Risk Factors for Comorbid Anxiety in Patients with Coronary Heart Disease and Association with Noise Exposure

Abstract

Objective:

The study aims to investigate the risk factors for comorbid anxiety in patients with coronary heart disease (CHD) and its association with noise exposure, to improve anxiety symptoms through noise reduction management, and to maintain optimal psychological well-being.

Methods:

Retrospective analysis was performed on clinical data from 138 CHD patients hospitalized between June 2023 and December 2024. Participants were stratified into an anxiety group (n = 78) and a nonanxiety group (n = 60) based on the presence of clinically significant anxiety during hospitalization. Between-group comparisons were performed on noise levels (dB), baseline characteristics, sleep quality [assessed by the Pittsburgh Sleep Quality Index (PSQI)], and disease-related parameters. The correlation between noise exposure and anxiety in CHD patients was quantified, and determinants of anxiety were identified. Noise reduction management was implemented, and a comparison of the Self-Rating Anxiety Scale (SAS) scores before and after noise reduction was performed.

Results:

The nonanxiety group showed significantly lower SAS scores (47.25 ± 2.47) than the anxiety group (55.64 ± 3.85; t = 14.706, P < 0.05). Pearson correlation analysis indicated a positive association between noise exposure and anxiety (r = 0.522, P < 0.05). Compared with the nonanxiety group, the anxiety group had higher rates of multivessel disease (≥3 vessels), prior percutaneous coronary intervention (PCI) history, PSQI scores, and noise levels (all P < 0.05). According to the univariate logistic regression analysis, the anxiety determinants included poor sleep quality, multivessel disease, elevated noise exposure, and prior PCI (odds ratios = 7.352, 2.240, 1.329, 2.266; all P < 0.05). Postmanagement data showed reduced noise levels and SAS scores compared with the baseline (all P < 0.05).

Conclusion:

Patients with CHD are prone to anxiety, with hospital noise being a potential risk factor. Risk factors include poor sleep quality, multiple diseased vessels, high noise levels, and prior PCI history. Clinical attention should be paid to patients’ emotional states, and noise reduction management can effectively alleviate anxiety.

KEY MESSAGES

• (1)Patients with coronary heart disease (CHD) display a relatively high incidence of anxiety, which is closely related to noise.
• (2)Sleep quality, the number of diseased blood vessels, noise decibels, and a history of percutaneous coronary intervention are all risk factors influencing concurrent anxiety in patients suffering from CHD.
• (3)Noise reduction management helps to minimize the influence of noise on patients’ physical and mental health and alleviate their anxiety.

INTRODUCTION

Coronary heart disease (CHD) is a common cardiovascular disorder caused by coronary atherosclerosis; it leads to vascular stenosis, occlusion, myocardial ischemia, and hypoxic injury.[1] Its irreversible chronic progression requires lifelong management, and the persistent risk of adverse cardiovascular events contributes to anxiety in patients.[2] Comorbid anxiety can activate the sympathetic nervous system, which promotes the release of vasoactive substances that elevates blood pressure and heart rate and thereby accelerates disease progression.[3] Karlsen et al.[4] further identified anxiety as a key predictor of poor prognosis in cardiovascular patients.

Dzhambov and Lercher[5] demonstrated that “chronic exposure to noise pollution may be associated with depressive and anxiety symptoms,” which suggests that noise may serve as a potential risk factor for anxiety in this population. Noise is defined as unwanted sound perceived as disruptive to normal rest, daily activities, or emotional equilibrium.[6] Intense acoustic stimuli can provoke negative affective states, including irritability, agitation, and psychological distress, which not only impair sleep quality but also exacerbate psychological burden. Aydin Sayilan et al.[7] documented noise’s detrimental effects on anxiety and sleep in patients. Nevertheless, the specific relationship between noise exposure and anxiety in CHD patients remains underexplored. Therefore, this retrospective study analyzed 138 hospitalized CHD cases and compared noise levels and clinical profiles between the anxiety and nonanxiety groups to examine risk factors and associations with environmental noise.

MATERIALS AND METHODS

Study Design

Clinical records of patients hospitalized with CHD between June 2023 and December 2024 from Shaanxi Provincial People’s Hospital were retrospectively reviewed.

The inclusion criteria were as follows: (1) diagnosis of CHD confirmed per established criteria with coronary angiography-documented stenosis ≥50% [8]; (2) age 45 to 80 years; (3) active inpatient status; (4) minimum primary education attainment with intact communication capacity; (5) complete clinical documentation.

