Background
Patients with chest pain are encountered on a regular basis in primary care. In different studies the incidence of chest pain varies according to setting, country, and inclusion criteria. There is a wide range of different underlying diseases including coronary heart disease (CHD).
In regard to the diagnostic work-up of chest pain patients primary care providers (PCPs) are trained to elicit, among other information, the exact pain location. Both clinical guidelines and standard textbooks recommend a detailed clinical history including pain location and radiation. Several diagnostic studies and meta-analyses have examined the diagnostic value of pain location mainly in regard to CHD and acute coronary syndrome (ACS). In these studies, pain locations were normally marked on a pictogram either by patients or the attending physician. Marked areas were consequently aggregated (e.g. 'upper left pain') for analysis, resulting in a loss of detailed data. Most of these investigations were performed in secondary care settings and results are setting-specific and inconsistent.
Pain maps have been used frequently in other areas of research like low back pain, migraine headaches, or temporomandibular disorders and fibromyalgia syndrome. While some of these studies still used conventional grid methods as the above quoted chest pain studies did, other authors applied advanced methodological techniques superimposing pain drawings and transforming data into two-dimensional color coded images.
In our study we applied a newly developed technique to analyze pain drawings of a large cohort of unselected and consecutively recruited primary care patients with chest pain in order to find out whether pain localization is helpful to discriminate between CHD and other diseases.