Abstract
Background:
Environmental noise is common in intensive care units (ICUs), but its relationship with myocardial injury progression in acute myocardial infarction (AMI) is not well understood. This study compared myocardial injury biomarker levels and clinical outcomes of patients with AMI in a noise-reduced and standard ICUs.
Methods:
This retrospective study included patients with AMI who were admitted to an ICU between December 2021 and December 2024. Patients were allocated to standard and noise-reduced ICU groups on the basis of inpatient environment. The key metrics assessed included myocardial injury biomarkers (hs-cTnI and hs-cTnT), which were measured at baseline and multiple time points up to 48 hours post-admission. The main noise exposure indicator is the 24 hours average equivalent sound level. Supportive treatments administered in the ICU, such as sedation, the incidence of major adverse cardiovascular events 30 days post-admission, ICU stay duration and total hospital stay duration were analysed.
Results:
A total of 218 patients with comparable baseline data were allocated to standard (n = 113) and noise-reduced ICU groups (n = 105). All data reported in this study are absolute values measured at each time point. The noise-reduced ICU group exhibited significantly lower noise levels (44.21 ± 2.78 dBA vs. 55.26 ± 3.84 dBA, P < 0.001) and hs-cTnI and hs-cTnT levels at 12, 24 and 48 hours (hs-cTnI at 48 h: 135.65 ± 11.38 vs. 143.17 ± 17.82 ng/L, P < 0.001), reduced sedation use (24.76% vs. 38.05%, P = 0.035), a lower incidence of malignant arrhythmia (3.81% vs. 11.50%, P = 0.034) and shorter ICU (4.39 ± 0.75 days vs. 4.68 ± 0.92 days, P = 0.010) and total hospital stays (10.73 ± 1.32 days vs. 11.25 ± 1.45 days, P = 0.006).
Conclusion:
The implementation of noise reduction measures in the ICU was associated with reduced levels of myocardial injury, decrease in complications and improved short-term outcomes in patients with AMI.
Keywords: intensive care unit, myocardial infarction, noise, treatment outcome, troponin
KEY MESSAGES
-
(1)
Implementing targeted noise-reducing measures in ICUs successfully created a considerably quiet environment with average sound levels below common ICU standards.
-
(2)
In patients with AMI in ICUs, exposure to a quiet environment was linked to reduced myocardial injury biomarker levels (hs-cTnI and hs-cTnT) during the first 48 hours.
-
(3)
ICU patients in a quiet environment needed less sedative medication and had fewer malignant arrhythmias and shorter ICU and hospital stays, receiving more clinical benefits.
-
(4)
Findings suggest that controlling environmental noise is a modifiable risk factor and a valuable nonpharmacological strategy that improves short-term prognosis in AMI care.
Introduction
Acute myocardial infarction (AMI) is a life-threatening condition characterised by interrupted blood flow to the heart and can lead to ischaemia and necrosis.[1] Common clinical symptoms include chest pain, shortness of breath and elevated cardiac biomarkers, which require immediate medical intervention to restore blood flow and prevent further cardiac damage.[2,3] Improvements in prognosis have been achieved through timely reperfusion therapy, antiplatelet medications and intensive care monitoring. High noise levels in hospital environments, particularly in intensive care units (ICUs), are associated with potential disruptions to the recovery of patients with AMI.[4] Despite advancements in pharmacological and interventional treatments, the specific association between ICU environments remains unclear, particularly persistent noise quantified by metrics, such as A-weighted equivalent sound levels, maximum sound levels and the percentage of time exceeding certain thresholds and myocardial injury and recovery in patients with AMI.[5]
Environmental stressors, such as noise, can exacerbate cardiovascular stress responses.[6] Mechanistically, noise exposure may lead to increased catecholamine release, elevated heart rate and blood pressure, sleep disturbances[7] and autonomic imbalance by activating the sympathetic nervous system and the hypothalamic–pituitary–adrenal axis.[8] Theoretically, these changes can increase myocardial oxygen consumption, trigger arrhythmias and affect the biomarkers of myocardial injury.[9] These pathophysiological associations are largely derived from basic research or observations in the general population. However, direct evidence linking noise exposure to dynamic changes in continuous high-sensitivity biomarkers of myocardial injury and malignant arrhythmias in patients with AMI within the ICU environment remains limited.[10,11]
To date, clinical practice recognises the need for ICU noise management and has proposed various noise reduction strategies, such as using earplugs, implementing ‘quiet time’ protocols, performing environmental acoustic modifications and educating healthcare staff.[12] However, current studies have focused on evaluating these measures’ impact on patient sleep quality, subjective stress or general physiological parameters. Research systematically assessing the effects of comprehensive noise reduction interventions on core myocardial injury biomarkers and clinical endpoints, malignant arrhythmias, in high-risk patients with AMI remains limited.
The primary aim of this study is to investigate the association of ICU environmental noise levels with the degree of myocardial injury and short-term clinical outcomes in patients with AMI. We compared noise levels, biomarker trends, supportive treatment requirements and clinical outcomes of standard ICU environments and noise-reduced ICUs. In addition, the association between the ICU acoustic environment and patient outcomes was systematically evaluated in a well-defined cohort of patients with AMI. The focus of this study is on the objective, continuous quantification of ICU noise exposure and analysis of its correlations with dynamic changes in the serial high-sensitivity biomarkers of myocardial injury and clinical hard endpoints. The findings will provide insights into the role of environmental factors in cardiac critical care recovery.
MATERIALS AND METHODS
Study Design
This retrospective study included 218 patients with AMI who were admitted to Zhuzhou Central Hospital from December 2021 to December 2024. The aim was to evaluate the relationship of ICU noise levels with myocardial injury biomarker levels and short-term prognosis in patients with AMI. The patients were grouped by established ward configuration and bed availability at the time of admission. The patients were then divided into two groups based on hospital environment: standard ICU (n = 113) and noise-reduced ICU groups (n = 105).
