Evaluation of alveolar recruitment maneuver on respiratory resistance during general anesthesia: a prospective observational study

A stepwise lung recruitment maneuver using I-gel can improve respiratory parameters: A prospective observational study

I-gel has been used in various clinical situations. The study investigated alterations in respiratory parameters following a stepwise lung recruitment maneuver (LRM) using the i-gel. The research involved 60 patients classified as American Society of Anesthesiologists class I-II, aged 30 to 75 years, undergoing elective urologic surgery. Various respiratory parameters, including lung compliance, airway resistance, leak volume, airway pressure, and oxygen reserve index, were recorded at different time points: before LRM, immediately after LRM, and at 5, 15, and 30 minutes after LRM, as well as at the end of the surgery. The primary outcome was to assess an improvement in lung compliance. Dynamic lung compliance (mean ± SD) was significantly increased from 49.2 ± 1.8 to 70.15 ± 3.2 mL/cmH2O (P < .05) after LRM. Static lung compliance (mean ± SD) was increased considerably from 52.4 ± 1.7 to 65.0 ± 2.5 mL/cmH2O (P < .05) after the LRM. Both parameters maintained a statistically significant increased status for a certain period compared to baseline despite a decreased degree of increment. Airway resistance (mean ± SD) was significantly reduced after the LRM from 12.05 ± 0.56 to 10.41 ± 0.64 L/cmH2O/s (P < .05). Stepwise LRM using i-gel may improve lung compliance and airway resistance. Repeated procedures could lead to prolonged improvements in respiratory parameters.

Pulmonary response prediction through personalized basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings.

METHODS

This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps.

RESULTS

The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95.

CONCLUSIONS

The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.

Recruitment s during mechanical ventilation with sequential high-flow nasal oxygen after extubation to prevent postoperative pulmonary complications in patients undergone thoracic surgery: a protocol, prospective randomised controlled trial

INTRODUCTION

The incidence of postoperative pulmonary complications (PPCs) following thoracic surgery is high, which increases the mortality rate, prolongs the length of hospital stay and increases medical costs. Some studies have confirmed that preoperative risk assessment, intraoperative anaesthesia methods and intraoperative mechanical ventilation strategies, including recruitment manoeuvres (RMs), can reduce the incidence of PPCs. Despite these improved strategies, the incidence of PPCs remains high. However, mechanical ventilation strategies have not been studied in the postoperative period.

METHODS AND ANALYSIS

We assume that RM during mechanical ventilation with sequential high-flow nasal oxygen therapy (HFNO) after extubation can maintain the opening of the postoperative alveoli and ultimately reduce the incidence of PPCs after thoracic surgery. We will include thoracic surgery patients and divide them into the RM with sequential HFNO group and the control group. They will be given RMs and sequential HFNO or be given conventional treatment. The sample size is 654 adult patients (327 per group) undergone thoracic surgery and presenting to the intensive care unit.

ETHICS AND DISSEMINATION

This study was approved by the Biomedical Research Ethics Committee of West China Hospital of Sichuan University (REC2019-730). It is expected that this study will lead to a randomised controlled trial. We assume that the findings will provide more evidence about PPCs and improve the management of patients undergone thoracic surgery.

TRIAL REGISTRATION NUMBER

ChiCTR2100046356.

The efficacy of different alveolar recruitment maneuvers in holmium laser lithotripsy surgery under general anesthesia using a laryngeal mask

Background

Alveolar recruitment maneuvers (ARMs) is an important part of lung-protective ventilation strategies (LPVSs), but the optimal duration and interval Remain unclear.

Methods

Patients:252 patients who underwent holmium laser lithotripsy surgery and meet inclusion criteria were included and randomized into three groups based on the duration and frequency of ARMs (Regular, one 30 s ARM (RARMs); Improved and intermittent, three 10s ARMs (IARMs); and Control (C), no ARMs).Interventions: Groups R and I received ARMs at 20 cmH2O pressures every 30 min. All patients received the same anesthesia and mechanical ventilation. Measurements:Outcomes included heart rate and mean arterial pressure changes during ARMs and postoperative pulmonary complications (PPCs) within the first 7 postoperative days.

Main results

Incidences of PPCs in groups R(7.1%) and I (5.0%)were slightly lower than those in group C (8.9%).This indicated the potential to reduce lung injury. Heart rate and mean arterial pressure fluctuations during ARMs were significantly higher in groups R and I than in group C (P < 0.01). The rate of blood pressure decrease was significantly higher in group R than in group I (P < 0.01).

Conclusions

IARMs can reduce cycle fluctuations than RARMs in patients Undergoing holmium laser lithotripsy surgery with laryngeal mask general anesthesia. Low tidal volume ventilation and low PEEP combined with ARM did not significantly reduce the incidence of PPCs in healthy lung patients, but tended to reduce lung injury.

Trial registration

The study was registered on the Chinese Clinical Trial Registry.

(ChiCTR2000030815,15/03/2020). This study was approved by the ethics committee of Chengdu Fifth People’s Hospital with approval number(2020–005(Study)-1).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01664-y.

Perioperative Pulmonary Atelectasis: Part II. Clinical Implications

The development of pulmonary atelectasis is common in the surgical patient. Pulmonary atelectasis can cause various degrees of gas exchange and respiratory mechanics impairment during and after surgery. In its most serious presentations, lung collapse could contribute to postoperative respiratory insufficiency, pneumonia, and worse overall clinical outcomes. A specific risk assessment is critical to allow clinicians to optimally choose the anesthetic technique, prepare appropriate monitoring, adapt the perioperative plan, and ensure the patient's safety. Bedside diagnosis and management have benefited from recent imaging advancements such as lung ultrasound and electrical impedance tomography, and monitoring such as esophageal manometry. Therapeutic management includes a broad range of interventions aimed at promoting lung recruitment. During general anesthesia, these strategies have consistently demonstrated their effectiveness in improving intraoperative oxygenation and respiratory compliance. Yet these same intraoperative strategies may fail to affect additional postoperative pulmonary outcomes. Specific attention to the postoperative period may be key for such outcome impact of lung expansion. Interventions such as noninvasive positive pressure ventilatory support may be beneficial in specific patients at high risk for pulmonary atelectasis (e.g., obese) or those with clinical presentations consistent with lung collapse (e.g., postoperative hypoxemia after abdominal and cardiothoracic surgeries). Preoperative interventions may open new opportunities to minimize perioperative lung collapse and prevent pulmonary complications. Knowledge of pathophysiologic mechanisms of atelectasis and their consequences in the healthy and diseased lung should provide the basis for current practice and help to stratify and match the intensity of selected interventions to clinical conditions.

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks.

METHODS

This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH₂O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH₂O ahead covering 6x2cmH₂O PEEP steps.

RESULTS

Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH₂O, compared with 4.76cmH₂O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R² = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH₂O. Predicted dynamic functional residual capacity (V_frc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R² of 0.84.

CONCLUSIONS

Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.