Lung mechanics at the bedside: make it simple

Mechanical Ventilation, Past, Present, and Future

Mechanical ventilation (MV) has played a crucial role in the medical field, particularly in anesthesia and in critical care medicine (CCM) settings. MV has evolved significantly since its inception over 70 years ago and the future promises even more advanced technology. In the past, ventilation was provided manually, intermittently, and it was primarily used for resuscitation or as a last resort for patients with severe respiratory or cardiovascular failure. The earliest MV machines for prolonged ventilatory support and oxygenation were large and cumbersome. They required a significant amount of skills and expertise to operate. These early devices had limited capabilities, battery, power, safety features, alarms, and therefore these often caused harm to patients. Moreover, the physiology of MV was modified when mechanical ventilators moved from negative pressure to positive pressure mechanisms. Monitoring systems were also very limited and therefore the risks related to MV support were difficult to quantify, predict and timely detect for individual patients who were necessarily young with few comorbidities. Technology and devices designed to use tracheostomies versus endotracheal intubation evolved in the last century too and these are currently much more reliable. In the present, positive pressure MV is more sophisticated and widely used for extensive period of time. Modern ventilators use mostly positive pressure systems and are much smaller, more portable than their predecessors, and they are much easier to operate. They can also be programmed to provide different levels of support based on evolving physiological concepts allowing lung-protective ventilation. Monitoring systems are more sophisticated and knowledge related to the physiology of MV is improved. Patients are also more complex and elderly compared to the past. MV experts are informed about risks related to prolonged or aggressive ventilation modalities and settings. One of the most significant advances in MV has been protective lung ventilation, diaphragm protective ventilation including noninvasive ventilation (NIV). Health care professionals are familiar with the use of MV and in many countries, respiratory therapists have been trained for the exclusive purpose of providing safe and professional respiratory support to critically ill patients. Analgo-sedation drugs and techniques are improved, and more sedative drugs are available and this has an impact on recovery, weaning, and overall patients' outcome. Looking toward the future, MV is likely to continue to evolve and improve alongside monitoring techniques and sedatives. There is increasing precision in monitoring global "patient-ventilator" interactions: structure and analysis (asynchrony, desynchrony, etc). One area of development is the use of artificial intelligence (AI) in ventilator technology. AI can be used to monitor patients in real-time, and it can predict when a patient is likely to experience respiratory distress. This allows medical professionals to intervene before a crisis occurs, improving patient outcomes and reducing the need for emergency intervention. This specific area of development is intended as "personalized ventilation." It involves tailoring the ventilator settings to the individual patient, based on their physiology and the specific condition they are being treated for. This approach has the potential to improve patient outcomes by optimizing ventilation and reducing the risk of harm. In conclusion, MV has come a long way since its inception, and it continues to play a critical role in anesthesia and in CCM settings. Advances in technology have made MV safer, more effective, affordable, and more widely available. As technology continues to improve, more advanced and personalized MV will become available, leading to better patients' outcomes and quality of life for those in need.

Accuracy of the estimations of respiratory mechanics using an expiratory time constant in passive and active breathing conditions: a bench study

BACKGROUND

Respiratory mechanics monitoring provides useful information for guiding mechanical ventilation, but many measuring methods are inappropriate for awake patients. This study aimed to evaluate the accuracy of dynamic mechanics estimation using expiratory time constant (RC_exp) calculation during noninvasive pressure support ventilation (PSV) with air leak in different lung models.

METHODS

A Respironics V60 ventilator was connected to an active breathing simulator for modeling five profiles: normal adult, restrictive, mildly and severely obstructive, and mixed obstructive/restrictive. Inspiratory pressure support was adjusted to maintain tidal volumes (V_T), achieving 5.0, 7.0, and 10.0 ml/kg body weight. PEEP was set at 5 cmH₂O, and the back-up rate was 10 bpm. Measurements were conducted at system leaks of 25-28 L/min. RC_exp was estimated from the ratio at 75% exhaled V_T and flow rate, which was then used to determine respiratory system compliance (C_rs) and airway resistance (R_aw).

RESULTS

In non-obstructive conditions (R_aw ≤ 10 cmH₂O/L/s), the C_rs was overestimated in the PSV mode. Peak inspiratory and expiratory flow and V_T increased with PS levels, as calculated C_rs decreased. In passive breathing, the difference of C_rs between different V_T was no significant. Underestimations of inspiratory resistance and expiratory resistance were observed at V_T of 5.0 ml/kg. The difference was minimal at V_T of 7.0 ml/kg. During non-invasive PSV, the estimation of airway resistance with the RC_exp method was accurately at V_T of 7.0 ml/kg.