We collected all patient data with a medical record system and de-identified them to ensure that they do not affect subsequent treatment or prognosis. This study was approved by Zhuzhou Central Hospital’s institutional review board and ethics committee (Approval Number: 202501015). Informed consent was obtained from all participants in accordance with regulatory and ethical standards governing retrospective studies.
The key metrics assessed included myocardial injury biomarkers, namely, high-sensitivity cardiac troponin I (hs-cTnI) and high-sensitivity cardiac troponin T (hs-cTnT), which were measured at baseline and multiple time points up to 48 hours post-admission. The main noise exposure indicator is the 24 hours average equivalent sound level. Supportive treatment use during the ICU stay, such as sedation, the incidence of major adverse cardiovascular events (MACE) within 30 days post-admission, ICU stay duration and total hospital stay duration were analysed.
Inclusion and Exclusion Criteria
The inclusion criteria were as follows: (1) having an age over 18 years; (2) having a clear diagnosis of AMI, which conforms to the guidelines of the American College of Cardiology/American Heart Association and is based on myocardial necrosis markers, clinical symptoms or imaging findings[13]; (3) having been admitted to the ICU for at least 48 hours and being conscious; (4) having received medication treatment, including antiplatelet drugs, anticoagulants, statins and other related therapies and (5) complete data.
Patients (1) who have other severe complications, such as severe renal insufficiency or liver failure, or diseases that could have affected the study results; (2) have known hearing impairments that might render them unsuitable for participation in noise exposure-related research; (3) have severe mental illness or cognitive impairment that could have prevented the patients to provide reliable data or comply with study requirements; (4) have undergone major surgery that may complicate the study outcomes during postoperative recovery and (5) have used extra-corporeal membrane oxygenation (ECMO) or similar devices that generate constant high levels of noise were excluded. Clinically unstable patients requiring cardiac catheterisation or those who had experienced cardiac arrest and resuscitation were also excluded.
Of the 248 patients admitted to the ICU for AMI [[Figure 1], 11 were excluded during the inclusion criteria screening phase as seven patients were confused during their ICU stay and four had incomplete medical records. Subsequently, 19 patients were excluded on the basis of the predefined exclusion criteria as six had other severe complications or comorbidities, eight used ECMO or similar high-noise devices and five had recently undergone major surgery. Overall, 218 patients were considered for analysis: 113 in the standard ICU group and 105 in the noise-reduced ICU group.
Figure 1.
Flowchart of patient selection and study design for evaluating the impact of noise reduction in ICU settings for patients with acute myocardial infarction. ECMO, extra-corporeal membrane oxygenation; ICU, intensive care unit.
Noise Reduction Measures
In the noise-reduced ICU group, a series of comprehensive measures was implemented to effectively reduce environmental noise levels, ensuring daily average noise level below 50 dB. The ward interiors were fitted with sound-absorbing materials, such as acoustic panels, soundproof mats and carpets, to minimise sound reflection and propagation. All medical instruments underwent regular maintenance and noise-reducing treatments, including the installation of silencers or soundproof covers. Machine alert sounds were adjusted to the minimum necessary volume, and visual prompt systems were used as auxiliary alarm methods to reduce unnecessary auditory disturbances. Medical staff received specialised training emphasising the importance of maintaining quietness within the ward, minimising unnecessary conversations and movements and using whispers or gestures when communication is needed. Furthermore, ward doors and windows were designed with soundproofing features, and buffer zones were established between corridors and wards to further isolate external noise. These comprehensive measures collectively contributed to considerably lowered noise levels within the ICU, providing a quiet and comfortable treatment environment for patients.
All other aspects of clinical care, including staffing ratios, treatment protocols and monitoring standards, were identical between the two settings.
Data Collection
Baseline Data
Baseline demographic data were obtained from the medical record system, and comprehensive information was collected during the initial hospital admission interview, including age, gender, education level and marital status. Clinical presentation and vital signs, namely, primary symptoms at presentation, systolic blood pressure, diastolic blood pressure, pulse rate and body mass index (BMI) were recorded. Medical history and risk factors covered hypertension, diabetes, hyperlipidaemia history, family history of myocardial infarction, current smoking status and current alcohol consumption. The severity of the disease and cardiac function were assessed using the Killip classification and the Global Myocardial Performance Score Index (Global MPSI).[14,15] Initial treatment strategies involved the use of dual antiplatelet therapy, anticoagulation therapy and high-intensity statin therapy.
Blood samples were collected at the time of admission and during ICU stays. Some samples were immediately used for diagnostic purposes, whereas the remaining blood samples were centrifuged at 3000 rpm for 10 minutes at 4 °C to separate serum or plasma. The resulting serum or plasma was aliquoted into sterile, labelled cryovials and stored at −80 °C for long-term storage. All samples were processed under aseptic conditions to prevent contamination.
The levels of estimated glomerular filtration rate (eGFR), total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), creatinine and triglycerides in serum samples collected at the time of admission were analysed using an automatic biochemical analyser (AU5800, Beckman Coulter, USA). Immunoassays, specifically enzyme-linked immunosorbent assay, were employed to measure immune markers. B-type natriuretic peptide (BNP) was quantified using a Triage BNP test kit (Alere, USA). Hs-cTnI was measured using another kit (ARCHITECT STAT hs-Troponin I, Abbott, USA). Copeptin was detected using an electrochemiluminescence immunoassay with an Elecsys Copeptin kit (Roche Diagnostics, Switzerland).
