KEY POINTS This prospective cohort study aimed to investigate the association between sleep disturbances on the night of operative day and the risk of postoperative delirium (POD) in elderly patients. According to their postoperative night Sleep Quality Numeric Rating Scale (SQ-NRS) scores, the participants were categorised into the sleep disturbance (SDT) and no sleep disturbance (Non-SDT) groups. Poor subjective sleep quality (SQ-NRS ≥6) was independently associated with an increased risk of POD (adjusted OR 1.43, 95% CI 1.11 to 1.82). Acute kidney injury, stroke, pulmonary infection, cardiovascular complications and all-cause mortality within 1 year postoperatively showed no significant differences. Introduction

Postoperative delirium (POD), a common and sudden change in consciousness that occurs after surgery, is characterised by fluctuating cognitive function, inattention, and altered levels of consciousness.1 This condition is associated with negative functional outcomes, increased morbidity and mortality, and higher healthcare costs. The prevalence of POD is particularly high among elderly patients, with contributing factors such as advanced age, cognitive impairment and preexisting medical conditions.2

Prior studies have examined various peri-operative risk factors for POD, such as advanced age, baseline cognitive impairment, comorbid illness and medications.3,4 Sleep disorders, encompassing conditions such as insomnia, sleep-disordered breathing and circadian rhythm abnormalities, are common in older individuals and have been identified as significant risk factors for postoperative delirium.5 For instance, pre-operative OSA and insomnia have been associated with increased postoperative delirium.6,7 The hospital environment, characterised by noise, irregular light exposure and patient care activities, can further exacerbate sleep disturbances during the peri-operative period.8–10 However, research on the effects of sleep disturbances during the immediate postoperative period on POD risk is limited.

While some studies have identified postoperative sleep disturbances as a risk factor for delirium, they have not specifically investigated the relationship between sleep quality on the night of surgery and subsequent POD incidence.11,12 There is a need for more robust evidence from prospective cohort studies focusing on subjective sleep quality during this critical postoperative period and its impact on POD development.

This prospective cohort study aimed to investigate the association between subjective sleep quality on the night of surgery and POD incidence in elderly surgical patients. We hypothesised that poor subjective sleep quality would independently predict an increased risk of POD. Our findings could highlight the need to optimise peri-operative sleep to reduce POD.

Materials and methods Study design and population

This prospective, observational study was conducted at the First Medical Centre of the Chinese People's Liberation Army (PLA) General Hospital, a tertiary hospital. The study was registered with chictr.org.cn (ChiCTR1900028545). Written informed consent was obtained from all eligible subjects or their legal surrogates.

We prospectively gathered clinical data from individuals aged 65 years or older scheduled to undergo elective surgery under general anaesthesia between 1 April 2020, and 30 April 2022. This manuscript adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Eligible participants were scheduled to undergo surgery under general anaesthesia. The types of surgery included general, thoracic, orthopaedic, urological or gynaecological procedures. A detailed list of types of surgeries has been provided in Supplemental Digital Content 1, https://links.lww.com/EJA/A916. The exclusion criteria were patients with a history of alcohol abuse, dementia or prior neurosurgery procedures; patients with significant cognitive impairment [as determined by the Chinese version of Mini-Mental State Examination (MMSE) score of 17 or lower]13,14; patients admitted to the ICU; and patients whose surgery ended at 8 p.m. or later.15

Pre-operative and intra-operative data collection

Prior to the scheduled surgical procedures, trained research assistants conducted a 60-min baseline assessment in the patient wards to collect demographic data. The demographic characteristics of the participants, such as age, sex, BMI, comorbidities and prehospital medications, were documented. Data regarding comorbidities, including hypertension, diabetes, chronic obstructive pulmonary disease (COPD), cerebrovascular disease, obstructive sleep apnoea-hypopnea syndrome (OSAHS) and coronary heart disease, were collected. In addition, the study also recorded the medications received by the patients, such as angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers, NSAIDs, β-blockers, benzodiazepines and statins.16 Furthermore, neuropsychological evaluations were conducted, including the MMSE.17

The following parameters were documented intra-operatively: surgical sites, medication administration (midazolam and dexmedetomidine), the incidence of hypotension [defined as a decrease in mean arterial pressure (MAP) by more than 20% from baseline or a MAP <60 mmHg], surgical duration, amount of blood loss and urine output. The pre-operative laboratory data, including haemoglobin (Hb) levels, were also recorded for subsequent analysis.