CONCLUSIONS

The difference between the calculated C_rs and the preset value was influenced by the volume, status and inspiratory effort in spontaneously breathing.

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.

Stochastic integrated model-based protocol for volume-controlled ventilation setting

BACKGROUND AND OBJECTIVE

Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration.

METHODS

A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each.

RESULTS

From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol.

CONCLUSIONS

Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.

Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients

BACKGROUND AND OBJECTIVE

Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses.

METHODS

This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.

RESULTS

The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH₂O and 0.55 [0.19 - 1.20] mL/kg were obtained.

CONCLUSIONS

Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

BACKGROUND

Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort.

METHODS

Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH₂O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results.

RESULTS

Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH₂O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH₂O, with range -7.9 - -1.9 cmH₂O. Per-patient E and R were 16.4 [13.6-21.8] cmH₂O/L and 9.2 [6.4-13.1] cmH₂O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R²=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure.

CONCLUSIONS

The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.

Continuous estimation of airway resistance in non-invasive ventilation

BACKGROUND

This study aimed to evaluate the accuracy of expiratory time constant (RC_exp) to continuously calculate the airway resistance (R_aw).

MATERIAL AND METHODS

A Respironics V60 ventilator was connected to a lung simulator for modeling different profiles of respiratory mechanics.

RESULTS

During assisted ventilation, the respiratory system compliance (C_rs) calculation was always overestimated in most lung models. The R_aw estimation using the expiratory resistance (R_exp) method was close to the calculated value with the occlusion method during volume-controlled ventilation (VCV). In expiratory flow limitation (EFL) lung models, similar results were obtained in the estimation of inspiratory resistance (R_insp), but different variations were observed in the calculation of the R_exp. The results estimated with RC_exp and with dynamic signal analysis had significant variation and accuracy (p < 0.001).

CONCLUSION

The RC_exp method is a robust approach to provide real-time assessments of R_insp and R_exp in spontaneously breathing patients during noninvasive ventilation. An underestimation of R_exp was observed in EFL lung models.

Response to first dose of inhaled albuterol in mechanically ventilated preterm infants

BACKGROUND

Bronchodilator responses among preterm infants are heterogeneous. Bedside measurements may identify responders.

STUDY DESIGN

Respiratory measurements (Resistance, Compliance, FiO2) and pulse oximetry (SpO2) patterns were downloaded from infants <30 weeks gestational age during the first 2 months of life. Mechanically ventilated infants who received albuterol were included (n = 33). Measurements were compared before and after first albuterol. Secondary analyses assessed subsequent doses.

RESULTS

Median gestation and birthweight were 25 3/7 weeks and 730 g, respectively. Mean Resistance decreased post-albuterol (p = 0.007). Sixty-eight percent of infants were responders based on decreased Resistance. Compliance and FiO2 did not significantly differ. Percent time in hypoxemia (SpO2 < 85%) decreased post albuterol (p < 0.02). In responders, Resistance changes diminished with subsequent administration (all p = 0.01).

CONCLUSIONS

Ventilator resistance decreased in two-thirds of preterm infants, consistent with studies that utilized formal pulmonary function testing. Albuterol had a variable effect on delivered FiO2; however, hypoxemia may be useful in evaluating albuterol response.

Accuracy of the dynamic signal analysis approach in respiratory mechanics during noninvasive pressure support ventilation: a bench study

OBJECTIVE

To evaluate the accuracy of respiratory mechanics using dynamic signal analysis during noninvasive pressure support ventilation (PSV).

METHODS

A Respironics V60 ventilator was connected to an active lung simulator to model normal, restrictive, obstructive, and mixed obstructive and restrictive profiles. The PSV was adjusted to maintain tidal volumes (V_T) that achieved 5.0, 7.0, and 10.0 mL/kg body weight, and the positive end-expiration pressure (PEEP) was set to 5 cmH₂O. Ventilator performance was evaluated by measuring the flow, airway pressure, and volume. The system compliance (C_rs) and airway resistance (inspiratory and expiratory resistance, R_insp and R_exp, respectively) were calculated.