The exclusion criteria included the following: (1) diagnosis of depression and anxiety in accordance with the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders,[9] intake of benzodiazepines and selective serotonin reuptake inhibitors and other psychotropic drugs in the past 3 months; (2) comorbid cardiac conditions (e.g., myocarditis, congenital or rheumatic heart disease); (3) coagulopathies or hematological disorders; (4) major adverse cardiovascular events (e.g., acute myocardial infarction and cardiac arrest) within 6 months before admission; (5) crucial life events or severe stressors within 6 months before admission; (6) auditory impairments (e.g., tinnitus and moderate-to-severe hearing loss); (7) active malignancies (e.g., hepatic or lung cancer); (8) interhospital transfer or premature discharge during hospitalization; (9) chronic occupational or residential noise exposure (e.g., factory workers and residents near airports); (10) incomplete medical records.

From an initial group of 145 consecutively screened patients, 138 were ultimately enrolled after the application of the inclusion/exclusion criteria (7 exclusions: 2 transfers, 3 severe hepatic dysfunctions, and 2 incomplete records). Participants were stratified into an anxiety group (n = 78) and a nonanxiety group (n = 60) based on the presence of clinically significant anxiety during hospitalization. Anxiety diagnosis was established using the Zung Self-Rating Anxiety Scale (SAS),[10] translated and adapted by Zhang.[11] The 20-item instrument demonstrated strong internal consistency (Cronbach’s α = 0.915) and test–retest reliability (r = 0.837). Each item employs a 4-point Likert scale (0 = rarely to 4 = most or all the time), with raw scores (0–80) converted to standard scores (0–100) by multiplying by 1.25. A cutoff score of ≥50 indicated anxiety caseness.

The study was conducted in accordance with the Declaration of Helsinki[12] and approved by Shaanxi Provincial People’s Hospital Ethics Committee (2024-027). Written informed consent was obtained from all participants prior to their enrollment in the study.

Data Collection Procedures

Documentation of baseline characteristics was achieved using a hospital-developed questionnaire, capturing demographic and clinical variables, including age, sex, educational level, body mass index (BMI), marital status, monthly household income per capita, and comorbidity profile. Sleep quality was assessed using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI), which was originally developed by Buysse et al.[13] and translated by Liu et al.[14] This 7-component scale evaluates sleep efficiency, latency, quality, disturbances, medication use, daytime dysfunction, and duration, with each component rated 0 to 3. Global scores range from 0 to 21, with high scores indicating great sleep impairment.

Additional clinical parameters included CHD duration, CHD classification, the number of diseased vessels, and prior percutaneous coronary intervention (PCI) history. In-hospital noise levels were measured using a DT-885 sound level meter (CEM Instruments, USA). The device underwent on-site calibration prior to each use, with verification performed using 94 dB/114 dB reference sound sources. Measurements were initiated only under a consistent calibration deviation of <0.5 dB. In accordance with Chinese regional environmental noise measurement standards, continuous monitoring was performed at the bedside position of 1.2 to 1.5 m above floor level. Sound levels were recorded during daytime (06:00–21:00) and nighttime (21:00–06:00), with the arithmetic mean of measurements used as the final noise level.

Noise Reduction Management

Noise Source Identification

Environmental noise sources were systematically categorized into the following: (1) interpersonal interactions (patient–patient, family–patient, and visitor communications generating conversational clutter); (2) electronic device emissions (mobile phone ringtones and call conversations from patients, caregivers, and medical staff); (3) equipment-generated sounds (friction from moved therapeutic devices, beds, chairs, and doors; alarm signals from medical monitors).

Management Measures Development

A comprehensive literature review was conducted using Boolean search operators (“coronary heart disease” AND “anxiety” AND “noise”) in PubMed, supplemented by Chinese database searches. Cardiological clinicians and nursing staff evaluated the retrieved evidence regarding noise-associated health hazards, noise management necessity, and acoustic optimization strategies, culminating in a multidisciplinary consensus protocol.

Implementation of Management Measures

• (1)Unit-wide Acoustic Education: Visual alerts (“Silence,” “No Loud Talking,” and “Noise Reduction Zone”) were displayed in patient rooms, doorways, and corridor bulletin boards to raise noise awareness and promote voluntary voice modulation.
• (2)Patient/Family Noise Control: Mobile devices were mandated to be set in silent/vibration mode. Structured education on noise pathophysiology was delivered, with an emphasis on the minimization of nonessential conversations.
• (3)Visitation Management: Nonessential visits, excluding primary caregivers, were restricted to one 15–20 min session daily per patient. Visitors received identical mobile device regulations.
• (4)Medical Equipment Modification: Antivibration pads were installed under furniture; wheelchairs and medical carts were fitted with noise-dampening casters. Monitor alarms were reconfigured to vibration mode or minimum audible threshold.
• (5)Clinical Staff Protocol: Strict adherence to the “Three-Quiet Principles,” namely, quiet footsteps, hushed communications, and noiseless task execution, was implemented. Nonessential staff conversations were prohibited, with personal devices set to vibration mode. Phone usage and shift handovers were excluded from patient areas.

