Can Cardiac Arrhythmia Risk Be Predicted By Evaluating The Electrocardiographs Of Patients Diag- nosed With Chronic Obstructive Pulmonary Disease Exacerbation In The Emergency Department?

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Kaya M., Aydın H., Yıldırım H., Genç E., Çoşkun A., Kadıoğlu E.

1. Uluslararası acil Tıp Kongresi, Antalya, Turkey, 28 - 31 October 2021, pp.523-531

  • Publication Type: Conference Paper / Full Text
  • City: Antalya
  • Country: Turkey
  • Page Numbers: pp.523-531
  • Kütahya Health Sciences University Affiliated: Yes


Can Cardiac Arrhythmia Risk Be Predicted By Evaluating The Electrocardiographs Of Patients Diagnosed With Chronic Obstructive Pulmonary Disease Exacerbation In The Emergency Department? Murtaza Kaya1 , Harun Yildirim1 , Abdil Coskun2 , Hasan Aydin2 , Esref Genc2 , Emine Kadioglu1 1 Kutahya Healthy Sciences University, Medical Faculty, Department of Emergency Medicine, Kutahya, Turkey 2 Kutahya Evliya Celebi Training and Research Hospital, Emergency Servises, Kutahya, Turkey Introduction: According to data from the World Health Organization, chronic obstructive pulmonary disease (COPD) accounted for 5% (3.7 million) of deaths across the world in 2015. On the contrary, the total number of patients reached 251 million in 2016 (1). When the causes of death in this group are examined, it is two to three times higher than the deaths due to sudden cardiac arrest in the normal population (2). Studies have shown that cardiac repolarization and increasing dispersion in patients with COPD cause malignant ventricular arrhythmia and sudden cardiac deaths (3–4). It has been observed in the follow-up of patients with COPD that various pathophysiological mechanisms—such as increasing ventricular afterload, right ventricular hypertrophy, and structural changes affiliated with emphysema—cause electrocardiographic (ECG) changes (5). Existing studies show that there is much greater arrhythmia risk in the case of acute COPD attacks (6–7). These arrhythmias generally arise as the result of abnormal atrial and ventricular repolarization depending on the increasing automaticity, which occurs at the conclusion of increased exacerbation and hypercapnia that is dependent on the changes in oxygen, carbon dioxide, and pH (8–9). Detecting ECG changes in patients diagnosed with COPD acute exacerbation may play an important role in patient outcome. When evaluating cardiac repolarization disorders, it should be kept in mind that COPD exacerbation may also disrupt the cardiac rhythm. In this study, we aimed to predict the possibility of arrhythmia by comparing the ECG changes of patients admitted to the emergency service for acute COPD attacks. Material And Methods Study Design and Participants Patients who were admitted to a tertiary emergency department with dyspnea and diagnosed with COPD acute attack between 01.01.2019-30.06.2019 were included in this study. A total of 709 patients identified as R06.0 according to the tenth revision of the International Classification of Diseases were retrospectively analyzed in this study. In this treatment group, 536 patients who did not have a follow-up concerning COPD before the application date were excluded from the study, and the emergency service follow-up file of 173 patients was looked into. Twenty-two patients whose ECG could not be reached and 85 patients whose artifact and arrhythmia were determined in their ECG were excluded from the study. Sixty-six patients whose data could be reached and evaluated as acute were included (Figure 1). Fifty-four individuals not having any complaint about the respiratory 534 ST tract and not diagnosed with any cardiac pathology (who had their ECG performed to exclude these pathologies) were included in the study as the control group for the same period. The demographic features (age and gender), ECG parameters (P wave, QT interval, and T wave peak–end duration [Tpe]), and electrolyte parameters (sodium and potassium) of the two groups included in the study were analyzed. The study was approved by the Noninvasive Ethnic Group of XXXX (March 17, 2021/05- 12). ECG Evaluation: After 12-lead ECG pictures of the patients taken using a smartphone and uploading these pictures to a computer, an experienced emergency medicine physician evaluated them (Figure 2). The ECG records of all the patients included in the study were procured using a MAC800 (2017) device. These ECG pictures were taken at a speed of 25 mm/s and at an amplitude of 10 mm/mV. After calculating the maximum (max) duration in the longest lead and the minimum (min) in the shortest lead in ECG parameters, the dispersion (disp) durations were determined by the difference of the maximum and minimum values. The QT