Changes In Mechanical Ventilation Research Articles

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

Background and ObjectiveModel-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment. MethodsThe virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated. ResultsThis research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol. ConclusionThe virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

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 rs. 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 rs = 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.

Exploration of the Utility of Speckle-Tracking Echocardiography During Mechanical Ventilation and Mechanical Circulatory Support.

OBJECTIVE:This narrative review aims to discuss the potential applicability of speckle-tracking echocardiography (STE) in patients under mechanical ventilation (MV) and mechanical circulatory support (MCS). Both its benefits and limitations were considered through critical analyses of the current available evidence.DATA SOURCES AND STUDY SELECTION:A literature search was conducted in PubMed and Excerpta Medica Database indexed databases (2012–2021). In addition, the reference lists of all selected studies were manually scanned for further identification of potentially relevant studies.DATA EXTRACTION:The terms “Speckle-Tracking Echocardiography,” “Mechanical Ventilation,” “Mechanical Circulatory Support,” “Extracorporeal Membrane Oxygenation,” “Ventricular Assist Devices,” and “Left Ventricular Unloading Devices” were searched for the identification of relevant articles for narrative synthesis.DATA SYNTHESIS:STE is a well-established post-processing method of analyzing myocardial function, with potentially greater clinical utility than conventional 2D echocardiography. STE has been incorporated into the guideline recommendations for both the diagnostic and prognostic evaluations of myocardial and valvular pathologies. However, the potential of STE application within critical care settings has not yet been fully realized. Its utility in the assessment of patients undergoing MV and MCS is substantial. Specifically, it may serve as an ideal modality in the assessment of subtle changes in cardiac function. In the limited number of studies reviewed, STE was consistently a more sensitive marker of myocardial functional change, compared with traditional markers of 2D and Doppler parameters during changes in MV and MCS.CONCLUSIONS:Although current evidence is extremely limited, STE strain is suggested to be a more sensitive and reproducible parameter of myocardial function than conventional echocardiographic parameters and may have value in the assessment of patients undergoing MV and MCS in critical care settings. Further studies in larger populations are required to elucidate STE’s prognostic capability and its value as a point-of-care tool in guiding clinical practice for subjects under MV and MCS.

Changes in Use of Respiratory Support for Preterm Infants in the US, 2008-2018

In preterm infants, mechanical ventilation (MV) is associated with adverse pulmonary and neurodevelopmental outcomes. Multiple randomized clinical trials over the past 2 decades have shown the effectiveness of early noninvasive ventilation (NIV) in decreasing the use of MV in preterm infants. The epidemiologic factors associated with respiratory support in US preterm infants and any temporal changes after these trials is unknown. To evaluate temporal changes in MV and noninvasive respiratory support in US preterm infants. In a cohort design, 2 large national data sets (Pediatrix Clinical Data Warehouse for the clinical cohort and National Inpatient Sample for the national cohort) were used to collect data on preterm infants (<35 weeks' gestation) without congenital anomalies who received active intensive care and were discharged home or died in the birth hospital from January 1, 2008, to December 31, 2018. Data analysis was conducted from December 10, 2019, to December 16, 2020. Discharge year. In the clinical cohort, detailed respiratory support data were generated, including days of MV and NIV modalities, and temporal trends were evaluated using multivariable modified Poisson or negative binomial regression models with discharge year as a continuous variable. In the national cohort, observed and expected national MV use were calculated. Among 259 311 infants (47.2% female) in 359 neonatal intensive care units in the clinical cohort, decreases were noted in the use (from 29.4% of infants in 2008 to 18.5% in 2018, relative risk for annual change, 0.96; 95% CI, 0.95-0.96) and duration (mean days, from 10.3 in 2008 to 9.7 in 2018; rate ratio for annual change, 0.98; 95% CI, 0.97-0.98) of MV. Noninvasive ventilation use increased from 57.9% of infants in 2008 to 67.4% in 2018 (adjusted relative risk for annual change, 1.02; 95% CI, 1.02-1.03), and mean NIV duration increased by 3.2 days (95% CI, 2.9-3.6 days). With increased use of continuous positive airway pressure and nasal intermittent positive-pressure ventilation as the main factors in the increase, the mean duration of respiratory support increased from 13.8 to 15.4 days (adjusted rate ratio for annual change, 1.03; 95% CI, 1.02-1.04) from 2008 to 2018. Among 1 169 441 infants in the national cohort, MV use decreased from 22.0% in 2008 to 18.5% in 2018, with an estimated 29 700 fewer ventilated infants and 142 000 fewer days of MV than expected during this period. These findings suggest that preterm respiratory support changed significantly from 2008 to 2018, with decreased use and duration of MV, increased use and duration of NIV, and an overall increase in respiratory support duration.

