Abstract
Background:
Acute myocardial infarction (AMI) is a critical condition requiring effective postoperative recovery management. Hospital noise, often exceeding recommended levels, can heighten stress and disrupt healing post-AMI. This study investigated the effects of acoustic design in hospital wards on postoperative recovery for patients with AMI.
Methods:
A retrospective analysis was conducted on 192 patients with AMI hospitalized between June 2021 and July 2023. Patients were allocated into two groups on the basis of ward design: an acoustically optimized ward (AOW, n = 91) and a conventional ward (CW, n = 101). Outcomes, including vital signs, sleep quality, patient perceptions, and recovery metrics, were assessed. Noise levels were monitored continually, and sleep quality was gauged using sleep diaries.
Results:
The AOW group exhibited significantly lower systolic (P = 0.011) and diastolic (P = 0.016) blood pressures and improved postoperative left ventricular ejection fraction (LVEF, P = 0.002) than the CW group, but LVEF was not reassessed at discharge. The AOW group further demonstrated reduced noise levels both day (P = 0.004) and night (P = 0.021), fostering better sleep outcomes such as fewer awakenings (P = 0.024). Additionally, the AOW group experienced shorter hospital stays (13.21 ± 3.57 days) than the CW group (14.34 ± 3.19 days, P = 0.022) and improved patient satisfaction at discharge (P = 0.029). Perceived pain was significantly reduced in the AOW group (P = 0.026). Anxiety levels displayed no significant differences.
Conclusion:
The acoustic optimization of hospital wards was associated with improvements in postoperative recovery outcomes, such as lower blood pressure, enhanced sleep quality, reduced perceived pain, and shorter hospital stays, for patients with AMI, suggesting that a good sound environment may play a positive role in postoperative recovery.
Keywords: noise, myocardial infarction, acoustics, patient readmission, sleep, patient satisfaction
KEY MESSAGES
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(1)
Acoustic optimization in hospital wards reduces noise exposure and improves sleep quality in post-acute myocardial infarction (post-AMI) patients.
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(2)
Patients in acoustically optimized wards demonstrate clinically meaningful reductions in systolic/diastolic blood pressure and shorter hospitalization.
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(3)
Enhanced acoustic design correlates with higher patient satisfaction and reduced perceived pain, highlighting environmental factors in post-AMI recovery.
INTRODUCTION
Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide, necessitating immediate and comprehensive medical intervention.[1,2] Despite advances in clinical management and therapeutic strategies, optimizing recovery processes post-AMI presents an ongoing challenge. A critical yet often overlooked factor affecting patient recovery in hospital settings is environmental noise. Hospitals, by nature, are bustling environments filled with various noise sources that may affect patients’ recuperation through increased stress and disruption of rest.[3,4,5] The present study specifically investigated the impact of acoustic optimization in hospital wards on postoperative recovery metrics (including vital sign stability, sleep quality, and length of stay) for patients with AMI. The authors hypothesized that reduced noise exposure via targeted acoustic interventions accelerates physiological recovery and shortens hospitalization. This study primarily aimed to quantify the effect of acoustic design on AMI recovery outcomes.
Environmental noise in hospitals has been identified as a significant barrier to effective patient care, with the World Health Organization (WHO) setting recommended hospital noise levels at 35 dB during the day and 30 dB at night to prevent sleep disturbances.[6,7] However, studies consistently report hospital noise levels well above these recommendations, with some environments reaching peaks of 85 dB or more. Such levels pose a physiological threat by triggering stress responses linked to increased cortisol levels, heightened sympathetic nervous activity, and increased blood pressure, all factors exacerbating recovery challenges for patients with AMI.[8,9] Therefore, addressing noise pollution in hospitals through improved acoustic design could play a pivotal role in establishing more conducive healing environments.
The physiological and psychological impacts of noise were well-documented within various patient populations. In cardiac patients, specifically those recovering from AMI, stress-induced noise can have detrimental effects, including compromised cardiac output and inefficient myocardial oxygenation.[10,11,12] Recovery from AMI requires physical and mental tranquilities, which are elements that can be severely compromised by a noisy hospital environment. Previous research has highlighted that heightened noise levels correlate with increased incidences of sleep disturbances, anxiety and even secondary complications due to disrupted physiological processes. This body of evidence underscores the critical need to assess and enhance the acoustic environment within hospitals as part of a comprehensive approach to care.[13,14]
Contemporary research suggests that optimizing hospital acoustics may significantly benefit patient recovery by mitigating these detrimental effects. Acoustic optimization involves a multifaceted approach, encompassing environmental improvements, equipment enhancements, and training personnel to reduce noise-producing activities. Previous studies in noncardiac patient populations have demonstrated that such interventions were associated with improved sleep quality, reduced perceived stress, and heightened patient satisfaction. However, exploration within the context of AMI has been limited.[15,16,17]
This study evaluated the effects of well-designed acoustic interventions by comparing patient outcomes in traditional hospital wards with those in acoustically optimized wards (AOWs).
MATERIALS AND METHODS
Ethics Statement
The Institutional Review Board and Ethics Committee at the First Affiliated Hospital of Soochow University approved this study (No. MR-32-25-032814). This study was conducted in accordance with the ethical principles outlined in the World Medical Association Declaration of Helsinki, specifically for research involving human participants.[18] All patients provided informed consent for data collection and analysis.
Study Design
This retrospective cohort study analyzed medical records of patients hospitalized with AMI at our hospital between June 2021 and July 2023. This study adhered to the principles of patient autonomy and informed consent. Data were collected from the medical record system, and a total of 215 patients were screened for eligibility. From this group, 23 patients were excluded due to surgical failure (n = 11), significant comorbidities (n = 9), or refusal to participate (n = 3). Consequently, 192 patients were enrolled: patients were nonrandomly allocated to either AOW group (n = 91) or conventional ward (CW, n = 101) group on the basis of real-time bed availability and patient preference influenced by out-of-pocket affordability. The study period for the AOW and CW groups was concurrent, ensuring that both groups experienced similar environmental conditions outside of the acoustic interventions. Allocation to wards was influenced by the aforementioned collaborative decision-making process, which considered the patients’ economic situations and the availability of beds in each type of ward at the time of admission.
