Marko Topalovic

Inspiratory muscle training improves breathing pattern during exercise in COPD patients

Dyspnoea is typically the main symptom limiting exercise capacity in patients with chronic obstructive pulmonary disease (COPD) [1–3]. Exertional dyspnoea has been linked to dynamic hyperinflation (DH), when lung expansion critically encroaches upon the inspiratory reserve volume (IRV) [4]. Consequently, patients develop a rapid and shallow breathing pattern, which is energetically opposite to the pattern required to minimise the work of breathing [5]. Furthermore, the restriction of tidal volume (VT) expansion has recently been linked to daily physical activity limitation [6]. The addition of IMT to a PR programme for selected COPD patients changes breathing pattern during exercise http://ow.ly/WWrFT

Computer quantification of airway collapse on forced expiration to predict the presence of emphysema

BackgroundSpirometric parameters are the mainstay for diagnosis of COPD, but cannot distinguish airway obstruction from emphysema. We aimed to develop a computer model that quantifies airway collapse on forced expiratory flow–volume loops. We then explored and validated the relationship of airway collapse with computed tomography (CT) diagnosed emphysema in two large independent cohorts.MethodsA computer model was developed in 513 Caucasian individuals with ≥15 pack-years who performed spirometry, diffusion capacity and CT scans to quantify emphysema presence. The model computed the two best fitting regression lines on the expiratory phase of the flow-volume loop and calculated the angle between them. The collapse was expressed as an Angle of collapse (AC) which was then correlated with the presence of emphysema. Findings were validated in an independent group of 340 individuals.ResultsAC in emphysema subjects (N = 251) was significantly lower (131° ± 14°) compared to AC in subjects without emphysema (N = 223), (152° ± 10°) (p < 0.0001). Multivariate regression analysis revealed AC as best indicator of visually scored emphysema (R2 = 0.505, p < 0.0001) with little significant contribution of KCO, %predicted and FEV1, %predicted to the total model (total R2 = 0.626, p < 0.0001). Similar associations were obtained when using CT-automated density scores for emphysema assessment. Receiver operating characteristic (ROC) curves pointed to 131° as the best cut-off for emphysema (95.5% positive predictive value, 97% specificity and 51% sensitivity). Validation in a second group confirmed the significant difference in mean AC between emphysema and non-emphysema subjects. When applying the 131° cut-off, a positive predictive value of 95.6%, a specificity of 96% and a sensitivity of 59% were demonstrated.ConclusionsAirway collapse on forced expiration quantified by a computer model correlates with emphysema. An AC below 131° can be considered as a specific cut-off for predicting the presence of emphysema in heavy smokers.

Unexpected improvements of lung function in chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is usually characterized by a progressive decline of lung function. We reported the 10 years follow-up of an elderly man, a heavy smoker with severe COPD and apical bullous emphysema. During 6 months pulmonary rehabilitation program the patient’s clinical state improved significantly and it associated with a steep increase in forced expiratory volume in one second (FEV1). This case report elaborates on the unexpected gain of FEV1 in the follow-up of a COPD patient.

Automated Interpretation of Pulmonary Function Tests in Adults with Respiratory Complaints

Background: The use of pulmonary function tests is primarily based on expert opinion and international guidelines. Current interpretation strategies are using predefined cutoffs for the description of a typical pattern. Objectives: We aimed to explore the predicted disease outcome based on the American Thoracic Society/European Respiratory Society (ATS/ERS) interpreting strategy. Subsequently, we investigated whether an unbiased machine learning framework integrating lung function with clinical variables may provide alternative decision trees resulting in a more accurate diagnosis. Methods: Our study included data from 968 subjects admitted for the first time to a pulmonary practice. The final clinical diagnosis was based on the combination of complete pulmonary function with the investigations that were decided at the physicians discretion. Clinical diagnoses were separated into 10 different groups and validated by an expert panel. Results: The ATS/ERS algorithm resulted in a correct diagnostic label in 38% of the subjects. Chronic obstructive pulmonary disease (COPD) was detected with an acceptable accuracy (74%), whereas all other diseases were poorly identified. The new data-based decision tree improved the general accuracy to 68% after 10-fold cross-validation when detecting the most common lung diseases, with a significantly higher positive predictive value and sensitivity for COPD, asthma, interstitial lung disease, and neuromuscular disorder (83/78, 66/82, 52/59, and 100/54%, respectively). Conclusions: Our data show that the current algorithms for lung function interpretation can be improved by a computer-based choice of lung function and clinical variables and their decision-making thresholds.

