BACKGROUND:Postoperative delirium (POD) is a common complication in neurosurgical patients, leading to worse outcomes. However, effective super-early prediction models incorporating preoperative frailty remain unclear. METHODS:A secondary analysis of 800 adults undergoing elective craniotomy at Beijing Tiantan Hospital (2017-2018) was conducted. POD was assessed using CAM-ICU on postoperative days 1-3. Preoperative frailty was measured by the 5-factor modified Frailty Index (mFI-5).Four models: super-early (only preoperative variables), early (additionally adding intraoperative variables) and respectively further integrating mFI-5, were developed by logistic regression. Model performance was evaluated by the area under receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).The incremental value of adding frailty was evaluated by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). SHAP analysis assessed feature importance. RESULTS:POD occurred in 157 patients (19.6%). Multivariate analysis identified four preoperative variables (age, educational level, nature of the intracranial lesions, and supratentorial lesions) and three intraoperative variables(operation duration, blood loss and hypotension) as independent risk factors. The early model with frailty had the highest AUC(0.843, 95% CI: 0.810-0.875), but the super-early model with mFI-5 also achieved an AUC of 0.813 (95% CI: 0.777-0.849), comparable to the early model without frailty (AUC: 0.804, 95% CI: 0.768-0.840; P = 0.582).Calibration curves demonstrated good agreement between predicted and observed POD risks, and DCA confirmed the clinical utility of all four models. Adding mFI-5 significantly improved both models and SHAP confirmed frailty as the top predictor. CONCLUSIONS:Preoperative frailty substantially enhances POD prediction. An super-early model integrating mFI-5 enables timely risk stratification before surgery, supporting proactive delirium prevention.

Objective:To investigate the effect of trigger sensitivity on ventilation homogeneity in patients under pressure support ventilation.Methods:We prospectively enrolled 20 patients with heterogeneous lung ventilation under pressure support ventilation that was defined by electrical impedance tomography as the distribution of tidal volume in dependent region lower than 45%. The low and high flow trigger sensitivity (the lowest and highest limits of Servo-i were 2 L/min and 0.2 L/min, respectively) were randomly applied for 20 mins. The distribution of tidal volume in dependent region and end-expiratory lung volume (EELV) were evaluated by electrical impedance tomography. The esophageal manometry was used to measure the inspiratory effort and work of breathing.Results:Comparing to the high trigger sensitivity, the low trigger sensitivity increased the relative distribution of tidal volume in dependent region [(33 ± 9)% vs. (36 ± 9)%, t = 3.735, P = 0.001], the esophageal pressure swings during inspiration [0.8 (0.4, 1.8) cmH2O vs. 1.6 (1.0, 2.1) cmH2O, Z = 2.722, P = 0.021], and pressure time product [29 (15, 54) cmH2O·s-1·min-1 vs. 48 (23, 74) cmH2O·s-1·min-1, Z = 3.298, P = 0.044]; whereas, the change of transpulmonary pressure did not significantly increase [(12.6 ± 4.3) cmH2O vs. (12.8 ± 4.2) cmH2O, t = 0.906, P = 0.376]. The global EELV of a low trigger sensitivity during pressure support ventilation was 78 (29, 170) mL, which mainly acted on the dependent region [75 (-6, 131) mL].Conclusion:Decreasing trigger sensitivity could allow more air to flow into the dependent lung region and improve homogeneity during pressure support ventilation by increasing inspiratory effort, while the working of breathing and transpulmonary pressure remain within acceptable ranges.