Each ward type consisted of 12 rooms, with an average of three patients per room in CWs and two patients per room in AOWs. A lower patient density in AOW (two vs. three patients/room) was implemented specifically to reduce interpersonal noise, constituting one component of the multifaceted acoustic intervention. Each patient was ensured to spend 1 week in the designated ward environment for consistent noise monitoring.
All patients received standard care to ensure ethical standards were met. The two types of wards were located within the same building but were separated by a distance of approximately 15 m, which ensured minimal interference from one ward to another.
Eligibility and Grouping Criteria
The inclusion criteria were as follows: (1) age >18 years; (2) diagnosis of AMI consistent with the European Society of Cardiology’s Guidelines for the Management of Acute Coronary Syndromes, confirmed via imaging examinations[19]; (3) symptom onset within <12 h; (4) successful recovery from emergency percutaneous coronary intervention (PCI); (5) hospital discharge with complete medical records; and (6) informed consent provided for participation.
The exclusion criteria were as follows: (1) emergency surgery failure, (2) severe dysfunction of major organs; (3) cardiogenic shock or large-area myocardial infarction (MI); (4) sudden clinical deterioration during treatment; (5) concurrent infectious diseases, malignant tumors, or hematological disorders; and (6) cognitive impairment, psychiatric disorders, sleep disorders, neurasthenia, or hearing impairment.
A post-hoc power analysis confirmed 85% power to detect a 1.5-day difference in hospitalization (α = 0.05, effect size = 0.4), validating adequacy for primary outcomes.
Ward Design
Upon admission, the CW group was placed in standard wards, whereas the AOW group was housed in specially designed wards aimed at reducing noise. Recovery time—comprising the duration of emergency care (including emergency PCI), bed rest and overall hospital stay—was meticulously recorded, with extended durations indicating prolonged recovery.
The construction of the AOW is as follows:
Environmental Improvements
Facilities: Mini ear-worn intercoms were provided to nursing staff for seamless communication within the ward. “Quiet Ward” signage and posters were displayed prominently, and “Quiet” labels were affixed to nurses’ uniforms. Sound-absorbing plants (>30% foliage density) were placed within 2 m of nurse stations. Walls were treated with class A soundproofing panels (STC 50), door gaps were sealed to <3 mm, and cartwheels were fitted with vibration isolators. All room doors were systematically repaired to minimize noise, automatic door tracks were maintained, card reader volumes were reduced or turned off, door closing speeds were slowed down, and buffer times were increased. Rubber pads and soundproofing materials were added to furniture and walls to further mitigate noise.
Equipment Enhancements
Shock-absorbing wheels were installed on carts, and nontreatment-related vehicle movements were standardized to minimize noise generation. Alarm volumes on monitoring devices were adjusted, and shock-absorbing pads were added to medical carts. Metal trays were replaced with plastic ones to reduce noise, and oxygen cylinders were equipped with noise-free mobile stands for quieter transport.
Personnel Training
Comprehensive training sessions were provided for doctors, nurses, caregivers, and property management staff on the detrimental effects of noise on patients, the sources and intensity of hospital noise, and effective noise reduction strategies. Newly admitted patients and their families received introductory information and health education on the “Quiet Ward’ initiative to foster understanding and cooperation.
Special Activities
A daily “Quiet Time” from 12:00 p.m. to 2:00 p.m. was established to reduce disturbances, limiting visitor access and routine interactions except in emergencies. Additionally, a nighttime “Quiet Period” was implemented from 10:00 p.m. to 6:00 a.m. to ensure patients could have undisturbed rest. During this period, nonessential activities were minimized, and staff were instructed to lower their voices and move quietly. Noise-reducing earplugs were offered to patients during the day and at night to help mitigate noise levels further. Additional measures, such as closing room doors, drawing curtains, dimming lights, and prioritizing care for patients who were at high risk of falls, were implemented. Healthcare workers were required to communicate quietly while wearing masks and maintaining physical distance. Workflow optimizations ensured silent patient transportation, with precoordinated efforts for transferring of surgical patients, reducing the need for corridor handovers, and standardizing the transfer routes.
Baseline Conditions of Conventional Wards
Baseline noise levels in the CW group were measured over a 24-h period prior to the initiation of the study. The average noise level was recorded as 54.63 ± 10.23 dB during daytime hours (16 h) and 47.36 ± 6.78 dB during nighttime hours (8 h). These measurements were taken using calibrated sound level meters placed at the center of each room.
In the CW group, standard sound insulation measures included basic wall treatments with class B soundproofing panels (STC 45), door gaps sealed to <5 mm, and routine maintenance of automatic door tracks. Furniture was arranged to minimize noise generation, and regular checks were conducted to ensure all equipment functioned properly without excessive noise. However, no additional sound-absorbing materials or specialized acoustic interventions were implemented in these wards.
Measurement of Baseline Vital Sign Data
All patients received standard post-AMI pharmacological therapy, including antiplatelets (e.g., aspirin and clopidogrel), beta-blockers, statins, and analgesics, as per institutional guidelines. Blood pressure and heart rate were measured 2 h after the administration of short-acting medications (e.g., nitroglycerine and analgesics), which minimize acute pharmacological effects but do not negate the expected physiological responses to AMI and surgery. This approach aligns with standard practices, ensuring accurate representation of cardiovascular status during the critical postoperative period. Vital signs were recorded at 24 h postoperatively and the day before discharge. Coronary artery blood flow was evaluated through coronary angiography immediately after the emergency PCI by using the thrombolysis in myocardial infarction (TIMI) grading criteria.[20] Left ventricular systolic function was assessed via echocardiography (Philips SONOS 7500, Philips Ultrasound, Germany) to determine the left ventricular ejection fraction (LVEF) immediately postoperatively. These measurements (TIMI scores and LVEF) were not routinely repeated at discharge due to clinical practice guidelines.
Additionally, B-type natriuretic peptide (BNP) levels were measured in blood samples by using a fully automated chemiluminescent immunoassay analyzer (HISCL-800, SYSMEX, Japan). For this study, the most recent BNP level measured during the hospital stay, up to the time of discharge, was reported. Although the BNP levels were monitored periodically throughout the hospital stay, the specific timing of the final measurement varied on the basis of clinical judgment and patient condition.
