Measures of respiratory inductance plethysmography (RIP) in children with neuromuscular disease

Work of Breathing: Physiology, Measurement, and Diagnostic Value in Childhood Pneumonia

In clinical practice, increased "work of breathing" (WOB) is used to rapidly identify the acutely ill child in need of immediate clinical care, and is commonly used to support a clinical diagnosis of pneumonia. However, this key clinical sign is poorly understood and inconsistently defined. This review discusses the physiology, measurement, and clinical assessment of WOB, highlighting its utility in the recognition of pneumonia in under-resourced settings, where access to diagnostic imaging may be limited.

The impact of kangaroo mother care on work of breathing and oxygen saturation in very low birth weight infants with respiratory insufficiency

BACKGROUND: Kangaroo mother care (KMC) is defined as prolonged skin to skin care between a mother and infant with the infant lying in prone position on mom’s chest. KMC decreases morbidity and mortality and promotes physiologic stability. The aim of this study is to measure work of breathing (WOB) during KMC in very low birth weight (VLBW) infants on non-invasive respiratory support. METHODS: A prospective observational pilot study was conducted comparing WOB indices during standard care (SC) and KMC. Respiratory inductive plethysmography (RIP) measured WOB indices non-invasively: phase angle and labored breathing index. VLBW infants who were stable on non-invasive respiratory support were randomized to receive RIP measurements during KMC or during SC first. Summary statistics and mixed linear models were used to compare WOB and vital signs. RESULTS: A total of 32 infants were consented for the study, data collection and analysis was completed on 28 infants. There were no significant differences in mean phase angle during KMC or SC (73.5±4.6 SE deg vs 66.8±3.9 SE deg, p = 0.25). No differences in WOB and vital signs were detected. Controlling for respiratory support or randomization/first location did not change the results. CONCLUSION: In this pilot cohort, infants demonstrated no differences in work of breathing indices or oxygen saturation during KMC or SC while receiving non-invasive respiratory support. KMC appears to be safe and well tolerated with no worsened WOB. Larger studies should be performed to confirm our findings.

Utilising pneuRIP device in determining the adequacy of respiratory support when weaning high-flow nasal cannula in paediatric patients with acute respiratory distress: A pilot study

AIM

Recognition of paediatric respiratory distress and timely intervention is critical, especially during the weaning phase of support in paediatric acute respiratory failure, as weaning too aggressively can lead to further setbacks in a patient's recovery. We aimed to determine if pulmonary function measurements obtained with the pneuRIP device, a noninvasive pulmonary function testing device that provides measurements of labored breathing index (LBI), phase angle and %rib cage (%RC) contribution to breathing, will provide predictive values to assess the adequacy of respiratory support while weaning from HFNC.

METHODS

We reviewed patients ages 0-18 years admitted to the PICU for respiratory distress due to respiratory infections receiving HFNC. Patients with history of chronic lung disease and chronic neuromuscular disease with baseline habnormal breathing patterns were excluded. Phase angle, LBI and %RC were obtained every hour and with every wean of HFNC. Nine patients were enroled.

RESULTS

Mean LBI range remained 1.27-1.68 when LBI was plotted as a function of the HFNC flow rate. Mean values of %RC contribution to breathing ranged 43.65-57.12 as a function of the HFNC flow rate. No significant deviations existed in either %RC (P = 0.16) or LBI (P = 0.16) during the weaning of HFNC. Mean phase angle for all subjects was 41.48°-74.12° for the duration of wean and showed significant deviation from baseline during the weaning process (p = 0.001).

CONCLUSIONS

Measurements of LBI and %RC on the pneuRIP device effectively demonstrated tolerance of weaning HFNC during the recovery phase of acute respiratory failure from a respiratory infection.

Respiratory Inductance Plethysmography to Assess Fatigability during Repetitive Work

Cumulative fatigue during repetitive work is associated with occupational risk and productivity reduction. Usually, subjective measures or muscle activity are used for a cumulative evaluation; however, Industry 4.0 wearables allow overcoming the challenges observed in those methods. Thus, the aim of this study is to analyze alterations in respiratory inductance plethysmography (RIP) to measure the asynchrony between thorax and abdomen walls during repetitive work and its relationship with local fatigue. A total of 22 healthy participants (age: 27.0 ± 8.3 yrs; height: 1.72 ± 0.09 m; mass: 63.4 ± 12.9 kg) were recruited to perform a task that includes grabbing, moving, and placing a box in an upper and lower shelf. This task was repeated for 10 min in three trials with a fatigue protocol between them. Significant main effects were found from Baseline trial to the Fatigue trials (p < 0.001) for both RIP correlation and phase synchrony. Similar results were found for the activation amplitude of agonist muscle (p < 0.001), and to the muscle acting mainly as a joint stabilizer (p < 0.001). The latter showed a significant effect in predicting both RIP correlation and phase synchronization. Both RIP correlation and phase synchronization can be used for an overall fatigue assessment during repetitive work.

