Abstract
Purpose
Postoperative cerebral complications may be related to abnormal intraoperative cerebral blood flow, and blood pressure fluctuations are common during thoracoscopic surgery and affect the cerebral blood supply, especially in elderly patients. Here, we investigated the clinical value of continuous cerebral autoregulation (CA) monitoring in protecting against cerebral dysfunction in elderly patients undergoing thoracoscopic surgery.
Methods
This study included 84 patients who underwent video-assisted thoracoscopic lobectomy under general anaesthesia and were divided by age into an elderly group (aged ≥ 65 years, 40 patients) and a middle-aged group (aged 40–64 years, 44 patients) based on age-related physiological differences that may influence cerebral autoregulation and postoperative outcomes. General patient information, intraoperative regional cerebral saturation oxygenation (rScO2), mean arterial pressure (MAP), and the surgery duration were recorded. The Pearson correlation coefficient between rScO2 and MAP was calculated to obtain the cerebral oximetry index (COx), which was used to assess CA function and determine the upper limit of cerebral autoregulation (ULA) and the lower limit of cerebral autoregulation (LLA), i.e., the optimal MAP range. Postoperative delirium (POD) was assessed using the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM). All patients underwent total intravenous anaesthesia. The primary observation was the difference in the cumulative time of impaired CA function between the elderly and middle-aged groups. The secondary observations included the incidence rates of POD and the optimal MAP range in both groups.
Results
Elderly patients had a significantly longer cumulative impaired CA time than middle-aged patients (cumulative impaired CA time ratio (%): middle-aged group, 9.72 ± 6.44 vs. elderly group, 20.41 ± 9.27; P<0.001). Two patients (5%) in the elderly group and 1 patient (2.27%) in the middle-aged group screened positive for POD on at least 1 day. Furthermore, based on the COx calculation, the optimal MAP range was determined to be (67.13 ± 5.46,96.40 ± 7.01) mmHg for the elderly group and (63.77 ± 5.74,101.66 ± 8.23) mmHg for the middle-aged group.
Conclusions
Compared with middle-aged patients, elderly patients have double the duration of intraoperative cerebral blood flow autoregulation impairment, and their optimal mean arterial pressure (MAP) range is narrower, with the maintenance interval being 10 mmHg smaller than that of middle-aged patients (67–96 mmHg in elderly patients vs. 63–102 mmHg in middle-aged patients).
Trial registration
The study was registered with the Chinese Clinical Trial Registry (http://www.chictr.org.cn/; registration no. ChiCTR2400080120; registration date: January 22, 2024).
Supplementary Information
The online version contains supplementary material available at 10.1186/s12871-026-03791-2.
Keywords: Cerebral autoregulation, Optimal mean arterial pressure, Delirium, Surgery, Neuropsychological tests
Background
Thoracic surgery is associated with a high incidence of postoperative neurologic complications with complex aetiologies. Most studies have revealed a potential link between transient hypoxia induced by intraoperative one-lung ventilation and postoperative neurologic complications [1–3]. Although the cerebral oxygen saturation value fluctuates during one-lung ventilation, it remains within a safe range in most cases. Dynamic changes in cerebral perfusion pressure, a core element in assessing the autoregulatory function of cerebral blood flow, play a pivotal role in maintaining the normal physiological function of the brain [4]. The uniqueness of thoracic surgery lies in its special anatomical location near large blood vessels, which, together with the common intraoperative lateral position, inevitably trigger complex fluctuations in haemodynamics, which in turn impact cerebral perfusion.Based on these confirmed physiological associations, there is a strong basis to reasonably infer that although thoracic surgery does not directly act on cerebral blood flow autoregulatory mechanisms, it indirectly impacts cerebral blood flow autoregulation by altering cerebral perfusion pressure.
Cerebral autoregulation (CA) is a critical physiological mechanism by which the cerebrovascular system responds to fluctuations in blood pressure, automatically regulates cerebral blood flow, and ensures stable brain tissue perfusion [5]. When this mechanism is impaired, a series of serious complications, such as ischaemic brain injury, cerebral oedema, and even cerebral haemorrhage, may occur, especially during surgery, and hypotension or hypertension may increase the risk of postoperative neurological complications such as postoperative delirium (POD) [6, 7]. However, most of these studies have focused on carotid endarterectomy [8] and extracorporeal circulation [5, 7], and more research is needed on thoracoscopic surgery.