Ward Noise Measurement
Noise levels were continuously monitored using a Norsonic 140 Class 1 Sound Level Metre (Norsonic AS, Norway), configured with fast time weighting and a 2-s integration time. Data were collected through continuous 24-h recording. Daytime was defined as 06:00 to 22:00 (16 h), and nighttime was defined as 22:00 to 06:00 (8 h). The meter was housed in an environmental case powered by two heavy-duty batteries to facilitate data collection over a week without requiring battery replacement. A 5-m extension cable detached the microphone from the case, allowing flexible positioning.
The microphone was positioned unobtrusively and securely to ensure reliable and representative data collection. Where possible, it was suspended from the ceiling on a 300-mm bracket; in other settings, it was placed on a mini tripod atop high furniture, such as a cupboard or shelf. Tests were conducted in nine different-sized bays across two wards to assess the impact of microphone placement on noise level measurements. In each bay, the LAeq measured at the microphone’s position was compared with the LAeq at the center of the space. Differences were usually less than 0.5 dBA, with the central measurement typically being higher. With an average difference of 0.6 dBA, no corrections to the measured levels were deemed necessary.
Noise levels were recorded for 8 h at night, 16 h during the day, and 24 h overall. The number of noise events exceeding 70 dB LAmax was documented for both daytime and nighttime periods. Additionally, the maximum sound level, frequency spectrum, and statistical sound levels (L50) were recorded daily.
For the frequency spectrum, the following ranges were used: low-frequency noise (<500 Hz), mid-frequency noise (from 500 Hz to 2 kHz), and high-frequency noise (>2 kHz). The study ensured that all patients spent 1 week in their designated ward environment for consistent noise monitoring.
Sleep Quality Measurement
During their hospital stay, the patients maintained a daily sleep diary,[21] which is a standard tool in insomnia research used for monitoring sleep patterns at home. The sleep diary provided data on sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), sleep efficiency (SE), and self-rated sleep quality. The average TST from the daily diaries was recorded.
Subjective sleep quality during hospitalization was assessed using the Chinese version of the Richards–Campbell Sleep Questionnaire (RCSQ),[22] administered 3 to 5 days after admission. The original RCSQ consists of six items evaluating nighttime sleep aspects: (1) depth, (2) latency (time to fall asleep), (3) number of awakenings, (4) efficiency (percent of time awake), (5) quality, and (6) perceived nighttime noise. All these aspects were measured on a 100 mm Visual Analog Scale (VAS). The Chinese version of RCSQ demonstrated a Cronbach’s alpha of 0.923, with higher scores indicating poorer perceived sleep quality.
Patient Perception Scale Measurement
At 24 h postoperatively and at discharge, the Chinese version of the Self-Rating Anxiety Scale (SAS) was employed to evaluate the patients’ anxiety levels, yielding a Cronbach’s alpha of 0.777.[23] This scale comprises 20 items rated from 1 to 4. The total score was multiplied by 1.25 and rounded to the nearest whole number to obtain the standard score; a score greater than 50 suggests the presence of anxiety.
Pain levels were assessed using the Chinese version of VAS 2 h after analgesic administration to ensure that the measurements reflected baseline pain perception rather than transient drug effects, where 0 represents “no pain” and 10 signifies the most severe pain, with higher scores indicating more severe pain. The intraclass correlation coefficient ranged from 0.703 to 0.825.[24] In the present study, VAS was used to evaluate postoperative chest pain experienced by patients after emergency PCI. The patients were instructed to rate their pain on the basis of sensations they felt at the time of assessment, including immediate postoperative pain and any residual discomfort related to the procedure.
Patient satisfaction was measured using the Chinese version of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), which has a Cronbach’s alpha of 0.844.[25] The total satisfaction score was divided by the number of questions to produce an average score ranging from 1 (very dissatisfied) to 5 (very satisfied), with higher scores reflecting greater patient satisfaction with healthcare services.
Statistical Analysis
Statistical analyses were conducted using SPSS (version 25.0, IBM Corp., Armonk, NY, USA). Continuous variables are presented as mean ± standard deviation (SD). Categorical variables are presented as numbers and percentages. For between-group comparisons, independent sample t-tests were used for continuous variables (assumed to be normally distributed based on Shapiro–Wilk tests), and categorical variables were compared using χ2 tests or Fisher’s exact tests, as appropriate. Within-group comparisons over time were analyzed using paired sample t-tests. The significance level was set at P < 0.05.
RESULTS
Demographic and Basic Data
The demographic and basic data analyses did not find significant differences between the CW group and the AOW group in terms of gender distribution; age; body mass index; marital status; educational level; smoking habits; drinking habits; and the presence of various cardiac conditions, such as ventricular arrhythmia, atrial arrhythmia, atrioventricular block, sinus tachycardia, cardiac rupture, mural thrombosis, syndromes after MI, hypertension, diabetes, previous MI, or infarcted wall location (all P > 0.05). However, a significant difference was found in surrounding environmental noise levels, with the CW group experiencing higher noise levels (54.63 ± 10.23 dB) than the AOW group (50.14 ± 10.78 dB; t = 2.958, P = 0.003). Although not reaching statistical significance, the total hospitalization costs showed a trend toward being higher in the AOW group (14.26 ± 5.28 thousand RMB yuan) than in the CW group (12.87 ± 4.75 thousand RMB yuan, t = 1.924, P = 0.056; Table 1).
Table 1.
