DOI: https://doi.org/10.21203/rs.3.rs-17613/v1
Background Patients with septic shock are prone to have impaired cerebral autoregulation and to have an imbalance in cerebral oxygen metabolism. Transcranial doppler (TCD) and tissue oxygen saturation monitoring were performed to observe the changes in cerebral hemodynamic indices of the middle cerebral artery, and in cerebral and peripheral tissue oxygen saturation (StO 2 ) to identify risk factors for sepsis-associated delirium (SAD).
Methods Patients with septic shock that were admitted to the Department of Critical Care Medicine of Xiangya Hospital of Central South University from May 2018 to March 2019 were prospectively enrolled, which were divided into an SAD group and a non-SAD group according to the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Both groups were compared with respect to patient characteristics, blood gas analysis indexes, organ function indicators, cerebral hemodynamic index, cerebrovascular automatic regulation function (transient hyperemia response rate, THRR index), the changes in regional cerebral oxygen saturation, and peripheral tissue oxygen saturation.
Results The incidence of SAD was 39% (20/51). The overall 28-day mortality rate was 43% (22/51). Compared with the non-SAD group, patients in the SAD group required a longer mechanical ventilation time (5 days [95% confidence interval (CI) 2, 6] vs 1day [95% CI 1, 4], p=0.015) and ICU stay (9 days [95% CI 5, 20] vs 5 days [95% CI 3, 9], p=0.042) and has a higher 28-day mortality rate(65% vs 29%, p=0.011).Multivariate regression analysis showed that the THRR index (OR=5.770, 95% CI:1.222-27.255; p=0.027)and mean value of regional cerebral oxygen saturation (rSO 2 ) <55% (OR=3.864, 95% CI:1.026-14.550;p=0.046) were independent risk factors for SAD.
Conclusions SAD has a high incidence in septic patients and is associated with poor prognosis. Our results provide a clinical basis for improving early detection and treatment of SAD.
Sepsis-associated delirium (SAD) is considered a diffuse cerebral dysfunction caused by a systemic inflammatory response to an infection without evidence of a central nervous system involvement (i.e., infection). SAD can develop in a short time and fluctuate transiently with time [1–2]. Studies have found that neuroinflammation, abnormal cerebral perfusion, neurotransmitter imbalances and neuronal degeneration may be involved in the pathogenesis of SAD [3–6]. Other studies suggest that changes in cerebral blood flow caused by hemodynamic instability, decreased brain oxygen uptake, blood-brain barrier disruption and cerebral edema are important predisposing factors for SAD [7–8]. Transcranial Doppler (TCD) ultrasound can visually detect the changes of cerebral perfusion and cerebral autoregulation in patients with sepsis. Pfister et al. [9] found a significant correlation between cerebral vascular autoregulation disorders and SAD. Some studies have found that the changes of cerebral hemodynamics detected by TCD ultrasound are closely related to the clinical symptoms of SAD [10].
Hypotension and hypoxia not only increase neuronal apoptosis but are also directly associated with poor prognosis [11]. At present, for both central venous oxygen saturation(ScvO2)and Mixed venous oxygen saturation(SvO2), the monitoring is non-continuous, requires invasive procedures, and does not allow specific monitoring of local brain tissue, which makes it impossible to quickly and effectively identify patients with sepsis who are at risk of brain or brain-tissue hypoxia [12]. Regional cerebral oxygen saturation (rSO2) and tissue oxygen saturation (StO2) monitoring which are derived from near infrared spectroscopy (NIRS) can provide non-invasive assessment of cerebral oxygen metabolism and local tissue oxygen metabolism, providing real-time and continuous information of the balance of oxygen supply and demand. In addition, rSO2 is a sensitive indicator of global cerebral hypoperfusion [13]. Studies have revealed a good correlation between tissue oxygen saturation monitoring and clinical evaluation of sepsis or septic shock [14–15].