RESULTS

Under active breathing conditions, the C_rs was overestimated in the normal and restrictive models, and it decreased with an increasing pressure support (PS) level. The R_insp calculated error was approximately 10% at 10.0 mL/kg of V_T, and similar results were obtained for the calculated R_exp at 7.0 mL/kg of V_T.

CONCLUSION

Using dynamic signal analysis, appropriate tidal volume was beneficial for R_rs, especially for estimating R_exp during assisted ventilation. The C_rs measurement was also relatively accurate in obstructive conditions.

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH₂O. Prediction errors for peak inspiratory volume for an increase of 16 cmH₂O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care.

Go with the flow-clinical importance of flow curves during mechanical ventilation: A narrative review

Most clinicians pay attention to tidal volume and airway pressures and their curves during mechanical ventilation. On the other hand, inspiratory–expiratory flow curves also provide a plethora of information, but much less attention is paid to them. Flow curves chronologically show the velocity and direction of inspiration and expiration and are influenced by the respiratory mechanics, the patient’s effort, and the mode of ventilation and its settings. When the ventilator setting does not synchronize with the patient’s respiratory pattern, the patient can easily have worsening breathing effort, patient–ventilator asynchrony, which can lead to prolonged ventilator support or lung injury. The information provided by the flow curves during mechanical ventilation, such as respiratory mechanics, the patient’s effort, and patient–ventilator interactions, are very helpful when adjusting the ventilator setting. If clinicians can monitor and assess the flow curves information appropriately, it can be a useful diagnostic and therapeutic tool at the bedside. There may be association between inspiratory effort and flow, and this may further guide us, especially in the weaning process and when patients are not synchronizing with the ventilator. In this review, we try to gather information about “flow” that is scattered around in the literature and textbooks in one place. We will summarize the different flow waveforms utilized in commonly used ventilator modes with their advantages and disadvantages, information gained by the flow curves (i.e., flow-time, flow-volume, and flow-pressure), how to detect and manage asynchronies, and some ideas for future uses. Flow waveforms shapes and patterns are very beneficial for the management of patients undergoing mechanical ventilatory support. Attention to those waveforms can potentially improve patient outcomes. Clinicians should be familiar with this information and how to act upon them.

Reproducibility of respiratory mechanics measurements in patients on invasive mechanical ventilation

Objective

To evaluate the intra- and interexaminer reproducibility of measurements of the resistance and static and dynamic compliance of the respiratory system in patients on mechanical ventilation.

Methods

This was an analytical study conducted with individuals aged ≥ 18 years who were on invasive mechanical ventilation and had no clinical diagnosis of respiratory system disease and/or chest abnormality. Three measurements of respiratory mechanics were performed with a 1-minute interval between them. The first and third measurements were performed by examiner A, the second by examiner B. The values for the resistance and static and dynamic compliance of the respiratory system were compared using the intraclass correlation coefficient.

Results

A total of 198 measurements of respiratory mechanics were performed for 66 patients on mechanical ventilation. The patients had a mean age of 52.6 ± 18.6 years and a mean body mass index of 21.6 ± 2.1kg/m²; a surgical profile (61.5%) and female sex (53.8%) were predominant. Mean values were obtained for the three measurements of respiratory system resistance (A1: 15.7 ± 6.8cmH₂O/L/s; B1: 15.7 ± 6.4cmH₂O/L/s and A2: 15.9 ± 6.2cmH₂O/L/s), respiratory system static compliance (A1: 42.1 ± 13.7mL/cmH₂O; B1: 42.4 ± 14.6mL/cmH₂O and A2: 42.2 ± 14.5mL/cmH₂O) and respiratory system dynamic compliance (A1: 21.3 ± 7.3mL/cmH₂O; B1: 21.4 ± 7.5mL/cmH₂O and A2: 21.3 ± 6.2mL/cmH₂O). The intraclass correlation coefficient was also calculated for respiratory system resistance (R = 0.882 and p = 0.001; R = 0.949 and p = 0.001 - interexaminer A1 versus B and B versus A2, respectively; R = 0.932 and p = 0.001 - intraexaminer); respiratory system static compliance (R = 0.951 and p = 0.001; R = 0.958 and p = 0.001 - interexaminer A1 versus B and B versus A2, respectively; R = 0.965 and p = 0.001 - intraexaminer) and respiratory system dynamic compliance (R = 0.957 and p = 0.001; R = 0.946 and p = 0.001 - interexaminer A1 versus B and B versus A2, respectively; R = 0.926 and p = 0.001 - intraexaminer).