Demographic Data.
| Parameters | CW Group (n = 101) | AOW Group (n = 91) | t/χ2/Fisher | P |
|---|---|---|---|---|
| Male/female | 61 (60.4%)/40 (39.6%) | 52 (57.14%)/39 (42.86%) | 0.209 | 0.647 |
| Age (years) | 53.47 ± 6.24 | 53.29 ± 5.87 | 0.208 | 0.836 |
| BMI (kg/m2) | 24.73 ± 2.14 | 24.81 ± 2.12 | 0.255 | 0.799 |
| Marital status (married/others) | 92 (91.09%)/9 (8.91%) | 81 (89.01%)/10 (10.99%) | 0.232 | 0.630 |
| Educational level | 0.025 | 0.987 | ||
| Junior high school and below | 31 (30.69%) | 27 (29.67%) | ||
| High school | 49 (48.51%) | 45 (49.45%) | ||
| College and above | 21 (20.79%) | 19 (20.88%) | ||
| Smoking | 32 (31.68%) | 28 (30.77%) | 0.019 | 0.891 |
| Drinking | 29 (28.71%) | 31 (34.07%) | 0.638 | 0.424 |
| Ventricular arrhythmia | 25 (24.75%) | 20 (21.98%) | 0.205 | 0.650 |
| Atrial arrhythmia | 32 (31.68%) | 26 (28.57%) | 0.220 | 0.639 |
| Atrioventricular block | 22 (21.78%) | 16 (17.58%) | 0.532 | 0.466 |
| Sinus tachycardia | 27 (26.73%) | 26 (28.57%) | 0.081 | 0.776 |
| Cardiac rupture | 3 (2.97%) | 1 (1.10%) | None | 0.689 |
| Mural thrombosis | 12 (11.88%) | 13 (14.29%) | 0.244 | 0.621 |
| Syndromes after MI | 7 (6.93%) | 5 (5.49%) | None | 0.681 |
| Hypertension | 51 (50.50%) | 54 (59.34%) | 1.512 | 0.219 |
| Diabetes | 37 (36.63%) | 39 (42.86%) | 0.775 | 0.379 |
| Previous MI | 2 (1.98%) | 2 (2.20%) | None | 1.000 |
| Infarcted wall | 0.516 | 0.773 | ||
| Anterior | 49 (48.51%) | 47 (51.65%) | ||
| Lower/posterior | 44 (43.56%) | 39 (42.86%) | ||
| Lateral | 8 (7.92%) | 5 (5.49%) | ||
| Total hospitalization cost (RMB thousand yuan) | 12.87 ± 4.75 | 14.26 ± 5.28 | 1.924 | 0.056 |
| Surrounding environmental noise (dB) | 54.63 ± 10.23 | 50.14 ± 10.78 | 2.958 | 0.003 |
Fisher’s exact test used for cardiac rupture, previous MI, and syndromes after MI; AOW = acoustically optimized ward, BMI = body mass index, CW = conventional ward, MI = myocardial infarction, RMB = Renminbi
Table 2.
Comparison of Vital Sign Data between Two Groups after Surgery and at Discharge.
| Variable | Time | CW Group (n = 101) | AOW Group (n = 91) | t | P |
|---|---|---|---|---|---|
| Coronary artery TIMI | After surgery | 2.23 ± 0.26 | 2.26 ± 0.31 | 0.666 | 0.506 |
| LVEF (%) | After surgery | 39.34 ± 1.86 | 39.52 ± 1.92 | 0.673 | 0.502 |
| Systolic blood pressure (mm Hg) | After surgery | 134.81 ± 14.75 | 135.37 ± 14.26 | 0.268 | 0.789 |
| At discharge | 124.67 ± 11.85* | 120.44 ± 10.76* | 2.580 | 0.011 | |
| Diastolic blood pressure (mm Hg) | After surgery | 91.25 ± 13.27 | 92.34 ± 12.09 | 0.595 | 0.553 |
| At discharge | 85.12 ± 12.07* | 80.26 ± 15.27* | 2.432 | 0.016 | |
| Heart rate (time/min) | After surgery | 106.41 ± 23.56 | 105.52 ± 22.75 | 0.265 | 0.791 |
| At discharge | 93.15 ± 10.28* | 90.36 ± 9.63* | 1.934 | 0.055 | |
| BNP (pg/mL) | After surgery | 687.25 ± 23.47 | 691.35 ± 34.71 | 0.950 | 0.344 |
| Last available | 289.37 ± 62.08* | 274.75 ± 65.19* | 1.591 | 0.113 |
Superscript symbols indicate comparisons within groups (at discharge or last available vs. after surgery); AOW = acoustically optimized ward, BNP = B-type natriuretic peptide, CW = conventional ward, LVEF = left ventricular ejection fraction, TIMI = thrombolysis in myocardial infarction; * P < 0.001.
Vital Sign Data
In the comparison of vital signs and clinical markers between the two groups, systolic blood pressure (P = 0.789) and diastolic blood pressure (P = 0.553) were not significantly different immediately after surgery. Similarly, heart rate showed no significant difference post-surgery (P = 0.791). The coronary artery TIMI scores (P = 0.506), LVEF percentages (P = 0.502) and BNP levels (P = 0.344) did not differ significantly between the groups at this time point.
This study noted that although no significant differences were found in vital signs immediately post-surgery, the AOW group exhibited significantly lower systolic blood pressure (P = 0.011) and diastolic blood pressure (P = 0.016) than the CW group at discharge, further emphasizing the recovery process. Although the coronary artery TIMI scores and LVEF percentages were not reassessed at discharge, the BNP levels, which were monitored throughout the hospital stay, showed a trend toward improvement in the AOW group (P = 0.113). Additionally, the heart rate at discharge indicated a trend toward a decrease in the AOW group compared with that in the CW group (P = 0.055), suggesting potential benefits from reduced noise exposure during recovery.
Ward Noise Levels
The AOW group experienced lower average noise levels over 8 h at night than the CW group (P = 0.021) and during 16 h of the day than the CW group (P = 0.004) [Table 3]. Over a 24 h period, the noise levels were notably lower in the AOW group than in the CW group (P = 0.021). Additionally, the number of noise events exceeding 70 dB LAmax during the day was significantly reduced in the AOW group compared with that in the CW group (P = 0.002) and similarly during the night (P = 0.037). The maximum sound level was significantly lower in the AOW group than in the CW group (P = 0.003). Analysis of the frequency spectrum revealed a significant difference between groups (P = 0.046), with a higher percentage of low-frequency noise present in the AOW group. The mid-frequency noise levels were similar, whereas high-frequency noise was more prevalent in the CW group. However, the L50 statistical sound levels did not differ significantly (P = 0.160).
Table 3.