Objective:To compare the recruitability between pulmonary (ARDSp) and extrapulmonary acute respiratory distress syndrome (ARDSexp) using electric impedance tomography (EIT) in pig models.Methods:Sixteen healthy Bama pigs were randomly divided into ARDSp and ARDSexp group with 8 animals each. ARDSp model was established using alveolar instillation of hydrochloric acid and ARDSexp was injected with oleic acid through atrium venous. After the model was established, the animal received lung protective ventilation. After a recruitment maneuver, low (5 cmH2O) and high (25 cmH2O) PEEP were applied in each animal remaining other setting unchanged for 1 hour. Then an end-inspiratory and end-expiratory occlusion were performed. The recruitment volume (VREC) generated between high and low PEEP was calculated, and the data of oxygenation and hemodynamic were recorded.Results:Significant difference was found in VREC between ARDSexp and ARDSp models (P=0.003), and VREC was (1572.8±314.9) ml and (1089.9±224.2) ml, respectively. The ratio of arterial partial pressure of oxygen to inspired oxygen fraction (PaO2/FiO2) increased from (214.3±128.6) mmHg to (447.5±96.0) mmHg (P=0.004) with increasing PEEP in ARDSexp. However, PaO2/FiO2 did not change significantly in ARDSp [(173.6±112.0) mmHg vs (178.4±128.2) mmHg, P=0.943].Conclusion:The recruitability between ARDSp and ARDSexp in pig models is different. Compared with ARDSp, ARDSexp may have better recruitability. Higher PEEP may improve the oxygenation of ARDSexp, while the effect on ARDSp is relatively small.

Airway closure is defined as the lacking of communication between proximal airways and distal alveoli because of intermediate airways collapse, which was first proposed in 1967.[1] Recently, complete airway closure has been reported in 1/3 of patients with acute respiratory distress syndrome (ARDS) and in 22% of obese anesthetized patients with healthy lungs.[23] For obese patients with ARDS, the incidence was even up to 65%.[4] When complete airway closure occurs, all alveoli do not communicate with the central airways.[5] Under this condition, no gas moves into the lung until the airway pressure reaches a critical level; the corresponding pressure is named as airway opening pressure (AOP), which varies from 5 to 20 cmH2O.[2-4] At bedside, complete airway closure can be detected with a low flow pressure-volume (P-V) curve.[5] In a typical P-V curve without airway closure, the slope is consistently above the compliance of the breathing circuit even with the presence of a low inflection point due to lung collapse [Figure 1a]. As demonstrated by Figure 1b, in a patient with complete airway closure, the slope of the initial part of the P-V curve is complanate (that is the compliance of an occluded ventilator breathing circuit, approximate 1.5-2.5 ml/cmH2O) and there are no signs of cardiac oscillations. Then, the slope of this curve abruptly increases when pressure exceeds AOP accompanying gas entering alveoli, which represents the compliance of the respiratory system. In Figure 1c, the low flow pressure-impedance (P-I) curve derived from electric impedance tomography (EIT) shows a similar shape to the P-V curve from the same patient with Figure 1b. EIT monitoring further testified that there was not any gas moving into the lungs when the pressure was lower than AOP.[6] In addition, EIT can provide low flow P-V curves or P-I curves of different lung regions. In patients with asymmetrical ARDS (the difference of the ventilation distribution between left and right lung was more than 20%), regional P-V curves from the right and left lung derived from EIT both exhibited a complanate initial portion, but AOP of the left and right lung in the same patient was different, AOP of the more seriously injured lung was even 1.9 times higher than the other.[7] The traditional P-V curves derived from ventilator only reflected the global lung condition, for patients with asymmetrical ventilation, AOP estimated by the global P-V curve was closed to the unilateral lung with a lower AOP level since both lungs are connected in parallel.