Zhang HW, Chang GD, Liu XM, Gao H, Xu XD, Lv SY. Analysis of epidemiological characteristics and psychopsychological factors of arrhythmia in the elderly. World J Psychiatry 2025; 15(4): 100281 [DOI: 10.5498/wjp.v15.i4.100281]
Corresponding Author of This Article
Su-Ying Lv, Department of Arrhythmia, Shangqiu First People’s Hospital, No. 292 Kaixuan South Road, Suiyang District, Shangqiu 476100, Henan Province, China. dr1314555@163.com
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Author contributions: Zhang HW wrote the paper; Zhang HW and Lv SY designed the research; Zhang HW, Chang GD, Liu XM, Gao H, and Xu XD performed the research; Zhang HW and Lv SY analyzed the data; and all authors made substantial intellectual contributions to this paper.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Shangqiu First People’s Hospital, No. 2023-0901.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data supporting this article will be shared upon reasonable request to the corresponding author.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Su-Ying Lv, Department of Arrhythmia, Shangqiu First People’s Hospital, No. 292 Kaixuan South Road, Suiyang District, Shangqiu 476100, Henan Province, China. dr1314555@163.com
Received: November 28, 2024 Revised: January 16, 2025 Accepted: February 21, 2025 Published online: April 19, 2025 Processing time: 117 Days and 2.9 Hours
Abstract
BACKGROUND
Irregular heart rhythms are a primary manifestation of cardiovascular disease, considerably contributing to global morbidity and mortality rates. Moreover, patients with cardiac arrhythmias often experience a higher prevalence of sleep disorders, anxiety, and depression owing to various factors.
AIM
To investigate the epidemiological characteristics and psychological factors associated with arrhythmia in the elderly and to establish a theoretical foundation for its prevention and treatment in older adults.
METHODS
A retrospective analysis was performed on 169 elderly patients admitted to the Shangqiu First People’s Hospital from December 2022 to December 2023. All subjects underwent 24-hour electrocardiogram monitoring to record heart rate, heart rate variability, and 24-hour ambulatory electrocardiogram data. Additionally, patients’ medical records were reviewed to gather information on their general condition, including age, gender, underlying diseases, and other relevant factors. Patients were divided into four groups based on their Hamilton Anxiety (HAMA) and Hamilton Depression Rating Scale (HAMD) scores: Group A (HAMA scores ≥ 7), Group B (HAMD scores ≥ 7), Group C (both HAMA and HAMD scores ≥ 7), and Group D (HAMA and HAMD scores < 7). Psychological factors such as depression, anxiety, sleep status, and quality of life were analyzed. Pearson correlation was used to examine the relationship between scores from the Pittsburgh Sleep Quality Index (PSQI), HAMA/HAMD scales, and the Short Form 36-item Health Survey (SF-36) with the presence of arrhythmia.
RESULTS
Among the 169 patients, 87 (51.5%) had concurrent arrhythmia. Atrial arrhythmias constituted the largest proportion at 34.8% (30 out of 87), followed by sinus tachycardia at 24.1% (21 out of 87), and ventricular arrhythmias at 9.2% (8 out of 87). Factors such as advanced age, coronary heart disease, hypertension, smoking, exposure to secondhand smoke, and residing in rural areas significantly increased the risk of developing arrhythmia. There was a statistically significant difference between the two groups regarding PSQI, HAMA-14, HAMD-17, and SF-36 scores. Pearson correlation analysis revealed that PSQI, HAMA-14, and HAMD-17 scores were positively correlated with arrhythmia in the elderly, while the SF-36 score was negatively correlated. The anxiety, depression, and combined anxiety–depression groups exhibited significantly higher PSQI, HAMA-14, and HAMD-17 scores compared to the nonanxiety and non-depression group.
CONCLUSION
Arrhythmia among the elderly is primarily found in individuals with advanced age and existing health conditions. It is also linked to psychological factors such as depression, anxiety, reduced quality of life, and sleep disturbances.
Core Tip: Arrhythmia is an important disease in cardiovascular disorders, which is expected to increase dramatically in the next decades owing to the prolongation of life expectancy and the improvements in diagnosis. Anxiety and depressive symptoms have been shown to increase with recurrent arrhythmic episodes and are associated with the severity of atrial fibrillation symptoms. This study aims to explore the epidemiological characteristics and psychological factors associated with arrhythmia in older adults.
