The role of computerized diagnostic proposals in the interpretation of the 12-lead electrocardiogram by cardiology and non-cardiology fellows.

INTRODUCTION: Most contemporary 12-lead electrocardiogram (ECG) devices offer computerized diagnostic proposals. The reliability of these automated diagnoses is limited. It has been suggested that incorrect computer advice can influence physician decision-making. This study analyzed the role of diagnostic proposals in the decision process by a group of fellows of cardiology and other internal medicine subspecialties. MATERIALS AND METHODS: A set of 100 clinical 12-lead ECG tracings was selected covering both normal cases and common abnormalities. A team of 15 junior Cardiology Fellows and 15 Non-Cardiology Fellows interpreted the ECGs in 3 phases: without any diagnostic proposal, with a single diagnostic proposal (half of them intentionally incorrect), and with four diagnostic proposals (only one of them being correct) for each ECG. Self-rated confidence of each interpretation was collected. RESULTS: Availability of diagnostic proposals significantly increased the diagnostic accuracy (p<0.001). Nevertheless, in case of a single proposal (either correct or incorrect) the increase of accuracy was present in interpretations with correct diagnostic proposals, while the accuracy was substantially reduced with incorrect proposals. Confidence levels poorly correlated with interpretation scores (rho≈2, p<0.001). Logistic regression showed that an interpreter is most likely to be correct when the ECG offers a correct diagnostic proposal (OR=10.87) or multiple proposals (OR=4.43). CONCLUSION: Diagnostic proposals affect the diagnostic accuracy of ECG interpretations. The accuracy is significantly influenced especially when a single diagnostic proposal (either correct or incorrect) is provided. The study suggests that the presentation of multiple computerized diagnoses is likely to improve the diagnostic accuracy of interpreters

Problems with Bazett QTc correction in paediatric screening of prolonged QTc interval

Background Bazett formula is frequently used in paediatric screening for the long QT syndrome (LQTS) and proposals exist that using standing rather than supine electrocardiograms (ECG) improves the sensitivity of LQTS diagnosis. Nevertheless, compared to adults, children have higher heart rates (especially during postural provocations) and Bazett correction is also known to lead to artificially prolonged QTc values at increased heart rates. This study assessed the incidence of erroneously increased QTc values in normal children without QT abnormalities. Methods Continuous 12-lead ECGs were recorded in 332 healthy children (166 girls) aged 10.7 ± 2.6 years while they performed postural manoeuvring consisting of episodes (in the following order) of supine, sitting, standing, supine, standing, sitting, and supine positions, each lasting 10 min. Detailed analyses of QT/RR profiles confirmed the absence of prolonged individually corrected QTc interval in each child. Heart rate and QT intervals were measured in 10-s ECG segments and in each segment, QTc intervals were obtained using Bazett, Fridericia, and Framingham formulas. In each child, the heart rates and QTc values obtained during supine, sitting and standing positions were averaged. QTc durations by the three formulas were classified to  480 ms. Results At supine position, averaged heart rate was 77.5 ± 10.5 beat per minute (bpm) and Bazett, Fridericia and Framingham QTc intervals were 425.3 ± 15.8, 407.8 ± 13.9, and 408.2 ± 13.1 ms, respectively. At sitting and standing, averaged heart rate increased to 90.9 ± 10.1 and 100.9 ± 10.5 bpm, respectively. While Fridericia and Framingham formulas showed only minimal QTc changes, Bazett correction led to QTc increases to 435 ± 15.1 and 444.9 ± 15.9 ms at sitting and standing, respectively. At sitting, Bazett correction identified 51, 4, and 0 children as having the QTc intervals 440–460, 460–480, and > 480 ms, respectively. At sitting, these numbers increased to 118, 11, and 1, while on standing these numbers were 151, 45, and 5, respectively. Irrespective of the postural position, Fridericia and Framingham formulas identified only a small number (< 7) of children with QT interval between 440 and 460 ms and no children with longer QTc. Conclusion During screening for LQTS in children, the use of Bazett formula leads to a high number of false positive cases especially if the heart rates are increased (e.g. by postural manoeuvring). The use of Fridericia formula can be recommended to replace the Bazett correction not only for adult but also for paediatric ECGs