Statistical Methods

SPSS 25.0 software (IBM Corporation, main version number: 25.0) was used. Count data were expressed as n and %, and chi-square test (χ²) was performed. The Shapiro–Wilk normality test was conducted to assess the normality of measurement data. Measurement data that conformed to a normal distribution were expressed as “x̄± s,” and an independent sample t-test was used for between-group comparisons. Paired t-test was implemented for intragroup comparisons. General linear bivariate Pearson correlation was used to detect the relationship between CHD combined with anxiety and noise. Univariate logistic regression was performed to identify factors associated with anxiety comorbidity in patients with CHD. All statistical tests were two-tailed, with P < 0.05 indicating statistical significance.

RESULTS

Prevalence of Anxiety Comorbidity in Coronary Heart Disease Patients

Among the 138 enrolled CHD patients, 78 (56.5%) were identified with anxiety comorbidity, and 60 (43.5%) were allotted to the nonanxiety group. A marked difference in SAS scores was observed between groups, with significantly lower values detected in nonanxious patients (P < 0.05; Table 1).

Table 1.

Comparison of Anxiety Scores between Groups (Points).

Group	SAS Score
Nonanxiety group (n = 60)	47.25 ± 2.47
Anxiety group (n = 78)	55.64 ± 3.85
T	14.706
P	<0.001

SAS = Self-Rating Anxiety Scale.

Comparison of Baseline Characteristics between Groups

Significant intergroup differences were observed in multivessel disease (≥3 vessels), prior PCI history, PSQI scores, and in-hospital noise levels (all P < 0.05). No statistically significant differences were identified in age, sex, BMI, marital status, monthly household income per capita, CHD duration or CHD classification (P > 0.05; Table 2).

Table 2.

Comparison of Baseline Characteristics between Groups.

Project		Nonanxiety Group (n=60)	Anxiety Group (n = 78)	Statistic	P
Age (years)		65.85 ± 4.72	66.74 ± 4.83	t = 1.097	0.275
Sex	Male	39(65.00)	45(57.69)	χ2 = 0.760	0.383
	Female	21(35.00)	33(42.31)
BMI (kg/m2)		23.85 ± 3.64	23.79 ± 3.62	t = 0.096	0.923
Marital status	With partner	51(85.00)	64(82.05)	χ2 = 0.212	0.645
	Without partner	9(15.00)	14(17.95)
Monthly income (¥)	≥5000	17(28.33)	26(33.33)	χ2 = 0.395	0.530
	<5000	43(71.67)	52(66.67)
Comorbidities (n)	≥2	15(25.00)	28(35.90)	χ2 = 1.878	0.171
	<2	45(75.00)	50(64.10)
PSQI score		8.86 ± 0.57	10.13 ± 0.85	t = 9.972	<0.001
CHD duration (years)		3.35 ± 0.64	3.51 ± 0.72	t = 1.357	0.177
CHD classification	Stable angina	28(46.67)	38(48.72)	χ2 = 0.057	0.811
	Unstable angina	32(53.33)	40(51.28)
Diseased vessels	≥3 Vessels	12(20.00)	28(35.90)	χ2 = 4.164	0.041
	<3 Vessels	48(80.00)	50(64.10)
Noise level (dB)		63.58 ± 3.43	72.68 ± 4.37	t = 15.214	<0.001
Prior PCI	Yes	29(48.33)	53(67.95)	χ2 = 4.177	0.041
	No	31(51.67)	25(32.05)

BMI = body mass index, CHD = coronary heart disease, PCI = percutaneous coronary intervention, PSQI = Pittsburgh Sleep Quality Index.

Univariate Logistic Regression Analysis of Anxiety Comorbidity in Coronary Heart Disease Patients

The dependent variable was defined as the presence or absence of anxiety comorbidity (1 = anxiety, 0 = no anxiety). Independent variables included the statistically significant indicators in Table 1 (PSQI score, the number of diseased vessels, hospital noise level, and PCI history). Univariate logistic regression analysis demonstrated poor sleep quality, multivessel disease (≥3 vessels), elevated hospital noise levels, and prior PCI history as significant determinants of anxiety comorbidity in CHD patients [odds ratios (ORs) = 7.352, 2.240, 1.329, 2.266; all P < 0.05; Table 3].

