gms | German Medical Science

Background: In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories.

Material/Methods: We intended to use multivariate statistical methods to identify diagnostic subgroups in patients with chest pain on the basis of a large, comprehensive data set with information on history, risk factors, and physical examination. We conducted a cross-sectional diagnostic study in 74 primary care practices with 1199 patients to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient’s chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.

Results: A multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: „chest wall syndrome“, „vital threat“, „stomach and bowel pain“, „angina pectoris“, „chest infection syndrome“, and „harmless chest pain“. MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.

Conclusions: Chest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level so that no natural groupings are possible. This has to be differentiated from the classification of patients into diagnostic categories with the help of clinical prediction rules which was shown to be effective in chest pain.