Online Monitoring of Swimmer Training Using a 3D Accelerometer - Identifying Swimming and Swimming Style

In the process of optimizing training efficiency and improving results of the athletes, technology has increasing share. Wearable sensors, especially those measuring motion are lately acquiring more and more interest. In this paper, we aimed to develop online monitoring tool of swimming training, more in particular algorithm for detection of swimming and turning events using 3D accelerometer. Additionally, algorithm should be able to discriminate between performed swimming styles. This study included data of 10 swimmers who swam on predefined protocol for 1200m. Each swimmer was equipped with wireless waterproof 3D accelerometer attached over right wrist. Algorithm showed high accuracy of 100% for detection of swimming and turning activity. Additionally, detection of swimming styles such as crawl, breaststroke and backstroke resulted of 100% true positive rate. However, true positive rate decreased to 95% for detection of butterfly event. To conclude, we demonstrate that swimming activity together with style recognition can be registered using wireless waterproof 3D accelerometer. Furthermore, we show that such detection can be automatized and performed in an online mode. Taken together, this development leads to a useful online monitoring tool of swimming training.

Non-linear parameters of specific resistance loops to characterise obstructive airways diseases

BackgroundSpecific resistance loops appear in different shapes influenced by different resistive properties of the airways, yet their descriptive ability is compressed to a single parameter - its slope. We aimed to develop new parameters reflecting the various shapes of the loop and to explore their potential in the characterisation of obstructive airways diseases.MethodsOur study included 134 subjects: Healthy controls (N = 22), Asthma with non-obstructive lung function (N = 22) and COPD of all disease stages (N = 90). Different shapes were described by geometrical and second-order transfer function parameters.ResultsOur parameters demonstrated no difference between asthma and healthy controls groups, but were significantly different (p < 0.0001) from the patients with COPD. Grouping mild COPD subjects by an open or not-open shape of the resistance loop revealed significant differences of loop parameters and classical lung function parameters. Multiple logistic regression indicated RV/TLC as the only predictor of loop opening with OR = 1.157, 95% CI (1.064–1.267), p-value = 0.0006 and R2 = 0.35. Inducing airway narrowing in asthma gave equal shape measures as in COPD non-openers, but with a decreased slope (p < 0.0001).ConclusionThis study introduces new parameters calculated from the resistance loops which may correlate with different phenotypes of obstructive airways diseases.

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential

Purpose of review The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Recent findings Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Summary Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Mechanical properties, safety and resistance values of Lemgruber ® elastic tubing

BACKGROUND Lemgruber® elastic tubing has been used as an adjunct to exercise training with positive effects in healthy adults and in patients with chronic lung disease. Despite its benefits, there is a lack of information on the specific resistance, elongation, reproducibility and safety of the different types of Lemgruber® elastic tubing. OBJECTIVES The primary outcome was to assess the length-resistance relation (E/R) of five Lemgruber® elastic tubing of different diameters. Secondary outcomes included the development of reference equations of resistance according to elongation of Lemgruber® elastic tubing types and; the description of Lemgruber® elastic tubing safety and; the description of elongation of Lemgruber® elastic tubing using a clinically useful outcome (i.e. range of motion, in degrees). METHODS The relation between elongation and resistance of Lemgruber® elastic tubing was investigated in a laboratory environment. Secondly, reference equations for the resistance according to the elongation in each Lemgruber® elastic tubing were calculated. Finally, the elongation of the tubing during movements in different degrees of range of motion were estimated using mathematical models, so that the resistance provided by the tubing for any exercise could be predicted. RESULTS Lemgruber® elastic tubing provided a large array of resistance varying from 3±0.1Newtons (N) to 537±13N (mean±standard deviation). The maximal resistance deemed safe for each of the five Lemgruber® elastic tubing were: 173±25N, 280±23N, 409±40N, 395±37N and 537±13N. Reference equations had nearly perfect predictive power (r2=0.99) for all polynomial non-linear models (p<0.001 for all). CONCLUSIONS Lemgruber elastic tubing progressively increased resistance with increased elongation. The large array of resistances delivered by Lemgruber® elastic tubing, along with its safety and good estimation of reference values, support its use in clinical practice.