Sex and rate change differences in QT/RR hysteresis in healthy subjects

While it is now well understood that the extent of the QT interval changes due to underlying heart rate differences (i.e., the QT/RR adaptation) needs to be distinguished from the speed with which the QT interval reacts to heart rate changes (i.e., the so-called QT/RR hysteresis), gaps still exist in the physiologic understanding of the QT/RR hysteresis processes. The present study was designed to address the questions whether the speed of QT adaptation to heart rate changes is driven by time or by the number of cardiac cycles; whether the QT interval adaptation speed is the same when heart rate accelerates and decelerates; and whether the characteristics of QT/RR hysteresis are related to age and sex. The study evaluated 897,570 measurements of QT intervals together with their 5-minute histories of preceding RR intervals, all made in 751 healthy volunteers (336 females) aged 34.3±9.5 years. Three different QT/RR adaptation models were combined with exponential decay models that distinguished time-based and interval-based basis of QT/RR hysteresis. In each subject and for each modelling combination, best-fit combination of modelling parameters was obtained by seeking minimal modelling residuals. The results showed that the response of QT/RR hysteresis appears to be driven by absolute time rather than by the numbers of cardiac cycles. The speed of the QT/RR hysteresis was found decreasing with increasing age whilst the duration of individually rate corrected QTc interval was found increasing with increasing age. Contrary to the longer QTc intervals, the QT/RR hysteresis speed was faster in females. The QT/RR hysteresis differences between heart rate acceleration and deceleration were not found physiologically systematic (i.e., they differ between different healthy subjects) but on average, the QT/RR hysteresis speed was found slower after heart rate acceleration than after rate deceleration

Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms.

INTRODUCTION: Interpretation of the 12‑lead Electrocardiogram (ECG) is normally assisted with an automated diagnosis (AD), which can facilitate an 'automation bias' where interpreters can be anchored. In this paper, we studied, 1) the effect of an incorrect AD on interpretation accuracy and interpreter confidence (a proxy for uncertainty), and 2) whether confidence and other interpreter features can predict interpretation accuracy using machine learning. METHODS: This study analysed 9000 ECG interpretations from cardiology and non-cardiology fellows (CFs and non-CFs). One third of the ECGs involved no ADs, one third with ADs (half as incorrect) and one third had multiple ADs. Interpretations were scored and interpreter confidence was recorded for each interpretation and subsequently standardised using sigma scaling. Spearman coefficients were used for correlation analysis and C5.0 decision trees were used for predicting interpretation accuracy using basic interpreter features such as confidence, age, experience and designation. RESULTS: Interpretation accuracies achieved by CFs and non-CFs dropped by 43.20% and 58.95% respectively when an incorrect AD was presented (p < 0.001). Overall correlation between scaled confidence and interpretation accuracy was higher amongst CFs. However, correlation between confidence and interpretation accuracy decreased for both groups when an incorrect AD was presented. We found that an incorrect AD disturbs the reliability of interpreter confidence in predicting accuracy. An incorrect AD has a greater effect on the confidence of non-CFs (although this is not statistically significant it is close to the threshold, p = 0.065). The best C5.0 decision tree achieved an accuracy rate of 64.67% (p < 0.001), however this is only 6.56% greater than the no-information-rate. CONCLUSION: Incorrect ADs reduce the interpreter's diagnostic accuracy indicating an automation bias. Non-CFs tend to agree more with the ADs in comparison to CFs, hence less expert physicians are more effected by automation bias. Incorrect ADs reduce the interpreter's confidence and also reduces the predictive power of confidence for predicting accuracy (even more so for non-CFs). Whilst a statistically significant model was developed, it is difficult to predict interpretation accuracy using machine learning on basic features such as interpreter confidence, age, reader experience and designation