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01.12.2014 | Research article | Ausgabe 1/2014 Open Access

BMC Emergency Medicine 1/2014

Validation of a diagnostic probability function for estimating probabilities of acute coronary syndrome

BMC Emergency Medicine > Ausgabe 1/2014
Lukas Zimmerli, Johann Steurer, Reto Kofmehl, Maria M Wertli, Ulrike Held
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1471-227X-14-23) contains supplementary material, which is available to authorized users.

Competing interests

All authors declare that they have no competing interests.

Authors’ contributions

All authors interpreted the data, discussed the results and commented on the manuscript. LZ and JS designed and coordinated the study. LZ, JS, MW and UH wrote the manuscript. RK, MW and UH conducted the statistical analyses. All authors read and approved the final version of the manuscript.



We recently reported about the derivation of a diagnostic probability function for acute coronary syndrome (ACS). The present study aims to validate the probability function as a rule-out criterion in a new sample of patients.


186 patients presenting with chest pain and/or dyspnea at one of the three participating hospitals’ emergency rooms in Switzerland were included in the study. In these patients, information on a set of pre-specified variables was collected and a predicted probability of ACS was calculated for each patient. Approximately two weeks after the initial visit in the emergency room, patients were contacted by phone to assess whether a diagnosis of ACS was established.


Of the 186 patients included in the study, 31 (17%) had an acute coronary syndrome. A risk probability for ACS below 2% was considered a rule-out criterion for ACS, leading to a sensitivity of 87% and a specificity of 17% of the rule. The characteristics of the study patients were compared to the cases from which the probability function was derived, and considerable deviations were found in some of the variables.


The proposed probability function, with a 2% cut-off for ruling out ACS works quite well if the patient data lie within the ranges of values of the original vignettes. If the observations deviate too much from these ranges, the predicted probabilities for ACS should be seen with caution.
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