Adjustment of high flow nasal cannula rates using real-time work of breathing indices in premature infants with respiratory insufficiency

OBJECTIVE

To assess the feasibility of real-time monitoring of work of breathing (WOB) indices and the impact of adjusting HFNC flow on breathing synchrony and oxygen stability in premature infants.

STUDY DESIGN

A prospective, observational study of infants stable on HFNC. The flow adjusted per predetermined algorithm. Respiratory inductive plethysmography (RIP) noninvasively measured WOB. A high-resolution pulse oximeter collected oxygen saturation and heart rate data. Summary statistics and mixed linear models were used.

RESULTS

Baseline data for 32 infants, final analysis of 21 infants. Eighty-one percent with abnormal WOB. Sixty-two percent demonstrated 20% improvement in WOB. For infants with gestational age <28 weeks, an incremental increase in HFNC flow rate decreased WOB (p < 0.001) and improved oxygen saturation and stability (p < 0.01).

CONCLUSIONS

Premature infants do not receive optimal support on HFNC. The use of a real-time feedback system to adjust HFNC is feasible and improves WOB, oxygen saturation, and oxygen stability. This technology may improve the utility of HFNC in premature infants.

Automated Assessment of Thoracic-Abdominal Asynchrony in Patients with Morquio Syndrome

Morquio syndrome is a rare disease caused by a disorder in the storage of mucopolysaccharides that affects multiple organs, including musculoskeletal, respiratory, cardiovascular, and digestive systems. Respiratory failure is one of the leading causes of mortality in Morquio patients; thus, respiratory function testing is vital to the management of the disease. An automated respiratory assessment methodology using the pneuRIP device and a machine-learning algorithm was developed. pneuRIP is a noninvasive approach that uses differences between thoracic and abdominal movements (thoracic-abdominal asynchrony) during respiration to assess respiratory status. The technique was evaluated on 17 patients with Morquio (9 females and 8 males) between the ages of 2 and 57 years. The results of the automated technique agreed with the clinical assessment in 16 out of the 17 patients. It was found that the inverse cumulative percentage representation of the time delay between the thorax and abdomen was the most critical variable for accurate evaluation. It was demonstrated that the technique could be successfully used on patients with Morquio who have difficulty breathing with 100% compliance. This technique is highly accurate, portable, noninvasive, and easy to administer, making it suitable for a variety of settings, such as outpatient clinics, at home, and emergency rooms.

Machine learning for automatic identification of thoracoabdominal asynchrony in children

BACKGROUND

The current methods for assessment of thoracoabdominal asynchrony (TAA) require offline analysis on the part of physicians (respiratory inductance plethysmography (RIP)) or require experts for interpretation of the data (sleep apnea detection).

METHODS

To assess synchrony between the thorax and abdomen, the movements of the two compartments during quiet breathing were measured using pneuRIP. Fifty-one recordings were obtained: 20 were used to train a machine-learning (ML) model with elastic-net regularization, and 31 were used to test the model's performance. Two feature sets were explored: (1) phase difference (ɸ) between the thoracic and abdominal signals and (2) inverse cumulative percentage (ICP), which is an alternate measure of data distribution. To compute accuracy of training, the model outcomes were compared with five experts' assessments.

RESULTS

Accuracies of 61.3% and 90.3% were obtained using ɸ and ICP features, respectively. The inter-rater reliability (i.r.r.) of the assessments of experts was 0.402 and 0.684 when they used ɸ and ICP to identify TAA, respectively.

CONCLUSIONS

With this pilot study, we show the efficacy of the ICP feature and ML in developing an accurate automated approach to identifying TAA that reduces time and effort for diagnosis. ICP also helped improve consensus among experts.