interval was defined as the time from the beginning of the QRS complex to the end of the T wave. Also, using the Bazett formula and calculating the variation of corrected QT (QTc) was obtained. The distance between the peak (Tp) of the T wave and the isoelectric line and even the last endpoint (Tp-e) was calculated using the tangent method (10). QT max, QT min, QT disp, QTc max, QTc min, QTc disp, Tp-e max, Tp-e min , Tp-e disp and P max, P min, P disp were calculated. Tp-e/QT and Tp-e/QTc proportions were included in the dataset by calculating these proportions. Statistical Analysis: SPSS (v.20.0; IBM Corp., Armonk, NY, USA) was used to perform all statistical analyses. Normality of the data was tested using the Kolmogorov–Smirnov test. Numeric variables indicating normal distribution were represented as mean ± standard deviations (SDs); abnormally numeric variables as median (interquartile ranges [IQRs]); and categorical variables as numbers and percentages. Student’s t test was used for numeric variables with normal distribution, and Mann-Whitney U test was used for abnormally distribution. The chi-squared or Fisher exact test was used for categorical variables. Receiver operating characteristic (ROC) analysis was performed for values with significant differences between groups. A four-cell chart had been created after the cutoff time, which was specified using the Youden Index. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV), and positive likelihood (LR+) and negative likelihood (LR−) proportions were measured. A p-value <0.05 was considered statistically significant. Results: A total of 120 patients were included in the study, with 66 in the COPD group and 54 in the control group. Forty-eight (64%) of the males included in the study were in the COPD group and 27 (60%) in the control group, whereas 18 (40%) of the females were in the COPD group and 27 (60%) in the control group. It was found that there was more COPD in men than women in the study groups (p = 0.011). The mean age in the COPD group was 68.5, while it was 66.5 in the control group. There was no significant difference between the two groups (p = 0.189). According to the electrolyte parameters, the mean levels of sodium in the COPD and control groups were 140 mmol/L and 140  mmol/L, while  the mean levels of potassium were 4.1 mmol/L and 4.2 mmol/L, respectively. There wasn’t a statistically significant difference the electrolyte values between two groups (p= 0.353, p= 0.071). Results are detailed in Table 1. ECG parameters were evaluated in milliseconds (ms). In the control and study groups, the median QT max was 360 ms and 400 ms; the QT min was 300 ms and 320 ms; and QT disp was 40 ms and 535 ST 40 ms. There was a significant difference between QT max and min values, but no statistically significant difference in terms of QT disp (p < 0.001, p < 0.001, and p = 0.490). The mean (±SD) QTc max duration in the control and study groups were 439.9 ± 32.11 and 449.9 ± 37.52/ms, while QTc min was 377.8 ±30.25 and 380.7 ± 31.18/ms. There wasn’t a statistically significant difference detected between the two groups (p = 0.122 and p = 0.595). The QTc disp was measured as 54.5 in the study group and as 51.0 ms in the control group.  There wasn’t significant difference between the groups (p = 0.820).  The median Tp-e max, min, and disp durations in the study and control groups were measured; 80ms-80ms, 50ms-60ms and 40ms-20ms, respectively. There was a statistically significant difference between the groups (p = 0.041, p < 0.001, and p = 0.001). The median P max, P min and P disp. were 100ms, 60ms, 40ms in the study group, and 80ms, 60ms, 20ms in the control group, respectively. There wasn’t difference in P max between the groups (p = 0.445), while there was a statistically significant difference in P min and disp p < 0.001 and p < 0.001). Results are detailed in Table 2. Cutoff values were determined by ROC analysis for P disp. and Tp-e disp, which were statistically different between groups. Area under the curve (AUC) values were 0.712 and 0.671, respectively (Figure 3). According to the highest Youden Index, the determined cutoff value was evaluated as 30 ms. According to the four-cell chart, which is based on this, sensitivity values were 87.88% and 80.3% and specificity values were 55.56% and 61.11% for P disp and Tp-e disp, respectively. The PPV, NPV, LR+, and LR− values (as measured on the website by entering the data of the four-cell chart) for P disp were 70.73%, 78.95%, 1.98, and 0.22, respectively, while 71.62%, 71.74%, 2.06, and 0.32 for Tp-e disp (Table 3).  