Therefore, our study uses TCD and tissue oxygen saturation monitoring to observe the changes in cerebral hemodynamic indices of the middle cerebral artery, and in cerebral and peripheral tissue oxygen saturation (StO2) to identify risk factors for sepsis-associated delirium (SAD).
Fifty-one patients with septic shock who were admitted to the Department of Intensive Care Unit, Xiangya Hospital of Central South University from May 2018 to March 2019 were enrolled in our study. Our study was a pilot observational trial with inclusion criteria were in accordance with the definition of sepsis 3.0. All included patients were treated according to the 2016 International Guidelines for the Treatment of Sepsis and Septic Shock [16] (i.e., Mean arterial pressure (MAP) reached 65 mmHg and lactic acid normalization was the target of initial resuscitation). Patients with diagnostic criteria for septic shock [17] were included in the study. Exclusion criteria: (1) age < 18 years ; (2) previous history of mental disorders or craniotomy; (3) neurological diseases (clear intracranial lesions such as cerebral hemorrhage, subarachnoid hemorrhage, craniocerebral trauma, stroke, intracranial infection, etc.); (4) liver failure with hepatic encephalopathy suspected; (5) pregnancy; (6) abnormal findings upon cervical vascular examination, by head MRI/CT and TCD ultrasound (such as carotid plaque with hemodynamically significant stenosis (eg > 50%)., thrombosis, etc.); (7) inability to detect blood flow signals in the temporal window by TCD. The primary outcome was delirium from admission to the ICU to Day 7 or discharge from the ICU.The secondary outcome was the 28-day mortality rate.
This study was conducted in line with the standards of medical ethics. It was approved by the Central South University Xiangya Hospital Ethics Committee (Ethics No.:2018101082), and informed consent was obtained from the patient's immediate families.
The diagnosis of delirium was based on the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), which was performed twice a day by trained researchers at the ICU (10:00–11:00 am; 16:00–17:00 for at first day of ICU admission up to 7 days. Patient assigned to the "SAD group" on the basis of a twice positive CAM-ICU screen performed by two researchers. All patients with septic shock were classified into a SAD group and non-SAD group according to the presence or absence of delirium, respectively.
General data, Acute physiological and chronic health status assessment scores (APACHE II) score, sequential [sepsis-related] organ failure assessment (SOFA) score, administration of sedation and analgesia drugs, continuous renal replacement therapy (CRRT) mechanical ventilation time (days), ICU time (days), and 28-day mortality (%) of septic shock patients were determined.
Arterial and central venous blood gas indicators, lactate clearance, Central venous pressure (CVP), Norepinephrine dosage, total resuscitation fluid, and urine output. Patients with septic shock included in the study completed critical echocardiography within 1 hour of admission, which measured cardiac output (CO), left ventricular ejection fraction (LVEF%), inferior vena cava diameter (IVCD), and Inferior Vena cava collapse index (IVC-CI).
Blood samples were monitored immediately after admission to the ICU, including organ function was assessed using routine blood indicators, liver and kidney function indicators, and routine coagulation indicators. Sepsis-associated biomarker indicators were procalcitonin (PCT); Neuron Specific Enolase (NSE), Central Nervous System Specific Protein (S100β).
TCD ultrasound (Shenzhen Delikai, EMS-9A dual channel, 1.6 MHz TCD probe) was used to obtain the bilateral middle cerebral arteries (MCAs) flow signal through the temporal window after 6 hours of initial resuscitation in patients with septic shock. Recording the diastole velocity of middle cerebral arteries (VdMCA), mean blood flow velocity (the mean velocity of middle cerebral arteries;VmMCA), systolic velocity of middle cerebral arteries (VsMCA),pulsatility index (PI;PI=(VsMCA-VdMCA)/VmMCA), and calculation of cerebral blood flow index (CBFi, CBFi = 10 × MAP/1.47 PI), record whether the S1 and S2 peaks are fused during systole, and complete the dynamic assessment of cerebrovascular autoregulation. Dynamic cerebrovascular autoregulation was assessed by the transient hyperemia response rate (THRR) method. That is, the blood flow velocity of the middle cerebral artery was stably decreased to 30–50% of the baseline value by confirmed carotid artery compression for 3–9 seconds, and then the ratio of the blood flow velocity to the baseline blood flow velocity was measured. A THRR index > 1.09 is considered evidence for a dynamic cerebrovascular autoregulation function, conversely, at a THRR index ≤ 1.09 cerebrovascular autoregulation is considered to be impaired [18].