Conclusion

The measurements of resistance and static and dynamic compliance of the respiratory system show good intra- and interexaminer reproducibility for ventilated patients.

Model-based PEEP titration versus standard practice in mechanical ventilation: a randomised controlled trial

Background

Positive end-expiratory pressure (PEEP) at minimum respiratory elastance during mechanical ventilation (MV) in patients with acute respiratory distress syndrome (ARDS) may improve patient care and outcome. The Clinical utilisation of respiratory elastance (CURE) trial is a two-arm, randomised controlled trial (RCT) investigating the performance of PEEP selected at an objective, model-based minimal respiratory system elastance in patients with ARDS.

Methods and design

The CURE RCT compares two groups of patients requiring invasive MV with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio ≤ 200; one criterion of the Berlin consensus definition of moderate (≤ 200) or severe (≤ 100) ARDS. All patients are ventilated using pressure controlled (bi-level) ventilation with tidal volume = 6–8 ml/kg. Patients randomised to the control group will have PEEP selected per standard practice (SPV). Patients randomised to the intervention will have PEEP selected based on a minimal elastance using a model-based computerised method. The CURE RCT is a single-centre trial in the intensive care unit (ICU) of Christchurch hospital, New Zealand, with a target sample size of 320 patients over a maximum of 3 years. The primary outcome is the area under the curve (AUC) ratio of arterial blood oxygenation to the fraction of inspired oxygen over time. Secondary outcomes include length of time of MV, ventilator-free days (VFD) up to 28 days, ICU and hospital length of stay, AUC of oxygen saturation (SpO₂)/FiO₂ during MV, number of desaturation events (SpO₂ < 88%), changes in respiratory mechanics and chest x-ray index scores, rescue therapies (prone positioning, nitric oxide use, extracorporeal membrane oxygenation) and hospital and 90-day mortality.

Discussion

The CURE RCT is the first trial comparing significant clinical outcomes in patients with ARDS in whom PEEP is selected at minimum elastance using an objective model-based method able to quantify and consider both inter-patient and intra-patient variability. CURE aims to demonstrate the hypothesized benefit of patient-specific PEEP and attest to the significance of real-time monitoring and decision-support for MV in the critical care environment.

Trial registration

Australian New Zealand Clinical Trial Registry, ACTRN12614001069640. Registered on 22 September 2014.

(https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366838&isReview=true) The CURE RCT clinical protocol and data usage has been granted by the New Zealand South Regional Ethics Committee (Reference number: 14/STH/132).

Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

BACKGROUND

Spirometry is conventionally used to diagnose airway diseases in children with sickle cell disease (C-SCD). However, spirometry is difficult for younger children to perform, is effort dependent, and it provides limited information on respiratory mechanics. Impulse oscillometry (IOS) is an effort-independent pulmonary function test (PFT), which measures total airway resistance (R5Hz) and reactance (AX). IOS could be advantageous without certain limitations of spirometry.

AIM

To compare the accuracy of IOS vs spirometry in making the diagnosis of asthma and assessing age-related pulmonary changes in C-SCD.

STUDY DESIGN

Retrospective chart review.

SUBJECT SELECTION

Fifty-six C-SCD and thirty-six controls (asthmatics without SCD) followed at Penn State with PFTs obtained during the initial pulmonary evaluation.

METHODOLOGY

We grouped C-SCD into asthmatics and non-asthmatics based on pre-referral diagnosis and compared PFTs between two groups. Receiver operating characteristic (ROC) curve analyses and machine learning tools (XGBoost and artificial neural network) were used to rank the spirometry and IOS measures based on their ability to predict a diagnosis of asthma. Robust linear regression was used to analyze association among height/age with various PFT measures.

RESULTS

Both ROC and XGBoost indicated that FEF_25-75 %, forced expiratory volume in 1 second (FEV1)/forced vital capacity, and R5Hz(%) were the top three predictors for asthma diagnosis. R5Hz(%) and AX had superior bronchodilator response (BDR) than FEV1. IOS parameters had significant association with height/age in C-SCD (possibly due to the stiff lungs) but not in controls.

CONCLUSION

IOS had advantages over spirometry in C-SCD because it is feasible in early childhood, provides insights into the pulmonary mechanics, and is more sensitive to detect BDR.

Optimising mechanical ventilation through model-based methods and automation

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation

Background

Mechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO₂) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective.

Discussion

This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions.

Conclusion

Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.

