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Pedro Lopes De Melo - One of the best experts on this subject based on the ideXlab platform.

  • high accuracy detection of airway obstruction in asthma using machine learning algorithms and Forced Oscillation measurements
    Computer Methods and Programs in Biomedicine, 2017
    Co-Authors: Jorge L. M. Amaral, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Juliana Veiga, Pedro Lopes De Melo
    Abstract:

    Our aim was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the Forced Oscillation technique (FOT) in the diagnosis of airway obstruction in asthma.We used different techniques, including k-nearest neighbour (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC).Our findings revealed that all classifiers improved the diagnostic accuracy; ADAB and KNN were very close to achieving high accuracy.The best performance was observed using the cross products of the FOT parameters associated with KNN, which was able to reach a high diagnostic accuracy.Our study and findings will contribute to assist clinicians in airway obstruction identification and guiding therapy in asthma. Background and ObjectivesThe main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the Forced Oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma. MethodsThe data consisted of FOT parameters obtained from 75 volunteers (39 with obstruction and 36 without). Different supervised machine learning (ML) techniques were investigated, including k-nearest neighbors (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC). ResultsThe first part of this study showed that the best FOT parameter was the resonance frequency (AUC=0.81), which indicates moderate accuracy (0.700.90). In the second part of this study, the use of the cited ML techniques was investigated. All the classifiers improved the diagnostic accuracy. Notably, ADAB and KNN were very close to achieving high accuracy (AUC=0.88 and 0.89, respectively). Experiments including the cross products of the FOT parameters showed that all the classifiers improved the diagnosis accuracy and KNN was able to reach a higher accuracy range (AUC=0.91). ConclusionsMachine learning classifiers can help in the diagnosis of airway obstruction in asthma patients, and they can assist clinicians in airway obstruction identification.

  • early diagnosis of respiratory abnormalities in asbestos exposed workers by the Forced Oscillation technique
    PLOS ONE, 2016
    Co-Authors: Hermano Albuquerque De Castro, Agnaldo Jose Lopes, Pedro Lopes De Melo
    Abstract:

    Background The current reference test for the detection of respiratory abnormalities in asbestos-exposed workers is spirometry. However, spirometry has several shortcomings that greatly affect the efficacy of current asbestos control programs. The Forced Oscillation technique (FOT) represents the current state-of-the-art technique in the assessment of lung function. This method provides a detailed analysis of respiratory resistance and reactance at different oscillatory frequencies during tidal breathing. Here, we evaluate the FOT as an alternative method to standard spirometry for the early detection and quantification of respiratory abnormalities in asbestos-exposed workers. Methodology/Principal findings Seventy-two subjects were analyzed. The control group was composed of 33 subjects with a normal spirometric exam who had no history of smoking or pulmonary disease. Thirty-nine subjects exposed to asbestos were also studied, including 32 volunteers in radiological category 0/0 and 7 volunteers with radiological categories of 0/1 or 1/1. FOT data were interpreted using classical parameters as well as integer (InOr) and fractional-order (FrOr) modeling. The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). Exposed workers presented increased obstruction (resistance p<0.001) and a reduced compliance (p<0.001), with a predominance of obstructive changes. The FOT parameter changes were correlated with the standard pulmonary function analysis methods (R = -0.52, p<0.001). Early respiratory abnormalities were identified with a high diagnostic accuracy (AUC = 0.987) using parameters obtained from the FrOr modeling. This accuracy was significantly better than those obtained with classical (p<0.001) and InOr (p<0.001) model parameters. Conclusions The FOT improved our knowledge about the biomechanical abnormalities in workers exposed to asbestos. Additionally, a high diagnostic accuracy in the diagnosis of early respiratory abnormalities in asbestos-exposed workers was obtained. This makes the FOT particularly useful as a screening tool in the context of asbestos control and elimination. Moreover, it can facilitate epidemiological research and the longitudinal follow-up of asbestos exposure and asbestos-related diseases.