Citation: Zhang HW, Chang GD, Liu XM, Gao H, Xu XD, Lv SY. Analysis of epidemiological characteristics and psychopsychological factors of arrhythmia in the elderly. World J Psychiatry 2025; 15(4): 100281
Arrhythmia refers to abnormalities in the frequency, rhythm, origin, conduction velocity, or excitation sequence of electrical impulses[1], making it a significant condition within cardiovascular disorders. It can occur independently or alongside other cardiovascular diseases and can be fatal during acute episodes. Mild cases may cause discomfort for patients, while severe cases can lead to hemodynamic changes, result in cardiac function decompensation, and exacerbate myocardial ischemia, worsening the overall condition[2]. Arrhythmia is common among middle-aged and older adults, with both its incidence and severity increasing with age[3]. Related research indicates that the occurrence of arrhythmia is associated with neuropsychological factors[4]. Normal human functioning is regulated by endocrine hormones and the autonomic nervous system. Emotional fluctuations and psychological conflicts can considerably affect cardiovascular function through the cerebral cortex–limbic system–hypothalamic–pituitary–adrenal cortex axis[5]. Additionally, various factors - including clinical symptoms, lifestyle changes, long-term medication, adverse reactions, and the uncertainty of prognosis - contribute to a high incidence of sleep disorders, anxiety, and depression in patients with arrhythmia[6]. Studies have shown a bidirectional relationship between depression and arrhythmias, where negative emotions can trigger or worsen cardiovascular disease symptoms[7]. Depression is linked to a higher incidence of adverse cardiovascular events, and emotional stress is a well-known trigger for ventricular arrhythmias and sudden cardiac death[8]. Evidence suggests that anxiety and depression are associated with sleep disturbances, and a decline in sleep quality can exacerbate these emotional disorders, creating a cyclical influence. Ultimately, both emotional disorders and sleep disturbances can lead to autonomic nerve dysfunction, which impacts the autonomic nervous system’s regulation of the heart, resulting in abnormalities in heart rate and blood pressure[9]. Anxiety and depressive symptoms have also been found to increase with recurrent arrhythmic episodes and are linked to the severity of atrial fibrillation symptoms[10,11]. The European Society of Cardiology guidelines for managing atrial fibrillation highlight the potential for mood disorders in this patient population[12]. Furthermore, the presence of depressive and anxiety symptoms can impact the treatment outcomes[13]. Many researchers currently believe that anxiety and depression may worsen the condition and increase mortality rates in elderly patients with arrhythmia. However, the influence and mechanisms by which anxiety and depression affect patients with arrhythmia remain unclear. Therefore, this study aims to explore the epidemiological characteristics and psychological factors related to arrhythmia in older adults, ultimately providing a theoretical foundation for the prevention and treatment of arrhythmia in the elderly population.
MATERIALS AND METHODS
Research subjects
Elderly patients treated at Shangqiu First People’s Hospital from December 2022 to December 2023 were selected as study participants. Inclusion criteria included: (1) Age ≥ 60 years; (2) Normal language communication abilities, intact audio-visual functions, stable mental state, and the ability to cooperate with clinical examinations and evaluations; (3) No consumption of alcohol and caffeine-containing food or drinks on the day of testing, nor involvement in strenuous exercise or experiencing emotional fluctuations; and (4) Complete clinical data. Exclusion criteria included: (1) History of cerebral hemorrhage or cerebral infarction; (2) Recent acute infections; (3) Left ventricular ejection fraction < 60%; (4) Past medical or family histories of mental illness; (5) Recent consumption of drugs impacting mental and neurological status; (6) Severe kidney and liver conditions; (7) Pericardial effusion and congenital heart disorders; (8) Malignant tumors or multiple organ dysfunction; and (9) Incomplete clinical data. This study received approval from the hospital’s ethics review committee. Arrhythmia was diagnosed based on the criteria for arrhythmia outlined in Internal Medicine, which includes sinus arrhythmia (sinus tachycardia/bradycardia), atrial arrhythmia (atrial premature beats, atrial tachycardia, atrial flutter, or atrial fibrillation), and ventricular arrhythmia (ventricular premature beats, ventricular tachycardia, atrioventricular block, etc.).