In addition, age-related variations in CA remain a topic of debate. Burkhart [9] et al. noted that although cerebral autoregulation may be slightly impaired in older people, its impairment may not be clinically significant, thus implying that elderly individuals can tolerate some degree of haemodynamic fluctuations. In contrast, Yunliang found that, compared with middle-aged adults, older adults aged 65 years and older experience a significant reduction in cerebral blood flow autoregulation and are more sensitive to haemodynamic fluctuations, which may increase the risk of POD [10].
Thus, we hypothesized that during thoracic surgery, elderly patients are more vulnerable to injury due to impaired CA, which increases the risk of POD. We tested this hypothesis by continuously monitoring regional cerebral saturation oxygenation (rScO2) using near-infrared spectroscopy (NIRS) and recording the mean arterial pressure (MAP). Pearson’s correlation coefficient was calculated to statistically analyse the relationship between the MAP and rScO2, and we calculated the cerebral oximetry index (COx). Plotting the COx values revealed a U-shaped curve, which reflected the effect of changes in the MAP on the CA status. A high COx value indicates that CA is impaired, and a low COx value suggests that CA is normal [11–13]. In this study, we plotted COx values on U-shaped graphs to determine the upper limit of cerebral autoregulation (ULA) and the lower limit of cerebral autoregulation (LLA), i.e., the optimal MAP range, which may indicate the optimal CA and a reduced risk of delirium, in elderly patients after thoracic surgery to optimize intraoperative blood pressure management.
Materials and methods
General data and collection
Patients who underwent video-assisted thoracic surgery (VATS) lobectomy under general anaesthesia at Shanxi Bethune Hospital between February and May 2024 were consecutively enrolled in this prospective observational study. The patients were divided into two age groups: the middle-aged group, which consisted of 44 patients aged 40–64 years, and the elderly group, which comprised 40 patients aged 65 years and older. The middle-aged group was selected for comparison to observe potential differences in cerebral autoregulatory function and postoperative delirium rates between these two age groups. By comparing CA function and outcomes between age groups, we aimed to identify age-specific vulnerabilities and optimize intraoperative blood pressure management. The inclusion criteria were as follows: (1) patients classified with an American Society of Anaesthesiologists (ASA) status of I to III; (2) patients scheduled for elective VATS lobectomy at our hospital; and (3) patients aged 40 years and older. The exclusion criteria are detailed in Appendix 1. This study was reviewed and approved by the Ethics Committee of Shanxi Bethune Hospital (approval number YXLL-2023-023). All patients signed an informed consent form. The study was registered with the Chinese Clinical Trial Registry (http://www.chictr.org.cn/; registration no. ChiCTR2400080120; registration date: January 22, 2024).
Anaesthesia methods
Preanaesthesia evaluation and preparation
Patients completed the Mini-Mental State Examination (MMSE) the day before surgery. Information such as sex, age, history of hypertension, and body mass index (BMI) was recorded. Upon entering the operating room, 5 L/min of oxygen was administered via a mask. Electrocardiograms, heart rate, pulse oximetry, noninvasive blood pressure, and the bispectral index (BIS)( Manufacturer: Kehui Medical Equipment International Trading Co, Shanghai, China) were monitored. Alcohol swabs were used to remove oils from the forehead skin first, and then after drying, electrode patches were placed 2 cm above the arch of the eyebrow and then covered with an opaque material to prevent bright light interference during the continuous monitoring of cerebral oxygen saturation (rScO2) (EGOS-600 NIR Tissue Blood Oxygen Parameter Nondestructive Monitor, Manufacturer: Suzhou Aiqin Biomedical Electronics Co, Suzhou, China). Peripheral venous access was established, and then the left radial artery was catheterized for continuous arterial blood pressure monitoring after a positive Allen test.
Anaesthesia induction
Anaesthesia was induced with sufentanil citrate (0.4 µg/kg), etomidate (0.2–0.3 mg/kg), and rocuronium bromide (0.6 mg/kg). After 3 min of assisted ventilation through a mask, double-lumen bronchial intubation was performed under direct laryngoscopy, and the position of the double-lumen bronchial tube was adjusted under fibre optic bronchoscopy, followed by connection to the anaesthesia machine for volume-controlled mechanical ventilation.