Table 3.

Univariate Logistic Regression Analysis of Anxiety Comorbidity.

Factor	β	SE	Wald	P	OR	95% CI
PSQI score	1.995	0.405	24.242	<0.001	7.352	3.323–16.266
Number of diseased vessels	0.806	0.400	4.068	0.044	2.240	1.023–4.905
Hospital noise level	0.284	0.073	15.151	<0.001	1.329	1.151–1.533
Prior PCI	0.818	0.354	5.328	0.021	2.266	1.131–4.539
Constant	−34.849	5.687	37.552	<0.001	-	-

CI = confidence interval, OR = odds ratio, PCI = percutaneous coronary intervention, PSQI = Pittsburgh Sleep Quality Index, SE = Standard Error

Correlation Analysis

Based on the SAS scores from Table 1 and noise level data from Table 2, bivariate Pearson linear correlation analysis was conducted, and the results revealed a significant positive correlation between anxiety comorbidity and noise exposure in CHD patients (r = 0.522, P < 0.05; Table 4).

Table 4.

Correlation between Anxiety and Noise Levels in Coronary Heart Disease Patients.

Variable	Anxiety	Noise Level
Anxiety	-	0.522 (P < 0.001)
Noise level	0.522 (P < 0.001)	-

Effects of Noise Reduction Management on Anxiety Symptoms

In this study, 138 patients who underwent noise reduction management showed significantly lower average ward noise levels and SAS scores than the pretreatment levels (P < 0.05; Table 5).

Table 5.

Comparative Analysis of Noise Levels and Self-Rating Anxiety Scale Scores before and after Noise Reduction.

Group	Noise Level (dB)	SAS Score
Before noise reduction (n = 138)	68.72 ± 5.64	51.99 ± 4.26
After noise reduction (n = 138)	57.48 ± 4.25	46.52 ± 3.48
t	13.354	11.682
P	<0.001	<0.001

SAS = Self-Rating Anxiety Scale.

DISCUSSION

CHD is recognized for its noncurative nature, which leads to persistent anxiety in most patients during disease coexistence. This anxiety-induced psychological stress promotes catecholamine and epinephrine secretion, which results in vasoconstriction, increased vascular resistance, elevated blood pressure, and heightened cardiovascular burden; such a phenomenon exacerbates clinical conditions and elevates the risk of adverse clinical outcomes.[2,15] Wang et al.[16] established anxiety as an independent risk factor for CHD development and progression. Multiple factors influence the development of anxiety in hospitalized patients. Noise serves as an environmental stressor that can provoke discomfort and irritability. When ward noise reaches certain intensity thresholds, it may induce psychological stress responses. Hospitalized patients already experiencing negative emotional states due to their underlying medical conditions become particularly vulnerable in uncontrolled acoustic environments, where intense noise exposure can amplify stress reactions and precipitate anxiety symptoms.[17,18] Consistent with this mechanism, Ok et al.[19] reported that “patients exposed to high-noise environments demonstrated worse anxiety states and sleep quality,” collectively suggesting that noise exposure may represent a risk factor for anxiety development in CHD patients.

Univariate logistic regression analysis identified poor sleep quality, multivessel disease, elevated hospital noise levels, and prior PCI history as significant risk factors for anxiety comorbidity in CHD patients. These findings confirm noise as a contributing factor rather than an independent determinant in anxiety development, with its effects being moderated by sleep quality, disease severity, and PCI history. CHD patients often present with substantial health-related apprehensions, which manifest as persistent worry and anxiety. When superimposed on this vulnerable psychological state, excessive environmental noise exposure exacerbates irritability and fear while concurrently inducing physiological manifestations, including tachycardia and hypertension, collectively elevating anxiety risk.[20] Furthermore, noise functions as a potent stressor that activates the hypothalamic–pituitary–adrenal axis, which stimulates cortisol secretion. Elevated cortisol levels enhance vascular sensitivity to catecholamines, which promotes vasoconstriction and increases vascular resistance and blood pressure. Concurrently, cortisol-mediated oxidative stress accelerates vascular endothelial damage, which aggravates the underlying coronary pathology. Sleep and emotional states exhibit a bidirectional relationship and often form a vicious cycle. Sleep deprivation disrupts physiological rhythms and circadian regulation, which leads to daytime fatigue, mental lethargy, and ultimately anxiety development.[21] Ward noise directly impairs sleep quality in CHD patients, with high noise levels correlating with poor sleep outcomes. Such a condition subsequently affects daytime functioning and emotional stability. Thus, noise indirectly contributes to anxiety through its detrimental effects on sleep architecture. Disease severity, as indicated by multivessel involvement, represents another crucial determinant. Patients with extensive coronary lesions (≥3 vessels) often develop exaggerated illness concerns, characterized by disproportionate worry, tension, and procedural phobia, which substantially increase vulnerability to anxiety comorbidity.[22] Similarly, prior PCI history independently predicts anxiety risk, likely due to reinforced illness perceptions from rehospitalization experiences. Patients with such a disease frequently develop procedure-related hypervigilance, which is characterized by intensified fears of disease progression and repeat revascularization, which further compound psychological burden.[23]