Research methods
General data collection: Data were collected from patients, including age, gender, body mass index, history of coronary heart disease, hypertension, diabetes, and lifestyle factors such as smoking, alcohol consumption, and exposure to secondhand smoke.
Anxiety and depression: Depression was assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17), where the score correlates positively with the severity of depression. A score of < 7 indicates no depression, 7-17 signifies mild depression, 18-24 indicates moderate depression, and a score of ≥ 25 represents severe depression. The degree of anxiety was assessed using the 14-item Hamilton Anxiety Rating Scale (HAMA-14), where the score is directly proportional to the severity of anxiety symptoms. Scores of ≥ 29 indicate possible severe anxiety, ≥ 21 indicate definite obvious anxiety, ≥ 14 indicate definite anxiety, and > 7 indicate possible anxiety, while a score of less than 7 suggests no anxiety symptoms. Patient grouping: Based on their anxiety and depression status, as determined by HAMA and HAMD scores, patients were categorized into four groups: Group A with only anxiety disorder (HAMA scores ≥ 7), group B with only depression disorder (HAMD scores ≥ 7), group C with both anxiety and depression disorders (HAMA scores ≥ 7 and HAMD scores ≥ 7), and group D without anxiety or depression disorders (HAMA scores < 7 and HAMD scores < 7).
Sleep status: All patients were evaluated for sleep quality using the Pittsburgh Sleep Quality Index (PSQI). This scale includes 19 self-assessment questions and 5 questions assessed by sleep partners. For this study, only the 19 self-assessment questions were scored, which are divided into 7 factors, with scores ranging from 0 to 3. The total PSQI score, calculated as the cumulative score of each factor, can range from 0 to 21. A higher score indicates poorer sleep quality.
Quality of life: The Short-Form 36-item Health Survey (SF-36; score range: 0-100) was used to evaluate the quality of life across 8 domains: Physical function, role-physical, bodily pain, general health status, vitality, social function, role-emotional, and mental health. Higher scores are associated with better quality of life.
Heart rate variability: Heart rate variability (HRV) time domain indicators were monitored for 24 h using the SA-3000P HRV analysis system. Key measurements included the root-mean-square of successive normal sinus two adjacent R waves (RR) interval difference (rMSSD), the standard deviation of all normal sinus RR intervals over 24 hours (SDNN), the standard deviation of the average RR intervals measured every 5 minutes during the 24 hours (SDANN), and the percentage of successive normal sinus RR intervals > 50 milliseconds (pNN50).
Statistical analysis
Data were analyzed using SPSS version 19.0. Continuous variables are presented as mean ± SD. Independent sample t-tests were used for group comparisons, while analysis of variance was used for comparisons among multiple groups, followed by pairwise comparisons using least significant difference-t tests. Categorical data are expressed as rates or percentages and were analyzed using χ2 tests. Correlations were assessed using Pearson correlation analysis. A P value < 0.05 was considered statistically significant.
RESULTS
Epidemiological characteristics
A total of 180 questionnaires were distributed for this survey, and 174 were returned. After removing 5 invalid questionnaires, 169 valid responses were obtained, resulting in a response rate of 93.9%. Among the 169 included patients, 87 (51.5%) had concurrent arrhythmia and were placed in the research group, while 82 (48.5%) nonarrhythmia cases formed the control group. Among the 87 patients with arrhythmia, atrial arrhythmia was the most prevalent, accounting for 34.8% (30 out of 87), followed by sinus tachycardia at 24.1% (21 out of 87), and ventricular arrhythmia at 9.2% (8 out of 87). Patients with arrhythmia were older than those without (P < 0.05). Additionally, a higher incidence of arrhythmia was observed among patients with coronary heart disease, hypertension, smoking habits, exposure to secondhand smoke, and those residing in rural areas (P < 0.05). Please refer to Table 1 for further details.
Table 1 Epidemiological characteristics of arrhythmia in 169 elderly individuals.