Anesthesia maintenance
The same anaesthesia induction method was used for both groups. Both groups received combined general intravenous anesthesia with unilateral erector spinae plane block (ESPB). Specifically, a dose of 20 ml of 0.2% ropivacaine was administered for the unilateral ESPB, which was performed prior to the induction of general intravenous anesthesia. Neither epidural anesthesia nor any other forms of peripheral nerve blocks were additionally applied in either group during the procedure. Anaesthesia was maintained with continuous intravenous infusions of 6–12 mg/(kg·h) of propofol and 0.1–1.0 µg/(kg·min) of remifentanil, with intermittent doses of sufentanil (0.15–0.7 µg/kg) and rocuronium bromide (0.15 mg/kg). During bilateral lung ventilation, the ventilator settings were adjusted to a tidal volume of 8–10 ml/kg, and a respiratory rate of 12 breaths/min. During single-lung ventilation(SLV), the respiratory parameters were set to a tidal volume of 6–8 ml/kg, a respiratory rate of 14–16 breaths/min. The lungs were inflated intermittently during surgery, and an appropriate positive end-expiratory pressure (PEEP) (3–5 cm H2O) was applied as a supplement with the aim of minimizing injury risks while ensuring effective gas exchange—key to optimizing outcomes during SLV [14]. (Due to the limited space in the main text, the detailed explanation has been placed in Appendix 2).Intraoperative hemodynamic stability was maintained as needed via intravenous infusion of norepinephrine, with the target of keeping the mean arterial pressure (MAP) within ± 20% of the baseline value. Meanwhile, bispectral index (BIS) monitoring was performed throughout the procedure, and the anesthetic dosage was adjusted to maintain the BIS value between 40 and 60, which ensured an appropriate depth of anesthesia. (The definition of hypotension, its management, and the criteria for norepinephrine administration are detailed in Appendix 3.)The end-tidal carbon dioxide pressure (PetCO2) was maintained at 35–45 mmHg. The nasopharyngeal temperature was monitored, and a heating device was used to maintain the nasal temperature between 36 and 37 °C. An arterial blood gas analysis was performed before single-lung ventilation and 30 min after single-lung ventilation.
Postoperative analgesia and follow-up
Postoperative multimodal analgesia using a paravertebral nerve block plus a uniform intravenous analgesic pump can maintain a numerical rating scale (NRS) pain score ≤ 3. On postoperative days 1–3, an anaesthesiologist who was not involved in the anaesthesia induction procedure but was familiar with the criteria used for diagnosing postoperative delirium screened patients for postoperative delirium using the 3-Minute Confusion Assessment Method (3D-CAM)(See Appendix 4 for details).
Data collection and processing
In this study, a Philips IntelliVue MX Series patient monitor (Model: M8004A; Manufacturer: Philips Medizin Systeme Böblingen GmbH, Germany) was used to record invasive MAP every 12 s. Postoperatively, the MAP recorded during the entire monitoring period was integrated to construct a detailed MAP fluctuation curve (Fig. 1A). An EGOS-600 NIR Tissue Blood Oxygen Parameter Nondestructive Monitor (manufacturer: Suzhou Aiqin Biomedical Electronics, Suzhou, China) was used to record regional cerebral oxygen saturation (rScO2) every 2 s. Similarly, postoperative rScO2 fluctuation curves were constructed (Fig. 1B). We developed a specific algorithm using the Python programming language and incorporating libraries such as Numpy, Scipy, and Pandas to precisely calculate the COx value. Initially, we applied a linear interpolation method with an interpolation factor of 1/6 to increase the frequency of MAP recordings to every 2 s, ensuring temporal alignment between the MAP and the rScO2 value. This process involved a linear estimation using adjacent MAP values to generate a new MAP value every 2 s, yielding 30 synchronized pairs of MAP and rScO2 data points per minute. Considering that cerebral oxygenation monitoring data may be affected by high-frequency noise and baseline drift, we employed signal processing techniques to filter out irrelevant high-frequency components and to correct baseline shifts, preserving low-frequency signals related to CA and thus enhancing data purity and reliability [15].Nonoverlapping processed data were recorded every 10 s on average, allowing the development of time series plots of the MAP and the rScO2 value (Fig. 1C) to provide a visual representation of the dynamic monitoring procedures used for each patient. The patients’ MAP values were sorted in ascending order and then grouped into multiple 2 mmHg intervals. Within each interval, the Pearson correlation coefficient between the MAP and rScO2 values was calculated to obtain the COx value for each interval. These COx values were plotted on the vertical axis against the corresponding MAP intervals on the horizontal axis, with the expectation of displaying a typical U-shaped curve (Fig. 2). (The specific calculation of the U-shaped curve is detailed