Preference of neuromuscular patients regarding equipment for daytime mouthpiece ventilation: A randomized crossover study.

BACKGROUND: Patients with neuromuscular disorders (NMDs) are likely to develop respiratory failure which requires noninvasive ventilation (NIV). Ventilation via a mouthpiece (MPV) is an option to offer daytime NIV. OBJECTIVES: To determine the preferred equipment for MPV by patients with NMDs. METHODS: Two MPV equipment sets were compared in 20 patients with NMDs. Set 1, consisted of a non-dedicated ventilator for MPV (PB560, Covidien) with a plastic angled mouthpiece. Set 2, consisted of a dedicated MPV ventilator (Trilogy 100, Philips Respironics) without backup rate and kiss trigger combined with a silicone straw mouthpiece. The Borg dyspnea score, ventilator free time, transcutaneous oxygen saturation (SpO2) and carbon dioxide tension (TcCO2 ) were recorded with and without MPV. Patient perception was assessed by a 17-items list. RESULTS: Carbon dioxide tension measurements and total perception score were not different between the two MPV sets. Dyspnea score was lower with the non-dedicated versus dedicated equipment, 1 (0.5) versus 3 (1-6), P < 0.01. All patients with a ventilator free time lower than 6 hours preferred a set backup rate rather than a kiss trigger. Sixty five percent of patients preferred the commercial arm support and 55% preferred the plastic angled mouthpiece. CONCLUSIONS: Dedicated and non-dedicated MPV equipment are deemed effective and comfortable. Individualization of arm support and mouthpiece is advised to ensure success of MPV. A ventilator free time lower than 6 hours seems to be a useful indicator to a priori set a backup rate rather than a rate at zero associated to the kiss trigger.

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark.

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.

The critical importance of mask seals on respirator performance: An analytical and simulation approach.

Filtering facepiece respirators (FFRs) and medical masks are widely used to reduce the inhalation exposure of airborne particulates and biohazardous aerosols. Their protective capacity largely depends on the fraction of these that are filtered from the incoming air volume. While the performance and physics of different filter materials have been the topic of intensive study, less well understood are the effects of mask sealing. To address this, we introduce an approach to calculate the influence of face-seal leakage on filtration ratio and fit factor based on an analytical model and a finite element method (FEM) model, both of which take into account time-dependent human respiration velocities. Using these, we calculate the filtration ratio and fit factor for a range of ventilation resistance values relevant to filter materials, 500-2500 Pa∙s∙m-1, where the filtration ratio and fit factor are calculated as a function of the mask gap dimensions, with good agreement between analytical and numerical models. The results show that the filtration ratio and fit factor are decrease markedly with even small increases in gap area. We also calculate particle filtration rates for N95 FFRs with various ventilation resistances and two commercial FFRs exemplars. Taken together, this work underscores the critical importance of forming a tight seal around the face as a factor in mask performance, where our straightforward analytical model can be readily applied to obtain estimates of mask performance.