P002 Targeted non-CPAP combination therapy resolves obstructive sleep apnoea

Introduction Mandibular advancement splint (MAS) therapy is an effective alternative to CPAP for many people with obstructive sleep apnoea (OSA) but ~50% have residual OSA. This study aimed to resolve OSA in these individuals by combining MAS with other targeted therapies based on OSA endotype characterisation. Methods Eleven people with OSA (apnoea-hypopnoea index (AHI): 35±13 events/h), not fully resolved with MAS alone (AHI>10 events/h) were recruited. Initially, OSA endotypes were assessed via a detailed physiology night. Step one of combination therapy focused on anatomical interventions including MAS plus an oral expiratory positive airway pressure valve (EPAP) and a supine-avoidance device. Participants with residual OSA (AHI>10 events/h) following the anatomical combination therapy night, were then given one or more targeted non-anatomical therapies according to endotype characterisation. This included oxygen (4L/min) to reduce unstable respiratory control (high loop gain), 10mg zolpidem to increase arousal threshold, or 80/5mg atomoxetine-oxybutynin (ato-oxy) for poor pharyngeal muscle responsiveness. Results OSA was successfully treated (AHI<10 events/h) in all participants with combination therapy. MAS combined with EPAP and supine-avoidance therapy resolved OSA in ~65% of participants (MAS alone vs. combination therapy: 17±4 vs. 5±3, events/h, n=7). For the remaining participants, OSA resolved with the addition of oxygen (n=2), one with 80/5mg ato-oxy and another required both oxygen and 80/5mg ato-oxy. Discussion Targeted combination therapy may be a viable treatment alternative for people with OSA who cannot tolerate CPAP or for those who have an incomplete therapeutic response with monotherapy.

Positive expiratory pressure in postoperative cardiac patients in intensive care: A randomized controlled trial

Objective: To evaluate effectiveness of positive expiratory pressure blow-bottle device compared to expiratory positive airway pressure and conventional physiotherapy on pulmonary function in postoperative cardiac surgery patients in intensive care unit. Design: A randomized controlled trial. Settings: Tertiary care. Subjects: 48 patients (16 in each group; aged 64.5 ± 9.1 years, 38 male) submitted to cardiac surgery. Interventions: Patients were randomized into conventional physiotherapy (G1), positive expiratory pressure blow-bottle device (G2) or expiratory positive airway pressure, both associated with conventional physiotherapy (G3). G2 and G3 performed three sets of 10 repetitions in each session for each technique. Main measures: Pulmonary function (primary); respiratory muscle strength, radiological changes, pulmonary complications, length of intensive care unit and hospital stay (secondary) assessed preoperatively and on the 3rd postoperative day. Results: Pulmonary function (except for forced expiratory volume in one second/ forced vital capacity % predicted) and respiratory muscle strength showed significant reduction from the preoperative to the 3rd postoperative in all groups ( P < 0.001), with no difference between groups ( P > 0.05). Regarding radiological changes, length of intensive care unit stay and length of hospital stay, there was no significant difference between groups ( P > 0.05). Conclusion: Both positive expiratory pressure techniques associated with conventional physiotherapy were similar, but there was no difference regarding the use of positive expiratory pressure compared to conventional physiotherapy. Clinical Trial Registration Number: NCT03639974. https://clinicaltrials.gov/ct2/show/NCT03639974

Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach

Background The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. Materials and methods A cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error. Results There were 364 indirect calorimetry measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index, use of vasopressors, expiratory positive airway pressure, MV, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69. Conclusion We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.

Proposal of a new equation for estimating resting energy expenditure for acute kidney injury patients on dialysis. A Machine Learning Approach.

The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. Material and methods: A cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error (RMSE). Results: There were 364 indirect calorimetry (IC) measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index (BMI), use of vasopressors, expiratory positive airway pressure, minute volume (MV), C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69. Conclusion: We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.

Proposal of a new equation for estimating resting energy expenditure for acute kidney injury patients on dialysis. A Machine Learning Approach.