Tissue oxygen saturation was determined using the non-invasive tissue oxygen saturation monitor (CASMED, FORE-SIGHT) to continuously collect forehead cerebral oxygen saturation (rSO2) (Large probe, detection depth 2.5 cm below the skull) and the thenar eminence tissue oxygen saturation (StO2) (small probe, detection depth 0.5-2 cm). rSO2 and StO2 values were continuously recorded for at least 1 hour after 6 hours of initial resuscitation in patients with septic shock, and these data were uniformly processed in the later period. The rSO2 and StO2 data were analyzed at multiple thresholds (༜65%, < 60%, < 55%, and < 50%) to determine 1 suitable for comparison[19].
SPSS version 24.0 (SPSS Inc, Armonk, New York) was used for data analysis. The data were tested for normality. The data of the continuous variable that conformed to or approximated the normal distribution were expressed as mean ± standard deviation (χ ± s). The comparison between two samples was performed by independent student t test. Data that did not conform to the normal distribution were expressed as median [interquartile range; IQR], and the comparison between the two samples was performed using the Wilcoxon rank-sum test. The chi-square (X2) test was used to compare categorical data. When the theoretical frequency was < 5, the continuous correction method was adopted. When the theoretical frequency was < 1, the exact probability method was adopted. Multivariate logistic regression analysis was used to detect independent predictors of delirium, the predictor variables were selected from the risk factors displaying p < 0.05 in tables. The Pearson analysis method was used to analyze the correlation of the above-mentioned normal distribution variables. P < 0.05 was considered statistically significant.
Of a total of 121 patients with septic shock that were screened in this study, 66 patients met the exclusion criteria and 4 patients did not complete delirium evaluation. Therefore, 51 patients were enrolled in the study after CAM-ICU evaluation was successfully completed. SAD incidence was 39%. (Fig. 1).
Of the 51 patients with septic shock, the average age was 53 ± 11 years, including 31 males, accounting for 61%. Patients in the SAD group had a higher SOFA score (12 ± 5 vs 6 ± 3, p < 0.001) and a higher APACHE II score (21 ± 7 vs 15 ± 6, p = 0.003) (Table 1). The overall median mechanical ventilation time was 5 [1, 5] days and median ICU stay was 6 [3, 12] days. Compared with the non-SAD group, the SAD group needed a longer mechanical ventilation time (5 [2, 6] days vs 1 [1, 4] days, p = 0.015), and ICU stay (9 [5, 20] days vs 5 [3, 9] days, p = 0.042). The 28-day mortality rate was 43% for the entire cohort and was significantly higher in the SAD group (65% vs 29%, p = 0.011) (Table 1).