IMPACT

Our article presents an automated approach to identifying thoracic abdominal asynchrony using machine learning and the pneuRIP device. It also shows how a modified statistical measure of cumulative frequency can be used to visualize the progression of the pulmonary functionality along time. The pulmonary testing method we developed gives patients and doctors a noninvasive and easy to administer and diagnose approach. It can be administered remotely, and alerts can be transmitted to the physician. Further, the test can also be used to monitor and assess pulmonary function continuously for prolonged periods, if needed.

A novel noninvasive approach for evaluating work of breathing indices in a developmental rat model using respiratory inductance plethysmography

Pulmonary function testing (PFT) is an important component for evaluating the outcome of experimental rodent models of respiratory diseases. Respiratory inductance plethysmography (RIP) provides a noninvasive method of PFT requiring minimal cooperation. RIP measures work of breathing (WOB) indices including phase angle (Ф), percent rib cage (RC %), breaths per minute (BPM), and labored breathing index (LBI) on an iPad. The aim of this study was to evaluate the utility of a recently developed research instrument, pneuRIP, for evaluation of WOB indices in a developmental rat model. Sprague Dawley rats (2 months old) were commercially acquired and anaesthetised with isoflurane. The pneuRIP system uses two elastic bands: one band (RC) placed around the rib cage under the upper armpit and another band (AB) around the abdomen. The typical thoracoabdominal motion (TAM) plot showed the abdomen and rib cage motion in synchrony. The plots of phase angle and LBI as a function of data point number showed that values were within the range. The distribution for phase angle and LBI was within a narrow range. pneuRIP testing provided instantaneous PFT results. This study demonstrated the utility of RIP as a rapid, noninvasive approach for evaluating treatment interventions in the rodent model.

Expiratory dysfunction in young dogs with golden retriever muscular dystrophy

Respiratory disease is a leading cause of morbidity in people with Duchenne muscular dystrophy and also occurs in the golden retriever muscular dystrophy (GRMD) model. We have previously shown that adult GRMD dogs have elevated expiratory flow as measured non-invasively during tidal breathing. This abnormality likely results from increased chest and diaphragmatic recoil associated with fibrosis and remodeling. Treatments must reverse pathologic effects on the diaphragm and other respiratory muscles to maximally reduce disease morbidity and mortality. Here, we extended our work in adults to younger GRMD dogs to define parameters that would be helpful in preclinical trials. Tidal breathing spirometry and respiratory inductance plethysmography were performed in GRMD dogs at approximately 3 and 6 months of age, corresponding to approximately 5-10 years in DMD, when clinical trials are often conducted. Expiratory flows were markedly elevated in GRMD versus normal dogs at 6 months. Values increased in GRMD dogs between 3 and 6 months, providing a 3-month window to assess treatment efficacy. These changes in breathing mechanics have not been previously identified at such an early age. Expiratory flow measured during tidal breathing of unsedated young GRMD dogs could be a valuable marker of respiratory mechanics during preclinical trials.

A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events and analyze people's smoking behavior. Respiratory inductive plethysmography (RIP) has been employed to track breathing and to identify characteristic breathing pattern specific to smoking. Pattern recognition algorithms such as Support Vector Machine (SVM), Hidden Markov Model, Decision tree, or ensemble approaches have been used to identify smoke inhalations. However, no deep learning approaches, which have been proved effective to many time series datasets, have ever been tested yet. Hence, a Convolutional Neural Network (CNN) and Long Term Short Memory (LSTM) based approach is presented in this paper to detect smoke inhalations in the breathing signal. To illustrate the effectiveness of this deep learning approach, a traditional machine learning (SVM) based approach was used for comparison. On the validation dataset of 120 smoking sessions performed in a laboratory setting by 30 moderate-to-heavy smokers, the CNN-LSTM approach achieved an F1-score of 72% in leave-one-subject-out (LOSO) cross-validation method whereas the classical SVM approach scored 63%. These results suggest that deep learning-based approaches might provide a better analytical method for detection of smoke inhalations than more conventional machine learning approaches.

Pediatric Pulmonology year in review 2018: Asthma, physiology/pulmonary function testing, and respiratory infections

Pediatric Pulmonology publishes original research, reviews, and case reports related to a wide range of children's respiratory disorders. In our "Year in Review" series, we summarize publications in our major topic areas from 2018, in the context of selected literature in these areas from other journals relevant to our discipline. This review covers selected articles on asthma, physiology/lung function testing, and respiratory infections.