Noise Level Detection
Average equivalent sound level (Leq) is the average noise energy over a given time period and is typically measured in decibels (dBA). It reflects the average intensity of noise. To measure Leq, high-precision sound level meters (Model: Type 2250, Manufacturer: Brüel & Kjær, Country: Denmark) were used for continuous monitoring. Measurements were conducted using A-frequency and fast time weighting. Sound level meters were placed at multiple key locations within the ward, including the centre of the room, near the head of the bed and at the doorway, to ensure comprehensive and representative data collection. Each location had data collected continuously for 5 days with 24 hours recordings each day to capture noise variations across different time periods. The maximum sound level refers to the highest instantaneous sound level recorded during the measurement period. This metric helps to identify sudden high-noise events that can adversely affect patient recovery. Similarly, high-precision sound level meters were used to automatically capture and record the maximum sound levels observed during the measurement period. Nighttime peak sound level (L10) refers to the sound level that exceeds during 10% of the nighttime period (from 10 PM to 6 AM). This metric is used in the assessment of the disturbance level of nighttime noise because a quiet environment during the night is particularly important for patient rest. By setting the sound level meter to operate in nighttime mode and record the data, L10 values can be calculated. Exceedance time refers to the duration each day when the sound level exceeds 55 dBA. The definitions of L10 and exceedance time are based on the Night Noise Guidelines for Europe.[16]
Myocardial Injury Markers
Blood samples were collected from all patients at four fixed time points within 48 hours of admission: at admission (0 hour) and 12, 24 and 48 hours. Some samples were used for immediate analysis, and the rest was processed and stored frozen until analysis. Blood samples showing visible haemolysis during collection were discarded and re-collected. In this study, hs-cTnI was designated as the primary biomarker for analysis, and hs-cTnT as the secondary biomarker.
The levels of hs-cTnI and hs-cTnT during the first 48 hours after admission were measured. These markers’ detection facilitates the assessment of myocardial injury and provides an important basis for clinical diagnosis and treatment.
hs-cTnI levels were detected using an ARCHITECT STAT hs-Troponin I assay kit (ARCHITECT STAT hs-Troponin I, Abbott, USA). After sample processing, an ARCHITECT i series automated analyser (ARCHITECT i series, Abbott, USA) was employed for detection. For the detection of hs-cTnT, the Elecsys hs-Troponin T assay kit (Elecsys hs-Troponin T, Roche Diagnostics, Switzerland) was utilised. After sample processing, the Cobas e series automated analyser (Cobas e series, Roche Diagnostics, Switzerland) was used for detection. This assay kit uses specific antibodies to bind to cardiac troponin T and measures its concentration through electrochemiluminescence signals. The Cobas e series analyser can automatically complete sample processing and data analysis.
Supportive Treatment Usage
Data on the use of supportive treatments, including analgesia, sedation and oxygen therapy, were collected from the medical record system. Sedation use was treated as a potential confounder (covariate) in this analysis.
Major Adverse Cardiac Events and Length of Stay
Data on MACE and length of stay were obtained from a medical record system, which meticulously documented the occurrence of MACE within 30 days of admission, including recurrent myocardial infarction, malignant arrhythmia and acute heart failure. Recurrent myocardial infarction was defined by the presence of clinical symptoms, new ischaemic changes on electrocardiography and a re-elevation of cardiac biomarkers.[17] Malignant arrhythmia specifically referred to newly developed sustained ventricular tachycardia or ventricular fibrillation.[18] Acute heart failure was defined as newly occurring or worsening heart failure reaching Killip class III or higher and requiring intensive treatment.[19] All events were evaluated through an independent review of medical records by a cardiologist who was blinded to the patients’ group allocation. Additionally, the system recorded the length of stay in the ICU and the total length of hospitalisation.
Statistical Analysis
Data analysis was performed using SPSS 29.0 statistical software (SPSS Inc., Chicago, IL, USA). Categorical data were expressed as [n (%)] and analysed using the chi-square test (χ2). Continuous variables were first evaluated for normal distribution using the Shapiro–Wilk test. Normally distributed continuous data were expressed as mean ± standard deviation (M ± SD) and analysed using the t-test. All statistical tests were conducted using a two-tailed significance level, and P-values less than 0.05 were considered statistically significant.
To validate the relationship between noise and outcomes, multivariate logistic regression analysis was employed, with the occurrence of MACE as the dependent variable. Clinically significant variables included in the analysis were group assignment, age, Killip classification and history of diabetes. Variable selection was performed using forward stepwise methods, with an inclusion criterion of α = 0.05. The results are presented as odds ratios and their 95% confidence intervals. The independent variables included in the model were as follows: Group (coded as: Standard ICU = 0, noise-reduced ICU = 1), age (continuous variable), Killip class ≥ III (yes = 1, no = 0) and history of diabetes (present = 1, absent = 0).
The sample size for this study was estimated using G*Power software (Version 3.1.9.7). The calculation was based on the following assumptions: a medium effect size (d) of 0.5 and a two-tailed significance level (α) of 0.05. At least 95 patients per group would be required to reject the null hypothesis of equal means with 95% statistical power when the two-sided, two-sample t-test with equal variances was used. Ultimately, this study included 218 patients: 113 in the standard ICU group and 105 in the noise-reduced ICU group. Thus, the actual sample size of this study met the minimum sample size requirement and provided sufficient statistical power, ensuring the reliability and validity of the results.
RESULTS
Demographic Information
In the demographic comparison between the standard and noise-reduced ICU groups, no significant differences were observed across most variables assessed, with all P-values of >0.05 for age, gender distribution, education level, marital status, main symptoms, including chest pain, chest tightness, shortness of breath and other symptoms, blood pressure, pulse rate, BMI, medical history of hypertension, diabetes mellitus, hyperlipidaemia, family history of myocardial infarction, current smoking, current alcohol consumption, Killip class ≥III, Global MPSI and treatment strategies, such as dual antiplatelet therapy, anticoagulation and high-intensity statin use [Table 1]. The data revealed a trend towards consistency in patient profiles between the two groups, indicating that the baseline characteristics were well balanced.
Table 1.