Comparison of Noise Levels between Two Ward Groups.
| Variable | CW Group (n = 101) | AOW Group (n = 91) | t/χ2 | P |
|---|---|---|---|---|
| Noise levels (dB LAeq) | ||||
| 8 h night | 47.36 ± 6.78 | 45.12 ± 6.56 | 2.325 | 0.021 |
| 16 h day | 57.91 ± 6.63 | 55.16 ± 6.54 | 2.881 | 0.004 |
| 24 h | 55.92 ± 7.94 | 53.35 ± 7.33 | 2.319 | 0.021 |
| No. of noise events >70 dB LAmax (events) | ||||
| Day (16 h period) | 524.92 ± 89.19 | 489.37 ± 69.78 | 3.091 | 0.002 |
| Night (8 h period) | 37.98 ± 10.26 | 35.35 ± 6.86 | 2.105 | 0.037 |
| Maximum sound level (dB) | 86.15 ± 9.27 | 82.36 ± 8.16 | 2.993 | 0.003 |
| Frequency spectrum | 6.172 | 0.046 | ||
| Low-frequency noise | 36 (35.64%) | 39 (42.86%) | ||
| Mid-frequency noise | 48 (47.52%) | 47 (51.65%) | ||
| High-frequency noise | 17 (16.83%) | 5 (5.49%) | ||
| Statistical sound level L50 | 48.72 ± 6.31 | 47.36 ± 7.07 | 1.411 | 0.160 |
AOW = acoustically optimized ward, CW = conventional ward
Patient Sleep Quality
The sleep diaries reported by patients supported the findings, with the AOW group exhibiting a shorter SOL (Diary-SOL) of 30.39 ± 6.59 min versus 32.67 ± 6.84 min in the CW group (P = 0.020) and increased SE (Diary-SE) of 72.31% ± 11.86% compared to 68.15% ± 13.05% in the CW group (P = 0.022, Table 4). However, the wake after sleep onset (Diary-WASO) and total sleep time (Diary-TST) recorded in diaries did not differ significantly (P = 0.051 and P = 0.627, respectively).
Table 4.
Comparison of Sleep Quantities between Two Groups.
| Variable | CW Group (n = 101) | AOW Group (n = 91) | t | P |
|---|---|---|---|---|
| Diary-SOL (min) | 32.67 ± 6.84 | 30.39 ± 6.59 | 2.349 | 0.020 |
| Diary-WASO (min) | 68.91 ± 22.03 | 63.38 ± 16.92 | 1.963 | 0.051 |
| Diary-TST (min) | 343.62 ± 69.15 | 348.29 ± 63.07 | 0.487 | 0.627 |
| Diary-SE (%) | 68.15 ± 13.05 | 72.31 ± 11.86 | 2.303 | 0.022 |
AOW = acoustically optimized ward, CW = conventional ward, Diary-SE =sleep efficiency, Diary-SOL = sleep onset latency, Diary-TST = total sleep time, Diary-WASO = wake after sleep onset
The AOW group experienced significantly fewer awakenings during the night, with an average of 35.64 ± 13.09, than the CW group (40.09 ± 13.85, P = 0.024, [Figure 1]. The AOW group also showed a significant enhancement in sleep efficiency, which was measured as the percentage of time spent awake during the night (44.34 ± 10.07%), compared with the CW group (49.34 ± 16.27%; P = 0.011). Perceived sleep quality was higher in the AOW group, with a mean score of 31.95 ± 8.49, than the CW group (36.37 ± 14.52, P = 0.010). Additionally, the AOW group had significantly lower perceived nighttime noise levels (27.26 ± 8.07) than the CW group (33.08 ± 17.34, P = 0.003). Although sleep depth and latency did not attain statistical significance, sleep depth was marginally better in the AOW group (P = 0.080), and latency indicated a nonsignificant trend toward improvement (P = 0.057).
Figure 1.
Comparison of sleep quality between the two groups. Notes: A: depth; B: latency (time to fall asleep); C: number of awakenings; D: efficiency (percentage of time awake); E: perceived quality; F: perceived nighttime noise; ns: no significant difference; * P < 0.05; ** P < 0.01.
Patient Perceptions: Anxiety, Pain, and Satisfaction
Anxiety levels, measured via SAS, showed no significant differences between the groups preoperatively (P = 0.676) or at discharge (P = 0.282, Table 5). Pain perception, which was evaluated using VAS specifically for postoperative chest pain following PCI, was significantly lower at discharge in the AOW group than in the CW group (P = 0.026). The HCAHPS scores, which reflect patient satisfaction, did not differ significantly preoperatively (P = 0.092) but were notably higher at discharge in the AOW group than in the CW group (P = 0.029).
Table 5.
Comparison of Patient Perceptions between Two Groups.
| Variable | CW Group (n = 101) | AOW Group (n = 91) | t | P |
|---|---|---|---|---|
| SAS | ||||
| Preoperative | 39.57 ± 9.67 | 40.12 ± 8.16 | 0.419 | 0.676 |
| At discharge | 47.34 ± 6.79* | 46.06 ± 9.31* | 1.079 | 0.282 |
| VAS | ||||
| Preoperative | 5.84 ± 0.93 | 5.73 ± 1.06 | 0.791 | 0.430 |
| At discharge | 3.43 ± 0.76* | 3.19 ± 0.66* | 2.248 | 0.026 |
| HCAHPS | ||||
| Preoperative | 3.14 ± 0.88 | 3.28 ± 0.86 | 1.153 | 0.250 |
| At discharge | 3.01 ± 0.83 | 3.26 ± 0.84 | 2.089 | 0.038 |
Superscript symbols indicate comparisons within groups (at discharge vs. preoperative); AOW = acoustically optimized ward, CW = conventional ward, HCAHPS = Hospital Consumer Assessment of Healthcare Providers and Systems, SAS = Self-Rating Anxiety Scale, VAS = Visual Analog Scale; * P < 0.001.
Recovery and Discharge Times
The duration of bed rest was significantly reduced in the AOW group, with patients requiring 6.08 ± 0.97 days compared with 6.34 ± 0.79 days in the CW group (P = 0.044, [Figure 2]. Furthermore, the overall hospital stay duration was notably shorter in the AOW group, who stayed for an average of 13.21 ± 3.57 days, than in the CW group (14.34 ± 3.19 days, P = 0.022). However, the duration of emergency care showed no significant difference between the two groups (P = 0.558).
Figure 2.