The objective of this study was, therefore, to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. Material and methods: A cross-sectional descriptive study has been carried out in 114 AKI patients on dialysis and mechanical ventilation consecutively selected, aged between 19 and 95 years. For the construction of the predictive model, 80% of the cases were randomly separated to train and 20% of unused cases for validation. Several machine learning models were tested in the training data: linear regression with Stepwise, rpart, support vector machine with radial kernel, generalized boosting machine, and random forest. The models were selected by 10-fold cross-validation and the performances evaluated by the root mean square error (RMSE). Results: There were 364 indirect calorimetry (IC) measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, BMI, use of vasopressors, expiratory positive airway pressure, minute volume, C-reactive protein, temperature, and serum urea. The final r-value in the validation set was 0.69. Conclusion: We propose a new predictive equation for estimating the REE for AKI patients on dialysis that use a non-linear approach with better performance than actual models.

Proposal of a New Equation for Estimating Resting Energy Expenditure for Acute Kidney Injury Patients on Dialysis. A Machine Learning Approach.

Objective: The objective of this study was, therefore, to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. Material and methods: A cross-sectional descriptive study has been carried out in 114 AKI patients on dialysis and mechanical ventilation consecutively selected, aged between 19 and 95 years. For the construction of the predictive model, 80% of the cases were randomly separated to train and 20% of unused cases for validation. Several machine learning models were tested in the training data: linear regression with Stepwise, rpart, support vector machine with radial kernel, generalized boosting machine, and random forest. The models were selected by 10-fold cross-validation and the performances evaluated by the root mean square error (RMSE). Results: There were 364 indirect calorimetry (IC) measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, BMI, use of vasopressors, expiratory positive airway pressure, minute volume, C-reactive protein, temperature, and serum urea. The final r-value in the validation set was 0.69. Conclusion: We propose a new predictive equation for estimating the REE for AKI patients on dialysis that use a non-linear approach with better performance than actual models.

Combination therapy with mandibular advancement and expiratory positive airway pressure valves reduces obstructive sleep apnea severity

Study ObjectivesMandibular advancement splint (MAS) therapy is a well-tolerated alternative to continuous positive airway pressure for obstructive sleep apnea (OSA). Other therapies, including nasal expiratory positive airway pressure (EPAP) valves, can also reduce OSA severity. However, >50% of patients have an incomplete or no therapeutic response with either therapy alone and thus remain at risk of adverse health outcomes. Combining these therapies may yield greater efficacy to provide a therapeutic solution for many incomplete/nonresponders to MAS therapy. Thus, this study evaluated the efficacy of combination therapy with MAS plus EPAP in incomplete/nonresponders to MAS alone.MethodsTwenty-two people with OSA (apnea–hypopnea index [AHI] = 22 [13, 42] events/hr), who were incomplete/nonresponders (residual AHI > 5 events/hr) on an initial split-night polysomnography with a novel MAS device containing an oral airway, completed an additional split-night polysomnography with MAS + oral EPAP valve and MAS + oral and nasal EPAP valves (order randomized).ResultsCompared with MAS alone, MAS + oral EPAP significantly reduced the median total AHI, with further reductions with the MAS + oral/nasal EPAP combination (15 [10, 34] vs. 10 [7, 21] vs. 7 [3, 13] events/hr, p < 0.01). Larger reductions occurred in supine nonrapid eye movement AHI with MAS + oral/nasal EPAP combination therapy (ΔAHI = 23 events/hr, p < 0.01). OSA resolved (AHI < 5 events/hr) with MAS + oral/nasal EPAP in nine individuals and 13 had ≥50% reduction in AHI from no MAS. However, sleep efficiency was lower with MAS + oral/nasal EPAP versus MAS alone or MAS + oral EPAP (78 ± 19 vs. 87 ± 10 and 88 ± 10% respectively, p < 0.05).ConclusionsCombination therapy with a novel MAS device and simple oral or oro-nasal EPAP valves reduces OSA severity to therapeutic levels for a substantial proportion of incomplete/nonresponders to MAS therapy alone.Clinical TrialsName: Targeted combination therapy: Physiological mechanistic studies to inform treatment for obstructive sleep apnea (OSA)URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372279 Registration: ACTRN12617000492358 (Part C)