Stress Index Can Be Accurately and Reliably Assessed by Visually Inspecting Ventilator Waveforms

Setting the optimal positive end-expiratory pressure: a narrative review

The primary goals of positive end-expiratory pressure (PEEP) are to restore functional residual capacity through recruitment and prevention of alveolar collapse. Through these mechanisms, PEEP improves arterial oxygenation and may reduce the risk of ventilator-induced lung injury (VILI). Because of the many potential negative effects associated with the use of PEEP, much research has concentrated on determining the optimal PEEP setting. Arterial oxygenation targets and pressure-volume loops have been utilized to set the optimal PEEP for decades. Several other techniques have been suggested, including the use of PEEP tables, compliance, driving pressure (DP), stress index (SI), transpulmonary pressures, imaging, and electrical impedance tomography. Each of these techniques has its own benefits and limitations and there is currently not one technique that is recommended above all others.

Intraoperative Ventilator Management of the Critically Ill Patient

Strategies for the intraoperative ventilator management of the critically ill patient focus on parameters used for lung protective ventilation with acute respiratory distress syndrome, preventing or limiting the deleterious effects of mechanical ventilation, and optimizing anesthetic and surgical conditions to limit postoperative pulmonary complications for patients at risk. Patient conditions such as obesity, sepsis, the need for laparoscopic surgery, or one-lung ventilation may benefit from intraoperative lung protective ventilation strategies. Anesthesiologists can use risk evaluation and prediction tools, monitor advanced physiologic targets, and incorporate new innovative monitoring techniques to develop an individualized approach for patients.

Non-invasive over-distension measurements: data driven vs model-based

Clinical measurements offer bedside monitoring aiming to minimise unintended over-distension, but have limitations and cannot be predicted for changes in mechanical ventilation (MV) settings and are only available in certain MV modes. This study introduces a non-invasive, real-time over-distension measurement, which is robust, predictable, and more intuitive than current methods. The proposed over-distension measurement, denoted as OD, is compared with the clinically proven stress index (SI). Correlation is analysed via R² and Spearman r_s. The OD safe range corresponding to the unit-less SI safe range (0.95-1.05) is calibrated by sensitivity and specificity test. Validation is fulfilled with 19 acute respiratory distress syndrome (ARDS) patients data (196 cases), including assessment across ARDS severity. Overall correlation between OD and SI yielded R² = 0.76 and Spearman r_s = 0.89. Correlation is higher considering only moderate and severe ARDS patients. Calibration of OD to SI yields a safe range defined: 0 ≤ OD ≤ 0.8 cmH₂O. The proposed OD offers an efficient, general, real-time measurement of patient-specific lung mechanics, which is more intuitive and robust than SI. OD eliminates the limitations of SI in MV mode and its less intuitive lung status value. Finally, OD can be accurately predicted for new ventilator settings via its foundation in a validated predictive personalized lung mechanics model. Therefore, OD offers potential clinical value over current clinical methods.

Stepwise Ventilator Waveform Assessment to Diagnose Pulmonary Pathophysiology

Clinicians can use mechanical waveform analysis as a diagnostic tool to identify pulmonary pathophysiology. This review offers an approach to develop a hypothesis of a patient’s lung pathophysiology.

Flow Index accurately identifies breaths with low or high inspiratory effort during pressure support ventilation

Background

Flow Index, a numerical expression of the shape of the inspiratory flow-time waveform recorded during pressure support ventilation, is associated with patient inspiratory effort. The aim of this study was to assess the accuracy of Flow Index in detecting high or low inspiratory effort during pressure support ventilation and to establish cutoff values for the Flow index to identify these conditions. The secondary aim was to compare the performance of Flow index,of breathing pattern parameters and of airway occlusion pressure (P_0.1) in detecting high or low inspiratory effort during pressure support ventilation.

Methods

Data from 24 subjects was included in the analysis, accounting for a total of 702 breaths. Breaths with high inspiratory effort were defined by a pressure developed by inspiratory muscles (P_musc) greater than 10 cmH₂O while breaths with low inspiratory effort were defined by a P_musc lower than 5 cmH₂O. The areas under the receiver operating characteristic curves of Flow Index and respiratory rate, tidal volume,respiratory rate over tidal volume and P_0.1 were analyzed and compared to identify breaths with low or high inspiratory effort.