[7]Figure 1:: Typical low flow pressure-volume curves and a low flow pressure-impedance curve in patients with and without complete airway closure. (a) An example of traditional low flow pressure-volume curve in a patient without complete airway closure. It shows a low inflection point at approximate 5 cmH2O, the pressure to reopen partial collapsed alveoli. The slope of the curve is consistently higher than the compliance of the occluded breathing circuit (red line), and the zigzagging of the curve from beginning to end indicates cardiac oscillations. (b) Low flow pressure-volume curve in a patient with complete airway closure. The initial part of pressure-volume curve is without the signs of cardiac oscillations and the compliance is close to that of the occluded breathing circuit (red line) until airway pressure exceeds airway opening pressure (About 9 cmH2O in this patients). (c) Low flow pressure impedance curve from electric impedance tomography monitoring of the same patient in Figure b. The low flow pressure impedance curve shows a similar shape, and the ventilation map by electric impedance tomography further testified that there was no any gas entering lungs when airway pressure was lower than airway opening pressureNow, the mechanism and anatomical location of complete airway closure are not clear yet. The structural collapse of airway walls may be the main reason for airway closure.[89] The obesity and head down position may promote airway closure by applying more pressure on airway walls. Moreover, the relaxation of tracheal smooth muscle during anesthesia also can make airways more easily closed.[10] Besides, surfactant depletion leads to airway instable by affecting surface tension at the liquid-gas interface and contributes to airway closure.[11] Expiratory flow limitation seems to be related to complete airway closure, the incidence of complete airway closure in patients with expiratory flow limitation was higher than those without expiratory flow limitation,[12] but these two phenomena were not the same thing. The prolonged expiration before the P-V curve can eliminate expiratory flow limitation and auto-PEEP, but AOP stayed unchanged.[2] Perhaps, some factors play an important role in both and the relationship needs further to be discussed. The computed tomography (CT) during complete airway closure did not find total lung collapse, suggesting that alveoli were still inflated and the location of closure was airways, but the specific location is still unknown.[5] Recently, the synchrotron radiation phase-contrast CT was used to directly observe individual airways characters in an ARDS model of rabbits and could visualize small airways with diameters of 600-1400 μ. The airway closure mainly occurred in the 18th generation airways and even might appear at more than one site along the airway.[8] These evidence support that small airways can occur closure, but if complete airway closure is located in small airways, it will need hundreds or thousands of small airways to be closed simultaneously, so may the large airway or even trachea be the real site of complete airway closure? The fiber-optic bronchoscopy found that third-level bronchus appeared closed during expiration and reopened during inspiration in a severe ARDS patient with complete airway closure (AOP = 9 cmH2O).[13] Hence, the sites of complete airway closure are probably not only in the terminal bronchioles but also more in the main bronchi or even the trachea, it needs further to be explored. The complete airway closure could bring several relevant clinical consequences. First, airway pressure is not able to reflect actual alveolar pressure in case of complete airway closure even if an end-expiratory occlusion being performed, that makes a misinterpretation of respiratory mechanics.[3] Second, complete airway closure could induce denitrogenation atelectasis due to intermittently open or continuously closed airways, especially in high fraction inspired oxygen.[14] The lung atelectasis can cause ventilation/perfusion mismatch and impair oxygenation. Third, cyclic opening and closing might result in inflammatory reactions and bronchiolar damages when the PEEP setting is lower than AOP.[15] Therefore, the PEEP setting is recommended to be higher than AOP. In conclusion, complete airway closure often occurs in ARDS and anesthesia obese patients, but it is easy to be ignored except for performing a low flow P-V curve and might increase the risk of ventilation-induced lung injury.

