2010
DOI: 10.3399/bjgp10x502137
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Accuracy of symptoms and signs for coronary heart disease assessed in primary care

Abstract: Diagnosing the aetiology of chest pain is challenging. There is still a lack of data on the diagnostic accuracy of signs and symptoms for acute coronary events in low-prevalence settings. AimTo evaluate the diagnostic accuracy of symptoms and signs in patients presenting to general practice with chest pain. Design of studyCross-sectional diagnostic study with delayed-type reference standard. SettingSeventy-four general practices in Germany. MethodThe study included 1249 consecutive patients presenting with che… Show more

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Cited by 46 publications
(31 citation statements)
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“…The data for this study came from a larger project aimed at investigating the accuracy of signs and symptoms for chest pain patients with CHD, and the methodology and results of the main study have been presented elsewhere (5,11). In the present study, a secondary analysis was conducted to assess the accuracy of GPs' diagnostic assessment of chest pain patients in primary care facilities in the state of Hesse, Germany.…”
Section: Methodsmentioning
confidence: 99%
“…The data for this study came from a larger project aimed at investigating the accuracy of signs and symptoms for chest pain patients with CHD, and the methodology and results of the main study have been presented elsewhere (5,11). In the present study, a secondary analysis was conducted to assess the accuracy of GPs' diagnostic assessment of chest pain patients in primary care facilities in the state of Hesse, Germany.…”
Section: Methodsmentioning
confidence: 99%
“…All the more it is important to incorporate measures of reliability into computer systems assisting the GP. Design and conduct of the study were described elsewhere [5].…”
Section: Data Setmentioning
confidence: 99%
“…The first one is standard logistic regression (LR), which is a quite common approach in medical data analysis in general and was also used for analyzing our data set in [5]. Since this classifier fits a linear decision boundary in the instance space, it is rather restrictive and makes strong model assumptions.…”
Section: Classifiersmentioning
confidence: 99%
“…Это правило сле-дует использовать в контексте другой клинической информации, как например, наличия кашля или острой жгучей боли (что делает диагноз ИБС менее вероятным). И наоборот, такие признаки, как ирра-диация боли в левую руку, известная сердечная недо-статочность и сахарный диабет делают диагноз ИБС более вероятным [55].…”
Section: симптомы и признаки (см Web-приложение)unclassified