In addition, pain levels at rest were evaluated using the numeric rating scale (NRS of Pain at rest) at 9 a.m. on the first postoperative day. This scale ranges from 0 (no pain) to 10 (maximum pain).18,19

Exposure

The subjective sleep quality of participants during their night of operative day was assessed using the Sleep Quality-Numeric Rating Scale (SQ-NRS) at 9 a.m. on the first postoperative day. The SQ-NRS is a numerical scale ranging from 0 to 10, where 0 represents excellent sleep, and 10 represents a complete inability to fall asleep throughout the night.20–22 On the basis of the sleep disturbance levels, the participants were divided into two groups: those with an SQ-NRS score 6 or higher, indicating sleep disturbance (SDT), and those with a score of below 6, representing no sleep disturbance (Non-SDT).23–25

Outcome

The primary outcome measure was the incidence of delirium from 24 h postoperatively until discharge, as determined by the 3-Minute Diagnostic Interview for Confusion Assessment Method (3D-CAM). This assessment was performed twice daily at 9 a.m. and 4 p.m. during the patient's hospital stay, up to a maximum of 7 days following surgery.26 Proficient research assistants recorded the incidences of delirium using systematic interviews.

The secondary outcomes of our study included acute kidney injury (AKI), stroke, pulmonary infection, cardiovascular complications during hospitalisation and all-cause mortality within 1 year postoperatively. AKI was assessed using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria.27 Cardiovascular complications included heart failure, cardiac arrest, angina pectoris, myocardial infarction or severe arrhythmia.

Ethics

Ethical approval for this study (approval number: S2019–311–02) was provided by the Ethical Committee of Chinese PLA General Hospital, China (Feng Huang) on 26 December 2019.

Sample size

The sample size for this study was calculated using PASS 11.0 software (NCSS, LLC, Kaysville, Utah, USA), setting a type I error of 0.05 and a power of 0.8. Previous research indicates a 14% incidence of SDT based on the NRS used to evaluate sleep quality postsurgery.23 Subsequently, other studies have shown that the incidence rate of postoperative delirium was 14.3% in patients with SDT, compared to 2.9% in patients without SDT.11 From these studies, we assumed that the incidence rate of POD would be 15% in patients with SDT and 9% in patients without SDT. Consequently, the sample size required was calculated as 1986 for the non-SDT group and 208 for the SDT group. To account for potential attrition during the study, we initially planned to enrol a total of 2437 participants.

Statistical analysis

Initially, a descriptive summary of the patient characteristics, stratified by delirium status, was conducted. Continuous variables with a normal distribution were presented as mean (standard deviation) and compared using Student's t-test. Nonnormally distributed continuous variables were reported as the median and interquartile range [IQR] and compared using the Mann--Whitney U tests. Categorical variables were compared using χ2 tests between groups.

Univariate and multivariate logistic regression models examined the relationship between SDT and POD incidence. In the multivariable logistic regression model, we employed a forced entry approach, where all selected variables were included simultaneously in the model. The potential covariates for the multivariable regression were selected based on univariate analysis, clinical relevance, and previous literature associations with POD.28,29 Potential confounders included age, BMI, cerebrovascular disease, intra-operative midazolam and dexmedetomidine, surgical sites (general, thoracic, orthopaedic, urological or gynaecological), surgical duration, blood loss, baseline laboratory values, for example, Hb, and pain NRS at rest.30

The entire patient cohort was also analysed using a subgroup analysis based on predetermined subgroups. This subgroup analysis was stratified by factors such as age, sex, ASA, Hb, intra-operative hypotension, surgical sites (general, thoracic, orthopaedic, urological or gynaecological) and education. There is substantial evidence linking lower Hb levels with poorer postoperative outcomes, and thus, it was thought necessary to explore if the impact of other predictors on POD varied with Hb levels.30

Lastly, the significance tests employed in this study utilised a two-tailed approach and a significance level of P value less than 0.05. Data analysis was performed using R software (R Statistical Software, version 4.0.1, R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/.

Results

Between April 2020 and April 2022, 3917 elderly patients were screened for inclusion in the study. Among them, 845 were excluded for various reasons (Fig. 1). Consequently, the final sample for analysis comprised 3072 patients.

Fig. 1:

Flowchart of the study.