  • Forced Oscillation integer and fractional order modeling in asthma
    Computer Methods and Programs in Biomedicine, 2016
    Co-Authors: Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Juliana Veiga, Pedro Lopes De Melo
    Abstract:

    Our aim was to evaluate the use of fractional-order (FrOr) modeling in asthma.We compared FrOr models with traditional parameters and an integer-order model FrOr parameters best fit the data, showing good associations with spirometry.They also showed a high accuracy in the detection of the mild obstruction in asthma FrOr models provide meaningful information in asthmatic patients. The purpose of this study was to evaluate the use of fractional-order (FrOr) modeling in asthma. To this end, three FrOr models were compared with traditional parameters and an integer-order model (InOr). We investigated which model would best fit the data, the correlation with traditional lung function tests and the contribution to the diagnostic of airway obstruction. The data consisted of Forced Oscillation (FO) measurements obtained from healthy (n=22) and asthmatic volunteers with mild (n=22), moderate (n=19) and severe (n=19) obstructions. The first part of this study showed that a FrOr was the model that best fit the data (relative distance: FrOr=4.3?2.4; InOr=5.1?2.6%). The correlation analysis resulted in reasonable (R=0.36) to very good (R=0.77) associations between FrOr parameters and spirometry. The closest associations were observed between parameters related to peripheral airway obstruction, showing a clear relationship between the FrOr models and lung mechanics. Receiver-operator analysis showed that FrOr parameters presented a high potential to contribute to the detection of the mild obstruction in a clinical setting. The accuracy area under the Receiver Operating Characteristic curve (AUC) observed in these parameters (AUC=0.954) was higher than that observed in traditional FO parameters (AUC=0.732) and that obtained from the InOr model (AUC=0.861). Patients with moderate and severe obstruction were identified with high accuracy (AUC=0.972 and 0.977, respectively). In conclusion, the results obtained are in close agreement with asthma pathology, and provide evidence that FO measurement associated with FrOr models is a non-invasive, simple and radiation-free method for the detection of biomechanical abnormalities in asthma.

  • correlations between Forced Oscillation technique parameters and pulmonary densitovolumetry values in patients with acromegaly
    Brazilian Journal of Medical and Biological Research, 2015
    Co-Authors: Gustavo Bittencourt Camilo, Pedro Lopes De Melo, Alysson R Carvalho, Dequitier Carvalho Machado, Roberto Mogami, Leandro Kasuki, Monica R Gadelha, Agnaldo Jose Lopes
    Abstract:

    The aims of this study were to evaluate the Forced Oscillation technique (FOT) and pulmonary densitovolumetry in acromegalic patients and to examine the correlations between these findings. In this cross-sectional study, 29 non-smoking acromegalic patients and 17 paired controls were subjected to the FOT and quantification of lung volume using multidetector computed tomography (Q-MDCT). Compared with the controls, the acromegalic patients had a higher value for resonance frequency [15.3 (10.9-19.7) vs 11.4 (9.05-17.6) Hz, P=0.023] and a lower value for mean reactance [0.32 (0.21-0.64) vs 0.49 (0.34-0.96) cm H2O/L/s2, P=0.005]. In inspiratory Q-MDCT, the acromegalic patients had higher percentages of total lung volume (TLV) for nonaerated and poorly aerated areas [0.42% (0.30-0.51%) vs 0.25% (0.20-0.32%), P=0.039 and 3.25% (2.48-3.46%) vs 1.70% (1.45-2.15%), P=0.001, respectively]. Furthermore, the acromegalic patients had higher values for total lung mass in both inspiratory and expiratory Q-MDCT [821 (635-923) vs 696 (599-769) g, P=0.021 and 844 (650-945) vs 637 (536-736) g, P=0.009, respectively]. In inspiratory Q-MDCT, TLV showed significant correlations with all FOT parameters. The TLV of hyperaerated areas showed significant correlations with intercept resistance (rs=−0.602, P<0.001) and mean resistance (rs=−0.580, P<0.001). These data showed that acromegalic patients have increased amounts of lung tissue as well as nonaerated and poorly aerated areas. Functionally, there was a loss of homogeneity of the respiratory system. Moreover, there were correlations between the structural and functional findings of the respiratory system, consistent with the pathophysiology of the disease.

