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Validation of a diagnostic probability function for estimating probabilities of acute coronary syndrome
Lukas Zimmerli, Johann Steurer, Reto Kofmehl
1Horten Centre for Patient Oriented-Research and Knowledge Transfer, University of Zurich, Pestalozzistrasse 24, Zurich 8091, Switzerland. ulrike.held@usz.ch.
Insights
A new study validates a diagnostic probability function for acute coronary syndrome (ACS). The rule-out criterion performed well when patient data matched original ranges, but caution is advised for deviations.
Area of Science:
- Cardiology
- Emergency Medicine
- Diagnostic Tools
Background:
- A diagnostic probability function for acute coronary syndrome (ACS) was previously developed.
- This study aimed to validate the function as a rule-out criterion in a new patient cohort.
Purpose of the Study:
- To validate a previously derived diagnostic probability function for acute coronary syndrome (ACS).
- To assess the utility of the function as a rule-out criterion in a new patient sample.
Main Methods:
- 186 patients with chest pain or dyspnea were enrolled from Swiss emergency rooms.
- Pre-specified variables were collected to calculate a predicted probability of ACS for each patient.
- Patient outcomes were assessed via phone two weeks post-visit to confirm ACS diagnosis.
Main Results:
- 17% of the 186 patients were diagnosed with ACS.
- A 2% probability cut-off yielded 87% sensitivity and 17% specificity for ruling out ACS.
- Significant deviations were observed in patient characteristics compared to the original derivation cohort.
Conclusions:
- The ACS probability function functions effectively as a rule-out criterion when patient data aligns with original ranges.
- Caution is recommended when applying the function if observed patient data significantly deviates from the original derivation values.
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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