Identification of patients with evolving coronary syndromes by using statistical models with data from the time of presentation. Issue 2 (6th June 2005)

• Record Type: Journal Article
• Authors: Kennedy, R L; Harrison, R F
• Is Part Of: Heart. Volume 92:Issue 2(2006)
• Page Start: 183
• Page End: 189
• Publication Date: 2005-06-06
• Subjects: ACI-TIPI, acute cardiac ischaemia-time insensitive predictive instrument -- ACS, acute coronary syndromes -- ANN, artificial neural network -- CK, creatine kinase -- log10 LR, log10 likelihood ratio -- MI, myocardial infarction -- non-STEMI, non-ST elevation myocardial infarction -- ROC, receiver operating characteristic -- STEMI, ST elevation myocardial infarction; statistical models -- logistic regression -- coronary syndromes; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/hrt.2004.055293
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Objective: To derive statistical models for the diagnosis of acute coronary syndromes by using clinical and ECG information at presentation and to assess performance, portability, and calibration of these models, as well as how they may be used with cardiac marker proteins. Design and methods: Data from 3462 patients in four UK teaching hospitals were used. Inputs for 8, 14, 25, and 43 factor logistic regression models were selected by using log10 likelihood ratios (log10 LRs). Performance was analysed by receiver operating characteristic curves. Results: A 25 factor model derived from 1253 patients from one centre was selected for further study. On training data, 98.2% of ST elevation myocardial infarctions (STEMIs) and 96.2% of non-ST elevation myocardial infarctions (non-STEMIs) were correctly classified, whereas only 2.1% of non-cardiac cases were incorrectly classified. On data from three other centres, 97.3% of STEMIs and 91.9% of non-STEMIs were correctly classified. Differences in log10 LRs for individual inputs from different centres accounted for the decline in performance when models were applied to unseen data. Classification was improved when output was combined with either clinical opinion or marker proteins. Conclusions: Logistic regression models based on data available at presentation can classify patients with chest pain with a high degree of accuracy, particularly when combined with clinical opinion or marker proteins.