  • on the respiratory mechanics measured by Forced Oscillation technique in patients with systemic sclerosis
    PLOS ONE, 2013
    Co-Authors: Ingrid Almeida Miranda, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Jose Manoel Jansen, Pedro Lopes De Melo
    Abstract:

    Background Pulmonary complications are the most common cause of death and morbidity in systemic sclerosis (SSc). The Forced Oscillation technique (FOT) offers a simple and detailed approach to investigate the mechanical properties of the respiratory system. We hypothesized that SSc may introduce changes in the resistive and reactive properties of the respiratory system, and that FOT may help the diagnosis of these abnormalities. Methodology/Principal Findings We tested these hypotheses in controls (n = 30) and patients with abnormalities classified using spirometry (n = 52) and pulmonary volumes (n = 29). Resistive data were interpreted with the zero-intercept resistance (Ri) and the slope of the resistance (S) as a function of frequency. Reactance changes were evaluated by the mean reactance between 4 and 32 Hz (Xm) and the dynamic compliance (Crs,dyn). The mechanical load was evaluated using the absolute value of the impedance in 4 Hz (Z4Hz). A compartmental model was used to obtain central (R) and peripheral (Rp) resistances, and alveolar compliance (C). The clinical usefulness was evaluated by investigating the area under the receiver operating characteristic curve (AUC). The presence of expiratory flow limitation (EFL) was also evaluated. For the groups classified using spirometry, SSc resulted in increased values in Ri, R, Rp and Z4Hz (p 0.90). In groups classified by pulmonary volume, SSc resulted in reductions in S, Xm, C and Crs,dyn (p 0.80). It was also observed that EFL is not common in patients with SSc. Conclusions/Significance This study provides evidence that the respiratory resistance and reactance are changed in SSc. This analysis provides a useful description that is of particular significance for understanding respiratory pathophysiology and to ease the diagnosis of respiratory abnormalities in these patients.

Raffaele Dellaca - One of the best experts on this subject based on the ideXlab platform.

  • Forced Oscillation measurements in the first week of life and pulmonary outcome in very preterm infants on noninvasive respiratory support
    Pediatric Research, 2019
    Co-Authors: Emanuela Zannin, Raffaele Dellaca, Roland P Neumann, Sven M Schulzke
    Abstract:

    BACKGROUND: We aimed at investigating whether early lung mechanics in non-intubated infants below 32 weeks of gestational age (GA) are associated with respiratory outcome. METHODS: Lung mechanics were assessed by the Forced Oscillation technique using a mechanical ventilator (Fabian HFOi, ACUTRONIC Medical Systems AG, Hirzel, Switzerland) that superimposed small-amplitude Oscillations (10 Hz) on a continuous positive airway pressure. Measurements were performed during regular tidal breathing using a face mask on days 2, 4, and 7 of life. Respiratory system resistance (Rrs) and reactance (Xrs) were computed from flow and pressure. RESULTS: One hundred and seventy-seven measurements were successfully performed in 68 infants. Infants had a mean (range) GA of 29.3 (24.1-31.7) weeks and a birth weight of 1257 (670-2350)g. Xrs was associated with the duration of respiratory support (R2 = 0.39, p < 0.001). A multilevel regression model, including Xrs and GA, explained the duration of respiratory support better than GA alone (R2 = 0.51 vs. 0.45, p = 0.005, likelihood ratio test). CONCLUSION: Assessment of Xrs in the first week of life is feasible and improves prognostication of respiratory outcome in very preterm infants on noninvasive respiratory support.

  • Forced Oscillation Technique
    Mechanics of Breathing, 2014
    Co-Authors: Daniel Navajas, Raffaele Dellaca, Ramon Farré
    Abstract:

    Forced Oscillation technique (FOT) is a noninvasive approach for assessing the mechanical properties of the respiratory system. The technique is based on applying a low-amplitude pressure Oscillation to the airway opening and computing respiratory impedance defined as the complex ratio of oscillatory pressure and flow. Impedance data are interpreted in terms of mechanical models of the respiratory system. Common clinical applications of FOT include assessment of airflow obstruction in patients with asthma and chronic obstructive pulmonary disease and airway responsiveness. New areas of interest are monitoring of airway patency in sleep and noninvasive mechanical ventilation.