Results

P_musc, P_0.1, Pressure Time Product and Flow Index differed between breaths with high, low and intermediate inspiratory effort, while RR, RR/V_T and V_T/kg of IBW did not differ in a statistically significant way. A Flow index higher than 4.5 identified breaths with high inspiratory effort [AUC 0.89 (CI 95% 0.85–0.93)], a Flow Index lower than 2.6 identified breaths with low inspiratory effort [AUC 0.80 (CI 95% 0.76–0.83)].

Conclusions

Flow Index is accurate in detecting high and low spontaneous inspiratory effort during pressure support ventilation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03855-4.

Flow Index: a novel, non-invasive, continuous, quantitative method to evaluate patient inspiratory effort during pressure support ventilation

Background

The evaluation of patient effort is pivotal during pressure support ventilation, but a non-invasive, continuous, quantitative method to assess patient inspiratory effort is still lacking. We hypothesized that the concavity of the inspiratory flow-time waveform could be useful to estimate patient’s inspiratory effort. The purpose of this study was to assess whether the shape of the inspiratory flow, as quantified by a numeric indicator, could be associated with inspiratory effort during pressure support ventilation.

Methods

Twenty-four patients in pressure support ventilation were enrolled. A mathematical relationship describing the decay pattern of the inspiratory flow profile was developed. The parameter hypothesized to estimate effort was named Flow Index. Esophageal pressure, airway pressure, airflow, and volume waveforms were recorded at three support levels (maximum, minimum and baseline). The association between Flow Index and reference measures of patient effort (pressure time product and pressure generated by respiratory muscles) was evaluated using linear mixed effects models adjusted for tidal volume, respiratory rate and respiratory rate/tidal volume.

Results

Flow Index was different at the three pressure support levels and all group comparisons were statistically significant. In all tested models, Flow Index was independently associated with patient effort (p < 0.001). Flow Index prediction of inspiratory effort agreed with esophageal pressure-based methods.

Conclusions

Flow Index is associated with patient inspiratory effort during pressure support ventilation, and may provide potentially useful information for setting inspiratory support and monitoring patient-ventilator interactions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03624-3.

Patient-ventilator dyssynchrony in the intensive care unit: A practical approach to diagnosis and management

Patient-ventilator dyssynchrony or asynchrony occurs when, for any parameter of respiration, discordance exists between the patient's spontaneous effort and the ventilator's provided support. If not recognised, it may promote oversedation, prolong the duration of mechanical ventilation, create risk for lung injury, and generally confuse the clinical picture. Seven forms of dyssynchrony are common: (a) ineffective triggering; (b) autotriggering; (c) inadequate flow; (d) too much flow; (e) premature cycling; (f) delayed cycling; and (g) peak pressure apnoea. 'Reverse triggering' also occurs and may mimic premature cycling. Correct diagnosis of these phenomena often permits management by simple ventilator optimisation rather than by less desirable measures.

The Basics of Ventilator Waveforms

Purpose of Review

Knowledge of ventilator waveforms is important for clinicians working with children requiring mechanical ventilation. This review covers the basics of how to interpret and use data from ventilator waveforms in the pediatric intensive care unit.

Recent Findings

Patient-ventilator asynchrony (PVA) is a common finding in pediatric patients and observed in approximately one-third of ventilator breaths. PVA is associated with worse outcomes including increased length of mechanical ventilation, increased length of stay, and increased mortality. Identification of PVA is possible with a thorough knowledge of ventilator waveforms.

Summary

Ventilator waveforms are graphical descriptions of how a breath is delivered to a patient. These include three scalars (flow versus time, volume versus time, and pressure versus time) and two loops (pressure-volume and flow-volume). Thorough understanding of both scalars and loops, and their characteristic appearances, is essential to being able to evaluate a patient’s respiratory mechanics and interaction with the ventilator.