| Characteristic | The overall population (n = 51) | SAD group (n = 20) | Non-SAD group(n = 31) | p value | |
|---|---|---|---|---|---|
| Age(years)a | 53 ± 11 | 56 ± 11 | 52 ± 14 | 0.239 | |
| Gender(man)b | 31(61) | 13(65) | 18(58) | 0.620 | |
| BMI(kg/m2)a | 22.59 ± 2.50 | 23.05 ± 2.48 | 22.29 ± 2.51 | 0.294 | |
| Education(years)b | 0.884 | ||||
| ༜6 | 19(37) | 8(40) | 11(36) | ||
| 6–9 | 13(25) | 4(20) | 9(29) | ||
| 9–12 | 11(22) | 5(25) | 6(19) | ||
| 12–16 | 8(16) | 3(5) | 5(16) | ||
| Hypertension b | 14(27) | 6(30) | 8(26) | 0.743 | |
| Coronary heart disease b | 8(16) | 4(20) | 4(13) | 0.775 | |
| Diabetes mellitus b | 9(18) | 3(15) | 6(19) | 0.982 | |
| Temperature(℃)a | 37.2 ± 0.9 | 37.2 ± 0.7 | 37.2 ± 1.0 | 0.936 | |
| Heart rate(beats/min)a | 109 ± 17 | 112 ± 18 | 106 ± 16 | 0.276 | |
| Breaths(/min)a | 22 ± 6 | 22 ± 6 | 22 ± 6 | 0.864 | |
| Pulse oxygen saturation (%)a | 98 ± 3 | 97 ± 4 | 98 ± 3 | 0.211 | |
| Blood glucose(mmol/L)a | 9.0 ± 4.5 | 9.4 ± 5.3 | 8.7 ± 3.9 | 0.597 | |
| MAP(mmHg)a | 81 ± 14 | 82 ± 18 | 80 ± 11 | 0.687 | |
| SOFA a | 8 ± 5 | 12 ± 5 | 6 ± 3 | ༜0.001 | |
| APACHE Ⅱ a | 18 ± 7 | 21 ± 7 | 15 ± 6 | 0.003 | |
| Sedative b | 25(49) | 11(55) | 14(45) | 0.493 | |
| Analgesic b | 34(67) | 15(75) | 19(61) | 0.311 | |
| Detection rate of pathogenic bacteria b | 29(57) | 13(65) | 16(52) | 0.242 | |
| CRRT b | 16(31) | 9(45) | 7(23) | 0.092 | |
| Mechanical ventilation time (days)c | 2[1, 5] | 5[2, 6] | 1[1, 4] | 0.002 | |
| ICU stay (days) c | 6[3, 12] | 9[5, 20] | 5[3, 9] | 0.030 | |
| 28-day mortality b | 22 (43) | 13 (65) | 9(29) | 0.011 | |
| a values shown as mean ± standard deviation; b values shown as number of patients (%); c values shown as as median [IRQ]. | |||||
Compared with the non-SAD group, the SAD group had significantly higher blood lactate (Lac) (t6h) (5.2 ± 4.3 mmol/L vs 2.1 ± 1.3 mmol/L, p = 0.005), lower lactate clearance rate (t6h) (-0.36 ± 0.73 mmol/L vs -0.002 ± 0.35 mmol/L, p = 0.049), and lower oxygenation index (t6h) (230 ± 116 vs 322 ± 121, p = 0.01). There were no significant differences in critical echocardiography indicators p > 0.05). An additional file shows this in more detail. [See Additional file 1 in Supplementary materials].
The white blood cell count (WBC) was significantly higher in the SAD group than in the non-SAD group (21.38 ± 14.16 vs 12.55 ± 11.61, p = 0.019). Likewise, urea nitrogen (BUN) (9.6 [5.7, 16.9] mmol/L vs 5.8 [2.8, 11.2] mmol/L, p = 0.046) and neuron specific enolase (NSE (18.24 [13.29, 27.08] vs 9.55 [6.13, 18.11], p = 0.031) were significantly higher in the SAD group than in the non-SAD group. An additional file shows this in more detail. [See Additional file 2 in Supplementary materials].
Compared with the non-SAD group, the SAD group had a lower cerebral artery diastolic blood flow velocity (VdMCA) (49.7 cm/s ± 20.3 vs 61.9 ± 17.3, p = 0.026), and a higher pulsation index (PI) (0.98 ± 0.19 vs 0.84 ± 0.20, p = 0.019). The dynamic cerebrovascular dysfunction (ie, THRR index < 1.09) was 22% in the entire cohort, but was significantly higher in the SAD group compared with the non-SAD group (40% vs 10%, p = 0.01) (Table 2).