  • positive end expiratory pressure optimization with Forced Oscillation technique reduces ventilator induced lung injury a controlled experimental study in pigs with saline lavage lung injury
    Critical Care, 2011
    Co-Authors: Peter Kostic, Pasquale Pompilio, A Pedotti, Emanuela Zannin, Marie Andersson Olerud, Goran Hedenstierna, Peter Frykholm, Anders Larsson, Raffaele Dellaca
    Abstract:

    Introduction Protocols using high levels of positive end-expiratory pressure (PEEP) in combination with low tidal volumes have been shown to reduce mortality in patients with severe acute respiratory distress syndrome (ARDS). However, the optimal method for setting PEEP is yet to be defined. It has been shown that respiratory system reactance (Xrs), measured by the Forced Oscillation technique (FOT) at 5 Hz, may be used to identify the minimal PEEP level required to maintain lung recruitment. The aim of the present study was to evaluate if using Xrs for setting PEEP would improve lung mechanics and reduce lung injury compared to an oxygenation-based approach.

  • optimisation of positive end expiratory pressure by Forced Oscillation technique in a lavage model of acute lung injury
    Intensive Care Medicine, 2011
    Co-Authors: Raffaele Dellaca, Pasquale Pompilio, A Pedotti, Emanuela Zannin, Peter Kostic, Marie Andersson Olerud, Goran Hedenstierna, Peter Frykholm
    Abstract:

    Purpose We evaluated whether oscillatory compliance (CX5) measured by Forced Oscillation technique (FOT) at 5 Hz may be useful for positive end-expiratory pressure (PEEP) optimisation.

  • home monitoring of within breath respiratory mechanics by a simple and automatic Forced Oscillation technique device
    Physiological Measurement, 2010
    Co-Authors: Raffaele Dellaca, A Pedotti, Alessandro Gobbi, Miriam Pastena, Bartolome Celli
    Abstract:

    Spirometry is the gold standard to determine the presence of airflow obstruction, but it requires volitional participation and needs qualified supervision. The Forced Oscillation technique (FOT) measures respiratory input impedance (Zrs) during spontaneous breathing and it could be useful for unsupervised monitoring of airway obstruction. We developed a FOT device for home monitoring of Zrs which transmits the data through the Internet. Its accuracy, stability and reliability were evaluated in a pilot study measuring the Zrs in the unsupervised self-measurements of five healthy subjects. Finally, to explore the applicability of the concept, 36 consecutive daily home measurements were recorded from one healthy subject and one chronic obstructive pulmonary disease (COPD) patient. The accuracy of the device fulfilled FOT guidelines, and the reliability test showed a mean discrepancy of resistance of 0.10 ± 0.01 cmH2O s L−1. The data from the healthy subjects demonstrated high repeatability in assessing Zrs. The measurements on the healthy subjects and the patient with COPD suggest the feasibility of unsupervised FOT measurements. The healthy subjects showed minimal daily variations in Zrs, whereas the patient with COPD had large differences in mean values and important fluctuations over day-to-day measurements. The results of the pilot study demonstrate that unsupervised home monitoring of Zrs using the FOT yields accurate and reproducible data. It could provide new insights into the dynamics of airway obstruction and improve the understanding and management of obstructive diseases.

Agnaldo Jose Lopes - One of the best experts on this subject based on the ideXlab platform.