Patient characteristics were stratified by delirium and are presented in Table 1. Briefly, among all patients, 373 patients (12.1%) developed delirium. In terms of comorbidities, cerebrovascular disease had a significantly higher prevalence in patients with delirium (17.2%) compared to those without delirium (10.7%, P < 0.001). No significant differences were detected in the prevalence of other comorbidities, including hypertension, diabetes and coronary heart disease. Regarding the history of medication use, no statistically significant differences were detected between the two groups. However, patients with delirium had a significantly higher prevalence of the SQ-NRS (32.4%) compared to those without delirium (24.8%, P = 0.002).

Table 1 - Baseline and demographic characteristics Overall (n = 3072) Nondelirium (n = 2699) Delirium (n = 373) P value Age (years) 70 [67 to 74] 70 [67 to 74] 71 [68 to 76] <0.001 Male (%) 1610 (52.4) 1423 (52.7) 187 (50.1) 0.377 BMI (kg m−2) 24.6 [22.6 to 26.9] 24.7 [22.6 to 27.1] 24.2 [22.2 to 26.2] 0.004 Smoke (%) 802 (26.1) 710 (26.3) 92 (24.7) 0.540 Alcohola (%) 792 (25.8) 702 (26.0) 90 (24.1) 0.474 Educationb (years) 3 (2.5) 3 (2.5) 3 (2.4) 0.115 Comorbidity (%)  Hypertension 1676 (54.6) 1469 (54.4) 207 (55.5) 0.739  Diabetes 989 (32.2) 868 (32.2) 121 (32.4) 0.961  Coronary heart disease 589 (19.2) 522 (19.3) 67 (18.0) 0.573  Cerebrovascular disease 354 (11.5) 290 (10.7) 64 (17.2) <0.001  OSAHS 8 (0.3) 6 (0.2) 2 (0.5) 0.252 History of medications (%)  ACEI 270 (8.8) 240 (8.9) 30 (8.0) 0.656  ARB 491 (16.0) 443 (16.4) 48 (12.9) 0.094  β-blocker 320 (10.4) 288 (10.7) 32 (8.6) 0.251  NSAIDs 114 (3.7) 107 (4.0) 7 (1.9) 0.064  Statin 321 (10.4) 290 (10.7) 31 (8.3) 0.177  Benzodiazepines 918 (29.9) 815 (30.2) 103 (27.6) 0.337 ASA Classification (%) 0.226  I 32 (1.0) 28 (1.0) 4 (1.1)  II 2494 (81.2) 2205 (81.7) 289 (77.5)  III 542 (17.6) 463 (17.2) 79 (21.2)  IV 4 (0.1) 3 (0.1) 1 (0.3) Hb (g dl−1) 13.1 [12.0 to 14.1] 13.1 [12.0 to 14.1] 12.7 [11.5 to 13.8] <0.001 SQ-NRS (%) 0.002  ≥6 791 (25.7) 670 (24.8) 121 (32.4)  <6 2281 (74.3) 2029 (75.2) 252 (67.6)

Data were presented as mean (SD), median [IQR], or n (%).ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blocker; ASA, American Society of Anaesthesiologists; Hb, haemoglobin; IQR, interquartile range; OSAHS, obstructive sleep apnoea-hypopnoea syndrome; SQ-NRS, sleep quality numeric rating scale.

aAlcohol was defined as having answered yes or no to any alcohol consumption ever;

bEducation was defined as the total number of years an individual has spent in formal education.

The intra-operative and postoperative characteristics are detailed in Table 2. In the delirium group, the median [IQR] surgical duration was observed to be significantly longer 167 min [110 to 236] compared to the nondelirium group, 140 min [100 to 200], demonstrating a statistically significant difference (P < 0.001). Moreover, the delirium group exhibited a statistically significant increase in blood loss compared with the nondelirium group (P = 0.043). In addition, the proportion of delirium cases was highest in general surgery (43.7%), followed by orthopaedic (26.5%), thoracic (10.2%), urological (17.4%) and gynaecological (2.4%). The difference in surgical sites distribution was statistically significant (P = 0.001). Interestingly, in terms of intra-operative medication use, both midazolam and dexmedetomidine were less frequently used in the delirium group (54.7 and 9.7%, respectively) than in the nondelirium group (65.2 and 6.3%, respectively), with these differences being statistically significant (P < 0.001 and P = 0.019, respectively).