What links ventilator driving pressure with survival in the acute respiratory distress syndrome? A computational study

BackgroundRecent analyses of patient data in acute respiratory distress syndrome (ARDS) showed that a lower ventilator driving pressure was associated with reduced relative risk of mortality. These findings await full validation in prospective clinical trials.MethodsTo investigate the association between driving pressures and ventilator induced lung injury (VILI), we calibrated a high fidelity computational simulator of cardiopulmonary pathophysiology against a clinical dataset, capturing the responses to changes in mechanical ventilation of 25 adult ARDS patients. Each of these in silico patients was subjected to the same range of values of driving pressure and positive end expiratory pressure (PEEP) used in the previous analyses of clinical trial data. The resulting effects on several physiological variables and proposed indices of VILI were computed and compared with data relating ventilator settings with relative risk of death.ResultsThree VILI indices: dynamic strain, mechanical power and tidal recruitment, showed a strong correlation with the reported relative risk of death across all ranges of driving pressures and PEEP. Other variables, such as alveolar pressure, oxygen delivery and lung compliance, correlated poorly with the data on relative risk of death.ConclusionsOur results suggest a credible mechanistic explanation for the proposed association between driving pressure and relative risk of death. While dynamic strain and tidal recruitment are difficult to measure routinely in patients, the easily computed VILI indicator known as mechanical power also showed a strong correlation with mortality risk, highlighting its potential usefulness in designing more protective ventilation strategies for this patient group.

What Does a First Order Model Tell Us about PEEP Wave Maneuvers?

Patients with acute respiratory distress syndrome (ARDS) are currently treated with a lung protective ventilation strategy and the application of positive end-expiratory pressure (PEEP), sometimes in combination with recruitment maneuvers. In this study, the respiratory system elastance and airway resistance of each breath before, during and after a specific recruitment maneuver (PEEP wave maneuver) were analyzed in two patient groups, ARDS and control group. A reduction of elastance after the maneuver was observed in ARDS patients. In addition, only healthy lungs exhibited a reduction of the elastance during the course of the maneuver, while the lungs of ARDS patients didn’t show that reduction of elastance. The capability of PEEP wave maneuvers to improve lung ventilation was shown and the dynamic behavior of the elastance after the maneuver was illustrated. Healthy lungs adapt faster to changes in mechanical ventilation than the lungs of ARDS patients.

A mathematical model approach quantifying patients' response to changes in mechanical ventilation: Evaluation in pressure support

PurposeThis article evaluates how mathematical models of gas exchange, blood acid-base status, chemical respiratory drive, and muscle function can describe the respiratory response of spontaneously breathing patients to different levels of pressure support. MethodsThe models were evaluated with data from 12 patients ventilated in pressure support ventilation. Models were tuned with clinical data (arterial blood gas measurement, ventilation, and respiratory gas fractions of O2 and CO2) to describe each patient at the clinical level of pressure support. Patients were ventilated up to 5 different pressure support levels, for 15 minutes at each level to achieve steady-state conditions. Model-simulated values of respiratory frequency (fR), arterial pH (pHa), and end-tidal CO2 (FeCO2) were compared to measured values at each pressure support level. ResultsModel simulations compared well to measured data with Bland-Altman bias and limits of agreement of fR of 0.7 ± 2.2 per minute, pHa of −0.0007 ± 0.019, and FeCO2 of −0.001 ± 0.003. ConclusionThe models describe patients' fR, pHa, and FeCO2 response to changes in pressure support with low bias and narrow limits of agreement.

A mathematical model approach quantifying patients’ response to changes in mechanical ventilation: Evaluation in volume support

This paper presents a mathematical model-approach to describe and quantify patient-response to changes in ventilator support. The approach accounts for changes in metabolism (V˙O2, V˙CO2) and serial dead space (VD), and integrates six physiological models of: pulmonary gas-exchange; acid–base chemistry of blood, and cerebrospinal fluid; chemoreflex respiratory-drive; ventilation; and degree of patients’ respiratory muscle-response.The approach was evaluated with data from 12 patients on volume support ventilation mode. The models were tuned to baseline measurements of respiratory gases, ventilation, arterial acid–base status, and metabolism. Clinical measurements and model simulated values were compared at five ventilator support levels.The models were shown to adequately describe data in all patients (χ2, p > 0.2) accounting for changes in V˙CO2, VD and inadequate respiratory muscle-response. F-ratio tests showed that this approach provides a significantly better (p < 0.001) description of measured data than: (a) a similar model omitting the degree of respiratory muscle-response; and (b) a model of constant alveolar ventilation. The approach may help predict patients’ response to changes in ventilator support at the bedside.