Characteristic
Research group (n = 87)
Control group (n = 82)
χ2/t
P value
Age (years)
70.22 ± 5.95
66.91 ± 4.21
4.143
< 0.0001
Sex
-
-
0.705
0.401
Male
47
39
-
-
Female
40
43
-
-
BMI (kg/m2)
23.46 ± 2.12
23.55 ± 1.95
0.310
0.757
Coronary heart disease
-
-
9.833
0.002
With
61
38
-
-
Without
26
44
-
-
Hypertension
-
-
7.987
0.005
With
56
35
-
-
Without
31
47
-
-
Diabetes
-
-
0.389
0.533
With
32
34
-
-
Without
55
48
-
-
Smoking
-
-
6.314
0.012
With
60
41
-
-
Without
27
41
-
-
Drinking
-
-
0.337
0.562
With
41
35
-
-
Without
46
47
-
-
Secondhand smoke exposure
-
-
9.079
0.003
With
65
43
-
-
Without
22
39
-
-
Residence
-
-
4.232
0.040
Rural
53
37
-
-
Urban
34
45
-
-
Analysis of psychopsychological factors of arrhythmia occurrence
According to the univariate analysis (Figure 1), patients with concurrent arrhythmia exhibited significantly higher PSQI scores (16.68 ± 2.66), HAMA-14 scores (11.00 ± 5.40), and HAMD-17 scores (12.61 ± 6.02), along with lower SF-36 scores (68.59 ± 7.43) compared to those without concurrent arrhythmia (PSQI scores: 15.17 ± 2.84; HAMA-14 scores: 9.26 ± 5.67; HAMD-17 scores: 9.95 ± 6.16; SF-36 scores: 72.23 ± 4.60) (P < 0.05).
Figure 1 Analysis of psychopsychological factors of arrhythmia occurrence in 169 elderly patients.
A: Pittsburgh Sleep Quality Index score; B: Hamilton Anxiety-14 score; C: Hamilton Depression-17 score; D: The Short-Form 36-item Health Survey score. aP < 0.05; cP < 0.05. PSQI: Pittsburgh Sleep Quality Index; HAMA-14: Hamilton Anxiety-14; HAMD-17: 17-item Hamilton Depression Rating Scale; SF-36: The Short-Form 36-item Health Survey.
Correlation between psychopsychological factors and concurrent arrhythmia
Through point-biserial correlation analysis (Table 2), we discovered a positive association between PSQI scores (r = 0.239, P = 0.002), HAMA-14 scores (r = 0.156, P = 0.042), and HAMD-17 scores (r = 0.191, P = 0.013) and arrhythmia in the elderly population. Conversely, there was an inverse correlation between SF-36 scores and arrhythmia, with a correlation coefficient of -0.283 (P < 0.05).
Table 2 Correlation between psychopsychological score and complicated arrhythmia.
Characteristic
r
P value
PSQI
0.239
0.002
HAMA-14
0.156
0.042
HAMD-17
0.191
0.013
SF-36
-0.283
0.000
Psychopsychological scores of patients in each group
Among the 87 patients with arrhythmias, 56 were classified as group A (anxiety), 59 as group B (depression), 47 as group C (both anxiety and depression), and 19 as group D (neither anxiety nor depression). There were significant statistical differences in PSQI scores (F = 8.255, P < 0.0001), HAMA-14 scores (F = 25.58, P < 0.0001), and HAMD-17 scores (F = 41.85, P < 0.0001) among the four groups, while SF-36 scores showed no significant differences (F = 0.044, P = 0.988). Groups A, B, and C had significantly higher PSQI, HAMA-14, and HAMD-17 scores compared to group D. Specifically, group A had higher HAMA-14 scores (14.14 ± 4.12) than group B (11.75 ± 4.60), and group B had higher HAMD-17 scores (16.20 ± 3.27) compared to group A (14.29 ± 5.15) (P < 0.05), as detailed in Table 3.
Table 3 Psychopsychological factor scores of patients in each group, mean ± SD.
The differences in rMSSD (F = 85.61, P < 0.0001), pNM50 (F = 38.25, P < 0.0001), SDNN (F = 11.62, P < 0.0001), and SDANN (F = 4.575, P = 0.004) among the four patient groups were statistically significant. However, no significant differences were found among groups A, B, and C (P > 0.05), as presented in Table 4.
Table 4 Comparison of heart rate variability among groups, mean ± SD.