Comparison of baseline demographic and clinical characteristics of patients
| Index | Standard ICU group (n = 113) | Noise-reduced ICU group (n = 105) | t/χ 2 | P |
|---|---|---|---|---|
| Age (years) | 61.23 ± 9.15 | 61.28 ± 10.13 | 0.039 | 0.969 |
| Gender [n, (%)] | 0 | 0.987 | ||
| -Female | 44 (38.94) | 41 (39.05) | ||
| -Male | 69 (61.06) | 64 (60.95) | ||
| Education level [n, (%)] | 2.551 | 0.279 | ||
| -Primary school or below | 36 (31.86) | 35 (33.33) | ||
| -Secondary school | 58 (51.33) | 60 (57.14) | ||
| -University or above | 19 (16.81) | 10 (9.52) | ||
| Marital status [n, (%)] | 3.233 | 0.199 | ||
| -Unmarried | 7 (6.19) | 4 (3.81) | ||
| -Married | 86 (76.11) | 90 (85.71) | ||
| -Divorced or widowed | 20 (17.70) | 11 (10.48) | ||
| Main symptoms [n, (%)] | ||||
| -Chest pain | 57 (50.44) | 57 (54.29) | 0.322 | 0.570 |
| -Chest tightness | 41 (36.28) | 38 (36.19) | 0 | 0.989 |
| -Shortness of breath | 11 (9.73) | 9 (8.57) | 0.088 | 0.766 |
| -Other | 7 (6.19) | 6 (5.71) | 0.022 | 0.881 |
| Systolic blood pressure (mmHg) | 145.28 ± 7.63 | 144.27 ± 7.38 | 0.993 | 0.322 |
| Diastolic blood pressure (mmHg) | 95.28 ± 7.21 | 94.77 ± 6.95 | 0.525 | 0.600 |
| Pulse rate (beats per minute) | 72.19 ± 6.95 | 72.20 ± 6.48 | 0.009 | 0.993 |
| BMI (kg/m2) | 22.76 ± 1.57 | 22.81 ± 1.22 | 0.257 | 0.798 |
| Medical history | ||||
| Hypertension [n, (%)] | 45 (39.82) | 46 (43.81) | 0.356 | 0.551 |
| Diabetes mellitus [n, (%)] | 18 (15.93) | 16 (15.24) | 0.020 | 0.888 |
| Hyperlipidaemia [n, (%)] | 47 (41.59) | 45 (42.86) | 0.036 | 0.850 |
| Family history of myocardial infarction [n, (%)] | 39 (34.51) | 39 (37.14) | 0.164 | 0.686 |
| Current smoking [n, (%)] | 30 (26.55) | 29 (27.62) | 0.032 | 0.859 |
| Current alcohol consumption [n, (%)] | 16 (14.16) | 17 (16.19) | 0.175 | 0.676 |
| Killip Class ≥III [n, (%)] | 27 (23.89) | 25 (23.81) | 0 | 0.988 |
| Global MPSI | 1.29 ± 0.14 | 1.31 ± 0.13 | 1.245 | 0.214 |
| Treatment [n, (%)] | ||||
| -DAPT | 102 (90.27) | 98 (93.33) | 0.676 | 0.411 |
| -Anticoagulation | 97 (85.84) | 93 (88.57) | 0.363 | 0.547 |
| -High-intensity statin | 98 (86.73) | 96 (91.43) | 1.229 | 0.268 |
Note: BMI, body mass index; DAPT, dual antiplatelet therapy; ICU, intensive care unit; MPSI, Global Myocardial Performance Score Index.
Baseline Blood Test Indicators between the Two Groups of Patients
When comparing the baseline blood test results between the standard and noise-reduced ICU groups, no significant differences were found for eGFR, total cholesterol, LDL cholesterol, HDL-cholesterol, C-reactive protein, natriuretic peptide type B, triglycerides and copeptin, and all P-values exceeded 0.05 [Table 2]. The only parameter that approached significance was creatinine (P = 0.079), which showed a slight trend towards low levels in the noise-reduced ICU group compared with the standard ICU group. However, this difference did not achieve statistical significance. The consistency in these baseline laboratory parameters indicates that both groups were comparable at the start of the study.
Table 2.
Baseline blood test indicators between the two groups of patients
| Index | Standard ICU group (n = 113) | Noise-reduced ICU group (n = 105) | t/χ 2 | P |
|---|---|---|---|---|
| eGFR (mL/min/1.73 m2) | 92.02 ± 5.01 | 91.26 ± 4.93 | 1.115 | 0.266 |
| Total cholesterol (mmol/L) | 4.81 ± 0.83 | 4.72 ± 0.81 | 0.803 | 0.423 |
| LDL cholesterol (mmol/L) | 3.01 ± 0.67 | 2.98 ± 0.55 | 0.367 | 0.714 |
| HDL-cholesterol (mg/dL) | 41.74 ± 6.42 | 42.12 ± 6.18 | 0.444 | 0.658 |
| Creatinine (mg/dL) | 0.83 ± 0.15 | 0.80 ± 0.12 | 1.764 | 0.079 |
| C-reactive protein (mg/L) | 18.31 ± 3.15 | 18.45 ± 3.46 | 0.317 | 0.751 |
| Natriuretic peptide type B (pg/mL) | 128.21 ± 10.88 | 127.68 ± 9.31 | 0.382 | 0.703 |
| Triglycerides (mg/dL) | 133.90 ± 10.58 | 132.92 ± 10.95 | 0.678 | 0.498 |
| Copeptin (pmol/L) | 5.43 ± 0.31 | 5.38 ± 0.27 | 1.289 | 0.199 |
Note: eGFR, estimated glomerular filtration rate; LDL cholesterol, low-density lipoprotein cholesterol; HDL-cholesterol, high-density lipoprotein cholesterol.
Noise Level Detection
Regarding noise detection results, significant differences were noted between the groups [Table 3]. The average equivalent sound level, maximum sound level and nighttime peak sound were significantly higher in the standard ICU group (all P-values <0.001). Additionally, the exceedance time (>55 dBA, h/day) was substantially longer in the standard ICU group (P < 0.001). These findings indicate that the noise reduction measures implemented in the noise-reduced ICU group were effective in considerably lowering various sound metrics.
Table 3.