Discussion: COPD is one of the leading causes of mortality and morbidity (11). Although it is seen largely in males who smoke, it is increasingly prevalence in females (12). In a systematic review and meta-analysis published in 2018 by Ntritsos et al., COPD prevalence is much higher in males than females, while the difference between the prevalence in terms of gender has been decreasing close to the age of 40 years. It was stated that, in urban areas, the prevalence was 13.03% in males and 8.34% in females, but in rural areas, it was 10.69% in males and 5.96% in females (13). When the mean age was taken into consideration, the incidence rate of COPD in males was much higher than that in females at the time the patients were included in the study (p = 0.011). A statistically significant difference wasn’t determined when the mean ages of the study and control groups were compared (p = 0.189). Electrolytes have a proarrhythmic effect by changing the cardiac ionic flow kinetics. In arrhythmia, potassium, calcium, and magnesium changes are much more effective, while sodium is less effective (14). In spite of the fact that there was a difference in K and Na electrolytes between the study and control groups in Ogan N et al. study, the mean values in the groups were within the normal reference range. When sodium and potassium values were examined, no significant difference was noted between the groups in terms of electrolytes (p = 0.353 and p = 0.071). Also, when the studies that were conducted before were compared to our study, the number of patients was found to be greater, but the mean values were similar to ours. It is known that COPD has a risk of cardiovascular disease, such as abnormality of transmission, arrhythmia, and ischemic heart disease. ECG abnormalities have been researched in patients with COPD, and it has been stated that fatal arrhythmias increase mortality in more than half of the 536 ST patients (16, 17). Atrial fibrillation, multifocal atrial tachycardia, and ventricular arrhythmia are the common types in patients with COPD (18). The risk factors associated with COPD such as inhaled bronchodilators (which are the basic treatment modality), age, smoking, hypoxemia, and respiration acidosis contribute to arrhythmia (19). We evaluated the ECG parameters as a whole to predict both atrial and ventricular arrhythmias in patients with COPD.  The QT duration, which is from the starting of the QRS complex to the end of the T wave, is a total duration for ventricular depolarization and repolarization. The QT duration must be straightened in regard to the heart rate to make the comparison with the reference value possible because the QT duration is affected by the heart rate (20). There was a difference between the COPD and control groups in terms of QT max and min values (p < 0.001 and p < 0.001), whereas no difference was observed in terms of QT disp (p = 0.490). There was no difference between the groups in terms of QTc max, which was straightened with regard to the heart rate, min, and disp (p = 0.122, p = 0.595, and p = 0.820). Although there was a difference between the COPD and control groups in terms of QTc disp in the study by Sarubbi et al., unlike our study, there might be a difference in the sense that the average age of the patients included in the study (62.7 ± 7.1) was younger than that in our study (68.5 [59–77]), and similarly, the average age in the control group was younger than ours (7). These ECG parameters were not used for predicting the hospitalization of patients, so these parameters are not determiners (21). In the study of Sievi et al., the average QTc of 91 patients with COPD (437.9 ± 29.5) was similar to that in our study (439.9 ± 32.11) (2). There was no difference between our control and treatment groups because the control group was at the same old age as the treatment group, and they had other chronic disorders apart from COPD. It was stated that the Tp-e duration was evaluated in different treatment groups as a sign of repolarization deformity and arrhythmia of the T wave in a review by Tse et al. Hypertension, ischemic heart disease, Brugada syndrome, Chagas disease, and pulmonary embolism took part in these treatment groups (22). Moreover, arrhythmia was tried to be seen in the treatment groups (early-stage sarcoidosis, tricyclic antidepressant intoxication, cardiac syndrome X, and acute ischemic stroke) in the literature (23–26). However, a study that predicts that the Tp-e duration foresees arrhythmia in the COPD group was not encountered in the literature. Tasolar et al. detected that the rates of Tp-e, corrected Tp-e, and Tp-e/ QTc in smokers were much higher than those in nonsmokers (27). Similarly, the difference between the COPD and control groups was significant for Tp-e max, min, and disp in our study (p = 0.041, p < 0.001, and p = 0.001). In contrast, we could not detect