Characteristic
Group A (n = 56)
Group B (n = 59)
Group C (n = 47)
Group D (n = 20)
F
P value
rMSSD (millisecond)
21.46 ± 3.51
21.52 ± 3.40
20.97 ± 3.50
36.78 ± 6.92
85.61
< 0.0001
pNM50 (%)
15.98 ± 2.28
16.10 ± 2.62
15.93 ± 2.24
22.04 ± 1.88
38.25
< 0.0001
SDNN (millisecond)
85.85 ± 12.54
84.82 ± 13.87
84.25 ± 12.16
102.8 ± 8.78
11.62
< 0.0001
SDANN (millisecond)
80.18 ± 11.40
79.69 ± 12.03
80.47 ± 12.17
90.32 ± 5.77
4.575
0.004
DISCUSSION
Cardiac dysfunction primarily manifests as arrhythmias and myocardial conduction disorders. In China, the prevention and treatment of atrial fibrillation face considerable challenges owing to its high prevalence, low awareness, and inadequate management[14,15]. Previous studies have examined the epidemiology of atrial fibrillation in China but have often underestimated the prevalence and incidence of the condition[16,17]. Therefore, it is essential to understand the epidemiological characteristics of arrhythmias. Additionally, among these patients, the incidence of depression and anxiety disorders is notably high. Reports indicate that 40%-60% of patients in this population experience depression and anxiety disorders[18]. Furthermore, depression has been found to have substantial negative effects on the health of patients with chronic cardiovascular issues and coronary heart disease[19,20].
This study found that among the 169 patients analyzed, 87 had concurrent arrhythmias, with atrial arrhythmias making up the largest portion, followed by sinus tachycardia. Patients with arrhythmias were significantly older than those without them. A notably higher incidence of arrhythmias was identified in patients with coronary heart disease, hypertension, a history of smoking, exposure to secondhand smoke, and those residing in rural areas. As individuals age, their cardiac function experiences degenerative changes, which increases the likelihood of developing arrhythmias[21]. Furthermore, there is a positive correlation between arrhythmias and myocardial ischemia, meaning that patients with coronary heart disease are at a heightened risk for arrhythmias[22]. Hypertensive patients exhibit increased sympathetic excitability, elevated catecholamine levels, and activation of the renin-angiotensin system. These factors can cause increased excitability of cardiac ectopic pacemakers, thereby increasing the risk of arrhythmias[23]. Smoking is one of the most prevalent behavioral risk factors associated with cardiovascular diseases, and there is substantial evidence indicating its impact on cardiovascular conditions in patients with atrial fibrillation[24,25]. Additionally, a previous study suggests that objectively measured higher levels of urinary cotinine, as well as exposure to secondhand smoke, may correlate with a 1.6-fold increased risk of atrial fibrillation in nonsmokers[26]. A significantly higher number of patients with arrhythmia are found in rural areas compared to urban areas. In China, the agricultural population constitutes over 50% of the national demographic. The dual urban–rural structure in China results in distinct lifestyle and behavioral risk factors for rural residents, differing considerably from those of urban residents and individuals in other countries and regions[27]. As living and dietary standards improve, the incidence of cardiovascular diseases in rural areas of China is increasing rapidly, outpacing growth in urban areas, and the majority of cases remain untreated. In recent years, the rural medical security system has been increasingly adopted, leading to an increase in the rates of cardiovascular disease consultations and hospitalizations among rural residents. However, economic conditions and disparities in health education and healthcare quality still create a substantial gap between rural and urban populations.
We subsequently analyzed patients’ sleep disorders along with their scores for anxiety, depression, and quality of life. Patients with concurrent arrhythmia exhibited significantly higher PSQI, HAMA-14, and HAMD-17 scores, along with lower SF-36 scores, compared to those without arrhythmia. Additionally, correlation analysis revealed a positive relationship between PSQI, HAMA-14, and HAMD-17 scores and the occurrence of arrhythmia in the elderly population, while an inverse relationship was identified between the SF-36 score and arrhythmia. In the study by Polikandriot et al[28], 170 patients with atrial fibrillation were included, with 70% of participants being male and 32.4% aged over 70 years. Additionally, 34.9% of the patients exhibited high levels of anxiety, and 20.2% had high levels of depression. The results indicated that patients’ characteristics were associated with anxiety and depression, emphasizing the need to assess these factors when treating this commonly encountered arrhythmia. Sleep disturbances are a common physiological manifestation in patients with chronic conditions, with emotional state being the primary determinant of sleep quality[29]. Elderly patients with cardiovascular diseases are particularly susceptible to negative emotions such as anxiety and depression, which can result from long-term illness, reduced quality of life, medical burden, concerns about prognosis, and loss of work ability. Clinical research has established arrhythmia as a psychosomatic condition, with patients exhibiting a higher prevalence of anxiety and depression compared to the general population[30]. Individuals experiencing anxiety, depression, or other negative emotions have been shown to be at an increased risk of developing arrhythmias; anxiety and depression have been identified as independent risk factors for arrhythmias[28]. In 2015, von Eisenhart Rothe et al[31] demonstrated that, after adjusting for factors such as the perceived frequency and duration of atrial fibrillation episodes, chronic obstructive pulmonary disease, and sex, depressive mood was associated with the burden of atrial fibrillation symptoms at 6 months. Additionally, anxiety is the primary postdiagnostic response in patients with atrial fibrillation who do not have other associated comorbidities[32]. Lane et al[32] further elaborated that elevated state anxiety was present in 38.5%, 30.9%, and 35.7% of patients with atrial fibrillation at baseline (diagnosis), 6 months, and 12 months, respectively.