Comparison of noise level monitoring results between standard and noise-reduced ICU environments
| Index | Standard ICU group (n = 113) | Noise-reduced ICU group (n = 105) | t | P |
|---|---|---|---|---|
| Average equivalent sound level (dBA) | 55.26 ± 3.84 | 44.21 ± 2.78 | 24.444 | <0.001 |
| Maximum sound level (dBA) | 85.81 ± 7.24 | 73.23 ± 5.15 | 14.868 | <0.001 |
| Nighttime peak sound level (dBA) | 68.67 ± 5.23 | 47.60 ± 2.17 | 39.348 | <0.001 |
| Exceedance time (>55 dBA, h/day) | 18.51 ± 2.14 | 5.26 ± 1.21 | 56.707 | <0.001 |
Note: ICU, intensive care unit.
Myocardial Injury Markers
In the monitoring of hs-cTnI levels within 48 hours, no significant difference in baseline measurement at admission was found between the standard and noise-reduced ICU groups (all P-values >0.05; Table 4). However, subsequent measurements revealed notable differences. At 12 hours, the standard ICU group exhibited higher hs-cTnI levels (P = 0.019). This trend continued at 24 hours, where the standard ICU group again showed higher levels (P = 0.003). By 48 hours, the difference was even more pronounced. The standard ICU group maintained higher levels (P < 0.001). The consistent pattern of elevated hs-cTnI levels in this group indicates a greater degree of myocardial stress or injury and is potentially influenced by environmental factors, such as noise levels.
Table 4.
Comparison of high-sensitivity troponin I levels dynamics within 48 hours after admission between the two groups of patients
| Index | Standard ICU group (n = 113) | Noise-reduced ICU group (n = 105) | t | P |
|---|---|---|---|---|
| At Admission (ng/L) | 81.73 ± 6.12 | 81.81 ± 5.95 | 0.100 | 0.920 |
| At 12 hours | 150.91 ± 19.26 | 145.51 ± 14.22 | 2.368 | 0.019 |
| At 24 hours | 182.95 ± 21.63 | 175.75 ± 13.20 | 2.988 | 0.003 |
| At 48 hours | 143.17 ± 17.82 | 135.65 ± 11.38 | 3.743 | <0.001 |
Note: ICU, intensive care unit.
The baseline measurements of hs-cTnT at admission did not differ significantly between the standard and noise-reduced ICU groups (all P-values >0.05; [Figure 2]. However, differences emerged over time. By 12 hours, the standard ICU group showed higher hs-cTnT levels (P = 0.027). This trend continued at 24 hours, where the same group maintained higher levels (P = 0.005). At 48 hours, the difference was further accentuated, and the standard ICU group showed notably higher levels (P = 0.001). The consistent elevation in hs-cTnT levels in this group over time implies heightened myocardial stress or injury.
Figure 2.
Monitoring of hs-cTnT levels within 48 hours. Hs-cTnT, high-sensitivity cardiac troponin T; ICU, intensive care unit.
Supportive Treatment Usage
Regarding the supportive treatment utilisation rates, no significant differences in the use of analgesia and oxygen therapy were found between the standard and noise-reduced ICU groups (all P-values >0.05; [Figure 3]. However, the utilisation rate of sedation significantly varied between the groups (P = 0.035), indicating a lower usage in the noise-reduced ICU group. These findings suggest that sedation is not only a confounding factor that needs to be statistically controlled but is likely a potential mediator. That is, reduced environmental noise may decrease patient agitation and stress, thereby reducing the need for sedative medications and ultimately mediating some of the improvements in clinical outcomes.
Figure 3.
Supportive treatment utilisation rate.
Note: ICU, intensive care unit.
Regression Analysis of Independent Influencing Factors for Major Adverse Cardiovascular Events
Regarding MACE and length of stay, no significant differences in the rates of recurrent myocardial infarction and acute heart failure were found between the standard and noise-reduced ICU groups (all P-values >0.05; Table 5). However, the incidence of malignant arrhythmia was significantly lower in the noise-reduced ICU group (P = 0.034). Additionally, the ICU length of stay (P = 0.010) and total hospital length of stay (P = 0.006) were significantly shorter in the noise-reduced ICU group compared to the Standard ICU group. These results indicate that the occurrence of malignant arrhythmia was notably reduced in the noise-reduced ICU group. Furthermore, the significant reduction in ICU and total hospital length of stay suggests that noise reduction strategies contribute to improvement in patient outcomes and to the efficient use of healthcare resources.
Table 5.
Comparison of major adverse cardiovascular events and length of hospital stay between standard and noise-reduced ICU groups
| Index | Standard ICU group (n = 113) | Noise-reduced ICU group (n = 105) | t/χ 2 | P |
|---|---|---|---|---|
| Major adverse cardiovascular events | ||||
| -Recurrent myocardial infarction | 7 (6.19%) | 2 (1.90%) | 1.563 | 0.211 |
| -Malignant arrhythmia | 13 (11.50%) | 4 (3.81%) | 4.482 | 0.034 |
| -Acute heart failure | 12 (10.62%) | 6 (5.71%) | 1.729 | 0.189 |
| ICU length of stay (days) | 4.68 ± 0.92 | 4.39 ± 0.75 | 2.589 | 0.010 |
| Total hospital length of stay (days) | 11.25 ± 1.45 | 10.73 ± 1.32 | 2.772 | 0.006 |
Note: ICU, intensive care unit.
The multivariate regression analysis of MACE revealed several significant predictors [Table 6]. Treatment in a noise-reduced ICU was significantly associated with a reduced risk of adverse cardiovascular events (P = 0.002). Age (per year increase) showed a significant association with an increased risk (P = 0.003). Patients classified as Killip Class ≥ III had a significantly higher likelihood of adverse cardiovascular events (P = 0.009). Diabetes mellitus did not reach statistical significance in this model (P = 0.067).
Table 6.