in Appendix 5).Based on related studies, a COx value of 0.3 was set as the threshold. When the COx value was ≤ 0.3, cerebral blood flow did not change with variations in the MAP, and the MAP was within the optimal range for optimal CA, maintaining constant cerebral blood flow. When the COx was > 0.3, the cerebral blood flow passively changed with the MAP, and the MAP was outside the range for an optimal CA and the patient was unable to maintain constant cerebral blood flow [10, 16]. Consequently, COx ≥ 0.3 was the diagnostic threshold for dysfunctional cerebral autoregulation (DCA) applied in primary analysis, and a horizontal line at COx = 0.3 was drawn to represent the transition from normal to impaired CA [10].The blood pressure range corresponding to the intersection points with the curve is referred to as the optimal blood pressure range (MAPOPT range), and the lowest point of the curve is considered the optimal intraoperative MAP (MAPOPT) (Fig. 2). In both groups, impaired CA was represented by the cumulative time ratio of DCA expressed as a percentage, which was calculated as the total amount of time the COx value exceeded the threshold divided by the total perioperative recording time, multiplied by 100% [DCA cumulative time ratio (%) = (cumulative amount of time the COx value exceeded the threshold value/the total perioperative recording time) × 100%].
Fig. 1.
Graph of MAP-rScO2 time series
Fig. 2.
Optimal Mean Arterial Pressure and Blood Pressure Range
Figure 1A and B are the original data curves collected: Fig. 1A shows the MAP data collected at 12-second intervals, and Fig. 1B shows the rScO2 data collected at 2-second intervals. Figure 1C is the MAP-rScO2 time series plot processed by the software program.
Since the increase in sampling frequency of MAP values is mainly achieved through interpolation, and interpolation calculations do not introduce additional high-frequency components, there is no need to remove high-frequency processing for MAP values.
These COx values were plotted on the vertical axis against the corresponding MAP intervals on the horizontal axis, with the expectation of displaying a typical U-shaped curve. Based on related studies, a COx value of 0.3 was set as the threshold. The blood pressure range corresponding to the intersection points with the curve is referred to as the optimal blood pressure range (MAPOPT range), and the lowest point of the curve is termed the optimal intraoperative MAP (MAPOPT).
Observation indicators
Primary observation indicator
The primary observation indicator was the difference in the cumulative duration of impaired CA between the elderly group and the middle-aged group.
Secondary observation indicators
The secondary observation indicators were the incidence rate of POD and the optimal range of MAP in both groups.
Statistical analysis
Statistical description and methods
The data were described and analysed using SPSS 24.0 statistical software. Normally distributed measurement data are presented as means ± standard deviations (xˉ±s), whereas data with a skewed distribution are presented as medians (Q1, Q3). Count data are presented as the numbers of cases (n, %). The Pearson correlation coefficient was calculated to assess the correlation between the mean arterial pressure (MAP) and regional cerebral oxygen saturation (rScO2). The chi-square test was applied to compare dichotomous variables between groups, and independent sample t tests were used to compare measurement data between groups.The P value was set as two-sided for all comparisons, and a value of P < 0.05 was considered statistically significant.
For the comparison of MAP ranges, nonparametric data were analysed using the Kruskal‒Wallis test, whereas parametric data were analysed using one-way ANOVA. These tests were selected based on the data distribution, which was initially assessed using normality tests, such as the Shapiro‒Wilk test.
For multiple comparisons, appropriate adjustments were made. In the case of one-way ANOVA, the Bonferroni correction was applied to control the familywise error rate, ensuring that the overall significance level remained at the predefined threshold (typically 0.05), thus minimizing the risk of false positives due to multiple comparisons. When the Kruskal‒Wallis test revealed significant differences, pairwise comparisons were conducted using Dunn’s test, applying the Bonferroni-corrected significance level.
Sample size calculation
The primary indicator in this study was the difference in the cumulative duration of impaired CA between the elderly group and the middle-aged group. Based on previous studies, A COx value ≥ 0.15 represents the minimum clinically meaningful between-group difference required to achieve statistical significance [16]. At least 32 patients are needed for each group to achieve a 90% confidence interval (α = 0.05). Considering the potential for missing intraoperative COx values, monitoring failure, and loss to follow-up, we decided to increase the sample size by 20%. Therefore, at least 40 patients were included in each group.