In this study, patients were categorized based on the presence of anxiety, depression, or both anxiety and depression. It was observed that patients with anxiety or depression had significantly higher sleep disturbance scores compared to those without. Although no significant difference was observed in sleep disturbance scores among the three groups, the data indicated that patients with anxiety and depression scored slightly higher. Literature suggests that anxiety and depression can reduce rapid eye movement sleep time and extend the latency of rapid eye movement sleep, resulting in an increased duration of slow-wave sleep. This, in turn, leads to a decline in sleep quality and contributes to sleep disorders over time. Finally, we analyzed the HRV of four groups of patients, which measures the periodic variation of sinus rhythm in a specific time domain. We found significant differences in rMSSD, pNN50, SDNN, and SDANN among these groups. pNN50 and rMSSD are sensitive indicators of parasympathetic nerve activity, while SDNN reflects the overall activity of both parasympathetic and sympathetic nerves. However, SDANN specifically reflects sympathetic nerve activity[33]. Reduced HRV is commonly associated with increased sympathetic nerve activity in the body, which can lower the ventricular activity threshold and potentially trigger severe ventricular arrhythmias[34]. This study does have some limitations. One limitation is the relatively small sample size of patients who participated in the questionnaire. Another limitation is that this was a single-center study, which may limit the generalizability of the findings. Additionally, the study did not assess the impact of other sociodemographic and clinical factors that may influence quality of life or contribute to the occurrence of anxiety and depressive symptoms in patients with arrhythmia.
CONCLUSION
In conclusion, arrhythmia in the elderly population is primarily prevalent among individuals of advanced age with underlying medical conditions. Elderly patients with arrhythmia frequently experience significant anxiety and/or depression, which can lead to sleep disturbances. Considering the high prevalence and mortality of arrhythmia in China, a comprehensive understanding and proactive intervention of anxiety and depressive disorders are of paramount importance for effective clinical management and improved prognosis of arrhythmia.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or Innovation: Grade B, Grade C
Scientific Significance: Grade B, Grade C
P-Reviewer: Han B; Oh H S-Editor: Bai Y L-Editor: A P-Editor: Zhang XD
Hu LX, Tang M, Hua W, Ren XQ, Jia YH, Chu JM, Zhang JT, Liu XN. Psychological Disturbances and Their Association with Sleep Disturbance in Patients Admitted for Arrhythmia Diseases.Neuropsychiatr Dis Treat. 2022;18:1739-1750.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 1][Reference Citation Analysis (0)]
von Eisenhart Rothe AF, Goette A, Kirchhof P, Breithardt G, Limbourg T, Calvert M, Baumert J, Ladwig KH. Depression in paroxysmal and persistent atrial fibrillation patients: a cross-sectional comparison of patients enroled in two large clinical trials.Europace. 2014;16:812-819.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 36][Cited by in RCA: 36][Article Influence: 3.0][Reference Citation Analysis (0)]
Dorian P, Jung W, Newman D, Paquette M, Wood K, Ayers GM, Camm J, Akhtar M, Luderitz B. The impairment of health-related quality of life in patients with intermittent atrial fibrillation: implications for the assessment of investigational therapy.J Am Coll Cardiol. 2000;36:1303-1309.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 435][Cited by in RCA: 433][Article Influence: 17.3][Reference Citation Analysis (0)]