The basics of respiratory mechanics: ventilator-derived parameters

Mechanical ventilation is a life-support system used to maintain adequate lung function in patients who are critically ill or undergoing general anesthesia. The benefits and harms of mechanical ventilation depend not only on the operator's setting of the machine (input), but also on their interpretation of ventilator-derived parameters (outputs), which should guide ventilator strategies. Once the inputs-tidal volume (V_T), positive end-expiratory pressure (PEEP), respiratory rate (RR), and inspiratory airflow (V')-have been adjusted, the following outputs should be measured: intrinsic PEEP, peak (Ppeak) and plateau (Pplat) pressures, driving pressure (ΔP), transpulmonary pressure (P_L), mechanical energy, mechanical power, and intensity. During assisted mechanical ventilation, in addition to these parameters, the pressure generated 100 ms after onset of inspiratory effort (P_0.1) and the pressure-time product per minute (PTP/min) should also be evaluated. The aforementioned parameters should be seen as a set of outputs, all of which need to be strictly monitored at bedside in order to develop a personalized, case-by-case approach to mechanical ventilation. Additionally, more clinical research to evaluate the safe thresholds of each parameter in injured and uninjured lungs is required.

Cardiogenic oscillations to detect intratidal derecruitment and overdistension in a porcine model of healthy and atelectatic lungs

BACKGROUND

Low positive end-expiratory pressure (PEEP) can result in alveolar derecruitment, and high PEEP or high tidal volume (V_T) in lung overdistension. We investigated cardiogenic oscillations (COS) in the airway pressure signal to investigate whether these oscillations can assess unfavourable intratidal events. COS induce short instantaneous compliance increases within the pressure-volume curve, and consequently in the compliance-volume curve. We hypothesised that increases in COS-induced compliance reflect non-linear intratidal respiratory system mechanics.

METHODS

In mechanically ventilated anaesthetised pigs with healthy (n=13) or atelectatic (n=12) lungs, pressure-volume relationships and the ECG were acquired at a PEEP of 0, 5, 10, and 15 cm H₂O. During inspiration, the peak compliance of successive COS (C_COS) was compared with intratidal respiratory system compliance (C_RS) within incremental volume steps up to the full V_T of 12 ml kg^-1. We analysed whether C_COS variation corresponded with systolic arterial pressure variation.

RESULTS

C_COS-volume curves showed characteristic intratidal patterns depending on the PEEP level and on atelectasis. Increasing C_RS- or C_COS-volume patterns were associated with intratidal derecruitment with low PEEP, and decreasing patterns above 6 ml kg^-1 and high PEEP showed overdistension. C_COS was not associated with systolic arterial pressure variations.

CONCLUSIONS

Heartbeat-induced oscillations within the course of the inspiratory pressure-volume curve reflect non-linear intratidal respiratory system mechanics. The analysis of these cardiogenic oscillations can be used to detect intratidal derecruitment and overdistension and, hence, to guide PEEP and V_T settings that are optimal for respiratory system mechanics.

Assessing Respiratory System Mechanical Function

The main goals of assessing respiratory system mechanical function are to evaluate the lung function through a variety of methods and to detect early signs of abnormalities that could affect the patient's outcomes. In ventilated patients, it has become increasingly important to recognize whether respiratory function has improved or deteriorated, whether the ventilator settings match the patient's demand, and whether the selection of ventilator parameters follows a lung-protective strategy. Ventilator graphics, esophageal pressure, intra-abdominal pressure, and electric impedance tomography are some of the best-known monitoring tools to obtain measurements and adequately evaluate the respiratory system mechanical function.

Assessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient

BACKGROUND

Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care.

METHODS

Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort.

RESULTS

The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort.

CONCLUSION

A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care.

[Respiratory monitoring of pediatric patients in the Intensive Care Unit]

Respiratory monitoring plays an important role in the care of children with acute respiratory failure. Therefore, its proper use and correct interpretation (recognizing which signals and variables should be prioritized) should help to a better understanding of the pathophysiology of the disease and the effects of therapeutic interventions. In addition, ventilated patient monitoring, among other determinations, allows to evaluate various parameters of respiratory mechanics, know the status of the different components of the respiratory system and guide the adjustments of ventilatory therapy. In this update, the usefulness of several techniques of respiratory monitoring including conventional respiratory monitoring and more recent methods are described. Moreover, basic concepts of mechanical ventilation, their interpretation and how the appropriate analysis of the information obtained can cause an impact on the clinical management of the patient are defined.