| Variable | The overall population (n = 51) | SAD group (n = 20) | Non-SAD group (n = 31) | P value |
|---|---|---|---|---|
| MAP (mmHg)a | 81 ± 14 | 82 ± 18 | 80 ± 11 | 0.687 |
| VsMCA(cm/s) a | 127.9 ± 37.9 | 122.0 ± 50.2 | 131.7 ± 27.7 | 0.436 |
| VmMCA(cm/s) a | 79.2 ± 26.6 | 74.0 ± 29.7 | 82.7 ± 24.3 | 0.258 |
| VdMCA(cm/s) a | 57.1 ± 19.3 | 49.7 ± 20.3 | 61.9 ± 17.3 | 0.026 |
| PI a | 0.89 ± 0.21 | 0.98 ± 0.19 | 0.84 ± 0.20 | 0.019 |
| CBFi a | 648.73 ± 180.02 | 596.72 ± 196.38 | 682.28 ± 163.15 | 0.098 |
| S1S2 coalesce b | 30(59) | 11(55) | 19(61) | 0.656 |
| THRR index༜1.09b | 11(22) | 8(40) | 3(10) | 0.010 |
| a values shown as mean ± standard deviation; b values shown as number of patients (%). | ||||
The mean rSO2 value of the SAD group was lower than that of the non-SAD group (55 ± 7 vs 60 ± 6, p = 0.01). In the overall study cohort, 16 patients had an average rSO2 value of < 55%, and this was significantly more predominant in the SAD group than in the non-SAD group (50% vs 19%, p = 0.021). In addition, 15 patients had a mean StO2 < 60%, which was also significantly more predominant in the SAD group than in the non-SAD group (45% vs 19%, p = 0.049) (Table 3).
| The overall population (n = 51) | SAD group (n = 20) | Non-SAD group(n = 31) | p | |
|---|---|---|---|---|
| rSO2 value(%)a | ||||
| rSO2min | 55 ± 7 | 53 ± 7 | 56 ± 6 | 0.055 |
| rSO2max | 62 ± 6 | 60 ± 6 | 63 ± 6 | 0.056 |
| rSO2mean | 58 ± 7 | 55 ± 7 | 60 ± 6 | 0.010 |
| Number of patients b | ||||
| rSO2mean༜60% | 29(57) | 14(70) | 15(48) | 0.128 |
| rSO2mean༜55% | 16(31) | 10(50) | 6(19) | 0.021 |
| rSO2mean༜50% | 7(14) | 5(25) | 2(6) | 0.144 |
| StO2value(%)a | ||||
| StO2min | 63 ± 8 | 62 ± 9 | 64 ± 7 | 0.391 |
| StO2max | 70 ± 7 | 69 ± 9 | 71 ± 6 | 0.373 |
| StO2mean | 67 ± 7 | 65 ± 9 | 68 ± 6 | 0.335 |
| Number of patients b | ||||
| StO2mean༜65% | 16(31) | 9(45) | 7(23) | 0.092 |
| StO2mean༜60% | 15(29) | 9(45) | 6(19) | 0.049 |
| StO2mean༜55% | 6(12) | 4(20) | 2(6) | 0.307 |
| a values shown as mean ± standard deviation; b values shown as number of patients (%). | ||||
Multivariate regression analysis of risk factors which focus on the brain parameters for SAD, Logistic regression analysis showed that there were a number of independent risks for predicting SAD, including the THRR index < 1.09 (odds ratio (OR) = 5.77, 95% confidence interval (CI) : 1.222–27.255, p = 0.027), and the mean rSO2 < 55% value (OR = 3.864, 95% CI: 1.026 to 14.550, p = 0.046) (Table 4).