目的 评价血气分析仪与自动生化分析仪检测开颅术后患儿血钠和血钾水平的一致性.方法 前瞻性连续收集2019-09—2020-08首都医科大学附属北京坛医院重症医学科收治的开颅术后同时进行动脉血气分析和生化分析仪检测的100例患儿的血钠和血钾水平,应用Pearson相关和Bland-Altman分析法评价两种方法的一致性和相关性.结果 两种不同方法检测的血钠和血钾水平具有明显的相关性(r=0.996和r=0.859);二者血钠的差异(95％的可信区间)为-2.2 mmol/L(-6.7～2.2 mmol/L),血钾的差异(95％的可信区间)为-0.2 mmol/L(-0.6～0.2 mmol/L).结论 血气分析仪检测血钠和血钾的水平与自动生化分析仪呈明显正相关,但二者之间仍存在一定差异,尤其对于血钠水平,无法完全互相替代检测结果.

Background: Postoperative delirium (POD) is a significant clinical problem in neurosurgical patients after intracranial surgery. Identification of high-risk patients may optimise individual perioperative management, but an adequate and simple risk model for use at super early phase after operation has not been developed. Methods: Adult patients were admitted to the ICU after elective intracranial surgery under general anaesthesia. The POD was diagnosed as Confusion Assessment Method for the ICU positive on postoperative day 1 to 3. Multivariate logistic regression analysis was used to develop the early prediction model (E-PREPOD-NS) and the final model was validated with 200 bootstrap samples. Results: Among 800 patients included in the study, POD occurred in 157 cases (19.6%). We identified nine variables independently associated with POD in the final E-PREPOD-NS model: age > 65 years [odds ratio (OR) = 3.336, 95% confidence interval (CI) = 1.765-6.305, 1 risk score point], education level < 9 years (OR = 2.528, 95% CI = 1.446-4.419, 1 point), history of smoking (OR = 2.582, 95% CI = 1.611-4.140, 1 point), history of diabetes (OR = 2.541, 95% CI = 1.201-5.377, 1 point), supra-tentorial lesions (OR = 3.424, 95% CI = 2.021-5.802, 1 point), anesthesia duration > 360 min (OR = 1.686, 95% CI = 1.062-2.674, 0.5 point), GCS <9 at ICU admission (OR = 6.059, 95% CI = 3.789-9.690, 1.5 points), metabolic acidosis (OR = 13.903, 95% CI = 6.248-30.938, 2.5 points), and positioning of neurosurgical drainage tube (OR = 1.924, 95% CI = 1.132-3.269, 0.5 point). The area under the receiver operator curve (AUROC) of the risk score for prediction of POD was 0.865 (95% CI = 0.835-0.895). After internal validation by bootstrap, the AUROC was 0.851 (95% CI = 0.791-0.912). The model showed good calibration (Hosmer-Lemeshow P = 0.593). Conclusions: The E-PREPOD-NS model based on nine perioperative risk factors can predict POD in patients admitted to the ICU after elective intracranial surgery with fairly good accuracy. External validation is needed before use in clinical practice.

Background Electrical impedance tomography (EIT) is a real-time tool used to monitor lung volume change at the bedside, which could be used to measure lung recruitment volume (VREC) for setting positive end-expiratory pressure (PEEP). We assessed and compared the agreement in VREC measurement with the EIT method versus the flow-derived method. Material/Methods In 12 Bama pigs, lung injury was induced by tracheal instillation of hydrochloric acid and verified by an arterial partial pressure of oxygen to inspired oxygen fraction ratio below 200 mmHg. During the end-expiratory occlusion, an airway release maneuver was conduct at 5 and 15 cmH2O of PEEP. VREC was measured by flow-integrated PEEP-induced lung volume change (flow-derived method) and end-expiratory lung impedance change (EIT-derived method). Linear regression and Bland-Altman analysis were used to test the correlation and agreement between these 2 measures. Results Lung injury was successfully induced in all the animals. EIT-derived VREC was significantly correlated with flow-derived VREC (R2=0.650, p=0.002). The bias (the lower and upper limits of agreement) was −19 (−182 to 144) ml. The median (interquartile range) of EIT-derived VREC was 322 (218–469) ml, with 110 (59–142) ml and 194 (157–307) ml in dependent and nondependent lung regions, respectively. Global and regional respiratory system compliance increased significantly at high PEEP compared to those at low PEEP. Conclusions Close correlation and agreement were found between EIT-derived and flow-derived VREC measurements. The advantages of EIT-derived recruitability assessment included the avoidance of ventilation interruption and the ability to provide regional recruitment information.