Following implementation of noise control measures, considerable reductions in ward noise levels and SAS scores were observed in CHD patients (P < 0.05), which suggests a potential association between noise management and improvement in anxiety symptoms. This effect may be attributed to the comprehensive approach employed: identification of noise sources combined with evidence retrieval from international and domestic medical databases led to the informed development of a scientifically grounded, feasible, and systematic noise reduction protocol. Strategic placement of “Noise Reduction” and “Quiet” signage enhanced awareness among patients and families, which promoted lowered vocal volumes and electronic device silencing and thereby effectively mitigated noise sources. Visitation restrictions minimized nonessential conversations and entry/exit frequency, which contributed to noise control. Medical equipment modifications reduced friction-induced noise, and strict adherence to the “Three-Quiet” protocol among healthcare staff further decreased iatrogenic noise pollution. The implementation of noise reduction protocols effectively mitigated acoustic pollution and created a tranquil hospitalization environment for CHD patients. This optimized setting promoted psychological equilibrium, alleviated irritability, and possibly contributed to the amelioration of anxiety symptoms or prevention of their development.[24] The single-arm pre–post design of this study precludes the complete exclusion of potential confounding from concurrent conventional treatments, nursing interventions, or other unmeasured variables. Consequently, randomized controlled trials with appropriate control groups must be conducted in the future to validate the causal relationship between noise reduction and anxiety improvement.

This study encountered several methodological limitations. Firstly, the single-arm pre–post design cannot rule out confounding from concurrent treatments or other unmeasured factors, which necessitates randomized controlled trials to establish causality between noise reduction and anxiety improvement. As a retrospective analysis, no formal sample size calculation was performed, which possibly limited statistical power. Secondly, using a small sample from a single-center restricted generalizability. In multivariate modeling, only variables significant in univariate analyses were included—although driven by sample size constraints and parsimony, this approach risks residual confounding (e.g., variables, such as age, sex, and BMI were excluded) and may overlook weakly associated predictors. Thus, findings are associative rather than causal. Future studies should use larger samples and prespecify covariates based on theoretical grounds. Thirdly, the broad age range (45–80 years) without stratification for patients over 75 probably introduced confounding. Fourthly, the uniform noise reduction implementation across wards without a control group prevented the exclusion of time-dependent confounders or Hawthorne effects. Fifthly, wide 95% CIs (e.g., PSQI: 3.323–16.266) indicated limited precision, likely due to sample size or outcome variability. Finally, although noise may indirectly affect anxiety via sleep impairment (PSQI), and expected correlations were observed, the sample size (N = 138) was insufficient for formal mediation analysis. Future longitudinal studies with larger samples should perform mediation analyses to clarify the causal pathway between noise exposure and psychological health.

CONCLUSION

Patients with CHD are prone to anxiety, with hospital noise being a potential risk factor. Poor sleep quality, multiple diseased vessels, high noise levels, and prior PCI history constitute risk factors. Clinical attention should be paid to patients’ emotional states, and noise reduction management can effectively alleviate anxiety.

Availability of data and materials

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Author contributions

Hui Chen collected and analyzed the data; both authors conducted the study; Qian Wang designed the study. Hui Chen and Qian Wang participated in drafting the manuscript, and both authors contributed to critical revision of the manuscript for important intellectual content. Both authors gave final approval of the version to be published. Both authors participated fully in the work, take public responsibility for appropriate portions of the content, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or completeness of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and was approved by Shaanxi Provincial People’s Hospital Ethics Committee (Approval No. 2024-027). All participants provided written informed consent.

Financial Support and Sponsorship

No.

Conflicts of Interest

The authors declare that there are no conflicts of interest.