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Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid-base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patients transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO2 respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

Background: In patients with chronic coronary syndromes (CCS), increased ventilation/carbon dioxide production ( V ̇ E/ V ̇ CO2) slope has been found to predict disease progression and mortality, similarly to patients with heart failure (HF); however, increased chemosensitivity, a well-established predictor for mortality in patients with HF, has rarely been assessed in patients with CCS. Method: Patients with CCS, HF with reduced ejection fraction (EF < 50%), healthy controls (45+ years), and young healthy adults (<35 years) were recruited. For patients, a V ̇ E/ V ̇ CO2 slope ≥36 was an inclusion criterion. The Duffin rebreathing method was used to determine the resting end-expiratory partial pressure of carbon dioxide (PETCO2), ventilatory recruitment threshold (VRT), and slope (sensitivity) during a hyperoxic (150 mmHg O2) and hypoxic (50 mmHg O2) rebreathing test to determine the central and peripheral chemosensitivity. Results: In patients with CCS, HF, controls, and young healthy adults, median V ̇ E/ V ̇ CO2 slopes were 40.2, 41.3, 30.5, and 28.0, respectively. Both patient groups had similarly reduced hyperoxic VRT (at PETCO2 42.1 and 43.2 mmHg) compared to 46.0 and 48.8 mmHg in the control and young healthy adults. Neither hypoxic VRT nor hyper- or hypoxic slopes were significantly different in patients compared to controls. Both patient groups had lower resting PETCO2 than controls, but only patients with HF had increased breathing frequency and rapid shallow breathing at rest. Conclusion: In patients with HF and/or CCS and excess ventilation, central chemoreflex VRT was reduced independently of the presence of HF. Low VRTs were related to resting excess ventilation in patients with CCS or HF; however, rapid shallow breathing at peak exercise was present only in patients with HF. Clinical trial registration number: NCT05057884. [ABSTRACT FROM AUTHOR]

Background: Cerebrovascular reactivity (CVR) is a measure of the change in cerebral blood flow (CBF) in response to a vasoactive challenge. It is a useful indicator of the brain's vascular health. Purpose: To evaluate the factors that influence successful and unsuccessful CVR examinations using precise arterial and end‐tidal partial pressure of CO2 control during blood oxygen level–dependent (BOLD) MRI. Study Type: Retrospective. Subjects: Patients that underwent a CVR between October 2005 and May 2021 were studied (total of 1162 CVR examinations). The mean (±SD) age was 46.1 (±18.8) years, and 352 patients (43%) were female. Field Strength/Sequence: 3 T; T1‐weighted images, T2*‐weighed two‐dimensional gradient‐echo sequence with standard echo‐planar readout. Assessment: Measurements were obtained following precise hypercapnic stimuli using BOLD MRI as a surrogate of CBF. Successful CVR examinations were defined as those where: 1) patients were able to complete CVR testing, and 2) a clinically useful CVR map was generated. Unsuccessful examinations were defined as those where patients were not able to complete the CVR examination or the CVR maps were judged to be unreliable due to, for example, excessive head motion, and poor PETCO2 targeting. Statistical Analysis: Successful and unsuccessful CVR examinations between hypercapnic stimuli, and between different patterns of stimulus were compared with Chi‐Square tests. Interobserver variability was determined by using the intraclass correlation coefficient (P < 0.05 is significant). Results: In total 1115 CVR tests in 662 patients were included in the final analysis. The success rate of generating CVR maps was 90.8% (1012 of 1115). Among the different hypercapnic stimuli, those containing a step plus a ramp protocol was the most successful (95.18%). Among the unsuccessful examinations (9.23%), most were patient related (89.3%), the most common of which was difficulty breathing. Data Conclusion: CO2‐BOLD MRI CVR studies are well tolerated with a high success rate. Evidence Level: 4 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

This document is an editorial published in Frontiers in Physiology titled "Measuring resting cerebral perfusion using magnetic resonance imaging (MRI)." The editorial discusses the importance of measuring cerebral perfusion metrics, such as cerebral blood flow (CBF), in assessing cerebrovascular health. It highlights the use of magnetic resonance imaging (MRI) as a non-invasive technique for measuring perfusion metrics and discusses various methods and techniques for obtaining accurate measurements. The editorial also emphasizes the interplay between cerebral perfusion and brain metabolic processes and the potential for using advanced physiological MRI in routine clinical practice. [Extracted from the article]