OBJECTIVE:To evaluate the feasibility of esophageal pressure (Pes) calibration by the esophageal balloon pressure-volume (P-V) curve during assisted mechanical ventilation.METHODS:A prospective study was conducted. The postoperative patients admitted to intensive care unit of Beijing Tiantan Hospital Affiliated to Capital Medical University from June 2017 to January 2019 who needed pressure support ventilation by tracheal intubation and Pes monitoring with stable breath were enrolled. The Pes monitoring was performed by the esophageal balloon with a small geometric volume (2.8 mL). (1) Balloon volume tests of esophageal balloon were performed by inflating intermittently 0.5 mL increments up to 2.5 mL, the end-expiratory and end-inspiratory Pes were recorded to obtain end-expiratory and end-inspiratory P-V curves. The intermediate section in end-expiatory P-V curve that showed linear correlation was identified (as intermediate linear section), whose volume range was balloon working volume (Vwork) and slope was esophageal wall elastance (Ees), the balloon volume with the largest difference between end-expiratory and end-inspiratory Pes was the best balloon volume (Vbest), and the product of Ees and Vbest was esophageal wall recoil pressure reacting to balloon filling. To minimize the effect of esophageal wall on Pes, the calibrated Pes was the difference of Pes and esophageal wall recoil pressure. The consistency of calibrated Pes obtained by balloon volume at Vbest and other Vwork were analyzed. (2) For the convenience of clinical application, a simplified method was introduced to calibrate Pes. Based on all Vwork of patients located in 0.5-1.5 mL, the difference of end-expiratory Pes between balloon volume at 0.5 mL and 1.5 mL divided by 1.0 mL was used to estimate Ees, and the Pes among 0.5-1.5 mL was calibrated by Ees obtained by the simple method. The consistency of calibrated Pes obtained by the simple method and standard method were observed.RESULTS:Totally 30 patients were enrolled, all end-expiratory and end-inspiratory P-V curves existed the intermediate linear section, the calibrated Pes at Vwork did not increase with the balloon being inflated and had a good consistency with the calibrated Pes at Vbest, mean difference and 95% confidence interval (95%CI) was -0.02 (-1.50-1.50) cmH2O (1 cmH2O = 0.098 kPa). The Ees and calibrated Pes estimated by the simple method had a good agreement with the standard method, mean difference and 95%CI was -0.2 (-1.0-0.6) cmH2O/mL and 0.2 (-1.1-1.4) cmH2O, respectively.CONCLUSIONS:During assisted mechanical ventilation, the use of a small geometric volume esophageal balloon to monitor Pes and balloon P-V curve to calibrate Pes is feasible. The simple method can be used for simplifying clinical application, that's only by monitoring Pes at balloon volume at 0.5, 1.0 and 1.5 mL to evaluate the Ees and calibrate Pes.

Objective Measurement of positive end-expiratory pressure (PEEP)-induced recruitment lung volume using passive spirometry is based on the assumption that the functional residual capacity (FRC) is not modified by the PEEP changes. We aimed to investigate the influence of PEEP on FRC in different models of acute respiratory distress syndrome (ARDS). Methods A randomized crossover study was performed in 12 pigs. Pulmonary (n = 6) and extra-pulmonary (n = 6) ARDS models were established using an alveolar instillation of hydrochloric acid and a right atrium injection of oleic acid, respectively. Low (5 cmH 2 O) and high (15 cmH 2 O) PEEP were randomly applied in each animal. FRC and recruitment volume were determined using the nitrogen wash-in/wash-out technique and release maneuver. Results FRC was not significantly different between the two PEEP levels in either pulmonary ARDS (299 ± 92 mL and 309 ± 130 mL at 5 and 15 cmH 2 O, respectively) or extra-pulmonary ARDS (305 ± 143 mL and 328 ± 197 mL at 5 and 15 cmH 2 O, respectively). The recruitment volume was not significantly different between the two models (pulmonary, 341 ± 100 mL; extra-pulmonary, 351 ± 170 mL). Conclusions PEEP did not influence FRC in either the pulmonary or extra-pulmonary ARDS pig model.