Shi S, Tang Y, Zhao Q, Yan H, Yu B, Zheng Q, Li Y, Zheng L, Yuan Y, Zhong J, Xu J, Wu Y, Xu J, Chen L, Li S, Jiang J, Wang J, Fan J, Chen M, Tang B, Li W, Wu Q, Shi B, Zhou S, Zhao X, Yin Y, Zhang Z, Zhong G, Han X, Liu F, Wu M, Gao L, Yang B, Huang H, Huang C; China Atrial Fibrillation Center Project Team). Prevalence and risk of atrial fibrillation in China: A national cross-sectional epidemiological study.Lancet Reg Health West Pac. 2022;23:100439.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 10][Cited by in RCA: 33][Article Influence: 11.0][Reference Citation Analysis (0)]
Steinberg BA, Gao H, Shrader P, Pieper K, Thomas L, Camm AJ, Ezekowitz MD, Fonarow GC, Gersh BJ, Goldhaber S, Haas S, Hacke W, Kowey PR, Ansell J, Mahaffey KW, Naccarelli G, Reiffel JA, Turpie A, Verheugt F, Piccini JP, Kakkar A, Peterson ED, Fox KAA; GARFIELD-AF; ORBIT-AF Investigators. International trends in clinical characteristics and oral anticoagulation treatment for patients with atrial fibrillation: Results from the GARFIELD-AF, ORBIT-AF I, and ORBIT-AF II registries.Am Heart J. 2017;194:132-140.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 127][Cited by in RCA: 153][Article Influence: 19.1][Reference Citation Analysis (0)]
Wei Y, Xu J, Wu H, Zhou G, Chen S, Wang C, Shen Y, Yang S, Wang B, He Z, Sun J, Sun W, Ouyang P, Liu S. Survey of Antithrombotic Treatment in Rural Patients (>60 years) with Atrial Fibrillation in East China.Sci Rep. 2018;8:6830.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 12][Cited by in RCA: 16][Article Influence: 2.3][Reference Citation Analysis (0)]
Deng H, Guo P, Zheng M, Huang J, Xue Y, Zhan X, Wang F, Liu Y, Fang X, Liao H, Wei W, Liang Y, Liu F, Liao Z, Feng Y, Wu S. Epidemiological Characteristics of Atrial Fibrillation in Southern China: Results from the Guangzhou Heart Study.Sci Rep. 2018;8:17829.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 28][Cited by in RCA: 34][Article Influence: 4.9][Reference Citation Analysis (0)]
Vaccarino V, Badimon L, Bremner JD, Cenko E, Cubedo J, Dorobantu M, Duncker DJ, Koller A, Manfrini O, Milicic D, Padro T, Pries AR, Quyyumi AA, Tousoulis D, Trifunovic D, Vasiljevic Z, de Wit C, Bugiardini R; ESC Scientific Document Group Reviewers. Depression and coronary heart disease: 2018 position paper of the ESC working group on coronary pathophysiology and microcirculation.Eur Heart J. 2020;41:1687-1696.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 219][Cited by in RCA: 212][Article Influence: 42.4][Reference Citation Analysis (0)]
Choi S, Chang J, Kim K, Kim SM, Koo HY, Cho MH, Cho IY, Lee H, Son JS, Park SM, Lee K. Association of smoking cessation after atrial fibrillation diagnosis on the risk of cardiovascular disease: a cohort study of South Korean men.BMC Public Health. 2020;20:168.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 10][Cited by in RCA: 16][Article Influence: 3.2][Reference Citation Analysis (0)]
Lin GM, Lloyd-Jones DM, Colangelo LA, Szklo M, Heckbert SR, Chen LY, Lima JAC, Liu K. Secondhand tobacco smoke exposure, urine cotinine, and risk of incident atrial fibrillation: The multi-ethnic study of atherosclerosis.Prog Cardiovasc Dis. 2022;74:38-44.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 10][Cited by in RCA: 4][Article Influence: 1.3][Reference Citation Analysis (0)]
von Eisenhart Rothe A, Hutt F, Baumert J, Breithardt G, Goette A, Kirchhof P, Ladwig KH. Depressed mood amplifies heart-related symptoms in persistent and paroxysmal atrial fibrillation patients: a longitudinal analysis--data from the German Competence Network on Atrial Fibrillation.Europace. 2015;17:1354-1362.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 28][Cited by in RCA: 30][Article Influence: 3.0][Reference Citation Analysis (0)]