Table 2 - Intra-operative and postoperative characteristics Overall (n = 3072) Nondelirium (n = 2699) Delirium (n = 373) P Intra-operative medication (%)  Midazolam 1963 (63.9) 1759 (65.2) 204 (54.7) <0.001  Dexmedetomidine 205 (6.7) 169 (6.3) 36 (9.7) 0.019 Surgical duration (min) 140 [100 to 205] 140 [100 to 200] 167 [110 to 236] <0.001 Blood loss (ml) 50 [30 to 200] 50 [30 to 200] 100 [50 to 200] 0.043 Urine output (ml kg−1 h−1) 1.2 [0.5 to 2.2] 1.2 [0.5 to 2.2] 1.3 [0.6 to 2.1] 0.221 Hypotension (%) 338 (11.0) 301 (11.2) 37 (9.9) 0.532 Blood transfusion (%) 263 (8.6) 224 (8.3) 39 (10.5) 0.195 Surgical sites (%) 0.001  General 1056 (34.4) 893 (33.1) 163 (43.7)  Orthopaedic 953 (31.0) 854 (31.6) 99 (26.5)  Thoracic 472 (15.4) 434 (16.1) 38 (10.2)  Urological 467 (15.2) 402 (14.9) 65 (17.4)  Gynaecological 126 (4.1) 117 (4.3) 9 (2.4) Type of surgery (%) 0.446  Invasive 1266 (41.2) 1105 (40.9) 161 (43.2)  Minimally invasive 1806 (58.8) 1594 (59.1) 212 (56.8) NRS of pain on postoperative day 1 at rest 1 [0 to 3] 1 [0 to 3] 1 [0 to 3] 0.005

IQR, interquartile range; NRS, numeric rating scale.

Univariate and multivariate logistic regression analyses examined the relationship between POD and SDT (Table 3). In the univariate analysis, SDT showed a significant positive association with POD (OR 1.45, 95% CI 1.15–1.83, P = 0.002). Other significant variables in the univariate analysis included age, BMI, cerebrovascular disease, Hb, intra-operative midazolam and dexmedetomidine, surgical sites, surgical duration, blood loss and pain NRS. SDT remained significantly associated with POD (OR 1.43, 95% CI 1.11–1.82, P = 0.005) after adjusting for age, BMI, ASA classification, cerebrovascular disease, intra-operative midazolam and dexmedetomidine, surgical sites, surgical duration, blood loss, baseline laboratory values (Hb) and pain NRS at rest in the multivariate logistic analysis (Table 3). Other variables that remained significant in the multivariate model included age, cerebrovascular disease, intra-operative midazolam and dexmedetomidine, surgical duration and NRS of pain at rest.

Table 3 - Univariate and multivariate analysis of factors associated with postoperative delirium Univariate Multivariatea Variable OR 95% CI OR 95% CI SQ-NRS (n) 1.45 1.15 to 1.83 1.43 1.11 to 1.82 Age (years) 1.07 1.05 to 1.09 1.07 1.05 to 1.09 BMI (kg m−2) 0.95 0.92 to 0.98 0.98 0.95 to 1.02 Cerebrovascular disease 1.72 1.27 to 2.30 1.62 1.19 to 2.20 Hb (g dl−1) 0.99 0.98 to 0.99 0.99 0.99 to 1.00 Intra-operative midazolam 0.65 0.52 to 0.80 0.70 0.56 to 0.88 Intra-operative dexmedetomidine 1.60 1.08 to 2.31 1.64 1.09 to 2.41 Surgical sites  General Reference Reference  Orthopaedic 0.64 0.49 to 0.83 0.81 0.59 to 1.10  Thoracic 0.48 0.33 to 0.69 0.75 0.50 to 1.11  Urological 0.89 0.65 to 1.21 1.24 0.89 to 1.73  Gynaecological 0.42 0.20 to 0.81 0.53 0.24 to 1.04 Surgical duration (min) 1.00 1.00 to 1.01 1.00 1.00 to 1.01 Blood loss (ml) 1.00 1.00 to 1.00 1.00 1.00 to 1.00 NRS of pain on postoperative day 1 at rest 1.10 1.04 to 1.15 1.08 1.02 to 1.14

CI, confidence interval; Hb, haemoglobin; NRS, numeric rating scale; OR, odds ratio; POD, postoperative delirium; SQ-NRS, sleep quality numeric rating scale.

aAdjusted for age, BMI, ASA classification, cerebrovascular disease, intra-operative midazolam and dexmedetomidine, surgical sites, surgical duration, blood loss, baseline laboratory values, for example haemoglobin (Hb), and NRS of pain at rest.