| Variable | B | S.E. | Wals | p | Exp(B) | 95% C.I. for EXP(B) | |
|---|---|---|---|---|---|---|---|
| upper | lower | ||||||
| PI | 1.160 | 2.109 | 0.303 | 0.582 | 3.191 | 0.051 | 199.021 |
| VdMCA | -0.035 | 0.024 | 2.177 | 0.140 | 0.965 | 0.921 | 1.012 |
| THRR index༜1.09 | 1.753 | 0.792 | 4.896 | 0.027 | 5.770 | 1.222 | 27.255 |
| rSO2 mean | -0.083 | 0.058 | 2.045 | 0.153 | 0.921 | 0.822 | 1.031 |
| rSO2mean༜55% | 1.352 | 0.676 | 3.993 | 0.046 | 3.864 | 1.026 | 14.550 |
| Sig. = significance; EXP(B) = Estimated odds ratio. | |||||||
Impaired cerebral hemodynamics and imbalance of cerebral oxygen metabolism play an important role in the development of SAD. Our study confirmed a high incidence of SAD among patients with sepsis, which was associated with poor prognosis. Cerebrovascular autoregulation dysfunction (THRR index < 1.09) and mean cerebral oxygen saturation < 55% were found to be independent risk factors for SAD.
First, our study showed that the incidence of SAD in patients with septic shock was 39% (20/51), and that the SAD group required longer mechanical ventilation and ICU stay, and had a significantly higher 28-day mortality rate, which highlighted the need for identifying risk factors allowing early identification of SAD. In the ICU, early identification of reversible risk factors is very important for improving the prognosis of patients with sepsis or septic shock [20]. Tse et al. [21] proposed that age as well as certain pre-existing diseases and conditions are predictors of delirium after cardiac surgery. There are no recommended drugs or drug regimens to prevent delirium, so the focus should be on non-pharmaceutical interventions, including active communication, early detection of patients' psychological problems, family companionship, early activities, noise reduction, and good sleep [22].
Second, our study showed that cerebral vascular autoregulation dysfunction was an independent risk factor for delirium in patients with septic shock. This suggests that there is a decrease in cerebral perfusion and a decrease in cerebral vascular compliance in patients with SAD 6 hours after initial resuscitation. These changes may further affect cerebrovascular autoregulation. Cerebral hemodynamics and cerebral oxygen metabolism disorders play a crucial role in the development of SAD, which also lays the foundation for clinical hemodynamic management strategies targeting cerebral perfusion. Central nervous system dysfunction caused by sepsis is closely related to cerebral hypoperfusion caused by decreased cerebral blood flow. TCD ultrasound can monitor the systolic, diastolic, and mean blood flow velocity of anterior, posterior, and middle cerebral arteries to evaluate cerebral perfusion [23]. TCD also provides an indirect assessment of the cerebral circulation including cerebral vascular autoregulation. When the cerebral vascular autoregulation is impaired, the changes of cerebral ischemia or cerebral congestion that may occur with the changes of perfusion pressure may cause nerve damage, which may adversely affect the prognosis. As early as 1997, Smielewski et al. [24] found that the disappearance of the transient cerebral congestion response rate (THRR) was associated with poor prognosis in patients with severe craniocerebral injury. Although TCD and THRR index evaluation cannot completely replace the results of brain imaging, they can provide early continuous monitoring of cerebrovascular autoregulation in patients with sepsis and septic shock [25]. The timely detection of the dysfunction of cerebrovascular autoregulation, will improve timely implementation of intervention measures.
Third, determining an imbalance between oxygen supply and demand in the optimization of cerebral perfusion requires real-time dynamic monitoring of oxygen saturation of brain. Our study found a lower mean rSO2 value in the SAD group than in the non-SAD group, and multivariate regression showed that a mean rSO2 < 55% was an independent risk factor for delirium in patients with septic shock. It is suggested that the overall trend of cerebral oxygen saturation in SAD patients persists at a low level. The mismatch between cerebral oxygen supply and oxygen consumption is crucial for the development of SAD, which also requires a continuous monitoring of cerebral oxygen saturation in patients with septic shock. It should be used as a routine monitoring indicator followed by blood pressure, heart rate, respiration, and pulse oxygen saturation. Studies have reported that intraoperative regional rSO2 < 40% is an independent predictor of cognitive impairment after cardiac surgery [26]. De Tournay-Jette et al. [27] also reported that patients with rSO2 < 50% during surgery experienced more post-operative cognitive dysfunction after 4 to 7 days of surgery (p = 0.04), and that a greater than 30% reduction in baseline rSO2 was associated with cognitive impairment 1-month post-surgery (p = 0.03). Monitoring of oxygen saturation provides important information about oxygen in brain tissues at risk of various diseases. [28].