Multivariate logistic regression analysis of factors influencing MACE in patients with acute myocardial infarction
| Index | Coefficient | Std. Error | Wald Stat | P | OR | 95% CI |
|---|---|---|---|---|---|---|
| Noise-reduced ICU | −0.960 | 0.380 | 2.528 | 0.002 | 0.383 | 0.175–0.784 |
| Age (per year increase) | 0.085 | 0.026 | 3.240 | 0.003 | 1.089 | 1.036–1.148 |
| Killip class ≥III | 1.105 | 0.358 | 3.083 | 0.009 | 3.018 | 1.487–6.098 |
| Diabetes mellitus | 1.326 | 0.639 | 2.528 | 0.067 | 2.067 | 1.156–9.501 |
Note: CI, confidence interval; ICU, intensive care unit; OR, odds ratio.
DISCUSSION
This study suggests an association of reducing environmental noise in ICUs with decreased myocardial injury and improved short-term clinical outcomes in patients with AMI. By implementing a series of targeted noise control measures, we achieved a considerably quiet patient environment, which was found to be associated with the potential cardiac protective effects of standard medical treatments. The findings indicate that the acoustic environment in ICUs is an often-overlooked factor that should be considered a modifiable variable influencing the pathological progression of AMI and recovery.
Analysis showed that all sound metrics in the noise-reduced ICU were markedly lower, demonstrating the effectiveness of the noise-reducing intervention. The decrease in average equivalent sound, maximum peak and especially nighttime peak levels was particularly notable. Theoretically, increased noise levels may trigger physiological stress responses, such as elevated catecholamine release, which can exacerbate myocardial injury.[20] In ICUs, sleep disruption is primarily caused by excessive noise, which is a known source of physiological and psychological stress.[21,22] In a notably quiet environment, especially during nighttime, improvement in patients’ sleep quality may play an important role in the regulation of stress hormones and cardiovascular stability.[23] This successful intervention laid the groundwork for explaining the subsequent observed biological and clinical differences between the two groups. By reducing noise, we not only provided a comfortable environment for patients but also helped them to cope with recovery after AMI.
The most notable finding of this study was the difference in trajectories of myocardial injury biomarkers between the two groups. Although baseline levels were similar, the group exposed to higher noise levels showed consistently elevated hs-cTnI and hs-cTnT concentrations from 12 to 48 hours after admission. This result suggests that stress from a noisy environment exacerbates myocardial injury during AMI, potentially through the activation of the sympathetic nervous system.[24,25] Catecholamine release is a known consequence of environmental stress, which increases myocardial oxygen demand, promotes platelet aggregation and induces direct myocardial toxicity, thereby worsening ischaemic injury and leading to extensive myocardial cell death.[26,27] Our findings align with other studies that link environmental stress to elevated troponin levels in different clinical settings, but we specifically extend this evidence to the ICU environment for patients with AMI and propose a potential pathway of injury that warrants further validation.[28] This discovery underscores the importance of considering environmental factors in intensive care, suggesting that reducing noise reduces the risk of myocardial injury and improves short-term outcomes for patients. This evidence points to the need to divert attention to these modifiable factors for the enhancement of overall care quality.
The noise-reduced group showed a marked decrease in the need for sedative medications, which had compelling clinical relevance to the biological findings. Noise affects a patient’s final clinical outcomes through a key mediating factor. This finding suggests that in a quiet environment, patients experienced minimal agitation, anxiety and stress, and thus demand for pharmacological sedation lessens.[29] This observation is notable because excessive sedation is itself associated with adverse outcomes, including delirium, prolonged mechanical ventilation and extended ICU stays.[30] By decreasing reliance on sedatives, noise control strategies can contribute to a physiological and safe management approach, aligning with the goals of modern light sedation protocols in intensive care.
Regarding short-term outcomes, a key finding was the lowered incidence of malignant arrhythmias in the noise-reduced group. Noise has been linked to disrupted sleep patterns and an increased risk of arrhythmias.[31] The mechanisms underlying arrhythmias after AMI are complex, involving electrical instability of infarcted tissue, ischaemia and imbalance of the autonomic nervous system.[32] Excessive noise is a major disruptor of autonomic function, often leading to sympathetic dominance and reduced heart rate variability, which is a known factor contributing to arrhythmias.[33,34] By contrast, a quiet environment may help to restore parasympathetic tone and autonomic balance, thereby preventing electrical instability and life-threatening arrhythmias. Notably, our findings demonstrate a direct connection between environmental interventions and clinical endpoints, further supporting the view of noise reduction as a protective strategy. The decrease in arrhythmia events in the noise-reduced ICU group indicates that controlling environmental factors can markedly reduce the risk of life-threatening complications in patients with AMI.
Additionally, the noise-reduced group showed a marked reduction in ICU and total hospital stay, highlighting the practical and economic implications of our findings. The shortened hospital stay is a strong indicator of smooth and efficient recovery and is typically associated with a lowered risk of hospital-acquired complications.[35] Reduced myocardial injury, decreased need for sedation and few arrhythmia events likely contributed to a more stable and rapid clinical course, allowing patients to be discharged early from the ICU and the hospital.[36] Early discharge not only benefits patients but also optimises the use of valuable medical resources.
Multivariate regression analysis further reinforced the independent protective role of a noise-reduced environment in preventing MACE even after accounting for other key predictors, such as age and Killip class. This finding indicates that the benefits of noise reduction are not merely a byproduct but are themselves an important factor in improving patient outcomes. By identifying a noise-reduced environment as a strong protective factor, our study confirms its value in enhancing patient results.
Despite these promising results, we must acknowledge several limitations of this study. First, the single-centre and retrospective design may introduce unmeasured or residual confounding factors that may have affected the observed outcomes. Although we have adjusted for key baseline variables and ensured consistency in core clinical protocols, other unaccounted differences between the two ICU environments or in patient management may have influenced the results. Therefore, our findings should be interpreted as demonstrating an association rather than establishing causality. Second, the generalisability of our conclusions may be limited because they are primarily derived from a specific patient population [patients with AMI not receiving direct percutaneous coronary intervention (PCI)] within a single ICU setting and may not extend to other clinical contexts, such as general wards or patients undergoing different treatment regimens. Third, the follow-up period was limited to 30 days post-discharge, precluding assessment of long-term outcomes. Future prospective multicentre studies are needed to confirm these findings and to explore the underlying mechanisms.