Results
Comparison of baseline characteristics
In this study, 99 patients who underwent thoracic surgery patients were included. A total of 46 patients in the elderly group and 53 patients in the middle-aged group were recruited from February 1, 2024, to May 20, 2024; 15 patients were excluded, and 84 patients were ultimately included in the analysis (40 patients in the elderly group and 44 patients in the middle-aged group) (Fig. 3). The Consolidated Standards of Reporting Trials (CONSORT) recruitment diagram is shown in Fig. 3.
Fig. 3.
CONSORT recruitment diagram
When the basic data from the two groups of patients were compared, no statistically significant differences in body mass index (BMI), history of smoking, history of hypertension, history of diabetes, history of coronary heart disease, duration of single-lung ventilation, duration in the lateral decubitus position, or perioperative recording time were observed between the two groups (P > 0.05) (Table 1).
Table 1.
Comparison of the basic information of the two groups
| Clinical information | Elderly group (n = 40) |
Middle-aged group(n = 44) | t/X2 value | P value |
|---|---|---|---|---|
| Age (yrs) | 69.80 ± 4.68 | 56.50 ± 5.96 | 11.30 | <0.001 |
| BMI(kg/m²) | 24.14 ± 2.73 | 23.59 ± 3.06 | 0.861 | 0.392 |
| Smoking | 18(45.0%) | 15(34.1%) | 1.045 | 0.307 |
| hypertension | 14(35.0%) | 17(38.6%) | 0.119 | 0.730 |
| diabetes | 8(20.0%) | 8(18.2%) | 0.045 | 0.098 |
| Coronary | 5(12.5%) | 1(2.3%) | 3.304 | 0.069 |
| Single lung ventilation(min) | 134.90 ± 48.74 | 119.45 ± 41.16 | 1.574 | 0.119 |
| Side-lying time(min) | 182.35 ± 49.29 | 163.23 ± 45.50 | 1.849 | 0.068 |
| Surgical time(min) | 192.50 ± 50.49 | 179.16 ± 45.94 | 1.268 | 0.208 |
Comparison of primary observation indicator
In terms of the primary observational indicators, the study revealed that all patients exhibited some degree of DCA during surgery. Compared with the middle-aged group, the elderly group had a significantly longer cumulative duration of DCA (16.86 ± 11.58 vs. 38.25 ± 19.61, 9.72 ± 6.44 vs. 20.41 ± 9.27, P < 0.001) (Table 2) (Fig. 4). Postoperative follow-up revealed that 2 elderly patients (5%) and 1 middle-aged patient (2.27%) experienced postoperative delirium, which persisted for at least one day.
Table 2.
Comparison of the incidences of intraoperative DCA of the two groups
| Index | Elderly group (n = 40) |
middle-aged group(n = 44) | t/X2 value | P value |
|---|---|---|---|---|
| total recording time(min) | 192.50 ± 50.49 | 179.16 ± 45.94 | 1.268 | 0.208 |
| DCA time(min) | 38.25 ± 19.61 | 16.86 ± 11.58 | 6.01 | < 0.001 |
| DCA-CTR(%) | 20.41 ± 9.27 | 9.72 ± 6.44 | 6.08 | < 0.001 |
DCA-CTR cumulative time ratio of DCA, DCA dysfunctional cerebral autoregulate
Fig. 4.
Comparison of the incidences of intraoperative DCA of the two groups
Figure 4A shows that the duration of DCA was significantly longer in the elderly group than in the middle-aged group (P<0.001), and Fig. 4B shows that the percentage of DCA was significantly higher in the elderly group than in the middle-aged group (P<0.001).
Comparison of secondary observation indicators
The calculations yielded the results described below (Table 3). The optimal blood pressure ranges for the elderly and middle-aged groups were (67.13 ± 5.46, 96.40 ± 7.01) mmHg and (63.77 ± 5.74, 101.66 ± 8.23) mmHg, respectively, with a statistically significant difference between the two ranges (P = 0.008 and P<0.001). The optimal blood pressure range was significantly narrower in the elderly group (P<0.001) (Fig. 5).
Table 3.
