PURPOSE: Computer algorithms are often used for cardiac rhythm interpretation and are subsequently corrected by an overreading physician. The purpose of this study was to assess the incidence and clinical consequences of misdiagnosis of atrial fibrillation based on a 12-lead electrocardiogram (ECG). METHODS: We retrieved 2298 ECGs with the computerized interpretation of atrial fibrillation from 1085 patients. The ECGs were reinterpreted to determine the accuracy of the interpretation. In patients in whom the interpretation was incorrect, we reviewed the medical records to assess the clinical consequences resulting from misdiagnosis. RESULTS: We found that 442 ECGs (19%) from 382 (35%) of the 1085 patients had been incorrectly interpreted as atrial fibrillation by the computer algorithm. In 92 patients (24%), the physician ordering the ECG had failed to correct the inaccurate interpretation, resulting in change in management and initiation of inappropriate treatment, including antiarrhythmic medications and anticoagulation in 39 patients (10%), as well as unnecessary additional diagnostic testing in 90 patients (24%). A final diagnosis of paroxysmal atrial fibrillation based on the initial incorrect interpretation of the ECGs was generated in 43 patients (11%). CONCLUSION: Incorrect computerized interpretation of atrial fibrillation, combined with the failure of the ordering physician to correct the erroneous interpretation, can result in the initiation of unnecessary, potentially harmful medical treatment as well as inappropriate use of medical resources. Greater efforts should be directed toward educating physicians about the electrocardiographic appearance of atrial dysrhythmias and in the recognition of confounding artifacts.
PURPOSE: Computer algorithms are often used for cardiac rhythm interpretation and are subsequently corrected by an overreading physician. The purpose of this study was to assess the incidence and clinical consequences of misdiagnosis of atrial fibrillation based on a 12-lead electrocardiogram (ECG). METHODS: We retrieved 2298 ECGs with the computerized interpretation of atrial fibrillation from 1085 patients. The ECGs were reinterpreted to determine the accuracy of the interpretation. In patients in whom the interpretation was incorrect, we reviewed the medical records to assess the clinical consequences resulting from misdiagnosis. RESULTS: We found that 442 ECGs (19%) from 382 (35%) of the 1085 patients had been incorrectly interpreted as atrial fibrillation by the computer algorithm. In 92 patients (24%), the physician ordering the ECG had failed to correct the inaccurate interpretation, resulting in change in management and initiation of inappropriate treatment, including antiarrhythmic medications and anticoagulation in 39 patients (10%), as well as unnecessary additional diagnostic testing in 90 patients (24%). A final diagnosis of paroxysmal atrial fibrillation based on the initial incorrect interpretation of the ECGs was generated in 43 patients (11%). CONCLUSION: Incorrect computerized interpretation of atrial fibrillation, combined with the failure of the ordering physician to correct the erroneous interpretation, can result in the initiation of unnecessary, potentially harmful medical treatment as well as inappropriate use of medical resources. Greater efforts should be directed toward educating physicians about the electrocardiographic appearance of atrial dysrhythmias and in the recognition of confounding artifacts.
Authors: David E Krummen; Mitul Patel; Hong Nguyen; Gordon Ho; Dhruv S Kazi; Paul Clopton; Marian C Holland; Scott L Greenberg; Gregory K Feld; Mitchell N Faddis; Sanjiv M Narayan Journal: J Cardiovasc Electrophysiol Date: 2010-11
Authors: Eric Shulman; Faraj Kargoli; Philip Aagaard; Ethan Hoch; Luigi Di Biase; John Fisher; Jay Gross; Soo Kim; Kevin J Ferrick; Andrew Krumerman Journal: Clin Cardiol Date: 2017-06-09 Impact factor: 2.882
Authors: Kenneth M Weinberg; Pablo Denes; Alan H Kadish; Jeffrey J Goldberger Journal: Ann Noninvasive Electrocardiol Date: 2008-04 Impact factor: 1.468