BACKGROUND Postoperative delirium (POD) has been confirmed as an important complication after major surgery. However, neurosurgical patients have usually been excluded in previous studies. To date, data on POD and risk factors in patients after intracranial surgery are scarce. OBJECTIVES To determine the incidence and risk factors of POD in patients after intracranial surgery. DESIGN Prospective cohort study. SETTING A neurosurgical ICU of a university-affiliated hospital, Beijing, China. INTERVENTIONS Adult patients admitted to the ICU after elective intracranial surgery under general anaesthesia were consecutively enrolled between 1March 2017 and 2 February 2018. Delirium was assessed using the Confusion Assessment Method for the ICU. POD was diagnosed as Confusion Assessment Method for the ICU positive on either postoperative day 1 or day 3. Patients were classified into groups with or without POD. Data were collected for univariate and multivariate analyses to determine the risk factors for POD. RESULTS A total of 800 patients were included. POD was diagnosed in 157 patients (19.6%, 95% confidence interval 16.9 to 22.4%). Independent risk factors for POD included age, nature of intracranial lesion, frontal approach craniotomy, duration of surgery, presence of an episode of low pulse oxygenation at ICU admission, presence of inadequate emergence and emergence delirium, postoperative pain and presence of immobilising events. POD was associated with adverse outcomes and high costs. CONCLUSION POD is prevalent in patients after elective intracranial surgery. The identified risk factors for and the potential association of POD with adverse outcomes suggest that a comprehensive strategy involving screening for predisposing factors and early prevention of modifiable factors should be established in this population.

Objective To investigate the accuracy of derecruitment volume (V DER ) assessed by pressure–impedance (P-I) curves derived from electrical impedance tomography (EIT). Methods Six pigs with acute lung injury received decremental positive end-expiratory pressure (PEEP) from 15 to 0 in steps of 5 cmH 2 O. At the end of each PEEP level, the pressure–volume (P-V) curves were plotted using the low constant flow method and release maneuvers to calculate the V DER between the PEEP of setting levels and 0 cmH 2 O (V DER-PV ). The V DER derived from P-I curves that were recorded simultaneously using EIT was the difference in impedance at the same pressure multiplied by the ratio of tidal volume and corresponding tidal impedance (V DER-PI ). The regional P-I curves obtained by EIT were used to estimate V DER in the dependent and nondependent lung. Results The global lung V DER-PV and V DER-PI showed close correlations (r = 0.948, P<0.001); the mean difference was 48 mL with limits of agreement of −133 to 229 mL. Lung derecruitment extended into the whole process of decremental PEEP levels but was unevenly distributed in different lung regions. Conclusions P-I curves derived from EIT can assess V DER and provide a promising method to estimate regional lung derecruitment at the bedside.

The ventilator-induced lung injury (VILI) was centered on the "static" characteristics of the mechanical ventilation in early phases (tidal volume, plateau pressure, positive end-expiratory pressure and driving pressure). But the "dynamic" characteristics of ventilation must not be ignored (respiratory rate and flow). Mechanical energy and mechanical power (the pace of performing energy load) regarding all factor have won wide spread attention. The energy generated by mechanical ventilation is mainly used to expand respiratory system and overcome resistance, a fraction of energy acts on lung tissues probably inducing "heat" and inflammation that is related to lung injury. The review described recent conceptual advances regarding the mechanical energy and power, and the relationship with VILI, hoping to help further understanding the risk factors for VILI.

The standard high-flow tracheal (HFT) interface was modified by adding a 5-cm H2O/L/s resistor to the expiratory port. First, in a test lung simulating spontaneous breathing, we found that the modified HFT caused an elevation in airway pressure as a power function of flow. Then, three tracheal oxygen treatments (T-piece oxygen at 10 L/min, HFT and modified HFT at 40 L/min) were delivered in a random crossover fashion to six tracheostomized pigs before and after the induction of lung injury. The modified HFT induced a significantly higher airway pressure compared with that in either T-piece or HFT (p < 0.001). Expiratory resistance significantly increased during modified HFT (p < 0.05) to a mean value of 4.9 to 6.7 cm H2O/L/s. The modified HFT induced significant augmentation in end-expiratory lung volume (p < 0.05) and improved oxygenation for lung injury model (p = 0.038) compared with the HFT and T-piece. There was no significant difference in esophageal pressure swings, transpulmonary driving pressure or pressure time product among the three treatments (p > 0.05). In conclusion, the modified HFT with additional expiratory resistance generated a clinically relevant elevation in airway pressure and lung volume. Although expiratory resistance increased, inspiratory effort, lung stress and work of breathing remained within an acceptable range.