CONCLUSION
This study found that patients with AMI in a noise-reduced ICU environment had lower levels of myocardial injury markers, a reduced need for sedation, a lower incidence of malignant arrhythmias and shorter hospital stays than those in a standard ICU. A quiet acoustic environment was independently associated with improved cardiovascular outcomes. These results suggest that noise control is a valuable and modifiable aspect of AMI care in ICUs. Further prospective studies are needed to confirm these findings and explore the underlying mechanisms.
Availability of Data and Materials
The datasets used and analysed during the current study are available from the corresponding author on reasonable request.
Author Contributions
MinZe Zheng: Responsible for the formulation and implementation of research designs, as well as the collection and processing of data.
Yang Wang: Responsible for the overall conception and framework construction of the thesis, and completed the final revision and proofreading of the thesis.
Ethics Approval and Consent to Participate
This study was approved by the Zhuzhou Central Hospital’s Institutional Review Board and Ethics Committee (Approval Number: 202501015) and was performed in accordance with the principles of the Declaration of Helsinki. All eligible participants signed an informed consent form.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgement
Not applicable.
Funding Statement
None.
REFERENCES
- 1.Mohammadi T, D’Ascenzo F, Pepe M, Bonsignore Zanghì S, Bernardi M, Spadafora L, et al. Unsupervised machine learning with cluster analysis in patients discharged after an acute coronary syndrome: insights from a 23,270-patient study. Am J Cardiol. 2023;193:44–51. doi: 10.1016/j.amjcard.2023.01.048. [DOI] [PubMed] [Google Scholar]
- 2.Huang X, Bai S, Luo Y. Advances in research on biomarkers associated with acute myocardial infarction: a review. Medicine (Baltimore. 2024;103:e37793. doi: 10.1097/MD.0000000000037793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Remy T, Danchin N, Puymirat E. Long-term clinical outcomes in patients with acute myocardial infarction with multivessel disease and complete revascularization: insights from the FLOWER-MI trial, the FRAME-AMI trial and the FAST-MI 2015 registry. Arch Cardiovasc Dis. 2025;118:199–201. doi: 10.1016/j.acvd.2024.10.329. [DOI] [PubMed] [Google Scholar]
- 4.Tronstad O, Flaws D, Patterson S, Holdsworth R, Fraser JF. Creating the ICU of the future: patient-centred design to optimise recovery. Crit Care. 2023;27:402. doi: 10.1186/s13054-023-04685-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Liu Y, Yan S, Zou L, Wen J, Fu W. Noise exposure and risk of myocardial infarction incidence and mortality: a dose-response meta-analysis. Environ Sci Pollut Res Int. 2022;29:46458–46470. doi: 10.1007/s11356-022-20377-w. [DOI] [PubMed] [Google Scholar]
- 6.Kumar V, Hemavathy S, Huligowda LKD, Umesh M, Chakraborty P, Thazeem B, et al. Environmental pollutants as emerging concerns for cardiac diseases: a review on their impacts on cardiac health. Biomedicines. 2025;13:241. doi: 10.3390/biomedicines13010241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hahad O, Gilan D, Michal M, Tüscher O, Chalabi J, Schuster AK, et al. Noise annoyance and cardiovascular disease risk: results from a 10-year follow-up study. Sci Rep. 2024;14:5619. doi: 10.1038/s41598-024-56250-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hahad O, Kuntic M, Al-Kindi S, Kuntic I, Gilan D, Petrowski K, et al. Noise and mental health: evidence, mechanisms, and consequences. J Expo Sci Environ Epidemiol. 2025;35:16–23. doi: 10.1038/s41370-024-00642-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hillman DR. Sleep loss in the hospitalized patient and its influence on recovery from illness and operation. Anesth Analg. 2021;132:1314–1320. doi: 10.1213/ANE.0000000000005323. [DOI] [PubMed] [Google Scholar]
- 10.Yang ZJ, Zhao CL, Liang WQ, Chen ZR, Du ZD, Gong SS. ROS-induced oxidative stress and mitochondrial dysfunction: a possible mechanism responsible for noise-induced ribbon synaptic damage. Am J Transl Res. 2024;16:272–284. doi: 10.62347/EVDE9449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Münzel T, Daiber A, Engelmann N, Röösli M, Kuntic M, Banks JL. Noise causes cardiovascular disease: it’s time to act. J Expo Sci Environ Epidemiol. 2025;35:24–33. doi: 10.1038/s41370-024-00732-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Vreman J, Lemson J, Lanting C, van der Hoeven J, van den Boogaard M. The effectiveness of the interventions to reduce sound levels in the ICU: a systematic review. Crit Care Explor. 2023;5:e0885. doi: 10.1097/CCE.0000000000000885. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Rao SV, O’Donoghue ML, Ruel M, Rab T, Tamis-Holland JE, Alexander JH, et al. 2025 ACC/AHA/ACEP/NAEMSP/SCAI guideline for the management of patients with acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2025;151:e771–e862. doi: 10.1161/CIR.0000000000001309. [DOI] [PubMed] [Google Scholar]
- 14.Ponce-Gallegos MA, Mendoza-Mujica M, Ponce-Gallegos J, García-Diaz JA, Zelada-Pineda JA, Araiza-Garaygordobil D. Killip and Kimball classification in the ultrasound era: is it time to redefine? Arch Peru Cardiol Cir Cardiovasc. 2024;5:153–156. doi: 10.47487/apcyccv.v5i3.413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Tei C, Ling LH, Hodge DO, Bailey KR, Oh JK, Rodeheffer RJ, et al. New index of combined systolic and diastolic myocardial performance: a simple and reproducible measure of cardiac function-a study in normals and dilated cardiomyopathy. J Cardiol. 1995;26:357–366. [PubMed] [Google Scholar]
