Validity of thoracic respiratory inductive plethysmography in high body mass index subjects

International audienceWe aim to evaluate thoracic Respiratory Inductive Plethysmography (RIP) in high body mass index (BMI) subjects with a pneumotachometer (PT) as a reference. We simultaneously evaluated spontaneous breathing by RIP and PT in 10 low and 10 high BMI subjects at rest and in moderate exercise. We then recorded RIP amplitude with different excursions mimicking respiratory thoracic deformation, with different sizes of RIP belts surrounding cylinders of different perimeters with or without deformable foam simulating adipose tissue. RIP responses correlated with PT values in low and high BMI groups for inspiratory time (r=0.86 and r=0.91, respectively), expiratory time (r=0.96 and r=0.91, respectively) and amplitude (r=0.82 for both) but with a bias (-0.23 +/-0.25L) for high BMI subjects. ANOVA revealed the effects of perimeter and simulated adiposity (p<0.001 for both). We concluded that thoracic perimeter and deformity of adipose tissue are responsible for biases in RIP response in high BMI subjects

New physiological bench test reproducing nocturnal breathing pattern of patients with sleep disordered breathing.

Previous studies have shown that Automatic Positive Airway Pressure devices display different behaviors when connected to a bench using theoretical respiratory cycle scripts. However, these scripts are limited and do not simulate physiological behavior during the night. Our aim was to develop a physiological bench that is able to simulate patient breathing airflow by integrating polygraph data. We developed an algorithm analyzing polygraph data and transformed this information into digital inputs required by the bench hardware to reproduce a patient breathing profile on bench. The inputs are respectively the simulated respiratory muscular effort pressure input for an artificial lung and the sealed chamber pressure to regulate the Starling resistor. We did simulations on our bench for a total of 8 hours and 59 minutes for a breathing profile from the demonstration recording of a Nox T3 Sleep Monitor. The simulation performance results showed that in terms of relative peak-valley amplitude of each breathing cycle, simulated bench airflow was biased by only 1.48% ± 6.80% compared to estimated polygraph nasal airflow for a total of 6,479 breathing cycles. For total respiratory cycle time, the average bias ± one standard deviation was 0.000 ± 0.288 seconds. For patient apnea events, our bench simulation had a sensitivity of 84.7% and a positive predictive value equal to 90.3%, considering 149 apneas detected both in polygraph nasal simulated bench airflows. Our new physiological bench would allow personalizing APAP device selection to each patient by taking into account individual characteristics of a sleep breathing profile

Addition of bacterial filter alters positive airway pressure and non-invasive ventilation performances

International audienceRecently, one manufacturer of home ventilators alerted about the potential risk of serious injury related to the use of some of their positive airway pressure (PAP) and non-invasive ventilator (NIV) [1]. The risk is caused by the polyurethane foam used in their ventilators. In some cases, the foam broke into the blower and could have been inhaled by patients. The manufacturer and some healthcare regulatory agencies advocated, as a temporary solution, to modify PAP and NIV circuits by adding an inline bacterial filter in order to reduce the risk of inhalation [2]. However, changing ventilator circuits can alter ventilators performances during PAP and NIV [3]

Symptoms assessment and decision to treat patients with advanced Parkinson’s disease based on wearables data

International audienceBody-worn sensors (BWS) could provide valuable information in the management of Parkinson’s disease and support therapeutic decisions based on objective monitoring. To study this pivotal step and better understand how relevant information is extracted from BWS results and translated into treatment adaptation, eight neurologists examined eight virtual cases composed of basic patient profiles and their BWS monitoring results. Sixty-four interpretations of monitoring results and the subsequent therapeutic decisions were collected. Relationship between interrater agreements in the BWS reading and the severity of symptoms were analyzed via correlation studies. Logistic regression was used to identify associations between the BWS parameters and suggested treatment modifications. Interrater agreements were high and significantly associated with the BWS scores. Summarized BWS scores reflecting bradykinesia, dyskinesia, and tremor predicted the direction of treatment modifications. Our results suggest that monitoring information is robustly linked to treatment adaptation and pave the way to loop systems able to automatically propose treatment modifications from BWS recordings information