A computer based telemedicine protocol to predict acute coronary syndrome in patients with chest pain at home.
The decision to admit a patient to a coronary care unit for acute coronary syndrome (ACS) has serious medical and financial consequences. In this study, we aimed to develop a computer program to predict the existence of ACS in patients with chest pain at home; it is intended that patients will be able to access the program via the website to test its validity. This study proceeded in two phases. In the first phase, a computer-based decision protocol was developed using recursive-partitioning analysis to predict ACS in 250 patients with chest pain on the basis of their historical data. In the second phase, this protocol was tested in 115 patients for diagnosis of ACS prospectively. Thirty-two of the patients answered the algorithm questions on the website. All of the patients who visited the website of this study were advised to go to the emergency department. Although the algorithm showed the presence of ACS in 82 of 115 patients, 60 of 115 patients were diagnosed as having ACS in the emergency department (n = 55) or at follow-up. The agreement between the diagnosis of the algorithm and the true diagnosis was moderate and statistically significant (Kappa coefficient 0.61, P < 0.001). The sensitivity of the algorithm was 100%, although its specificity was 60%. The accuracy of the algorithm in diagnosing ACS was 81%. The algorithm diagnoses patients with ACS at a high ratio and decreases the number of patients being unnecessarily admitted to the emergency with non-ACS.
Coskun
Osman
O
Erciyes University, Faculty of Engineering, Electronics Engineering Department, Biomedical Engineering Division Kayseri, Istanbul, Turkey.
Eren
Ali
A
Eren
Mehmet
M
D000465:Algorithms; D000789:Angina, Unstable / Q000175:diagnosis*; D002637:Chest Pain / Q000175:diagnosis* / Q000209:etiology; D002985:Clinical Protocols / Q000592:standards*; D003658:Decision Making, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D005500:Follow-Up Studies; D006699:Home Care Services; D006801:Humans; D020407:Internet; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D013577:Syndrome; D017216:Telemedicine / Q000379:methods*