Aaron Peace

Human Factors Analysis of the CardioQuick Patch ®: A Novel Engineering Solution to the Problem of Electrode Misplacement during 12-lead Electrocardiogram Acquisition

INTRODUCTIONnThe CardioQuick Patch® (CQP) has been developed to assist operators in accurately positioning precordial electrodes during 12-lead electrocardiogram (ECG) acquisition. This study describes the CQP design and assesses the device in comparison to conventional electrode application.nnnMETHODSnTwenty ECG technicians were recruited and a total of 60 ECG acquisitions were performed on the same patient model over four phases: (1) all participants applied single electrodes to the patient; (2) all participants were then re-trained on electrode placement and on how to use the CQP; (3) participants were randomly divided into two groups, the standard group applied single electrodes and the CQP group used the CQP; (4) after a one day interval, the same participants returned to carry out the same procedure on the same patient (measuring intra-practitioner variability). Accuracy was measured with reference to pre-marked correct locations using ultra violet ink. NASA-TLK was used to measure cognitive workload and the Systematic Usability Scale (SUS) was used to quantify the usability of the CQP.nnnRESULTSnThere was a large difference between the minimum time taken to complete each approach (CQP=38.58s vs. 65.96s). The standard group exhibited significant levels of electrode placement error (V1=25.35mm±29.33, V2=18.1mm±24.49, V3=38.65mm±15.57, V4=37.73mm±12.14, V5=35.75mm±15.61, V6=44.15mm±14.32). The CQP group had statistically greater accuracy when placing five of the six electrodes (V1=6.68mm±8.53 [p<0.001], V2=8.8mm±9.64 [p=0.122], V3=6.83mm±8.99 [p<0.001], V4=14.90mm±11.76 [p<0.001], V5=8.63mm±10.70 [p<0.001], V6=18.13mm±14.37 [p<0.001]). There was less intra-practitioner variability when using the CQP on the same patient model. NASA TLX revealed that the CQP did increase the cognitive workload (CQP group=16.51%±8.11 vs. 12.22%±8.07 [p=0.251]). The CQP also achieved a high SUS score of 91±7.28.nnnCONCLUSIONnThe CQP significantly improved the reproducibility and accuracy of placing precordial electrodes V1, V3-V6 with little additional cognitive effort, and with a high degree of usability.

Using computerised interactive response technology to assess electrocardiographers and for aggregating diagnoses

The 12-lead electrocardiogram (ECG) is a crucial diagnostic tool. However, the ideal method to assess competency in ECG interpretation remains unclear. We sought to evaluate whether keypad response technology provides a rapid, interactive way to assess ECG knowledge. 75 participants were enrolled [32 (43%) Primary Care Physicians, 24 (32%) Hospital Medical Staff and 19 (25%) Nurse Practitioners]. Nineteen ECGs with 4 possible answers were interpreted. Out of 1425 possible decisions 1054 (73.9%) responses were made. Only 570/1425 (40%) of the responses were correct. Diagnostic accuracy varied (0% to 78%, mean 42%±21%) across the entire cohort. Participation was high, (median 83%, IQR 50%-100%). Hospital Medical Staff had significantly higher diagnostic accuracy than nurse practitioners (50±20% vs. 38±19%, p=0.04) and Primary Care Physicians (50±20% vs. 40±21%, p=0.07) although not significant. Interactive voting systems can be rapidly and successfully used to assess ECG interpretation. Further education is necessary to improve diagnostic accuracy.

A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram

BACKGROUNDnThe 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named Interactive Progressive based Interpretation (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreters annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation.nnnOBJECTIVESnTo improve interpretation accuracy and reduce missed co-abnormalities.nnnMETHODSnThe Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach.nnnRESULTSnA total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated.nnnCONCLUSIONnAlthough results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size.

Improved recording of atrial activity by modified bipolar leads derived from the 12-lead electrocardiogram

This study investigates the use of multivariate linear regression to estimate three bipolar ECG leads from the 12-lead ECG in order to improve P-wave signal strength. The study population consisted of body surface potential maps recorded from 229 healthy subjects. P-waves were then isolated and population based transformation weights developed. A derived P-lead (measured between the right sternoclavicular joint and midway along the costal margin in line with the seventh intercostal space) demonstrated significant improvement in median P-wave root mean square (RMS) signal strength when compared to lead II (94μV vs. 76μV, p<0.001). A derived ES lead (from the EASI lead system) also showed small but significant improvement in median P-wave RMS (79μV vs. 76μV, p=0.0054). Finally, a derived modified Lewis lead did not improve median P-wave RMS when compared to lead II. However, this derived lead improved atrioventricular RMS ratio. P-wave leads derived from the 12-lead ECG can improve signal-to-noise ratio of the P-wave; this may improve the performance of detection algorithms that rely on P-wave analysis.

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

INTRODUCTIONnInterpretation 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.nnnMETHODSnThis 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.nnnRESULTSnInterpretation accuracies achieved by CFs and non-CFs dropped by 43.20% and 58.95% respectively when an incorrect AD was presented (pu202f<u202f0.001). 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, pu202f=u202f0.065). The best C5.0 decision tree achieved an accuracy rate of 64.67% (pu202f<u202f0.001), however this is only 6.56% greater than the no-information-rate.nnnCONCLUSIONnIncorrect ADs reduce the interpreters 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 interpreters 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.

Eye Tracking the Visual Attention of Nurses Interpreting Simulated Vital Signs Scenarios: Mining Metrics to Discriminate Between Performance Level

Nurses welcome innovative training and assessment methods to effectively interpret physiological vital signs. The objective is to determine if eye-tracking technology can be used to develop biometrics for automatically predict the performance of nurses whilst they interact with computer-based simulations. A total of 47 nurses were recruited, 36 nursing students (training group) and 11 coronary care nurses (qualified group). Each nurse interpreted five simulated vital signs scenarios whilst “thinking-aloud.” The participants visual attention (eye-tracking metrics), verbalisation, heart rate, confidence level (1–10, 10 = most confident), and cognitive load (NASA-TLX) were recorded during performance. Scenario performances were scored out of ten. Analysis was used to find patterns between the eye-tracking metrics and performance score. Multiple linear regression was used to predict performance score using eye tracking metrics. The qualified group scored higher than the training group (6.85 ± 1.5 versus 4.59 ± 1.61, <italic>p</italic> = <0.0001) and reported greater confidence (7.51 ± 1.2 versus 5.79 ± 1.39, <italic>p</italic> = <0.0001). Regression using a selection of eye-tracking metrics was shown to adequately predict score (adjusted <italic>R</italic>² = 0.80, <italic>p </italic> = <0.0001). This shows that eye tracking alone could predict a nurses performance and can provide insight to the performance of a nurse when interpreting bedside monitors.

Role of tumour necrosis factor alpha converting enzyme (TACE/ADAM17) and associated proteins in coronary artery disease and cardiac events

Tumour necrosis factor alpha converting enzyme (TACE/ADAM17) is a member of the Axa0disintegrin and metalloproteinase (ADAM) family of ectodomain shedding proteinases. It regulates many inflammatory processes by cleaving several transmembrane proteins, including tumour necrosis factor alpha (TNFα) and its receptors tumour necrosis factor alpha receptorxa01 and tumour necrosis factor alpha receptorxa02. There is evidence that TACE is involved in several inflammatory diseases, such as ischaemia, heart failure, arthritis, atherosclerosis, diabetes and cancer as well as neurological and immune diseases. This review summarizes the latest discoveries regarding the mechanism of action and regulation of TACE. It also focuses on the role of TACE in atherosclerosis and coronary artery disease (CAD), highlighting clinical studies that have investigated its expression and protein activity. The multitude of substrates cleaved by TACE make this enzyme an attractive target for therapy and a candidate for biomarker research and development in CAD.

Using Eye-Tracking Technology to Capture the Visual Attention of Nurses During Interpretation of Patient Monitoring Scenarios from a Computer Simulated Bedside Monitor

Introduction:This study analysed the utility of eye tracking technology for gaining insight into the decision making processes of nurses during their interpretation of patient scenarios and vital signs.Methods:Five patient monitoring scenarios (vignette, vital signs [ECG, BP etc.] and scoring criteria) were designed and validated by critical care experts. Participants were asked to interpret these scenarios whilst ‘thinking aloud’. Visual attention was measured using infrared light- based eye-tracking technology. Each interpretation was scored out of 10. Subjects comprised of students (n=36) and qualified nurses (n=11). Scores and self-rated confidence (where 1=low, 10=high) are presented using mean±SD. Significance testing was performed using a t-test and ANOVA where appropriate (α = 0.05). Multivariate regression was performed to determine if a machine could use eye gaze features to accurately predict competency (dependent variable=score). Independent eye gaze only variables were used in the regression models if they statistically significantly (p<0.05) correlated with the score.Results:Scores across all scenarios were calculated (students=4.58±1.13 vs. qualified=6.85±0.82) with statistical significance between groups (p=<0.01). Mean self-rated confidence was also calculated (students=5.79±1.05 vs. qualified=7.49±1.00, p=<0.01). There was a weak positive correlation between confidence and score amongst students (r=0.323, p=0.06), although no meaningful correlation with qualified nurses (r=-0.099, p=0.77). However, for all participants there was a moderate correlation between confidence and score (r=0.592, p=<0.01).The fitness of the regression models for predicting competency based on eye gaze features only is as follows:• Scenario 1: R2=0.407, Std Error=1.243 (p=0.09)• Scenario 2: R2=0.746, Std Error=1.439 (p=0.01)• Scenario 3: R2=0.385, Std Error=1.564 (p=0.03)• Scenario 4: R2=0.687, Std Error=1.340 (p=0.44)• Scenario 5: R2=0.766, Std Error=0.960 (p=0.02)The following table also shows where subjects fixated the most and least on the different vital signs on the bedside monitor (note the lower fixation duration on the ECG by students in comparison to qualified nurses).Conclusion: The study has shown that eye-tracking measurements can provide insight into the decision- making of nurses and can be used to predict competency.

Interactive progressive-based approach to aid the human interpretation of the 12-lead Electrocardiogram

The 12-lead Electrocardiogram (ECG) is an important diagnostic support tool but is frequently incorrectly interpreted. This is partly due to the fact that even expert clinicians can impulsively provide a diagnosis based on first impression/intuition. It is therefore imperative to optimise how physicians interpret the 12-lead ECG. Hence, a set of interactive questions and prompts has been developed to guide an observer through a series of tasks when interpreting an ECG. This has been named `Interactive Progressive based Interpretation (IPI). Using this model, the 12-lead ECG is segmented into five parts and presented over five web based user interfaces. The IPI model was implemented using emerging web technologies such as HTML5. Thus, a new model has been proposed to aid ECG interpretation where observers systematically and sequentially interpret the 12-lead ECG as a series of sub-tasks. We hypothesize that this will reduce the number of errors and increase diagnostic accuracy.