a difference in the rate of Tp-e/QT and Tp-e/ QTc differently from this study. Even if COPD stems mostly from cigarettes, it may also be seen in nonsmokers, and the patients in the control group may have other chronic disorders that would affect these consequences. The increase in the Tp-e duration is a useful parameter to foresee the events of cardiovascular disease and ventricular arrhythmia (28). In our opinion, increasing the Tp-e disp in patients with COPD contributes to the literature about predicting ventricular arrhythmia. In the study of Atar et al., P max, min, and disp were evaluated to predict arrhythmia. The COPD and control groups were compared, and no difference was found between the groups (29). We detected a significant difference between the COPD and control groups in terms of P disp. The reason for this might be that the ECG evaluation of patients during COPD attacks was not stable. In the study of Cimci et al., P wave dispersion increased in the COPD group compared to the control group (30). 537 ST The increases in P disp and Tp-e disp in which we detected the cutoff values in differences according to ROC analysis were 30 ms. Even though a high meaningfulness rate was not detected in the AUC and susceptibility tests, LR− values were evaluated much more meaningfully. In our opinion, our study would serve as the base for future prospective large serial studies because there is currently not enough research on this subject. Limitations: The average age of the control group in our study was similar to the treatment group, but ECG parameters might have been affected because of ignoring the chronic diseases of the patients in the control group, except COPD. Although ECG was not evaluated microscopically, required aggrandizement was done manually in a digital environment and was measured. The small number of patients involved in the study and the retrospective nature of the study are also limitations. In this study, we aimed to predict a possible arrhythmia by evaluating the ECG parameters of patients diagnosed with COPD attacks. However, we could not evaluate whether arrhythmia developed in these patients. Prospective long-term cohort studies are needed on this subject. Conclusion: To our knowledge, this is the first study that the rise of the dispersions of P wave and Tp-e intervals (without rise of QTc disp.) is detected on the evaluation of atrial and ventricular arrhythmia risks on COPD acute attacks. It is very important that a detailed ECG evaluation of these patients is performed by doctors working in the emergency service where the attack treatment is provided to diagnose early mortality, which is dependent on arrhythmia. References 1. copd)(Date of Access 16.04.2021) 2. Sievi NA, Clarenbach CF, Camen G, Rossi VA, van Gestel AJ, Kohler M. 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PMID: 32281955. 540 ST Table 1: General characteristics and electrolyte values Control group (N = 54) COPD group (N = 66) p Age (year)a 66.5, 14 (40–82) 68.5, 18 (38–95) 0.189* Gender Men 27 (36%) 48 (64%) 0.011** Women 27 (60%) 18 (40%) Sodium (mmol/L)a 140, 3 (129–143) 140, 5 (126–146) 0.353* Potassium (mmol/L)a 4.20, 0.58 (3.30–6.30) 4.10, 0.90 (2.70–6.10) 0.071* * Mann–Whitney U-test; ** Chi-squared test. IQR: Interquartile Range a : Median, IQR (min-max) Table 2: Comparison of ECG parameters of the two groups. ECG parameters (ms) Control group (N = 54) COPD group (N = 66) p QT maxa 400,45 (320–480) 360,60 (280–520) <0.001* QT mina 320,40 (280–400) 300,40 (240–440) <0.001* QT dispa 40,40 (0–140) 40,20 (20–120) 0.490* QTc maxb 449.9 ± 37.52 439.9 ± 32.11 0.122** QTc minb 380.7 ± 31.18 377.8 ± 30.25 0.595** QTc dispa 51,49 (0–153) 55,30 (26–134) 0.820* Tp-e maxa 80,40 (40–160) 80,20 (50–120) 0.041* Tp-e mina 60,20 (40–100) 50,20 (30–80) <0.001* Tp-e dispa 20,40 (0–80) 40,10 (20–80) 0.001* Tp-e /QTa 0.240, 0.05 (0.12–0.38) 0.250, 0.04 (0.15–0.33) 0.615* Tp-e/QTc a 0.190, 0.080 (0.11–0.34) 0.189, 0.054 (0.11–0.27) 0.301* P maxa 80,40 (40–200) 100,20 (50–160) 0.445* P mina 60,40 (40–80) 60,20 (30–80) <0.001* P dispa 20,20 (0–140) 40,13 (10–100) <0.001* * Mann–Whitney U-test; ** Student’s t-test. Bold p values are statistically significant. IQR: Interquartile Range, SD: Standard deviation a : Median, IQR (min-max) b : Mean ± SD Table 3: Diagnostic sensitivity tests for P disp and Tp-e disp according to ROC analysis. Cutoff (ms) Sensitivity (%) Specificity (%) PPV (%) NPV (%) LR+ LR− Youden Index AUC P dispersion 30 87.88 55.56 70.73 78.95 1.98 0.22 0.434 0.712 Tp-e dispersion 30 80.3 61.11 71.62 71.74 2.06 0.32 0.414 0.671 541 ST Figure 1: Flowchart of the patients selected for this study. Figure 2: ECG parameters. Figure 3: P disp and Tp-e disp according to ROC analysis