The optimal blood pressure range during the two groups of patients
| Index | Elderly group (n = 40) |
Middle-aged group (n = 44) |
t/X2 value | P value |
|---|---|---|---|---|
| LLA(mmHg) | 67.13 ± 5.46 | 63.77 ± 5.74 | 2.73 | 0.008 |
| ULA(mmHg) | 96.40 ± 7.01 | 101.66 ± 8.23 | 3.14 | <0.001 |
| CA change range(mmHg) | 29.28 ± 6.03 | 37.89 ± 7.17 | 5.92 | <0.001 |
LLA the lower limit of cerebral autoregulation, ULA the upper limit of cerebral autoregulation, CA cerebral autoregulation
Fig. 5.
Comparison of the optimal blood pressure range during the two groups of patients
Discussion
The present study demonstrated that, compared with middle-aged patients, elderly patients had weaker cerebral blood flow autoregulation and were more susceptible to hemodynamic fluctuations, which may increase the risk of POD.
The decision to select the “cumulative duration” as the primary outcome was based on several considerations. During the perioperative period, cerebrovascular CA plays a central role in maintaining stable cerebral perfusion. Various factors during surgery dynamically affect CA function, and a measurement at a single time point cannot fully capture these changes. The cumulative duration, however, integrates the periods of CA dysfunction throughout the entire surgical process, providing a comprehensive quantitative indicator. This approach not only allows for a more precise assessment of how surgical factors impact the cerebrovascular system but also provides a holistic view of CA dysfunction during the procedure, offering valuable data to explore the relationship between surgery and brain function changes more thoroughly.
This study revealed that the cumulative duration of impaired CA during surgery in elderly patients significantly exceeded that in middle-aged patients and that the range for optimal CA was notably restricted in elderly patients. This finding is the cumulative duration of CA dysfunction was longer in elderly patients than in middle-aged patients [10]. According to previous reports, the cumulative incidence of POD in elderly patients who underwent elective surgery is 19% [17]. However, this study revealed a lower incidence of POD in elderly individuals, with only two patients (aged 74 and 66 years) exhibiting symptoms, and the durations of impaired CA in these two patients, as well as their percentages, were 33 min and 27 min and 14% and 23%, respectively, which were below the average. Given the small number of positive cases, a direct correlation between POD and CA impairment could not be established. Additionally, our postoperative follow-up period was only three days, which was insufficient to assess the long-term effects of impaired CA on the incidence of POD. Although a direct relationship between POD and impaired CA was not observed in elderly patients in this study, other studies revealed that impaired cerebral autoregulation and a MAP exceeding the range for optimal CA in patients who underwent cardiac surgery were associated with POD [18]. Impaired CA function is considered a key factor in the pathogenesis of perioperative neurocognitive disorders. Nakano [16] et al. conducted research in the intensive care unit (ICU) and reported that impaired CA in ICU patients is related to delirium, a direct association that is less commonly observed in the operating room. However, in both the operating room and the ICU, abnormal MAPs affecting cerebral autoregulation are associated with an increased risk of delirium. The incidence of impaired CA in the operating room is higher, despite its unclear mechanism, and may be related to factors such as medication, inflammation, and temperature. Therefore, optimized monitoring of CA is necessary during surgery, especially for elderly patients with a reduced regulatory capacity. Optimizing strategies to manage anaesthesia and control blood pressure to minimize intraoperative fluctuations in blood pressure and reduce the extent of the CA impairment is essential. Although impaired cerebral autoregulation is not an independent predictor of POD in elderly people, its potential role in the pathogenesis of delirium should not be overlooked and thus warrants further exploration and research.
