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

Zimmerli L, Steurer J, Kofmehl R, Wertli MM, Held U - BMC Emerg Med (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Horten Centre for Patient Oriented-Research and Knowledge Transfer, University of Zurich, Pestalozzistrasse 24, Zurich 8091, Switzerland. ulrike.held@usz.ch.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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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Calibration plot showing observed versus predicted probabilities for ACS.
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Fig1: Calibration plot showing observed versus predicted probabilities for ACS.

Mentions: Within 18 months 186 patients agreed to participate in the study. The median age of patients was 52 years (IQR 35-67), 130 (70%) of patients had male gender. In 168 (90%) patients the chief complaint was acute chest pain, and in 18 (10%) it was acute dyspnea, in 49 (26%) patients both symptoms were present at presentation in the emergency room (ER). In 42 patients troponin levels were elevated at the initial exam. Not myocardial ischemia only but infections, atrial fibrillation, heart failure, pulmonary embolism and other illnesses can cause an increase of troponin. So the number of patients with increased troponin levels is higher than the number of patients with myocardial ischemia. Further details about patient characteristics are shown in Table 1.In 31 (17%) patients an ACS was diagnosed, all of them have been hospitalized. In the two weeks after attending the emergency room in no further patient an ACS occurred. In eleven of the predictor variables of the prediction model there was a small percentage of missing values (range 1% to 9% of observations, median = 3%). The missing values were multiply imputed and the calculated probabilities of an ACS ranged from 0 to 1, median probability was 0.20 (IQR: 0.05-0.46). The calculated probabilities for more than one third of patients (36%) were less than 10%. The distribution of calculated versus observed probabilities is shown in Figure 1.Table 1


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

Zimmerli L, Steurer J, Kofmehl R, Wertli MM, Held U - BMC Emerg Med (2014)

Calibration plot showing observed versus predicted probabilities for ACS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4289321&req=5

Fig1: Calibration plot showing observed versus predicted probabilities for ACS.
Mentions: Within 18 months 186 patients agreed to participate in the study. The median age of patients was 52 years (IQR 35-67), 130 (70%) of patients had male gender. In 168 (90%) patients the chief complaint was acute chest pain, and in 18 (10%) it was acute dyspnea, in 49 (26%) patients both symptoms were present at presentation in the emergency room (ER). In 42 patients troponin levels were elevated at the initial exam. Not myocardial ischemia only but infections, atrial fibrillation, heart failure, pulmonary embolism and other illnesses can cause an increase of troponin. So the number of patients with increased troponin levels is higher than the number of patients with myocardial ischemia. Further details about patient characteristics are shown in Table 1.In 31 (17%) patients an ACS was diagnosed, all of them have been hospitalized. In the two weeks after attending the emergency room in no further patient an ACS occurred. In eleven of the predictor variables of the prediction model there was a small percentage of missing values (range 1% to 9% of observations, median = 3%). The missing values were multiply imputed and the calculated probabilities of an ACS ranged from 0 to 1, median probability was 0.20 (IQR: 0.05-0.46). The calculated probabilities for more than one third of patients (36%) were less than 10%. The distribution of calculated versus observed probabilities is shown in Figure 1.Table 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Horten Centre for Patient Oriented-Research and Knowledge Transfer, University of Zurich, Pestalozzistrasse 24, Zurich 8091, Switzerland. ulrike.held@usz.ch.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus