Between the Q and T

Jana Steyn 

Age 14 | Kamloops, B. C. 

Canada-Wide Science Fair Intermediate Excellence Award: Gold Medallist | Health Challenge Award | The Acturial Foundation of Canada Award


Approximately 1 in 2000 people suffer from a condition known as Long QT Syndrome (LQTS), a genetic condition that severely prolongs the QT interval in the heart’s electrical cycle. (Ioakeimidis, 2017) Many measurements can be taken from an EKG to help physicians better understand the workings of heart muscles. The QT interval is a measurement taken from the heart’s electrical cycle that is contained within the QRS complex which represents the polarization of heart muscles. In order to measure the QT interval, an electronic calliper is used to measure the interval from the start of the QRS complex to the end of the T-wave in the electrical cycle.  LQTS is one of the risk factors for Sudden Cardiac Arrest (SCA) and other fatal arrhythmias. The purpose of this experiment was to identify the most accurate lead(s) for QT interval measurements on a standard 12-lead electrocardiograph (EKG). The population of subjects for this experiment were women between 18 and 50 years of age who presented an EKG that was interpreted as normal by a qualified cardiologist. After reading through similar studies, an analytical method was devised. (Davey, 2000) The ultimate goal of this project was to improve protocol for obtaining QT interval measurements in the specified demographic and indirectly increase the diagnosis rate of LQTS to allow physicians to educate patients and take other steps to help prevent SCA.

HYPOTHESIS

If QT interval measurements are obtained from each individual lead on normal electrocardiographs from adult women between 18 and 50 years of age and then analyzed against the mean QT from the corresponding EKG, then individual measurements from lead II will present with the highest correlation coefficient (using Pearson’s correlation coefficient) when compared to QT interval measurements from each corresponding EKG.

MATERIALS

The materials used in this experiment were all digital. In order to obtain the measurements, an electronic calliper was used. Spreadsheet software was used to record the measurements and obtain the correlation coefficient. A digital electrocardiograph interpretation program was used to access the fifty electrocardiographs selected for this experiment. A laptop computer was used to access all these resources.

METHODS

Data Collection

1. Open an electrocardiogram from a woman between 18 and 50 years of age reported as normal and recorded electronically within the last year at a single healthcare institution. 

2. Blind confidential patient information. 

3. Using an electronic caliper, find the location to begin the measurement of the QT interval on the first lead. The starting point of the caliper should intersect with the commencement of the 

QRS complex (Goldenberg, 2006). 

4. Adjust the end point of the caliper to the intersection of the descending limb of the T wave with the isoelectric line on the electrocardiograph.

5. Record the measurement. 

6. Repeat steps 1-5 for each remaining lead.

7. Find the mean of all twelve measurements; round the mean to the nearest millisecond. 

8. Repeat steps 1-7 for each remaining electrocardiograph. 

Data Analysis

1. Transfer individual measurements from lead I on each EKG into a table using spreadsheet software.

2. For each measurement, transfer the mean QT interval from the corresponding EKG into the ad-jacent column.

3. Repeat steps 1-2 for each remaining EKG.

4. Use the built-in function on spreadsheet software to obtain the correlation coefficient for the relationship between individual lead measurements and the mean QT from the corresponding EKG.

5. Record the correlation coefficient using Pearson’s correlation coefficient.

6. Repeat steps 1-5 for each remaining lead.

OBSERVATIONS

After completing the experiment, the data was formatted into a table (Table 1). The values in the right column represent the strength of the relationship between measurements from each lead in comparison to the mean QT from the same EKG. In other words, the highest value related most closely to the mean QT interval and that lead could be deemed most accurate. The highest correlation coefficient can be observed on lead V6, followed closely by lead II and lead V2.

Table 1. Correlation Coefficient by Lead.  The values on the table above are representative of the strength of relationship between individual lead measurements and the mean QT.

Table 1. Correlation Coefficient by Lead. The values on the table above are representative of the strength of relationship between individual lead measurements and the mean QT.

DISCUSSION

After correlation coefficients had been calculated for each of the twelve leads on a standard 12-lead EKG, several interesting observations could be made in regards to the results.

The results of this experiment contrast the hypothesis. Lead II was the second most accurate lead overall. The results of this study suggest that lead V6 is slightly more accurate with a cor-relation coefficient of  r = 0.98994, which is greater than the correlation coefficient of r = 0.98984 obtained from lead II. 

The reason for these results likely relates to the proximity of lead V6 to the muscle wall of the left ventricle. The results of this small scale-experiment provide evidence to support the switch from lead II to lead V6 for QT interval measurements in EKGs from adult women. 

CONCLUSION

The results of this experiment provided some insightful information. Standard protocol dictates that lead II should be used to obtain QT interval measurements. (Salvi, 2012) Based on this protocol, a fair assumption could be made to assume that measurements from lead II would prove to be most accurate. Although lead II was proven to be an accurate source of QT interval measurements, this study has shown that lead V6 may be a more accurate alternative, although further experimentation is definitely required. In addition, lead II was also proven to be an accurate lead.  A small change such as the lead used to measure QT interval length could have an impact on the accuracy of measurements. Using a more accurate lead may offer a valuable tool to screen for long QT syndrome (LQTS) and possibly contribute to the prevention of fatalities associated with SCA as well as other arrhythmias. Based solely on the results of this small-scale study, switching to use of lead V6 for QT interval measurements may have an impact on the accuracy of QT interval measurements on normal EKGs from adult women between 18 and 50 years of age.

No significant issues were encountered while gathering or interpreting data for this study. However, applying the procedure to electrocardiographs with shaky baselines required more effort to obtain an accurate measurement. In these cases, a ruler was needed to judge the end of the T wave. 

Along with the emergence of handheld EKG technology, validation of a single lead QT measurement might impact the validity of mass screening programs for LQTS, providing a possible direction for future research. Information from this experiment could be used to validate meas-urements from these handheld devices.  

REFERENCES

Ajam, T. (2017, April 17). Electrocardiography. Retrieved from https://emedicine.medscape.com/article/1894014-overview 

Burns, E., & Lam, F. (2017, November 21). QT interval LITFL ECG Library Basics. Retrieved from https://lifeinthefastlane.com/ecg-library/basics/qt_interval/  

Davey, P. P., (2000). Which lead for Q-T interval measurements? Cardiology, 94(3), 159-164. 

Goldenberg I., Moss A.J., Zareba W., (2006) QT interval: how to measure it and what is “nor-mal” Journal of Cardiovascular Electrophysiology. 17:333–336.

Ioakeimidis NS, Papamitsou T, Meditskou S. and Iakovidou-Kritsi Z. (2017). Sudden infant death syndrome due to long QT syndrome: a brief review of the genetic substrate and prevalence. Journal of Biological Research (Thessalon) 24:6. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348737/  

Nordqvist, C. (2017, December 08). Arrhythmia: Causes, symptoms, types, and treatment. Retrieved from https://www.medicalnewstoday.com/articles/8887.php  

Potter, L. (2018, March 31). Understanding an ECG. Retrieved from                                       https://geekymedics.com/understanding-an-ecg/ 

Salvi, V., Karnad, D. R., Kerkar, V., Panicker, G. K., Manohar, D., Natekar, M., Lokhandwala, Y. (2012). Choice of an alternative lead for QT interval measurement in serial ECGs when Lead II is not suitable for analysis. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861229/

Schwartz, P. J., Crotti, L., & Insolia, R. (2012, August 01). Long-QT Syndrome. Retrieved from http://circep.ahajournals.org/content/5/4/868  


JANA STEYN

Jana Steyn Photo.png

Hi there! My name is Jana Steyn. I am in grade 9 at an independent school in Kamloops, British Columbia. I live with my parents, younger brother, and dog. I love to read, write, bike, and spend time with my friends. I presented my project “Between The Q & T” at the 2018 Canada Wide Science Fair in Ottawa and was awarded a several awards and a gold medal. My favourite subjects in school are science and math. In the future I hope to pursue a career in science, specifically medicine.