We conducted a subgroup analysis to explore the association between SDT and POD across various subgroups (Fig. 2). These subgroups were based on factors such as age, sex, ASA classification, Hb levels, intra-operative hypotension, surgical duration, surgical sites and education. The analysis revealed significant associations between several factors and POD. However, some factors did not show a significant association with the outcome. No interaction was observed between the subgroups. For detailed results of the subgroup analysis, please refer to Fig. 2.

Fig. 2:

Subgroup analyses of the association between the SDT and non-SDT groups.

No significant differences were observed between the groups concerning secondary outcomes (Supplemental Digital Content 2, https://links.lww.com/EJA/A917). The incidence of postoperative complications, such as AKI (9 vs. 27%, P = 0.129), stroke (0.8 vs. 0.6%, P = 0.507), pulmonary infection (8.3 vs. 9.1%, P = 0.796) and cardiovascular complications (1.3 vs. 1.1%, P = 0.888), did not exhibit any notable disparities between the SDT and non-SDT groups. Furthermore, the mortality rates between the two groups did not exhibit a statistically significant difference (0.2 vs. 0.0%, P = 0.996).

Discussion

Our study explored the relationship between sleep quality, as evaluated through the SQ-NRS, and the incidence of POD among a large cohort of surgical patients. Our results revealed a significant correlation between poor sleep quality on the night of operative day, indicated by higher SQ-NRS scores, and an increased risk of POD. This correlation persisted even after adjusting for potential confounders such as age, BMI, cerebrovascular disease, intra-operative midazolam and dexmedetomidine, surgical sites, surgical duration, blood loss, Hb and pain NRS at rest.

Our study emphasises the potential role of sleep disturbances occurring on the night of surgery as a potent predictor for developing POD. This adds a crucial dimension to existing research, highlighting the impact of sleep quality on determining postoperative outcomes. It calls for healthcare professionals to closely monitor the sleep quality of elderly patients during the peri-operative period, as it could possibly function as an early warning sign for the onset of POD. However, it is important to note that while our study found this association, it does not establish poor sleep quality as a direct cause of POD. Sleep disturbances could potentially be an early symptom of ongoing delirium rather than a cause.

While our observational study cannot determine causality between sleep disturbances and delirium, our findings still carry valuable clinical implications. The identification of sleep disturbances on the night of operative day could help flag patients at higher risk of developing delirium for more intensive monitoring. Prior studies have suggested sleep disturbances as an early warning sign for POD. For instance, a systematic review and meta-analysis highlighted preexisting sleep disturbances as a strong risk factor for POD.31 Similarly, another study on cardiac surgery patients identified pre-operative sleep disorder as a significant predictor of POD, particularly in older individuals.32 Our research extends these findings by focusing on the operative night, a period that has not been adequately studied. This focus is particularly relevant given evidence suggesting that general anaesthesia can lead to postoperative sleep disturbances, which can increase the risk of delirium and other adverse outcomes.7,33 A growing body of research proposes that sleep disturbances might serve as a warning sign for POD. For instance, a study by Zhang et al.12 found that patients who developed delirium had more frequent postoperative complications, suggesting that peri-operative risk factors, including sleep disturbances, impact the development of POD. Similarly, a study by Wang et al.11 identified postoperative sleep disorders as one of the seven factors significantly contributing to POD development in geriatric patients. Therefore, recognising and addressing sleep disturbances in surgical patients could potentially serve as a strategy for preventing POD. This could involve more intensive monitoring of patients with sleep disturbances, as well as interventions to improve sleep quality in the peri-operative period.34,35

Several strategies have been proposed to manage sleep disturbances in postoperative patients. Recent research includes the Prophylactic Melatonin for Delirium in Intensive Care (ProMEDIC) study, which is currently investigating whether prophylactic melatonin administered to ICU patients helps decrease the rate and severity of delirium.33 A systematic review and meta-analysis found that these substances significantly reduced delirium incidence, with a risk reduction of 49% in surgical patients and 34% in ICU patients.34 Another study found that ramelteon, when administered nightly to elderly patients admitted for acute care, was associated with a lower risk of delirium.35 Several nonpharmacological interventions have been suggested to manage postoperative sleep disturbances. These include controlling environmental factors such as nocturnal noise and light levels, which can help manage postoperative sleep disturbances.36 Sleep-promotion therapy, such as the use of ear plugs and eye masks in ICU patients, has been shown to decrease the degree of delirium.10 In addition, interventions such as muscle relaxation, posture and relaxation training can also be beneficial.37