Fourth, the mean value of StO2 fluctuated between 60–74% after initial resuscitation in patients with septic shock. In optimizing hemodynamics in critically ill patients, although global parameters are normalized, microcirculation and regional perfusion may change. The persistence of these changes is associated with poor prognosis. StO2 can serve as an early warning signal for the initiation of low perfusion and tissue hypoxia [29].
There are certain limitations to this study. First, the effectiveness of TCD ultrasound is influenced by the operator's technique. Second, we only evaluated the changes of cerebral hemodynamics, and cerebral and peripheral tissue oxygen saturation 6 hours following the initial resuscitation and without combined neuroimaging or neurophysiological results. In addition, in view of the small sample size and the observational nature of our study, large-scale randomized controlled clinical trials may be needed for further verification of our results.
This group of septic patients had a high incidence of SAD and this was associated with poor prognosis. Cerebrovascular dysregulation (THRR < 1.09), and mean cerebral oxygen saturation < 55% are independent risk factors for SAD. The high-quality and adequately powered RCTs are warranted in the future.
BMI body mass index; MAP = mean arterial pressure; SOFA = Sequential [sepsis-related] organ failure assessment; APACHE II=Acute physiological and chronic health status assessment scores; CRRT = Continuous renal replacement therapy. MAP = mean arterial pressure; VsMCA = systolic velocity of middle cerebral arteries; VmMCA= mean velocity of middle cerebral arteries; VdMCA= diastole velocity of middle cerebral arteries; PI = pulsatility index; CBFi=calculation of cerebral blood flow index; THRR = transient hyperemia response rate. rSO2 = regional cerebral oxygen saturation; StO2 = tissue oxygen saturation.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgements
Not applicable.
Funding
The present study was supported by the grants from the Chinese National Natural Science Foundation (81873956).
Contributions
Qing Feng performed statistical analyses, searched the scientific literature and drafted the manuscript. Meilin Ai, Li Huang, Qianyi Peng helped to collect the data. Yuhang Ai, contributed to conception, design, and data interpretation. Lina Zhang contributed to conception, design, data interpretation, manuscript revision for critical intellectual content, and supervision of the study. All authors read and approved the manuscript.
Ethics declarations
Ethics approval and consent to participate
We performed a prospective observation study at an urban academic hospital in China. All subjects provided written informed consent to participate in the study. All procedures of this study were approved by the ethics committee of Xiangya Hospital of Central South University (ethics no: 2018101082).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
[1] Tsuruta R, Oda Y. A clinical perspective of sepsis-associated delirium. J Intensive Care. 2016; 4(1):18.
[2] Souza-Dantas VC, Povoa P, Bozza F, et al. Preventive strategies and potential therapeutic interventions for delirium in sepsis. Hosp Pract (Minneap) .2016; 44:190-202.
[3] Hughes CG, Patel MB, Pandharipande PP. Pathophysiology of acute brain dysfunction: what's the cause of all this confusion? Curr Opin Crit Care .2012; 18:518-26.
[4] Burkhart CS, Siegemund M, Steiner LA. Cerebral perfusion in sepsis. Crit Care. 2010; 14:215.
[5] Van Gool WA, van de Beek D, Eikelenboom P. Systemic infection and delirium: when cytokines and acetylcholine collide. Lancet. 2010; 375:773-5.
[6] Ritter C, Tomasi CD, Dal-Pizzol F, et al. Inflammation biomarkers and delirium in critically ill patients. Crit Care. 2014;18: R106.
[7] Papadopoulos MC, Davies DC, Moss RF, et al. Pathophysiology of septic encephalopathy: a review. Crit Care Med .2000; 28:3019-24.
[8] Schramm P, Klein KU, Falkenberg L, et al. Impaired cerebrovascular autoregulation in patients with severe sepsis and sepsis-associated delirium. Crit Care .2012; 16: R181.