Ventilation causes pulmonary vascular dilation and modulates the NOS and VEGF pathway on newborn rats with CDH

Background/PurposeCongenital diaphragmatic hernia (CDH) is a defect that presents high mortality because of pulmonary hypoplasia and hypertension. Mechanical ventilation changes signaling pathways, such as nitric oxide and VEGF in the pulmonary arterioles. We investigated the production of NOS2 and NOS3 and expression of VEGF and its receptors after ventilation in rat fetuses with CDH. MethodsCDH was induced by Nitrofen. The fetuses were divided into 6 groups: 1) control (C); 2) control ventilated (CV); 3) exposed to nitrofen (N−); 4) exposed to nitrofen ventilated (N-V), 5) CDH and 6) CDH ventilated (CDHV). Fetuses were harvested and ventilated. We assessed body weight (BW), total lung weight (TLW), TLW/BW ratio, the median pulmonary arteriolar wall thickness (MWT). We analyzed the expression of NOS2, NOS3, VEGF and its receptors by immunohistochemistry and Western blotting. ResultsBW, TLW, and TLW/BW ratio were greater on C than on N− and CDH (p<0.05). The MWT was higher in CDH than in CDHV (p<0.001). CDHV showed increased expression of NOS3 (p<0.05) and VEGFR1 (p<0.05), but decreased expression of NOS2 (p<0.05) and VEGFR2 (p<0.001) compared to CDH. ConclusionVentilation caused pulmonary vasodilation and changed the expression of NOS and VEGF receptors.

Respiratory mechanics and plasma levels of tumor necrosis factor alpha and interleukin 6 are affected by gas humidification during mechanical ventilation in dogs.

The use of dry gases during mechanical ventilation has been associated with the risk of serious airway complications. The goal of the present study was to quantify the plasma levels of TNF-alpha and IL-6 and to determine the radiological, hemodynamic, gasometric, and microscopic changes in lung mechanics in dogs subjected to short-term mechanical ventilation with and without humidification of the inhaled gas. The experiment was conducted for 24 hours in 10 dogs divided into two groups: Group I (n = 5), mechanical ventilation with dry oxygen dispensation, and Group II (n = 5), mechanical ventilation with oxygen dispensation using a moisture chamber. Variance analysis was used. No changes in physiological, hemodynamic, or gasometric, and radiographic constants were observed. Plasma TNF-alpha levels increased in group I, reaching a maximum 24 hours after mechanical ventilation was initiated (ANOVA p = 0.77). This increase was correlated to changes in mechanical ventilation. Plasma IL-6 levels decreased at 12 hours and increased again towards the end of the study (ANOVA p>0.05). Both groups exhibited a decrease in lung compliance and functional residual capacity values, but this was more pronounced in group I. Pplat increased in group I (ANOVA p = 0.02). Inhalation of dry gas caused histological lesions in the entire respiratory tract, including pulmonary parenchyma, to a greater extent than humidified gas. Humidification of inspired gases can attenuate damage associated with mechanical ventilation.

A mathematical model for simulating respiratory control during support ventilation modes

Mathematical model simulations may assist in the selection of mechanical ventilator settings. Previously, simulations have been limited to control ventilator modes, as these models lacked representation of respiratory control. This paper presents integration of a chemoreflex respiratory control model with models describing: ventilation and pulmonary gas exchange; oxygenation and acid-base status of blood; circulation; interstitial fluid and tissue buffering; and metabolism. A sensitivity analysis showed that typical response to changing ventilator settings can be described by base excess (BE), production of CO2 (VCO2), and model parameters describing central chemoreceptor behavior. Since BE and VCO2, can be routinely measured, changes in ventilator support may therefore be used to identify patient-specific chemoreceptor drive, enabling patient-specific predictions of the response to changes in mechanical ventilation.