Various types of sleep disorders and factors contributing to sleep disturbances can lead to POD. Numerous studies on home sleep disturbance and cognitive problems have demonstrated the relevance of SDT and delirium.38,39 Obstructive sleep apnoea (OSA) is a common sleep disorder characterised by repeated episodes of complete or partial airway obstruction during sleep, which can increase the risk of POD as disrupted sleep and low oxygen levels can lead to fatigue and decreased cognitive functioning.40 Insomnia is a sleep condition characterised by trouble falling asleep, remaining asleep, or waking up too early, which can also raise the risk of POD.9 Pain, medication use, environmental factors, OSA, insomnia and other factors can contribute to postoperative sleep disturbances resulting in poor sleep quality.41 This may be partly attributable to interference between variables, such as postoperative discomfort, that impact postoperative sleep quality and are risk factors for a delay in neurocognitive recovery.42 In our study, we did not further clarify the category of sleep disturbances, although a small number of patients have apnoea syndrome. In future studies, it would be beneficial to further categorise sleep disturbances to better understand their individual impacts on the incidence of POD.

Our study has several limitations. Firstly, its single-centre setting may limit the generalisability of our findings. Conducted amid the acute severe stage of the COVID-19 pandemic, the potential for admission bias cannot be ignored. The exigencies of the ongoing pandemic could have influenced the selection of surgical patients. Secondly, we used the Sleep Quality Numeric Rating Scale (SQ-NRS) as a subjective measure of sleep quality. The discretionary definition of cut-off values for the SQ-NRS might introduce categorical bias, as differing thresholds could result in varying categorisations of sleep quality. In addition, our delirium evaluation may have overlooked instances of hypoactive delirium due to the more conspicuous presence of hyperactive delirium during interviews or early postoperative stages. This potential omission could have introduced a misclassification bias into our research. Thirdly, our investigation focused solely on the association between sleep disruption on the initial postoperative night and delirium. However, other research indicates a relationship between sleep disturbances on the second postoperative day and the severity of delirium.43 This suggests that our study might not have captured the full impact of postoperative sleep disturbances on delirium occurrences. Fourth, it is important to remember that correlation does not imply causation. While our study identified a link between poor sleep quality and postoperative delirium, it does not establish a causal relationship. This differentiation between association and causation in this context is vital. Additional research is necessary to unravel the causal relationship.

Despite these limitations, our study provides important contributions to the existing body of knowledge on the impact of sleep quality on postoperative outcomes, particularly POD. Further exploration is needed to clarify whether sleep disturbances on subsequent postoperative days similarly influence the onset and severity of delirium.

In conclusion, our study demonstrated a significant association between poor sleep quality, as assessed by the SQ-NRS, and the increased risk of POD incidence in surgical patients. It is important to note that while our study found this association, it does not establish poor sleep quality as a direct cause of POD. Therefore, while addressing sleep disturbances in the pre-operative and postoperative periods remains essential, it should be done with an understanding of this potential relationship. Further research is needed to elucidate the underlying mechanisms of this association and explore whether interventions to improve sleep quality could impact the incidence of POD in surgical patients.

Acknowledgements relating to this article

Assistance with the article: we appreciate the assistance of Hangzhou Le9 Healthcare Technology Co., Ltd. with the data extraction and statistical analysis.

Financial support and sponsorship: this study was supported by the National Key Research and Development Program of China (grant no. 2018YFC2001901).

Conflicts of interest: none.

Presentation: none.

This manuscript was handled by Giovanna A.L. Lurati Buse.

References 1. Aldecoa C, Bettelli G, Bilotta F, et al. European Society of Anaesthesiology evidence-based and consensus-based guideline on postoperative delirium. Eur J Anaesthesiol 2017; 34:192–214. 2. Hughes CG, Boncyk CS, Culley DJ, et al. American Society for Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on Postoperative Delirium Prevention. Anesth Analg 2020; 130:1572–1590. 3. Pisani MA, Friese RS, Gehlbach BK, et al. Sleep in the intensive care unit. Am J Respir Crit Care Med 2015; 191:731–738. 4. Vacas S, McInrue E, Gropper MA, et al. The feasibility and utility of continuous sleep monitoring in critically ill patients using a portable electroencephalography monitor. Anesth Analg 2016; 123:206–212. 5. Park MJ, Yoo JH, Cho BW, et al. Noise in hospital rooms and sleep disturbance in hospitalized medical patients. Environ Health Toxicol 2014; 29:e2014006. 6. Flink BJ, Rivelli SK, Cox EA, et al. Obstructive sleep apnea and incidence of postoperative delirium after elective knee replacement in the nondemented elderly. Anest