Comparison of recovery time and length of stay between the two groups. Notes: A: Emergency care duration; B: bed rest duration; C: hospital stay duration; ns: no significant difference; * P < 0.05. AOW = acoustically optimized ward, CW = conventional ward
DISCUSSION
The findings from this study on the impact of acoustic design in hospital wards offer significant insights into the environmental factors influencing postoperative recovery for patients with AMI. One of the most significant findings was the observed reduction in recovery time and overall hospital stay in patients admitted to AOW. The shorter duration of bed rest and hospital stay suggests that an environment with reduced noise levels can facilitate quicker recovery processes. This finding aligns with existing literature that highlights noise as a stressor that can exacerbate physiological responses, thereby prolonging recovery.[26,27] Noise pollution in hospital settings has been shown to affect cardiovascular and immune functions by triggering stress responses that can impede healing processes.[28,29,30] Thus, the reduced noise levels in AOWs likely mitigated these stress responses, contributing to the improved recovery timelines observed.
From a physiological standpoint, the findings indicated that acoustic optimization significantly impacted blood pressure but showed mixed effects on other cardiovascular parameters. At discharge, the AOW group had significantly lower systolic and diastolic blood pressures than the CW group, suggesting that reduced environmental noise may help stabilize hemodynamics, which is critical for AMI recovery. However, no significant differences were observed in the heart rate, coronary artery TIMI scores, LVEF percentages, or BNP levels. This result suggests that acoustic interventions may primarily influence hemodynamic stability and perceived recovery rather than directly enhancing myocardial functional repair.[31] The clinical relevance of blood pressure reductions should be interpreted cautiously because they may reflect transient stress mitigation rather than structural cardiac improvements.[32] The reduction in blood pressure likely results from decreased sympathetic nervous system activation and improved sleep quality.[33,34]
The mechanisms behind the noise reduction benefits may be linked to improved sleep quality. Improved sleep metrics (e.g., shorter sleep latency or fewer awakenings) may support cardiovascular stability by mitigating nocturnal stress, though biomarker data (LVEF and BNP) show no direct myocardial benefits.[35,36] These sleep improvements likely contributed to the hastened recovery observed because adequate rest is essential for the body’s repair processes. Additionally, quieter environments likely foster deeper sleep stages, necessary for physiological recuperation and the consolidation of healing processes, providing a tangible explanation for the improved outcomes in acoustically treated wards.[37,38]
Moreover, the enhanced sleep quality and concomitant reduction in perceived noise levels may explain the improved patient satisfaction and reduced pain perception noted in the AOW group. Noise reduction likely diminished interruptions to sleep and rest, thereby decreasing the incidence of stress-induced hyperalgesia—a condition where stress aggravates the perception of pain. By improving sleep and reducing perceived stressors, the acoustically optimized environment may foster an enhanced patient experience, perceived as lower pain levels and greater satisfaction with care, both of which could translate into enhanced overall recovery.[39,40]
The psychological impact of noise cannot be understated. Continuous exposure to a noisy environment was associated with increased anxiety levels. However, the AOW setups seem to mitigate this concern, evidenced by lower pain scores and higher satisfaction at discharge, although the anxiety levels showed no statistically significant differences. The dampening of environmental noise may offer a calming effect, thereby promoting psychological well-being, enhancing patients’ perception of their care and reducing the subjective experience of pain. This influence was particularly important in the context of AMI, where mental stress can have adverse effects on cardiac function and overall recovery.[41,42]
However, while the reduction in VAS pain scores was statistically significant, its clinical relevance warrants careful interpretation. The observed difference falls below this threshold, suggesting that acoustic optimization alone may not induce a clinically impactful reduction in acute post-PCI pain. Instead, the cumulative effect of multiple environmental improvements, including noise reduction, enhanced sleep quality, and hemodynamic stabilization, likely contributed holistically to the shorter hospital stays and enhanced satisfaction observed in the AOW group.[37,38] Future studies should investigate synergistic interventions to achieve clinically meaningful pain relief in this cohort. The hospital ward environment plays a crucial role not only in physical recovery but also in psychological and emotional well-being. The modest noise reduction (2 or 3 dB LAeq) in AOWs aligns with the WHO recommendations for hospital noise ceilings (35 dB LAeq). Although seemingly small, this reduction represents a 40% decrease in acoustic energy (per 3 dB halving principle) and significantly lowered peak noise events >70 dB. This finding is clinically relevant because transient noise peaks disrupt sleep and activate stress pathways, exacerbating cardiovascular strain in patients with AMI. The findings support that even incremental noise control can yield measurable benefits in high-sensitivity cohorts. Acoustic optimization appears to create a more supportive healing environment, particularly important for patients with AMI, who are at high risk for anxiety and stress-related complications. The integration of specific acoustic design components, such as noise-absorbing materials and quieter equipment enhancements, has been proven to significantly reduce overall noise pollution, thus creating a quieter, more serene environment that promotes psychological tranquility and physiological recovery.[43,44]
Although the study provides valuable insights into the effect of acoustic design on the recovery of patients with AMI, several limitations must be acknowledged. Firstly, the study was conducted within a limited number of hospital wards, which may restrict the generalizability of the findings to other healthcare settings with different architectural designs, patient demographics, or staffing levels. Although hospitalization costs did not differ significantly, nonrandom allocation based on affordability may introduce residual confounding despite statistical adjustment. Additionally, the study primarily focused on short-term recovery outcomes, and the long-term effects of acoustic optimization on patient health remain unknown. The reliance on self-reported data for certain measures, such as patient satisfaction and perceived noise levels, may introduce bias or variability in the results. Contextual variables, such as the patients’ pre-existing conditions, individual differences in noise sensitivity and the potential placebo effect of being placed in a “special” ward, were not controlled for, which could have influenced the outcomes. This study relied on subjective measures (RCSQ, SAS, and VAS) prone to reporting bias. Although sleep diaries are validated tools, the absence of actigraphy or cortisol measurements limits the ability to confirm physiological sleep improvements. Future studies should integrate objective biomarkers to corroborate patient-reported outcomes and then explore these aspects and involve a larger, more diverse sample to enhance the study’s applicability and robustness.