Ventilatory muscle strength and quality of life in obese patients undergoing bariatric surgery. Prelimianry results

Introduction Obesity is one of the public health problems more relevant in modern society, leading to a series of changes in mechanical ventilation. Obesity treatment is aimed at improving the health and quality of life. Materials and methods The study included 13 female patients, obese grade III, recruited from two bariatric surgery services and referred to the Sleep Laboratory at the Nove de Julho University. Inclusion criteria included patients who were obese grade III (BMI ⩾ 40 ) or grade II (BMI 35.0–39.9), age between 18 and 65years, accepting voluntarily to participate in the study by reading and signing the informed consent form. Results Mean age was 40.08 ± 9.86, after bariatric surgery the mean body mass index was 36.91 ± 6.67, with p =0.004 and maximum inspiratory pressures were 84.12 ± 9.36 and 82.36 ± 12.21 the maximum expiratory, with a p ?0.001 and p ?0.001 respectively. With the BAROS questionnaire there was an report of good to excellent response to surgery in terms of quality of life in 75% of patients. Conclusion After bariatric surgery an optimization in maximum inspiratory and expiratory pressures was observed. The BAROS questionnaire is standardized and easily applied to evaluate the results after bariatric surgery. Acknowledgements The Sleep Laboratory receives funding from the Universidade Nove de Julho (Brazil) and research projects approved by the Brazilian fostering agencies Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP). LVFO received a grant from CNPq (Research Productivity modality - PQID, process number 307618/2010–2).

Integrating mathematical models of pulmonary gas exchange, acid base, and respiratory control for simulation of changes in mechanical ventilation during support modes

Integrating mathematical models of pulmonary gas exchange, acid base, and respiratory control for simulation of changes in mechanical ventilation during support modes

The change in strategy of mechanical ventilation: a single center study in China

To observe the treatment strategy and its changes in mechanical ventilation (MV) in a single medical center. Five hundred and two patients undergoing MV for at least 24 hours from January 1994 to December 1997 (control group) and from January 2004 to December 2006 (study group) in a total of 1 090 patients who were admitted to intensive care unit (ICU) Fuxing Hospital, Capital Medical University during the 2 periods were investigated. Datas including causes for the initiation of MV, ventilator modes and treatment parameters, weaning methods, and prognosis of patients were collected. The total incidence of MV was 46.1% (502/1 090). The incidence of MV in control group was 48.9% (184/376), and that in study group was 44.5% (318/714), respectively. The main causes for MV of 502 patients were pneumonia 18.3% (92 cases), acute exacerbation of chronic obstructive pulmonary disease (AECOPD) 16.3% (82 cases), postoperation 13.7% (69 cases), coma 14.1% (71 cases) , and acute respiratory distress syndrome (ARDS) 12.7% (64 cases). The initial ventilator mode: 59.8% (110/184) or 23.0%(73/318) in control or study group was assist/control ventilation (A/C), and 57.2% (182/318) or 20.7%(38/184) in study or control group was pressure support ventilation (PSV), and there was significant difference between the two groups (both P<0.01). The use of noninvasive ventilation (NPPV) in study group was obviously increased compared with control group [10.4% (33/318) vs. 3.8% (7/184), P<0.01]. The mean pressure level of pressure support (PS) of all patients was 14.0 cm H(2)O (1 cm H(2)O=0.098 kPa), the mean positive end-expiratory pressure (PEEP) of both groups was 5.0 cm H(2)O. Compared with control group, PEEP (cm H(2)O) level in patients with ARDS was significantly higher (8.0 vs. 6.0, P<0.01) and volume tidal (V(T), ml) was significantly lower (400 vs. 550, P<0.01) in study group. The most frequently used weaning methods of both groups were T-piece, T-piece+PSV and PSV. The use of T-piece in study group was significantly higher than that in control group [84.4% (184/218) vs. 35.1% (40/114), P<0.01], and PSV was lower than that in control group [2.8% (6/218) vs. 29.8% (34/114), P<0.01]. The total mortality of MV patients in two groups in ICU was 49.6%(249/502). There was no significant difference of the mortality between study group and control group (54.6% vs. 55.4%, P=0.887). The ventilator modes and settings had been changed in a single medical center in the past 10 years. It is speculated that the changes are related with the results observed in some multicenter randomized controlled trials (RCTs).

Early Tracheotomy After Cardiac Surgery: Not Ready for Prime Time

In this issue, Trouillet and colleagues report a randomized trial of early versus late tracheotomy in patients requiring mechanical ventilation 4 days after cardiac surgery that showed improvements...

