How Useful Are Clinical Features in the Diagnosis of Acute, Undifferentiated Chest Pain?

Abstract

Abstract. Objectives: To measure the predictive value and diagnostic performance of clinical features used to diagnose coronary syndromes in patients presenting with acute, undifferentiated chest pain. Methods: The clinical features of patients presenting to the authors' chest pain unit with acute, undifferentiated chest pain were prospectively recorded on a standard form. Admitted patients were followed up by case note review. Discharged patients were followed up as outpatients three days later. Six months after the emergency department visit, evidence of adverse events was searched for from the hospital computer database, case notes, and the patient's primary care physician. The authors tested the power of each feature to predict: 1) acute myocardial infarction (AMI) by World Health Organization criteria, and 2) any acute coronary syndrome (ACS), evidenced by cardiac testing, AMI, arrhythmia, death, or revacsularization procedure within six months. Results:

Part 10: Acute Coronary Syndromes

“…Most often the patient will note chest or upper body discomfort and dyspnea as the predominant presenting symptoms accompanied by diaphoresis, nausea, vomiting, and dizziness. [17][18][19] Isolated diaphoresis, nausea, vomiting, or dizziness are unusual predominant presenting symptoms. 20 Atypical or unusual symptoms are more common in women, the elderly, and diabetic patients.…”

“…Although the presence of clinical signs and symptoms may increase suspicion of ACS, evidence does not support the use of any single sign or combination of clinical signs and symptoms alone to confirm the diagnosis. [17][18][19]112 When the patient presents with symptoms and signs of potential ACS, the clinician uses ECG findings (Figure 1, Box 4) to classify the patient into 1 of 3 groups: The interpretation of the 12-lead ECG is a key step in this process, allowing not only for this classification but also the selection of the most appropriate diagnostic and management strategies. Not all providers are skilled in the interpretation of the ECG; as a consequence, the use of computer-aided ECG interpretation has been studied.…”

A clinical scoring system in undifferentiated chest pain predicting undetectable troponin concentration

“…20 In addition, four of the five parameters in the final model had p values of less than 0.0016, the cut-off for significance after Sidak's correction for multiple hypothesis testing. Only pain radiating to the arms did not meet this strict criterion, but as this has been shown in other studies 11,19 to be relevant, we believe the association is genuine and therefore retained it in our model. We believe this clinical score shows promise as a tool to predict the likelihood of an elevated troponin level based upon clinical features in acute admissions with chest pain.…”

“…This increases the possibility of a type 1 error allowing clinical features that have no genuine relationship with troponin being included in the model by chance. However, each independent variable retained in our model has been previously associated with acute coronary syndromes or its mortality risk: previous MI, age and tachycardia are used to calculate risk in GRACE mortality risk scoring systems 18 ; pain radiating to the arms has been previously shown to be a feature useful in the diagnosis of acute coronary syndromes 11,19 ; and diabetes has for a long time been known to be a risk factor for myocardial infarction. 20 In addition, four of the five parameters in the final model had p values of less than 0.0016, the cut-off for significance after Sidak's correction for multiple hypothesis testing.…”

Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

“…This has the potential to improve clinical care, since many triage and treatment decisions have to be made early, and could also optimize the use of resources, including chest pain units. Studies confirming that clinical, as well as ECG, factors are highly discriminatory for evolving ACS have strengthened research in this area recently [16,17]. Various statistical and computer-based methods have been used to analyze clinical and ECG data from chest pain patients with a view to improving identification of high-risk patients at presentation.…”