FLOW-i ventilator performance in the presence of a circle system leak

Recently, the FLOW-i anaesthesia ventilator was developed based on the SERVO-i intensive care ventilator. The aim of this study was to test the FLOW-i's tidal volume delivery in the presence of a leak in the breathing circuit. We ventilated a test lung model in volume-, pressure-, and pressure-regulated volume-controlled modes (VC, PC, and PRVC, respectively) with a FLOW-i. First, the circuit remained airtight and the ventilator was tested with fresh gas flows of 6, 1, and 0.3 L/min in VC, PC, and PRVC modes and facing 4 combinations of different resistive and elastic loads. Second, a fixed leak in the breathing circuit was introduced and the measurements repeated. In the airtight system, FLOW-i maintained tidal volume (VT) and circuit pressure at approximately the set values, independently of respiratory mode, load, or fresh gas flow. In the leaking circuit, set VT = 500 mL, FLOW-i delivered higher VTs in PC (about 460 mL) than in VC and PRVC, where VTs were substantially less than 500 mL. Interestingly, VT did not differ appreciably from 6 to 0.3 L/min of fresh air flow among the 3 ventilatory modes. In the absence of leakage, peak inspiratory pressures were similar, while they were 35-45 % smaller in PRVC and VC than in PC mode in the presence of leaks. In conclusion, FLOW-i maintained VT (down to fresh gas flows of 0.3 L/min) to 90 % of its preset value in PC mode, which was 4-5 times greater than in VC or PRVC modes.

Pressure-Controlled vs Volume-Controlled Ventilation in Acute Respiratory Failure: A Physiology-Based Narrative and Systematic Review

BACKGROUND

Mechanical ventilation is a cornerstone in the management of acute respiratory failure. Both volume-targeted and pressure-targeted ventilations are used, the latter modes being increasingly used. We provide a narrative review of the physiologic principles of these two types of breath delivery, performed a literature search, and analyzed published comparisons between modes.

METHODS

We performed a systematic review and meta-analysis to determine whether pressure control-continuous mandatory ventilation (PC-CMV) or pressure control-inverse ratio ventilation (PC-IRV) has demonstrated advantages over volume control-continuous mandatory ventilation (VC-CMV). The Cochrane tool for risk of bias was used for methodologic quality. We also introduced physiologic criteria as quality indicators for selecting the studies. Outcomes included compliance, gas exchange, hemodynamics, work of breathing, and clinical outcomes. Analyses were completed with RevMan5 using random effects models.

RESULTS

Thirty-four studies met inclusion criteria, many being at high risk of bias. Comparisons of PC-CMV/PC-IRV and VC-CMV did not show any difference for compliance or gas exchange, even when looking at PC-IRV. Calculating the oxygenation index suggested a poorer effect for PC-IRV. There was no difference between modes in terms of hemodynamics, work of breathing, or clinical outcomes.

CONCLUSIONS

The two modes have different working principles but clinical available data do not suggest any difference in the outcomes. We included all identified trials, enhancing generalizability, and attempted to include only sufficient quality physiologic studies. However, included trials were small and varied considerably in quality. These data should help to open the choice of ventilation of patients with acute respiratory failure.

Feasibility of titrating PEEP to minimum elastance for mechanically ventilated patients

Background

Selecting positive end-expiratory pressure (PEEP) during mechanical ventilation is important, as it can influence disease progression and outcome of acute respiratory distress syndrome (ARDS) patients. However, there are no well-established methods for optimizing PEEP selection due to the heterogeneity of ARDS. This research investigates the viability of titrating PEEP to minimum elastance for mechanically ventilated ARDS patients.

Methods

Ten mechanically ventilated ARDS patients from the Christchurch Hospital Intensive Care Unit were included in this study. Each patient underwent a stepwise PEEP recruitment manoeuvre. Airway pressure and flow data were recorded using a pneumotachometer. Patient-specific respiratory elastance (E_rs) and dynamic functional residual capacity (dFRC) at each PEEP level were calculated and compared. Optimal PEEP for each patient was identified by finding the minima of the PEEP-E_rs profile.

Results

Median E_rs and dFRC over all patients and PEEP values were 32.2 cmH₂O/l [interquartile range (IQR) 25.0–45.9] and 0.42 l [IQR 0.11–0.87]. These wide ranges reflect patient heterogeneity and variable response to PEEP. The level of PEEP associated with minimum E_rs corresponds to a high change of functional residual capacity, representing the balance between recruitment and minimizing the risk of overdistension.