Inspiratory muscle training reduces diaphragm activation and dyspnea during exercise in COPD

Among patients with chronic obstructive pulmonary disease (COPD), those with the lowest maximal inspiratory pressures experience greater breathing discomfort (dyspnea) during exercise. In such individuals, inspiratory muscle training (IMT) may be associated with improvement of dyspnea, but the mechanisms for this are poorly understood. Therefore, we aimed to identify physiological mechanisms of improvement in dyspnea and exercise endurance following inspiratory muscle training (IMT) in patients with COPD and low maximal inspiratory pressure (Pimax). The effects of 8 wk of controlled IMT on respiratory muscle function, dyspnea, respiratory mechanics, and diaphragm electromyography (EMGdi) during constant work rate cycle exercise were evaluated in patients with activity-related dyspnea (baseline dyspnea index <9). Subjects were randomized to either IMT or a sham training control group ( n = 10 each). Twenty subjects (FEV1 = 47 ± 19% predicted; Pimax  = -59 ± 14 cmH2O; cycle ergometer peak work rate = 47 ± 21% predicted) completed the study; groups had comparable baseline lung function, respiratory muscle strength, activity-related dyspnea, and exercise capacity. IMT, compared with control, was associated with greater increases in inspiratory muscle strength and endurance, with attendant improvements in exertional dyspnea and exercise endurance time (all P < 0.05). After IMT, EMGdi expressed relative to its maximum (EMGdi/EMGdimax) decreased ( P < 0.05) with no significant change in ventilation, tidal inspiratory pressures, breathing pattern, or operating lung volumes during exercise. In conclusion, IMT improved inspiratory muscle strength and endurance in mechanically compromised patients with COPD and low Pimax. The attendant reduction in EMGdi/EMGdimax helped explain the decrease in perceived respiratory discomfort despite sustained high ventilation and intrinsic mechanical loading over a longer exercise duration. NEW & NOTEWORTHY In patients with COPD and low maximal inspiratory pressures, inspiratory muscle training (IMT) may be associated with improvement of dyspnea, but the mechanisms for this are poorly understood. This study showed that 8 wk of home-based, partially supervised IMT improved respiratory muscle strength and endurance, dyspnea, and exercise endurance. Dyspnea relief occurred in conjunction with a reduced activation of the diaphragm relative to maximum in the absence of significant changes in ventilation, breathing pattern, and operating lung volumes.

Quantifying the shape of the maximal expiratory flow–volume curve to address flow limitation

We read with great pleasure the recent article written by ominelli et al. (2015) published in the Journal of Respiratory hysiology & Neurobiology. The authors examined three different athematical methods to quantify the shape of maximal expiraory flow–volume (MEFV) curves in 34 subjects: ̌ angle, Slope ratio SR) and Flow-ratio (Kapp et al., 1988; Mead, 1978; O’Donnell and ose, 1990). Moreover they explored how these shape descriptors ary between subjects with mild chronic obstructive pulmonary isease (COPD) and healthy subjects. Key findings show that the R and ̌ angle methods of MEFV curve analysis can sufficiently etect group differences. Since many years it is known that the hape of flow-volume loops graphically visualizes flow limitation hat is occurring in the airways of patients with COPD, obstrucive asthma, Asthma-COPD Overlap Syndrome (ACOS) and other bstructive airways diseases (Jayamanne et al., 1980; Ohwada and akahashi, 2012). Although the shape analysis in clinical practice s usually reduced to a comparison of measured flows with preicted values of healthy peers, a more detailed quantification may dentify dynamical changes in the airways structure which evenually could be used to label specific subgroups within obstructive iseases. In this context, we would like to draw attention of the ournal’s readership on other mathematical methods that have ackled the shape of the maximal expiratory curve. These novel ethods are not only confirming what Dominelli et al. demonstrate ut are adding significant information on the phenotypic characeristics of the individual patient. In 816 subjects we showed that ngle of collapse (AC) describes the shape of MEFV in a similar maner as ̌ angle yet more precise as it is not restricted to the search n angle (concavity or convexity) at flows of 50% of vital capacty but computing the angle at the mathematical point where the urve is changing (tipping point or spirographic kink) (Topalovic t al., 2013). With the second method that includes 474 subjects Topalovic et al., 2014, 2015) we showed that complex mathematcal data-based modeling of flow changes over the time can be sed to distinguish between clinical COPD and healthy subjects, s well as between mild obstruction and healthy in a general poplation sample at risk. Here, we describe the shape of expiratory urve with dynamic components. Median (IQR) values of parameers pole1 and SSG were significantly different (p < 0.0001) between ild COPD and healthy subjects [0.9895 (0.9882–0.9922) vs. 0.9868 0.9810–0.9892) and 6.8 (5.7–7.8) vs. 8.2 (7.1–9.3), respectively]. his change in parameters or actual fastening of dynamics is due to ore sudden drops in expiratory airflow related to intraluminal bliteration (Koulouris and Hardavella, 2011), but also expiraory collapse due to reduced airway tethering (Papandrinopoulou t al., 2012). Most importantly, our approach was able to reclassify