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- Hakacova, Nina, et al.
(författare)
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Aspects of Left Ventricular Morphology Outperform Left Ventricular Mass for Prediction of QRS Duration
- 2010
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Ingår i: Annals of Noninvasive Electrocardiology. - 1082-720X. ; 15:2, s. 124-129
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Tidskriftsartikel (refereegranskat)abstract
- Methods: The study population of healthy adult volunteers was divided into a sample for development of a prediction model (n = 63) and a testing sample (n = 30). Magnetic resonance imaging data were used to assess anatomical characteristics of the left ventricle: the angle between papillary muscles (PMA), the length of the left ventricle (LVL) and left ventricular mass (LVM). Twelve-lead electrocardiogram (ECG) was used for measurement of the QRS duration. Multiple linear regression analysis was used to develop a prediction model to estimate the QRS duration. The accuracy of the prediction model was assessed by comparing predicted with measured QRS duration in the test set. Results: The angle between PMA and the length of the LVL were statistically significant predictors of QRS duration. Correlation between QRS duration and PMA and LVL was r = 0.57, P = 0.0001 and r = 0.45, P = 0.0002, respectively. The final model for prediction of the QRS was: QRS(Predicted) = 97 + (0.35 x LVL) - (0.45 x PMA). The predicted and real QRS duration differed with median 1 ms. Conclusions: The model for prediction of QRS duration opens the ability to predict case-specific normal QRS duration. This knowledge can have clinical importance, when determining the normality on case-specific basis. Ann Noninvasive Electrocardiol 2010;15(2):124-129.
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- Hakacova, Nina, et al.
(författare)
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Computer-based rhythm diagnosis and its possible influence on nonexpert electrocardiogram readers.
- 2012
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Ingår i: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 45, s. 18-22
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Systems providing computer-based analysis of the resting electrocardiogram (ECG) seek to improve the quality of health care by providing accurate and timely automatic diagnosis of, for example, cardiac rhythm to clinicians. The accuracy of these diagnoses, however, remains questionable. OBJECTIVES: We tested the hypothesis that (a) 2 independent automated ECG systems have better accuracy in rhythm diagnosis than nonexpert clinicians and (b) both systems provide correct diagnostic suggestions in a large percentage of cases where the diagnosis of nonexpert clinicians is incorrect. METHODS: Five hundred ECGs were manually analyzed by 2 senior experts, 3 nonexpert clinicians, and automatically by 2 automated systems. The accuracy of the nonexpert rhythm statements was compared with the accuracy of each system statement. The proportion of rhythm statements when the clinician's diagnoses were incorrect and the systems instead provided correct diagnosis was assessed. RESULTS: A total of 420 sinus rhythms and 156 rhythm disturbances were recognized by expert reading. Significance of the difference in accuracy between nonexperts and systems was P = .45 for system A and P = .11 for system B. The percentage of correct automated diagnoses in cases when the clinician was incorrect was 28% ± 10% for system A and 25% ± 11% for system B (P = .09). CONCLUSION: The rhythm diagnoses of automated systems did not reach better average accuracy than those of nonexpert readings. The computer diagnosis of rhythm can be incorrect in cases where the clinicians fail in reaching the correct ECG diagnosis.
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