A Bayesian sensitivity analysis of out-of-hospital 12-lead electrocardiograms: implications for regionalization of cardiac care.
BACKGROUND
The effectiveness of out-of-hospital regionalization of ST-elevation myocardial infarction (STEMI) patients to hospitals providing primary percutaneous coronary intervention depends on the accuracy of the out-of-hospital 12-lead electrocardiogram (PHTL). Although estimates of sensitivity and specificity of PHTL for STEMI have been reported, the impact of out-of-hospital STEMI prevalence on positive predictive value (PPV) has not been evaluated.


OBJECTIVE
To describe the relationship between varying population STEMI prevalences and PHTL predictive values, using ranges of PHTL sensitivity and specificity.


METHODS
The authors performed a Bayesian analysis using PHTL, where values for sensitivities (60%-70%), specificities (98%), and two prevalence ranges (0.5%-5% and 5%-20%) were derived from a literature review. PPV prediction intervals were compared with three months of prospective data from the Los Angeles County Emergency Medical Services Agency STEMI regionalization program.


RESULTS
When the estimated prevalence of STEMI in the out-of-hospital population is 5%-20%, the median PPV of the PHTL is 83% (95% credible interval [CrI] = 53% to 97%). However, if the population prevalence of STEMI is between 0.5% and 5%, the median PPV is 43% (95% CrI = 12% to 86%). When the PPV prediction intervals were incorporated with the Los Angeles County Emergency Medical Services Agency data, the PPV was 66%.


CONCLUSIONS
Even when assuming high specificity for PHTL, the false-positive rate will be considerable if applied to a population at low risk for STEMI. Before broadening application of PHTL to low-risk patients, the implications of a high false-positive rate should be considered.
Youngquist
Scott T
ST
Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA, USA. syoungquist@emedharbor.edu
Kaji
Amy H
AH
Lipsky
Ari M
AM
Koenig
William J
WJ
Niemann
James T
JT
D000465:Algorithms; D015906:Angioplasty, Balloon, Coronary; D001499:Bayes Theorem; D004562:Electrocardiography; D004632:Emergency Medical Services / Q000379:methods*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D006764:Hospitals, Community / Q000706:statistics & numerical data; D006801:Humans; D015141:Los Angeles / Q000453:epidemiology; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000453:epidemiology / Q000628:therapy; D009204:Myocardial Revascularization; D010043:Outcome and Process Assessment, Health Care; D010360:Patient Transfer; D011237:Predictive Value of Tests; D012040:Regional Health Planning; D012680:Sensitivity and Specificity; D014218:Triage / Q000379:methods