[9] Pfister D, Siegemund M, Dell-Kuster S, et al. Cerebral perfusion in sepsis-associated delirium. Crit Care .2008; 12: R63.
[10] Pierrakos C, Attou R, Decorte L, et al. Transcranial Doppler to assess sepsis-associated encephalopathy in critically ill patients. BMC Anesthesiol .2014; 14:45.
[11] Stocchetti N, Taccone FS, Citerio G, et al. Neuroprotection in acute brain injury: an up-to-date review. Crit Care .2015; 19:186.
[12] Polonen P, Ruokonen E, Hippelainen M, et al. A prospective, randomized study of goal-oriented hemodynamic therapy in cardiac surgical patients. Anesth Analg .2000; 90:1052-9.
[13] Fischer GW. Recent advances in application of cerebral oximetry in adult cardiovascular surgery. Semin Cardiothorac Vasc Anesth .2008; 12:60-9.
[14] Al Tayar A, Abouelela A, Mohiuddeen K. Can the cerebral regional oxygen saturation be a perfusion parameter in shock? J Crit Care. 2017; 38:164-7.
[15] Schenkman KA, Carlbom DJ, Bulger EM, et al. Muscle oxygenation as an indicator of shock severity in patients with suspected severe sepsis or septic shock. PLoS One. 2017; 12: e0182351.
[16] Rhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Crit Care Med. 2017; 45:486-552.
[17] Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).Jama. 2016;315:801-10.
[18] Cavill G, Simpson EJ, Mahajan RP. Factors affecting assessment of cerebral autoregulation using the transient hyperaemic response test. Br J Anaesth. 1998; 81:317-21.
[19] Kim J, Shim J K, Song J W, et al. Postoperative Cognitive Dysfunction and the Change of Regional Cerebral Oxygen Saturation in Elderly Patients Undergoing Spinal Surgery. Anesthesia & Analgesia, 2016；
[20] Mori S, Takeda JR, Carrara FS, et al. Incidence and factors related to delirium in an intensive care unit. Rev Esc Enferm USP .2016; 50:587-93.
[21] Tse L, Schwarz SK, Bowering JB, et al. Incidence of and Risk Factors for Delirium After Cardiac Surgery at a Quaternary Care Center: A Retrospective Cohort Study. J Cardiothorac Vasc Anesth .2015; 29:1472-9.
[22] Whitehorne K, Gaudine A, Meadus R, et al. Lived experience of the intensive care unit for patients who experienced delirium. Am J Crit Care. 2015; 24:474-9.
[23] Pierrakos C, Antoine A, Velissaris D, et al. Transcranial doppler assessment of cerebral perfusion in critically ill septic patients: a pilot study. Ann Intensive Care .2013; 3:28.
[24] Smielewski P, Czosnyka M, Kirkpatrick P, et al. Evaluation of the transient hyperemic response test in head-injured patients. J Neurosurg. 1997; 86:773-8.
[25] Sharma A, Aryal D. Problems with interpretation of transient hyperemic response ratio (THRR). Crit Ultrasound J .2018; 10:11.
[26] Yao FS, Tseng CC, Ho CY, et al. Cerebral oxygen desaturation is associated with early postoperative neuropsychological dysfunction in patients undergoing cardiac surgery. J Cardiothorac Vasc Anesth .2004; 18:552-8.
[27] De Tournay-Jette E, Dupuis G, Bherer L, et al. The relationship between cerebral oxygen saturation changes and postoperative cognitive dysfunction in elderly patients after coronary artery bypass graft surgery. J Cardiothorac Vasc Anesth .2011; 25:95-104.
[28] Huan Chen, Xiaoting Wang. Noninvasive cerebral oxygen monitoring:clinical relevance and challenges. Chinese J Crit Care & Intensive Care Med (Electronic Edition) .2018; 4:231-7.
[29] Nguyen HB. Central venous oxygen saturation: not easily replaced*. Crit Care Med. 2013; 41:1570-1.