The assessment of pain in patients with brain injury is challenging due to impaired ability to communicate. We aimed to test the reliability and validity of the critical-care pain observation tool (CPOT) and the bispectral index (BIS) for pain detection in critically brain-injured patients.This prospective observational study was conducted in a neurosurgical intensive care unit in a University-Affiliated Hospital. Adult brain-injured patients undergoing mechanical ventilation were enrolled. Nociceptive (endotracheal suctioning) and non-nociceptive (gentle touching) procedures were performed in a random crossover fashion. Before and immediately after the procedure, CPOT was evaluated by 2 residents and 2 chief nurses, and BIS was documented. The ability to self-report pain was also assessed. The inter-observer reliability of CPOT was analyzed. The criterion and discriminant validities of the CPOT and the BIS were tested.During the study, we enrolled 400 brain-injured patients. The ability to self-report pain was maintained in 214 (54%) and 218 (55%) patients during suctioning and gentle touching, respectively. The intraclass correlation coefficients (95% confidence interval) for inter-observer reliability of CPOT ranged from 0.86 (0.83-0.89) to 0.93 (0.91-0.94). Using self-reported pain as the reference, the area under the receiver operating characteristic curve (95% confidence interval) was 0.84 (0.80-0.88) for CPOT and 0.76 (0.72-0.81) for BIS. When the 2 instruments were combined as either CPOT 2 or BIS 88 after the procedure, the sensitivity and specificity were 0.90 (0.85-0.93) and 0.59 (0.52-0.66), respectively; and when the 2 instruments were combined as both CPOT 2 and BIS 88, the sensitivity and specificity were 0.62 (0.550.68) and 0.89 (0.83-0.93). Both CPOT and BIS increased significantly after suctioning (all P<.001) but remained unchanged after gentle touching (P ranging from .06 to .14).Our criterion and discriminant validity results supported the use of CPOT and BIS to detect pain in critically brain-injured patients. Combining use of CPOT and BIS in different ways might provide comprehensive pain assessment for different purposes.

BACKGROUND: The dynamic occlusion test is used to guide balloon catheter placement during esophageal pressure (P-es) monitoring. We introduced a cardiac cycle locating method to attenuate the influence of cardiac artifacts on Pes measurement. The aim was to provide a reliable analytic algorithm for the occlusion test. METHODS: Esophageal balloon catheters were placed in subjects receiving pressure support ventilation. During balloon position adjustment, end-expiratory occlusion was performed to induce 3 consecutive inspiratory efforts. Pes and airway pressure (P-aw) data were collected for off-line analysis. For each occluded inspiratory effort, the change in Pes (Delta P-es) was plotted against the change in Paw (Delta P-aw), and the slope of the regression line was calculated. The Delta P-es/Delta P-aw ratio was also measured with the cardiac cycle locating method and peak-to-peak method. Bland-Altman analysis was used to assess the agreement between the Delta P-es/Delta P-aw ratio and the slope. We defined the occlusion test with all fitted slopes for the 3 inspiratory efforts within 0.8 to 1.2 to indicate optimal balloon position; otherwise, the position was deemed non-optimal. Using the slope as the reference, the diagnostic accuracy of the Delta P-es/Delta P-aw ratio in distinguishing the optimal and the non-optimal balloon position was analyzed. RESULTS: A total of 86 occlusion tests containing 258 inspiratory efforts were collected from 15 subjects. The median (interquartile range) slope of Delta P-es versus Delta P-aw plot was 0.85 (0.76, 0.91). Bias (lower and upper limit of agreement) of Delta P-es/Delta P-aw ratio measured by the cardiac cycle locating method and the peak-to-peak method was 0.02 (-0.13 to 0.16) and 0.06 (-0.18 to 0.31), respectively. Forty-five (52.3%) occlusion tests indicated optimal balloon positions. Compared to the peak-to-peak method, the cardiac cycle locating method was more specific in detecting the non-optimal position. CONCLUSIONS: The cardiac cycle locating method provided reliable and precise measurement for the occlusion test. This method can accurately detect non-optimal balloon position during catheter adjustment.