- 16.Kim R, Berg M. Summary of night noise guidelines for Europe. Noise Health. 2010;12:61–63. doi: 10.4103/1463-1741.63204. [DOI] [PubMed] [Google Scholar]
- 17.Jneid H, Alam M, Virani SS, Bozkurt B. Redefining myocardial infarction: what is new in the ESC/ACCF/AHA/WHF Third Universal Definition of myocardial infarction? Methodist Debakey Cardiovasc J. 2013;9:169–172. doi: 10.14797/mdcj-9-3-169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zeppenfeld K, Tfelt-Hansen J, de Riva M, Winkel BG, Behr ER, Blom NA, et al. 2022 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. Eur Heart J. 2022;43:3997–4126. doi: 10.1093/eurheartj/ehac262. [DOI] [PubMed] [Google Scholar]
- 19.Tsutsui H, Isobe M, Ito H, Ito H, Okumura K, Ono M, et al. JCS 2017/JHFS 2017 guideline on diagnosis and treatment of acute and chronic heart failure- digest version. Circ J. 2019;83:2084–2184. doi: 10.1253/circj.CJ-19-0342. [DOI] [PubMed] [Google Scholar]
- 20.Lee Y, Lee S, Lee W. Occupational and environmental noise exposure and extra‐auditory effects on humans: a systematic literature review. Geohealth. 2023;7:e2023GH000805. doi: 10.1029/2023GH000805. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Knauert MP, Ayas NT, Bosma KJ, Drouot X, Heavner MS, Owens RL, et al. Causes, consequences, and treatments of sleep and circadian disruption in the ICU: an official American Thoracic Society Research Statement. Am J Respir Crit Care Med. 2023;207:e49–e68. doi: 10.1164/rccm.202301-0184ST. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Tahvili A, Waite A, Hampton T, Welters I, Lee PJ. Noise and sound in the intensive care unit: a cohort study. Sci Rep. 2025;15:10858. doi: 10.1038/s41598-025-94365-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jin T, Kosheleva A, Castro E, Qiu X, James P, Schwartz J. Long-term noise exposures and cardiovascular diseases mortality: a study in 5 U.S. states. Environ Res. 2024;245:118092. doi: 10.1016/j.envres.2023.118092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Rabinovich-Nikitin I, Kirshenbaum LA. Circadian regulated control of myocardial ischemia-reperfusion injury. Trends Cardiovasc Med. 2024;34:1–7. doi: 10.1016/j.tcm.2022.09.003. [DOI] [PubMed] [Google Scholar]
- 25.Nuszkiewicz J, Rzepka W, Markiel J, Porzych M, Woźniak A, Szewczyk-Golec K. Circadian rhythm disruptions and cardiovascular disease risk: the special role of melatonin. Curr Issues Mol Biol. 2025;47:664. doi: 10.3390/cimb47080664. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Shlobin NA, Thijs RD, Benditt DG, Zeppenfeld K, Sander JW. Sudden death in epilepsy: the overlap between cardiac and neurological factors. Brain Commun. 2024;6:fcae309. doi: 10.1093/braincomms/fcae309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Xiang W, Wang X, Li L, Zeng J, Lu H, Wang Y. Unveiling catecholamine dynamics in cardiac health and disease: mechanisms, implications, and future perspectives. Int J Drug Discov Pharmacol. 2023;2:12–22. [Google Scholar]
- 28.Qi S, Li X, Jiang Y, Zhu T, Ze L, Li Z, et al. Analysis of risk factors and development of predictive model for acute myocardial injury in patients with acute exacerbation of chronic obstructive pulmonary disease. J Thorac Dis. 2025;17:1977–1990. doi: 10.21037/jtd-2024-1992. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Istanboulian L, Dale C, Terblanche E, Rose L. Clinician-perceived barriers and facilitators for the provision of actionable processes of care important for persistent or chronic critical illness. J Adv Nurs. 2024;80:1619–1629. doi: 10.1111/jan.15924. [DOI] [PubMed] [Google Scholar]
- 30.Ceric A, Holgersson J, May TL, Skrifvars MB, Hästbacka J, Saxena M, et al. Effect of level of sedation on outcomes in critically ill adult patients: a systematic review of clinical trials with meta-analysis and trial sequential analysis. EClinicalMedicine. 2024;71:102569. doi: 10.1016/j.eclinm.2024.102569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Smith MG, Cordoza M, Basner M. Environmental noise and effects on sleep: an update to the WHO Systematic Review and Meta-Analysis. Environ Health Perspect. 2022;130:76001. doi: 10.1289/EHP10197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Frampton J, Ortengren AR, Zeitler EP. Arrhythmias after acute myocardial infarction. Yale J Biol Med. 2023;96:83. doi: 10.59249/LSWK8578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Zaman M, Muslim M, Jehangir A. Environmental noise-induced cardiovascular, metabolic and mental health disorders: a brief review. Environ Sci Pollut Res Int. 2022;29:76485–76500. doi: 10.1007/s11356-022-22351-y. [DOI] [PubMed] [Google Scholar]
- 34.Polito R, Valenzano A, Monda V, Cibelli G, Monda M, Messina G, et al. Heart rate variability and sympathetic activity is modulated by very low-calorie ketogenic diet. Int J Environ Res Public Health. 2022;19:2253. doi: 10.3390/ijerph19042253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Hirani R, Podder D, Stala O, Mohebpour R, Tiwari RK, Etienne M. Strategies to reduce hospital length of stay: evidence and challenges. Medicina (Kaunas) 2025;61:922. doi: 10.3390/medicina61050922. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.van Bakel BMA, de Koning IA, Bakker EA, Pop GAM, Cramer E, van Geuns RM, et al. Rapid improvements in physical activity and sedentary behavior in patients with acute myocardial infarction immediately following hospital discharge. J Am Heart Assoc. 2023;12:e028700. doi: 10.1161/JAHA.122.028700. [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 and analysed during the current study are available from the corresponding author on reasonable request.