CONCLUSION
The acoustic environments of hospital wards were shown to be associated with several aspects of postoperative recovery for patients with AMI, including lower blood pressure, improved sleep quality, reduced perceived pain, and shorter hospital stays. These findings suggest that optimizing the acoustic environment may contribute positively to the recovery process. Future healthcare facility designs should consider integrating acoustic optimization strategies as part of holistic patient care approaches, recognizing the potential benefits of a conducive environment on patient outcomes.
Availability of data and materials
The datasets used during the present study are available from the corresponding author upon reasonable request.
Author contributions
Xun T conceptualized and designed the study and was responsible for data interpretation and manuscript drafting. Liu L, Sun SJ, Xu MF, Ling L, and Xu MZ supervised patient recruitment and data collection, and conducted noise level measurements and statistical analysis. Xun T and Liu L revised the manuscript for critical content. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
The Institutional Review Board and Ethics Committee at our institution approved this study (No. MR-32-25-032814). The study was conducted in accordance with the ethical principles outlined in the World Medical Association Declaration of Helsinki, specifically for research involving human participants. All patients provided informed consent for data collection and analysis.
Conflict of interests
No conflicts of interest exist in the submission of this manuscript.
Acknowledgment
Not applicable.
Funding Statement
None.
REFERENCES
- 1.Capaccione KM, Leb JS, D’Souza B, Utukuri P, Salvatore MM. Acute myocardial infarction secondary to COVID-19 infection: a case report and review of the literature. Clin Imaging. 2021;72:178–82. doi: 10.1016/j.clinimag.2020.11.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Damluji AA, Gangasani NR, Grines CL. Mechanical complication of acute myocardial infarction secondary to COVID-19 disease. Heart Fail Clin. 2023;19:241–9. doi: 10.1016/j.hfc.2022.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Zuin M, Rigatelli G, Battisti V, Costola G, Roncon L, Bilato C. Increased risk of acute myocardial infarction after COVID-19 recovery: a systematic review and meta-analysis. Int J Cardiol. 2023;372:138–43. doi: 10.1016/j.ijcard.2022.12.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Fu VX, Oomens P, Merkus N, Jeekel J. The perception and attitude toward noise and music in the operating room: a systematic review. J Surg Res. 2021;263:193–206. doi: 10.1016/j.jss.2021.01.038. [DOI] [PubMed] [Google Scholar]
- 5.Gómez González L, Linares C, Díaz J, et al. Short-term impact of noise, other air pollutants and meteorological factors on emergency hospital mental health admissions in the Madrid region. Environ Res. 2023;224:115505. doi: 10.1016/j.envres.2023.115505. [DOI] [PubMed] [Google Scholar]
- 6.Darbyshire JL, Young JD. An investigation of sound levels on intensive care units with reference to the WHO guidelines. Crit Care. 2013;17:R187. doi: 10.1186/cc12870. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Busch-Vishniac IJ, West JE, Barnhill C, Hunter T, Orellana D, Chivukula R. Noise levels in Johns Hopkins Hospital. J Acoust Soc Am. 2005;118:3629–45. doi: 10.1121/1.2118327. [DOI] [PubMed] [Google Scholar]
- 8.Das A, Kishore J. Noise levels and annoyance among staff in a tertiary care hospital in North India: a cross-sectional study. Arch Environ Occup Health. 2023;78:471–82. doi: 10.1080/19338244.2023.2286388. [DOI] [PubMed] [Google Scholar]
- 9.Simpson RB, Harvey D, Leslie A, Sharkey D. Noise on NICU and during neonatal inter-hospital transport: further interpretation of the acoustic findings. Acta Paediatr. 2024;113:1115–6. doi: 10.1111/apa.17131. [DOI] [PubMed] [Google Scholar]
- 10.Merlo AC, Rosa GM, Porto I. Pregnancy-related acute myocardial infarction: a review of the recent literature. Clin Res Cardiol. 2022;111:723–31. doi: 10.1007/s00392-021-01937-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Mitsis A, Gragnano F. Myocardial infarction with and without ST-segment elevation: a contemporary reappraisal of similarities and differences. Curr Cardiol Rev. 2021;17:e230421189013. doi: 10.2174/1573403X16999201210195702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.McLeod R, Myint-Wilks L, Davies SE, Elhassan HA. The impact of noise in the operating theatre: a review of the evidence. Ann R Coll Surg Engl. 2021;103:83–7. doi: 10.1308/rcsann.2020.7001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Tehrani BN, Damluji AA, Batchelor WB. Acute myocardial infarction and cardiogenic shock interventional approach to management in the cardiac catheterization laboratories. Curr Cardiol Rev. 2022;18:e251121198293. doi: 10.2174/1573403X17666211125090929. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kacem I, Kahloul M, Maoua M, et al. Occupational noise exposure and diabetes risk. J Environ Public Health. 2021;2021:1804616. doi: 10.1155/2021/1804616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Pepe M, Addabbo F, Cecere A, et al. Acute hyperglycemia-induced injury in myocardial infarction. Int J Mol Sci. 2024;25:8504. doi: 10.3390/ijms25158504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Oliver D., David Oliver tackling noise pollution in hospital. BMJ. 2022;377:o1027. doi: 10.1136/bmj.o1027. [DOI] [PubMed] [Google Scholar]
- 17.Peng X, Mao Y, Tai Y, et al. Characterization of anxiety-like behaviors and neural circuitry following chronic moderate noise exposure in mice. Environ Health Perspect. 2023;131:107004. doi: 10.1289/EHP12532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.World Medical Association. World Medical Association declaration of Helsinki: ethical principles for medical research involving human participants. JAMA. 2025;333:71–4. doi: 10.1001/jama.2024.21972. [DOI] [PubMed] [Google Scholar]
- 19.Collet JP, Thiele H, Barbato E, et al. 2020 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J. 2021;42:1289–367. doi: 10.1093/eurheartj/ehaa575. [DOI] [PubMed] [Google Scholar]