Objective To analyze the relationship between bi-frontal pneumocephalus (BFP) and acute postoperative agitation following craniotomy.Methods A total of 365 consecutive adult patients underwent elective craniotomy at Department of Critical Care Medicine,Beijing Tiantan Hospital,Capital Medical University from July 2016 to December 2016 and their clinical data were retrospectively analyzed in this study.Sedation-agitation scale (SAS) was used to evaluate the patients 24 hours after the enrollment.Agitation was defined as an SAS score equal to or above 5.Patients were divided into 2 groups according to the presence or absence of BFP in postoperative CT scans within 12 hours post operation:the BFP group (n =83) and non bi-frontal pneumocephalus (NBFP) group (n =282).PSM (propensity score matching) was conducted to reduce confounding bias between the groups.The intergroup analysis was conducted on acute postoperative agitation and other recovery parameters.Results Among 365 patients,45 (12.3％) developed acute postoperative agitation and 83 (22.7％) developed BFP.With PSM,83 pairs of patients were successfully matched.After matching,compared to NBFP group,the incidence of postoperative agitation was higher (24.1％ vs.3.6％,P ＜ 0.001),use of sedation was much more (14.5％ vs.2.4％,P =0.005) and hospital length of stay was longer (10 vs.8 days,P ＜0.001).Conclusion Brain tumor patients following elective craniotomy with BFP are more likely to develop acute postoperative agitation.

BACKGROUND:Stress index provides a noninvasive approach to detect injurious ventilation patterns and to personalize ventilator settings. Obtaining the stress index (SI), however, requires quantitatively analyzing the shape of pressure-time curve with dedicated instruments or a specific ventilator, which may encumber its clinical implementation. We hypothesized that the SI could be qualitatively determined through a visual inspection of ventilator waveforms. METHODS:Thirty-six adult subjects undergoing volume controlled ventilation without spontaneous breathing were enrolled. For each subject, 2 trained clinicians visually inspected the pressure-time curve directly from the ventilator screen. They then qualitatively categorized the shape of pressure-time curve as linear, a downward concavity, or an upward concavity at the bedside. We simultaneously recorded airway pressure and flow signals using a dedicated instrument. A quantitative off-line analysis was performed to calculate the SI using specific research software. This quantitative analysis of the SI served as the reference method for classifying the shape of the pressure-time curve (ie, linear, a downward concavity, or an upward concavity). We compared the SI categorized by visual inspection with that by the reference. RESULTS:We obtained 200 SI assessments of pressure-time curves, among which 125 (63%) were linear, 55 (27%) were a downward concavity, and 20 (10%) were an upward concavity as determined by the reference method. The overall accuracy of visual inspection and weighted kappa statistic (95% CI) was 93% (88-96%) and 0.88 (0.82-0.94), respectively. The sensitivity and specificity to distinguish a downward concavity from a linear shape were 91% and 98%, respectively. The respective sensitivity and specificity to distinguish an upward concavity from a linear shape were 95% and 95%. CONCLUSIONS:Visual inspection of the pressure-time curve on the ventilator screen is a simple and reliable approach to assess SI at the bedside. This simplification may facilitate the implementation of SI in clinical practice to personalize mechanical ventilation. (ClinicalTrials.gov registration NCT03096106.).

OBJECTIVE:Esophageal pressure monitoring provides a minimally invasive method to assess the pleural pressure, which can be used to differentiate the lung and chest wall mechanics. The information of transpulmonary pressure, work of breathing, intrinsic positive end-expiratory pressure and respiratory muscle performance can facilitate the proper setting of mechanical ventilation. Esophageal pressure monitoring is still not routinely used in the clinical setting because of difficulty in esophageal balloon catheter placement and data interpretation due to esophageal pressure monitoring has certain technical requirements, and the measurement results are influenced by many factors such as airbag volume, location, esophageal wall elasticity and mediastinal organ weight. In this review, we introduced technique for esophageal pressure measurement and calculation of transpulmonary pressure aiming to promote the clinical application of esophageal pressure monitoring.