In this study, a typical U-shaped curve was observed in most cases, and U-shaped graphs are crucial for analysing the intraoperative optimal mean arterial pressure (MAPOPT) and the optimal MAP range in middle-aged and elderly patients. This study revealed significant differences in the upper and lower limits of the optimal blood pressure range between the two age groups, with the optimal blood pressure range for elderly patients being significantly narrower than that for middle-aged patients, which is consistent with the physiological decline in elderly individuals. Because elderly people have fragile cerebral vessels and a decreased regulatory capacity, CA is reduced. Additionally, the blood pressures of two patients in the middle-aged group remained within the ideal range for optimal CA throughout the procedure, suggesting a better cerebral blood flow regulatory capacity in this age group. Effective intraoperative blood pressure management can protect the brain from the effects of low perfusion or overperfusion. The greater the percentage of time exceeding the range for optimal CA is, the greater the risk of postoperative brain injury and adverse functional outcomes, and the correlation is significant [10]. A study of patients who underwent neurosurgery revealed that individualized perioperative blood pressure management strategies targeting individuals with unimpaired CA may reduce the incidence of perioperative complications [19]. This study found optimal blood pressure ranges for elderly and middle-aged groups, differing numerically from those in other research—such as for radical rectal cancer surgery patients(71–113 mmHg) [10], ICU patients (55–100 mmHg) [20], and healthy adults ((lower limit ~ 70 mmHg) [21].A consensus exists, however, that optimal cerebral autoregulation requires a lower limit above 55 mmHg. Compared to the traditional broad range (50–150 mmHg), maintaining blood pressure with a lower limit of 55 mmHg (ideally ≥ 70 mmHg), especially for elderly patients, better optimizes anesthesia management and protects brain function. Thus, CA monitoring technology can provide personalized, precise MAP targets to guide intraoperative anesthesia and maximize brain protection.
A major strength of this study is the improved method for calculating the COx value, which has significant advantages over previous methods. Traditional studies typically synchronize the recordings of MAP and rScO2 and then calculate the Pearson correlation coefficient between the two over various lengths of time (such as 5 min, 15 min, and 30 min) to obtain the COx value [10, 16, 19, 22]. However, this method may diminish the impacts of extreme blood pressure values due to the averaging of data within the time window. A more refined strategy was adopted for this study: first, the MAP and rScO2 data were synchronized; then, the MAP values were sorted from low to high, and the corresponding rScO2 values were paired. Subsequently, blood pressure intervals were subdivided into units of 2 mmHg, and the Pearson correlation coefficient for each subdivided interval was calculated separately, thereby obtaining a more precise COx value. The advantage of this method is that through the secondary subdivision and sorting of blood pressure values, even minor fluctuations in blood pressure and their direct impacts on cerebral oxygen saturation can be captured, avoiding the “dilution” of extreme blood pressure values by averaging broad time windows, which could obscure the true impacts. Therefore, the calculation method used in this study can more accurately reflect the specific impact of blood pressure changes on cerebral oxygen saturation, providing a more precise guide for intraoperative blood pressure management and brain protection strategies.
This study has some limitations, with a low incidence of postoperative delirium, which may be related to the small sample size and short follow-up time. Future studies should expand the sample size and extend the follow-up period to allow for a more comprehensive investigation.
Conclusions
In conclusion, while our study could not directly establish a definitive correlation between the duration of CA dysfunction and the incidence of postoperative delirium, we made several key clinical contributions. Our findings highlight that elderly individuals possess diminished brain regulation capabilities compared with younger adults, emphasizing the need for tailored management strategies. Additionally, we determined that the optimal blood pressure range for elderly individuals is narrower, with the mean arterial pressure (MAP) ideally maintained between 60 and 100 mmHg. These insights provide valuable guidance for personalized, precise blood pressure management in surgical settings, improving care and outcomes for elderly patients.
Supplementary Information
Acknowledgements
All authors would like to express their sincere gratitude to the entire staff of the Department of Anaesthesiology at Baiqiu’en Hospital, Shanxi Province, for their generous support and invaluable assistance with this study.
Abbreviations
- Abbreviation
Full Form
- CA
Cerebral autoregulation
- NIRS
Near-infrared spectroscopy
- rScO2
Regional cerebral saturation oxygenation
- MAP
Mean arterial pressure
- COx
Cerebral oximetry index
- MMSE
Mini Mental Status Examination
- BMI
Body mass index
- POD
Postoperative delirium
- LLA
Lower limit of cerebral autoregulation
- ULA
Upper limit of cerebral autoregulation
- DCA
Dysfunctional cerebral autoregulation
- VATS
Video-assisted thoracic surgery
- PEEP
Positive end-expiratory pressure
- NRS
Numeric rating scale
- 3D-CAM
3-Minute Confusion Assessment Method
Authors' contributions
All authors contributed to the study’s conception and design. TJQ, HYW, HXR, and LY performed material preparation, data collection, and analysis. SMY wrote the first draft of the manuscript. ZP did a revision of the first draft, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Data availability
The datasets used and analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethical approval and consent to participate
This study was reviewed and approved by the Ethics Committee of Shanxi Bethune Hospital (approval number YXLL-2023-023).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and analysed during the current study are available from the corresponding author on reasonable request.