Conclusions

Monitoring patient-specific E_rs can provide clinical insight to patient-specific condition and response to PEEP settings. The level of PEEP associated with minimum-E_rs can be identified for each patient using a stepwise PEEP recruitment manoeuvre. This ‘minimum elastance PEEP’ may represent a patient-specific optimal setting during mechanical ventilation.

Trial registration

Australian New Zealand Clinical Trials Registry: ACTRN12611001179921.

Electronic supplementary material

The online version of this article (doi:10.1186/s40814-015-0006-2) contains supplementary material, which is available to authorized users.

Time-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing

BACKGROUND

Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics.

METHODS

A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison.

RESULTS

At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator.

CONCLUSION

The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.

The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management

BACKGROUND

Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment.

IMPLEMENTATION

CURE Soft is a desktop application developed in JAVA. It has two modes of operation, 1) Online real-time monitoring decision support and, 2) Offline for user education purposes, auditing, or reviewing patient care. The CURE Soft has been tested in mechanically ventilated patients with respiratory failure. The clinical protocol, software testing and use of the data were approved by the New Zealand Southern Regional Ethics Committee.

RESULTS AND DISCUSSION

Using CURE Soft, patient's respiratory mechanics response to treatment and clinical protocol were monitored. Results showed that the patient's respiratory elastance (Stiffness) changed with the use of muscle relaxants, and responded differently to ventilator settings. This information can be used to guide mechanical ventilation therapy and titrate optimal ventilator PEEP.

CONCLUSION

CURE Soft enables real-time calculation of model-based respiratory mechanics for mechanically ventilated patients. Results showed that the system is able to provide detailed, previously unavailable information on patient-specific respiratory mechanics and response to therapy in real-time. The additional insight available to clinicians provides the potential for improved decision-making, and thus improved patient care and outcomes.

Newer nonconventional modes of mechanical ventilation

The conventional modes of ventilation suffer many limitations. Although they are popularly used and are well-understood, often they fail to match the patient-based requirements. Over the years, many small modifications in ventilators have been incorporated to improve patient outcome. The ventilators of newer generation respond to patient's demands by additional feedback systems. In this review, we discuss the popular newer modes of ventilation that have been accepted in to clinical practice. Various intensive care units over the world have found these modes to improve patient ventilator synchrony, decrease ventilator days and improve patient safety. The various modes discusses in this review are: Dual control modes (volume assured pressure support, volume support), Adaptive support ventilation, proportional assist ventilation, mandatory minute ventilation, Bi-level airway pressure release ventilation, (BiPAP), neurally adjusted ventilatory assist and NeoGanesh. Their working principles with their advantages and clinical limitations are discussed in brief.

Visualisation of time-varying respiratory system elastance in experimental ARDS animal models

BACKGROUND

Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.

METHODS

The single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject.

RESULTS

Six time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP.

CONCLUSIONS

Real-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

[Monitorization of respiratory mechanics in the ventilated patient]

Monitoring during mechanical ventilation allows the measurement of different parameters of respiratory mechanics. Accurate interpretation of these data can be useful for characterizing the situation of the different components of the respiratory system, and for guiding ventilator settings. In this review, we describe the basic concepts of respiratory mechanics, their interpretation, and their potential use in fine-tuning mechanical ventilation.

Expiratory model-based method to monitor ARDS disease state

Introduction

Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection.

Methods

Three fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty.

Results

The overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics.

Conclusion

Overall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.

Model-based respiratory mechanics to titrate PEEP and monitor disease state for experimental ARDS subjects

Modelling the respiratory mechanics of mechanically ventilated (MV) patients can provide useful information to guide MV therapy. Two model-based methods were evaluated based on data from three experimental acute respiratory distress syndrome (ARDS) induced piglets and validated against values available from ventilators. A single compartment lung model with integral-based parameter identification was found to be effective in capturing fundamental respiratory mechanics during inspiration. The trends matched clinical expectation and provided better resolution than clinically derived linear model metrics. An expiration time constant model also captured the same trend in respiratory elastance. However, the assumption of constant resistance and a slightly higher fitting error results in less insight than the single compartment model. Further research is required to confirm its application in titrating to optimal MV settings.