Design and implementation of a portable physiologic data acquisition system*

To describe and report the reliability of a portable, laptop-based, real-time, continuous physiologic data acquisition system (PDAS) that allows for synchronous recording of physiologic data, clinical events, and event markers at the bedside for physiologic research studies in the intensive care unit. Descriptive report of new research technology. Adult and pediatric intensive care units in three tertiary care academic hospitals. Sixty-four critically ill and injured patients were studied, including 34 adult (22 males and 12 females) and 30 pediatric (19 males and 11 females). None. Data transmission errors during bench and field testing were measured. The PDAS was used in three separate research studies, by multiple users, and for repeated recordings of the same set of signals at various intervals for different lengths of time. Both parametric (1 Hz) and waveform (125-500 Hz) signals were recorded and analyzed. Details of the PDAS components are explained and examples are given from the three experimental physiology-based protocols. Waveform data include electrocardiogram, respiration, systemic arterial pressure (invasive and noninvasive), oxygen saturation, central venous pressure, pulmonary arterial pressure, left and right atrial pressures, intracranial pressure, and regional cerebral blood flow. Bench and field testing of the PDAS demonstrated excellent reliability with 100% accuracy and no data transmission errors. The key feature of simultaneously capturing physiologic signal data and clinical events (e.g., changes in mechanical ventilation, drug administration, clinical condition) is emphasized. The PDAS provides a reliable tool to record physiologic signals and associated clinical events on a second-to-second basis and may serve as an important adjunctive research tool in designing and performing clinical physiologic studies in critical illness and injury.

A physiology simulator: validation of its respiratory components and its ability to predict the patient's response to changes in mechanical ventilation

We aimed to validate the mathematical validity and accuracy of the respiratory components of the Nottingham Physiology Simulator (NPS), a computer simulation of physiological models. Subsequently, we aimed to assess the accuracy of the NPS in predicting the effects of a change in mechanical ventilation on patient arterial blood-gas tensions. The NPS was supplied with the following measured or calculated values from patients receiving intensive therapy: pulmonary shunt and physiological deadspace fractions, oxygen consumption, respiratory quotient, cardiac output, inspired oxygen fraction, expired minute volume, haemoglobin concentration, temperature and arterial base excess. Values calculated by the NPS for arterial oxygen tension and saturation (PaO2 and SaO2), mixed venous oxygen tension and saturation (PvO2 and SvO2), arterial and mixed venous carbon dioxide tension (PaCO2 and PvCO2) and arterial pH were accurate compared with measured values. Subsequently, arterial gas responses to changes in minute volume of FiO2 were measured in 31 patients and were compared with the NPS prediction for each response. The 95% limits of agreement in predicting the magnitude of change were: arterial oxygen tension -2.07 to 2.47 kPa; PaCO2 -0.33 to 0.67 kPa; and pH -0.023 to 0.033. This investigation has validated respiratory components of the NPS. We recommend the NPS as a clinical tool for predicting the effects of alterations in mechanical ventilation in stable patients in the intensive care unit.

End-tidal Carbon Dioxide in Critically ill Patients during Changes in Mechanical Ventilation

Values of end-tidal CO2 (PETCO2) approximate PaCO2 in spontaneous breathing normal subjects and in stable patients receiving mechanical ventilatory support (MVS). Because marked inequality of ventilation/perfusion ratios in critically ill patients might affect this correlation, we assessed changes of PETCO2 in predicting changes in PaCO2 (delta PaCO2) and changes in minute ventilation (delta Ve) in this patient population. Twenty consecutive intubated patients 38 to 89 yr of age (mean, 70 yr) with respiratory failure while receiving MVS with indwelling arterial lines were studied. Settings on the mechanical ventilator were varied for frequency and tidal volume, and after a minimum of 5 to 10 min equilibration, PaCO2 and PETCO2 were measured. Vt and Ve were recorded from the digital indicator of the pneumotachygraph within the mechanical ventilator and corrected for compression volume in the respirator circuit. A total of 116 simultaneous measurements were performed. PETCO2 correlated well with PaCO2 (r = 0.78, p less than 0.001). The 95% confidence interval for the mean difference in PaCO2-PETCO2 was 4.0 +/- 0.97 mm Hg. However, delta PETCO2 (measured from baseline) did not correlate as well with delta PaCO2 (r = 0.58, p = less than 0.001). In four patients, the trend in their PETCO2 during changes in mechanical ventilation were in the opposite direction from the trend in their PaCO2. Thus, many critically ill patients, who cannto be preidentified, have an inconstant PaCO2-PETCO2 gradient with changes of ventilation. Utilization of PETCO2 as a noninvasive monitoring substitute for trends in PaCO2 in critically ill patients may be misleading despite establishing an initial PaCO2-PETCO2 relationship.