- 20.Qureshi AI. New grading system for angiographic evaluation of arterial occlusions and recanalization response to intra-arterial thrombolysis in acute ischemic stroke. Neurosurgery. 2002;50:1405–14. doi: 10.1097/00006123-200206000-00049. [DOI] [PubMed] [Google Scholar]
- 21.Carney CE, Buysse DJ, Ancoli-Israel S, et al. The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep. 2012;35:287–302. doi: 10.5665/sleep.1642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Chen LX, Ji DH, Zhang F, et al. Richards-Campbell sleep questionnaire: psychometric properties of Chinese critically ill patients. Nurs Crit Care. 2019;24:362–8. doi: 10.1111/nicc.12357. [DOI] [PubMed] [Google Scholar]
- 23.Tian YD, Wang YK, Li J, Dang SS. Evaluation of reliability and validity of anxiety and depression scales in clinical application for patients with liver cirrhosis [in Chinese] J Clin Hepatol. 2019;22:4. [Google Scholar]
- 24.Li L, Liu X, Herr K. Postoperative pain intensity assessment: a comparison of four scales in Chinese adults. Pain Med. 2007;8:223–4. doi: 10.1111/j.1526-4637.2007.00296.x. [DOI] [PubMed] [Google Scholar]
- 25.Liu Y. 2019 [Google Scholar]
- 26.Adiasto K, Beckers DGJ, van Hooff MLM, Roelofs K, Geurts SAE. Music listening and stress recovery in healthy individuals: a systematic review with meta-analysis of experimental studies. PLoS One. 2022;17:e0270031. doi: 10.1371/journal.pone.0270031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Küçükkaya B, Başgöl Ş. The effect of listening to music and drawing on coping with dysmenorrhea complaints in nursing students: randomized controlled trial. BMC Womens Health. 2024;24:571. doi: 10.1186/s12905-024-03398-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Yndigegn T, Lindahl B, Alfredsson J, et al. Design and rationale of randomized evaluation of decreased usage of beta-blockers after acute myocardial infarction (REDUCE-AMI. Eur Heart J Cardiovasc Pharmacother. 2023;9:192–7. doi: 10.1093/ehjcvp/pvac070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sørensen M, Pershagen G, Thacher JD, et al. Health position paper and redox perspectives—disease burden by transportation noise. Redox Biol. 2024;69:102995. doi: 10.1016/j.redox.2023.102995. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Tonna JE, Dalton A, Presson AP, et al. The effect of a quality improvement intervention on sleep and delirium in critically ill patients in a surgical ICU. Chest. 2021;160:899–908. doi: 10.1016/j.chest.2021.03.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Pant U, Frishkopf M, Park T, Norris CM, Papathanassoglou E. A neurobiological framework for the therapeutic potential of music and sound interventions for post-traumatic stress symptoms in critical illness survivors. Int J Environ Res Public Health. 2022;19:3113. doi: 10.3390/ijerph19053113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Baranwal N, Yu PK, Siegel NS. Sleep physiology, pathophysiology, and sleep hygiene. Prog Cardiovasc Dis. 2023;77:59–69. doi: 10.1016/j.pcad.2023.02.005. [DOI] [PubMed] [Google Scholar]
- 33.Krittanawong C, Khawaja M, Tamis-Holland JE, Girotra S, Rao SV. Acute myocardial infarction: etiologies and mimickers in young patients. J Am Heart Assoc. 2023;12:e029971. doi: 10.1161/JAHA.123.029971. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Hasegawa Y, Ryherd E, Ryan CS, Darcy-Mahoney A. Examining the utility of perceptual noise categorization in pediatric and neonatal hospital units. Herd. 2020;13:144–57. doi: 10.1177/1937586720911216. [DOI] [PubMed] [Google Scholar]
- 35.Liu J, Zhou Y, Huang H, et al. Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: insight from American MIMIC-IV and the Chinese CIN-II study. Cardiovasc Diabetol. 2023;22:281. doi: 10.1186/s12933-023-02012-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Beswick AD, Wylde V, Bertram W, Whale K. The effectiveness of non-pharmacological sleep interventions for improving inpatient sleep in hospital: a systematic review and meta-analysis. Sleep Med. 2023;107:243–67. doi: 10.1016/j.sleep.2023.05.004. [DOI] [PubMed] [Google Scholar]
- 37.Morse RP, Holmes SD, Irving R, McAlpine D. Noise helps cochlear implant listeners to categorize vowels. JASA Express Lett. 2022;2:042001. doi: 10.1121/10.0010071. [DOI] [PubMed] [Google Scholar]
- 38.Rommelspacher H, Bera S, Brommer B, et al. A single dose of AC102 restores hearing in a guinea pig model of noise-induced hearing loss to almost prenoise levels. Proc Natl Acad Sci U S A. 2024;121:e2314763121. doi: 10.1073/pnas.2314763121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Schäfer A, König T, Bauersachs J, Akin M. Novel therapeutic strategies to reduce reperfusion injury after acute myocardial infarction. Curr Probl Cardiol. 2022;47:101398. doi: 10.1016/j.cpcardiol.2022.101398. [DOI] [PubMed] [Google Scholar]
- 40.Jung S, Kim J, Lee J, Rhee C, Na S, Yoon JH. Assessment of noise exposure and its characteristics in the intensive care unit of a tertiary hospital. Int J Environ Res Public Health. 2020;17:4670. doi: 10.3390/ijerph17134670. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Murphy A, Goldberg S. Mechanical complications of myocardial infarction. Am J Med. 2022;135:1401–9. doi: 10.1016/j.amjmed.2022.08.017. [DOI] [PubMed] [Google Scholar]
- 42.Occhipinti G, Bucciarelli-Ducci C, Capodanno D. Diagnostic pathways in myocardial infarction with non-obstructive coronary artery disease (MINOCA. Eur Heart J Acute Cardiovasc Care. 2021;10:813–22. doi: 10.1093/ehjacc/zuab049. [DOI] [PubMed] [Google Scholar]
- 43.Jentzer JC, Monroe RE. Acute myocardial infarction cardiogenic shock in younger adults: a patient’s experience. J Card Fail. 2023;29:30–2. doi: 10.1016/j.cardfail.2022.12.001. [DOI] [PubMed] [Google Scholar]
- 44.Chen X, Liu M, Zuo L, et al. Environmental noise exposure and health outcomes: an umbrella review of systematic reviews and meta-analysis. Eur J Public Health. 2023;33:725–31. doi: 10.1093/eurpub/ckad044. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used during the present study are available from the corresponding author upon reasonable request.