The flow-time waveform predicts respiratory system resistance and compliance

PURPOSE

Knowledge of patients' lung compliance and resistance aids clinical management. We investigated whether these values, readily measured during volume assist-control ventilation (VACV), could also be estimated during pressure assist-control ventilation (PACV).

METHODS

Data were collected in 12 mechanically ventilated human subjects. During VACV, peak pressure, plateau pressure, end-expiratory pressure, tidal volume, and inspiratory flow rate were measured. During PACV, inspiratory pressure, end-expiratory pressure, and tidal volume were recorded. The linear component of the pressure-time waveform was extrapolated to time and flow axes. Using the equation of motion for the respiratory system, assuming a nonlinear resistance, we calculated inspiratory resistance and compliance. During VACV, compliance and inspiratory resistance were calculated in the conventional manner.

RESULTS

In ventilated subjects, mean compliance during PACV was 37.06 ± 15.65 mL/cm H(2)O, and during VACV, 36.93 ± 12.18 mL/cm H(2)O. Mean inspiratory resistance during PACV was 15.17 ± 5.14 cm H(2)O/L per second, whereas during VACV, it was 12.50 ± 2.99 cm H(2)O/L per second. A strong correlation is evident between compliance and inspiratory resistance calculated during PACV vs VACV (r(2) of 0.73 and 0.51, respectively).

CONCLUSIONS

During PACV, the inspiratory flow waveform is linear, and its slope contains information regarding inspiratory resistance and compliance. Calculated values correlate with those during VACV.

[Interpretation of ventilator curves in patients with acute respiratory failure]

Mechanical ventilation is a therapeutic intervention involving the temporary replacement of ventilatory function with the purpose of improving symptoms in patients with acute respiratory failure. Technological advances have facilitated the development of sophisticated ventilators for viewing and recording the respiratory waveforms, which are a valuable source of information for the clinician. The correct interpretation of these curves is crucial for the correct diagnosis and early detection of anomalies, and for understanding physiological aspects related to mechanical ventilation and patient-ventilator interaction. The present study offers a guide for the interpretation of the airway pressure and flow and volume curves of the ventilator, through the analysis of different clinical scenarios.

State-of-the-art sensor technology in Spain: invasive and non-invasive techniques for monitoring respiratory variables

The interest in measuring physiological parameters (especially arterial blood gases) has grown progressively in parallel to the development of new technologies. Physiological parameters were first measured invasively and at discrete time points; however, it was clearly desirable to measure them continuously and non-invasively. The development of intensive care units promoted the use of ventilators via oral intubation ventilators via oral intubation and mechanical respiratory variables were progressively studied. Later, the knowledge gained in the hospital was applied to out-of-hospital management. In the present paper we review the invasive and non-invasive techniques for monitoring respiratory variables.

Recruitability of the lung estimated by the pressure volume curve hysteresis in ARDS patients

OBJECTIVE

To assess the hysteresis of the pressure-volume curve (PV curve) as to estimate, easily and at the bedside, the recruitability of the lung in ARDS patients.

DESIGN

Prospective study.

SETTING

Twelve medico-surgical ICU beds of a general hospital.

PATIENTS

Twenty-six patients within the first 24 h from meeting ARDS criteria.

INTERVENTION

A Quasi-static inflation and deflation PV curve from 0 to 40 cmH(2)O and a 40 cmH(2)O recruitment maneuver (RM) maintained for 10 s were successively done with an interval of 30 min in between. RECORDINGS AND CALCULATION: Hysteresis of the PV curve (H(PV)) was calculated as the ratio of the area enclosed by the pressure volume loop divided by the predicted body weight (PBW). The volume increase during the RM (V(RM)) was measured by integration of the flow required to maintain the pressure at 40 cmH(2)O and divided by PBW, as an estimation of the volume recruited during the RM.

RESULTS

A positive linear correlation was found between H(PV) and V(RM) (r = 0.81, P < 0.0001).

CONCLUSIONS

The results suggest using the hysteresis of the PV curve to assess the recruitability of the lung.

Monitoring the mechanically ventilated patient

In the intensive care setting, monitored data relevant to the output, efficiency, and reserve of the respiratory system alert the clinician to sudden untoward events, aid in diagnosis, help guide management decisions, aid in determining prognosis, and enable the assessment of therapeutic response. This review addresses those aspects of monitoring we find of most value in the care of patients receiving ventilatory support. We concentrate on those modalities and variables that are routinely available or easily calculated from data readily collected at the bedside.