytical Performance to Enhanced Risk Stratification","volume":"54","author":"Kozinski","year":"2017","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.jacc.2017.07.718","article-title":"Clinical Use of High-Sensitivity Cardiac Troponin in Patients with Suspected Myocardial Infarction","volume":"70","author":"Twerenbold","year":"2017","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9713326","DOI":"10.1155\/2022\/9713326","article-title":"High-Sensitivity Troponin: A Review on Characteristics, Assessment, and Clinical Implications","volume":"2022","author":"Lazar","year":"2022","journal-title":"Dis. Markers"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1161\/CIRCULATIONAHA.122.059678","article-title":"High-Sensitivity Cardiac Troponin and the 2021 AHA\/ACC\/ASE\/CHEST\/SAEM\/SCCT\/SCMR Guidelines for the Evaluation and Diagnosis of Acute Chest Pain","volume":"146","author":"Sandoval","year":"2022","journal-title":"Circulation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1093\/clinchem\/hvad185","article-title":"Lower Limits for Reporting High-Sensitivity Cardiac Troponin Assays and Impact of Analytical Performance on Patient Misclassification","volume":"70","author":"Aakre","year":"2024","journal-title":"Clin. Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1001\/jama.2011.1896","article-title":"Serial Changes in Highly Sensitive Troponin I Assay and Early Diagnosis of Myocardial Infarction","volume":"306","author":"Keller","year":"2011","journal-title":"JAMA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1373\/clinchem.2018.286906","article-title":"Clinical Validation of a Novel High-Sensitivity Cardiac Troponin I Assay for Early Diagnosis of Acute Myocardial Infarction","volume":"64","author":"Boeddinghaus","year":"2018","journal-title":"Clin. Chem."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Citarella, A. (2022). ENTAIL: YEt ANoTher Amyloid FIbrils CLassifier. BMC Bioinform., 23.","DOI":"10.21203\/rs.3.rs-2041454\/v1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Marco, D. (2022). Classification of QRS Complexes to Detect Premature Ventricular Contraction Using Machine Learning Techniques. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0268555"},{"key":"ref_15","first-page":"1","article-title":"Standardization and Clinical Applications of Retinal Imaging Biomarkers for Cardiovascular Disease: A Roadmap from an NHLBI Workshop","volume":"22","author":"Chew","year":"2024","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"De Marco, F., Finlay, D., and Bond, R. (2020, January 13\u201316). Classification of Premature Ventricular Contraction Using Deep Learning. Proceedings of the 2020 Computing in Cardiology Conference (CinC), Rimini, Italy.","DOI":"10.22489\/CinC.2020.311"},{"key":"ref_17","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). Lightgbm: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st Annual Conference on Neural Information Processing System, Long Beach, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1007\/s11053-023-10249-6","article-title":"A Novel Scheme for Mapping of MVT-Type Pb\u2013Zn Prospectivity: LightGBM, a Highly Efficient Gradient Boosting Decision Tree Machine Learning Algorithm","volume":"32","author":"Hajihosseinlou","year":"2023","journal-title":"Nat. Resour. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rufo, D.D., Debelee, T.G., Ibenthal, A., and Negera, W.G. (2021). Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM). Diagnostics, 11.","DOI":"10.3390\/diagnostics11091714"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23366","DOI":"10.1109\/ACCESS.2023.3253885","article-title":"Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison","volume":"11","author":"Yang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yuan, Y., Yao, Z., Yang, J., Wang, X., and Tian, J. (2022). Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm. Electronics, 11.","DOI":"10.3390\/electronics11091495"},{"key":"ref_23","first-page":"267","article-title":"ESC Guidelines for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent STsegment Elevation: Task Force the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology(ESC)","volume":"37","author":"Roffi","year":"2016","journal-title":"Eur. Soc. Cardiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1093\/eurheartj\/ehaa575","article-title":"2020 ESC Guidelines for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation","volume":"42","author":"Collet","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1161\/CIR.0000000000000617","article-title":"Fourth Universal Definition of Myocardial Infarction (2018)","volume":"138","author":"Thygesen","year":"2018","journal-title":"Circulation"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Asl, S.K., and Rahimzadegan, M. (2022). The Recent Progress in the Early Diagnosis of Acute Myocardial Infarction Based on Myoglobin Biomarker: Nano-Aptasensors Approaches. J. Pharm. Biomed. Anal., 211.","DOI":"10.1016\/j.jpba.2022.114624"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, Y.-W., Ho, S.K., Tseng, W.-K., Yeh, H.-I., Leu, H.-B., Yin, W.-H., Lin, T.-H., Chang, K.-C., Wang, J.-H., and Wu, C.-C. (2020). Potential Impacts of High-Sensitivity Creatine Kinase-MB on Long-Term Clinical Outcomes in Patients with Stable Coronary Heart Disease. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-61894-3"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1373\/clinchem.2016.257816","article-title":"C-Type Natriuretic Peptides in Coronary Disease","volume":"63","author":"Prickett","year":"2017","journal-title":"Clin. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1373\/clinchem.2017.277186","article-title":"Clinical Laboratory Practice Recommendations for the Use of Cardiac Troponin in Acute Coronary Syndrome: Expert Opinion from the Academy of the American Association for Clinical Chemistry and the Task Force on Clinical Applications of Cardiac Bio-Markers of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"64","author":"Wu","year":"2018","journal-title":"Clin. Chem."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lai, J.P., Lin, Y.L., Lin, H.C., Shih, C.Y., Wang, Y.P., and Pai, P.F. (2023). Tree-based machine learning models with optuna in predicting impedance values for circuit analysis. Micromachines, 14.","DOI":"10.3390\/mi14020265"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-Year Anniversary","volume":"61","author":"Fernandez","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, Y., Que, H., Cai, Q., Zhao, J., Li, J., Kong, Z., and Wang, S. (2022). Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy. Energies, 15.","DOI":"10.3390\/en15134751"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s41512-021-00102-w","article-title":"A Relationship between the Incremental Values of Area under the ROC Curve and of Area under the Precision-Recall Curve","volume":"5","author":"Zhou","year":"2021","journal-title":"Diagn. Progn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable Machine-Learning Predictions for the Prevention of Hypoxaemia during Surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Merrick, L., and Taly, A. (2020). The Explanation Game: Explaining Machine Learning Models Using Shapley Values. Lecture Notes in Computer Science, Springer International Publishing.","DOI":"10.1007\/978-3-030-57321-8_2"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.hrthm.2019.07.019","article-title":"The Electrocardiogram in the Diagnosis and Management of Patients with Hypertrophic Cardiomyopathy","volume":"17","author":"Finocchiaro","year":"2020","journal-title":"Heart Rhythm"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1093\/ehjacc\/zuae085","article-title":"Clinical Decision Support Using Machine Learning and Cardiac Troponin for the Diagnosis of Myocardial Infarction","volume":"13","author":"Than","year":"2024","journal-title":"Eur. Heart J. Acute Cardiovasc. Care"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/14779072.2023.2264779","article-title":"Biomarkers to Monitor the Prognosis, Disease Severity, and Treatment Efficacy in Coronary Artery Disease","volume":"21","author":"Yazdani","year":"2023","journal-title":"Expert Rev. Cardiovasc. Ther."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/20\/2322\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:16:07Z","timestamp":1760112967000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/20\/2322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":38,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["diagnostics14202322"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14202322","relation":{},"ISSN":["2075-4418"],"issn-type":[{"type":"electronic","value":"2075-4418"}],"subject":[],"published":{"date-parts":[[2024,10,18]]}}}