  • high accuracy detection of airway obstruction in asthma using machine learning algorithms and Forced Oscillation measurements
    Computer Methods and Programs in Biomedicine, 2017
    Co-Authors: Jorge L. M. Amaral, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Juliana Veiga, Pedro Lopes De Melo
    Abstract:

    Our aim was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the Forced Oscillation technique (FOT) in the diagnosis of airway obstruction in asthma.We used different techniques, including k-nearest neighbour (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC).Our findings revealed that all classifiers improved the diagnostic accuracy; ADAB and KNN were very close to achieving high accuracy.The best performance was observed using the cross products of the FOT parameters associated with KNN, which was able to reach a high diagnostic accuracy.Our study and findings will contribute to assist clinicians in airway obstruction identification and guiding therapy in asthma. Background and ObjectivesThe main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the Forced Oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma. MethodsThe data consisted of FOT parameters obtained from 75 volunteers (39 with obstruction and 36 without). Different supervised machine learning (ML) techniques were investigated, including k-nearest neighbors (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC). ResultsThe first part of this study showed that the best FOT parameter was the resonance frequency (AUC=0.81), which indicates moderate accuracy (0.700.90). In the second part of this study, the use of the cited ML techniques was investigated. All the classifiers improved the diagnostic accuracy. Notably, ADAB and KNN were very close to achieving high accuracy (AUC=0.88 and 0.89, respectively). Experiments including the cross products of the FOT parameters showed that all the classifiers improved the diagnosis accuracy and KNN was able to reach a higher accuracy range (AUC=0.91). ConclusionsMachine learning classifiers can help in the diagnosis of airway obstruction in asthma patients, and they can assist clinicians in airway obstruction identification.

  • early diagnosis of respiratory abnormalities in asbestos exposed workers by the Forced Oscillation technique
    PLOS ONE, 2016
    Co-Authors: Hermano Albuquerque De Castro, Agnaldo Jose Lopes, Pedro Lopes De Melo
    Abstract:

    Background The current reference test for the detection of respiratory abnormalities in asbestos-exposed workers is spirometry. However, spirometry has several shortcomings that greatly affect the efficacy of current asbestos control programs. The Forced Oscillation technique (FOT) represents the current state-of-the-art technique in the assessment of lung function. This method provides a detailed analysis of respiratory resistance and reactance at different oscillatory frequencies during tidal breathing. Here, we evaluate the FOT as an alternative method to standard spirometry for the early detection and quantification of respiratory abnormalities in asbestos-exposed workers. Methodology/Principal findings Seventy-two subjects were analyzed. The control group was composed of 33 subjects with a normal spirometric exam who had no history of smoking or pulmonary disease. Thirty-nine subjects exposed to asbestos were also studied, including 32 volunteers in radiological category 0/0 and 7 volunteers with radiological categories of 0/1 or 1/1. FOT data were interpreted using classical parameters as well as integer (InOr) and fractional-order (FrOr) modeling. The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). Exposed workers presented increased obstruction (resistance p<0.001) and a reduced compliance (p<0.001), with a predominance of obstructive changes. The FOT parameter changes were correlated with the standard pulmonary function analysis methods (R = -0.52, p<0.001). Early respiratory abnormalities were identified with a high diagnostic accuracy (AUC = 0.987) using parameters obtained from the FrOr modeling. This accuracy was significantly better than those obtained with classical (p<0.001) and InOr (p<0.001) model parameters. Conclusions The FOT improved our knowledge about the biomechanical abnormalities in workers exposed to asbestos. Additionally, a high diagnostic accuracy in the diagnosis of early respiratory abnormalities in asbestos-exposed workers was obtained. This makes the FOT particularly useful as a screening tool in the context of asbestos control and elimination. Moreover, it can facilitate epidemiological research and the longitudinal follow-up of asbestos exposure and asbestos-related diseases.

  • Forced Oscillation integer and fractional order modeling in asthma
    Computer Methods and Programs in Biomedicine, 2016
    Co-Authors: Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Juliana Veiga, Pedro Lopes De Melo
    Abstract:

    Our aim was to evaluate the use of fractional-order (FrOr) modeling in asthma.We compared FrOr models with traditional parameters and an integer-order model FrOr parameters best fit the data, showing good associations with spirometry.They also showed a high accuracy in the detection of the mild obstruction in asthma FrOr models provide meaningful information in asthmatic patients. The purpose of this study was to evaluate the use of fractional-order (FrOr) modeling in asthma. To this end, three FrOr models were compared with traditional parameters and an integer-order model (InOr). We investigated which model would best fit the data, the correlation with traditional lung function tests and the contribution to the diagnostic of airway obstruction. The data consisted of Forced Oscillation (FO) measurements obtained from healthy (n=22) and asthmatic volunteers with mild (n=22), moderate (n=19) and severe (n=19) obstructions. The first part of this study showed that a FrOr was the model that best fit the data (relative distance: FrOr=4.3?2.4; InOr=5.1?2.6%). The correlation analysis resulted in reasonable (R=0.36) to very good (R=0.77) associations between FrOr parameters and spirometry. The closest associations were observed between parameters related to peripheral airway obstruction, showing a clear relationship between the FrOr models and lung mechanics. Receiver-operator analysis showed that FrOr parameters presented a high potential to contribute to the detection of the mild obstruction in a clinical setting. The accuracy area under the Receiver Operating Characteristic curve (AUC) observed in these parameters (AUC=0.954) was higher than that observed in traditional FO parameters (AUC=0.732) and that obtained from the InOr model (AUC=0.861). Patients with moderate and severe obstruction were identified with high accuracy (AUC=0.972 and 0.977, respectively). In conclusion, the results obtained are in close agreement with asthma pathology, and provide evidence that FO measurement associated with FrOr models is a non-invasive, simple and radiation-free method for the detection of biomechanical abnormalities in asthma.

  • correlations between Forced Oscillation technique parameters and pulmonary densitovolumetry values in patients with acromegaly
    Brazilian Journal of Medical and Biological Research, 2015
    Co-Authors: Gustavo Bittencourt Camilo, Pedro Lopes De Melo, Alysson R Carvalho, Dequitier Carvalho Machado, Roberto Mogami, Leandro Kasuki, Monica R Gadelha, Agnaldo Jose Lopes
    Abstract:

    The aims of this study were to evaluate the Forced Oscillation technique (FOT) and pulmonary densitovolumetry in acromegalic patients and to examine the correlations between these findings. In this cross-sectional study, 29 non-smoking acromegalic patients and 17 paired controls were subjected to the FOT and quantification of lung volume using multidetector computed tomography (Q-MDCT). Compared with the controls, the acromegalic patients had a higher value for resonance frequency [15.3 (10.9-19.7) vs 11.4 (9.05-17.6) Hz, P=0.023] and a lower value for mean reactance [0.32 (0.21-0.64) vs 0.49 (0.34-0.96) cm H2O/L/s2, P=0.005]. In inspiratory Q-MDCT, the acromegalic patients had higher percentages of total lung volume (TLV) for nonaerated and poorly aerated areas [0.42% (0.30-0.51%) vs 0.25% (0.20-0.32%), P=0.039 and 3.25% (2.48-3.46%) vs 1.70% (1.45-2.15%), P=0.001, respectively]. Furthermore, the acromegalic patients had higher values for total lung mass in both inspiratory and expiratory Q-MDCT [821 (635-923) vs 696 (599-769) g, P=0.021 and 844 (650-945) vs 637 (536-736) g, P=0.009, respectively]. In inspiratory Q-MDCT, TLV showed significant correlations with all FOT parameters. The TLV of hyperaerated areas showed significant correlations with intercept resistance (rs=−0.602, P<0.001) and mean resistance (rs=−0.580, P<0.001). These data showed that acromegalic patients have increased amounts of lung tissue as well as nonaerated and poorly aerated areas. Functionally, there was a loss of homogeneity of the respiratory system. Moreover, there were correlations between the structural and functional findings of the respiratory system, consistent with the pathophysiology of the disease.

  • on the respiratory mechanics measured by Forced Oscillation technique in patients with systemic sclerosis
    PLOS ONE, 2013
    Co-Authors: Ingrid Almeida Miranda, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Jose Manoel Jansen, Pedro Lopes De Melo
    Abstract:

    Background Pulmonary complications are the most common cause of death and morbidity in systemic sclerosis (SSc). The Forced Oscillation technique (FOT) offers a simple and detailed approach to investigate the mechanical properties of the respiratory system. We hypothesized that SSc may introduce changes in the resistive and reactive properties of the respiratory system, and that FOT may help the diagnosis of these abnormalities. Methodology/Principal Findings We tested these hypotheses in controls (n = 30) and patients with abnormalities classified using spirometry (n = 52) and pulmonary volumes (n = 29). Resistive data were interpreted with the zero-intercept resistance (Ri) and the slope of the resistance (S) as a function of frequency. Reactance changes were evaluated by the mean reactance between 4 and 32 Hz (Xm) and the dynamic compliance (Crs,dyn). The mechanical load was evaluated using the absolute value of the impedance in 4 Hz (Z4Hz). A compartmental model was used to obtain central (R) and peripheral (Rp) resistances, and alveolar compliance (C). The clinical usefulness was evaluated by investigating the area under the receiver operating characteristic curve (AUC). The presence of expiratory flow limitation (EFL) was also evaluated. For the groups classified using spirometry, SSc resulted in increased values in Ri, R, Rp and Z4Hz (p 0.90). In groups classified by pulmonary volume, SSc resulted in reductions in S, Xm, C and Crs,dyn (p 0.80). It was also observed that EFL is not common in patients with SSc. Conclusions/Significance This study provides evidence that the respiratory resistance and reactance are changed in SSc. This analysis provides a useful description that is of particular significance for understanding respiratory pathophysiology and to ease the diagnosis of respiratory abnormalities in these patients.

Alvaro Camilo Dias Faria - One of the best experts on this subject based on the ideXlab platform.

  • high accuracy detection of airway obstruction in asthma using machine learning algorithms and Forced Oscillation measurements
    Computer Methods and Programs in Biomedicine, 2017
    Co-Authors: Jorge L. M. Amaral, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Juliana Veiga, Pedro Lopes De Melo
    Abstract:

    Our aim was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the Forced Oscillation technique (FOT) in the diagnosis of airway obstruction in asthma.We used different techniques, including k-nearest neighbour (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC).Our findings revealed that all classifiers improved the diagnostic accuracy; ADAB and KNN were very close to achieving high accuracy.The best performance was observed using the cross products of the FOT parameters associated with KNN, which was able to reach a high diagnostic accuracy.Our study and findings will contribute to assist clinicians in airway obstruction identification and guiding therapy in asthma. Background and ObjectivesThe main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the Forced Oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma. MethodsThe data consisted of FOT parameters obtained from 75 volunteers (39 with obstruction and 36 without). Different supervised machine learning (ML) techniques were investigated, including k-nearest neighbors (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC). ResultsThe first part of this study showed that the best FOT parameter was the resonance frequency (AUC=0.81), which indicates moderate accuracy (0.700.90). In the second part of this study, the use of the cited ML techniques was investigated. All the classifiers improved the diagnostic accuracy. Notably, ADAB and KNN were very close to achieving high accuracy (AUC=0.88 and 0.89, respectively). Experiments including the cross products of the FOT parameters showed that all the classifiers improved the diagnosis accuracy and KNN was able to reach a higher accuracy range (AUC=0.91). ConclusionsMachine learning classifiers can help in the diagnosis of airway obstruction in asthma patients, and they can assist clinicians in airway obstruction identification.

  • Forced Oscillation integer and fractional order modeling in asthma
    Computer Methods and Programs in Biomedicine, 2016
    Co-Authors: Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Juliana Veiga, Pedro Lopes De Melo
    Abstract:

    Our aim was to evaluate the use of fractional-order (FrOr) modeling in asthma.We compared FrOr models with traditional parameters and an integer-order model FrOr parameters best fit the data, showing good associations with spirometry.They also showed a high accuracy in the detection of the mild obstruction in asthma FrOr models provide meaningful information in asthmatic patients. The purpose of this study was to evaluate the use of fractional-order (FrOr) modeling in asthma. To this end, three FrOr models were compared with traditional parameters and an integer-order model (InOr). We investigated which model would best fit the data, the correlation with traditional lung function tests and the contribution to the diagnostic of airway obstruction. The data consisted of Forced Oscillation (FO) measurements obtained from healthy (n=22) and asthmatic volunteers with mild (n=22), moderate (n=19) and severe (n=19) obstructions. The first part of this study showed that a FrOr was the model that best fit the data (relative distance: FrOr=4.3?2.4; InOr=5.1?2.6%). The correlation analysis resulted in reasonable (R=0.36) to very good (R=0.77) associations between FrOr parameters and spirometry. The closest associations were observed between parameters related to peripheral airway obstruction, showing a clear relationship between the FrOr models and lung mechanics. Receiver-operator analysis showed that FrOr parameters presented a high potential to contribute to the detection of the mild obstruction in a clinical setting. The accuracy area under the Receiver Operating Characteristic curve (AUC) observed in these parameters (AUC=0.954) was higher than that observed in traditional FO parameters (AUC=0.732) and that obtained from the InOr model (AUC=0.861). Patients with moderate and severe obstruction were identified with high accuracy (AUC=0.972 and 0.977, respectively). In conclusion, the results obtained are in close agreement with asthma pathology, and provide evidence that FO measurement associated with FrOr models is a non-invasive, simple and radiation-free method for the detection of biomechanical abnormalities in asthma.

  • on the respiratory mechanics measured by Forced Oscillation technique in patients with systemic sclerosis
    PLOS ONE, 2013
    Co-Authors: Ingrid Almeida Miranda, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Jose Manoel Jansen, Pedro Lopes De Melo
    Abstract:

    Background Pulmonary complications are the most common cause of death and morbidity in systemic sclerosis (SSc). The Forced Oscillation technique (FOT) offers a simple and detailed approach to investigate the mechanical properties of the respiratory system. We hypothesized that SSc may introduce changes in the resistive and reactive properties of the respiratory system, and that FOT may help the diagnosis of these abnormalities. Methodology/Principal Findings We tested these hypotheses in controls (n = 30) and patients with abnormalities classified using spirometry (n = 52) and pulmonary volumes (n = 29). Resistive data were interpreted with the zero-intercept resistance (Ri) and the slope of the resistance (S) as a function of frequency. Reactance changes were evaluated by the mean reactance between 4 and 32 Hz (Xm) and the dynamic compliance (Crs,dyn). The mechanical load was evaluated using the absolute value of the impedance in 4 Hz (Z4Hz). A compartmental model was used to obtain central (R) and peripheral (Rp) resistances, and alveolar compliance (C). The clinical usefulness was evaluated by investigating the area under the receiver operating characteristic curve (AUC). The presence of expiratory flow limitation (EFL) was also evaluated. For the groups classified using spirometry, SSc resulted in increased values in Ri, R, Rp and Z4Hz (p 0.90). In groups classified by pulmonary volume, SSc resulted in reductions in S, Xm, C and Crs,dyn (p 0.80). It was also observed that EFL is not common in patients with SSc. Conclusions/Significance This study provides evidence that the respiratory resistance and reactance are changed in SSc. This analysis provides a useful description that is of particular significance for understanding respiratory pathophysiology and to ease the diagnosis of respiratory abnormalities in these patients.

  • Contrasting diagnosis performance of Forced Oscillation and spirometry in patients with rheumatoid arthritis and respiratory symptoms
    Clinics, 2012
    Co-Authors: Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Wellington Ribeiro Barbosa, Geraldo Da Rocha Castelar Pinheiro, Pedro Lopes De Melo
    Abstract:

    OBJECTIVES: Pulmonary involvement in rheumatoid arthritis is directly responsible for 10% to 20% of all mortality. The best way to improve the prognosis is early detection and treatment. The Forced Oscillation technique is easy to perform and offers a detailed exam, which may be helpful in the early detection of respiratory changes. This study was undertaken to (1) evaluate the clinical potential of the Forced Oscillation technique in the detection of early respiratory alterations in rheumatoid arthritis patients with respiratory complaints and (2) to compare the sensitivity of Forced Oscillation technique and spirometric parameters. METHODS: A total of 40 individuals were analyzed: 20 healthy and 20 with rheumatoid arthritis (90% with respiratory complaints). The clinical usefulness of the parameters was evaluated by investigating the sensibility, the specificity and the area under the receiver operating characteristic curve. ClinicalTrials.gov: NCT01641705. RESULTS: The early adverse respiratory effects of rheumatoid arthritis were adequately detected by the Forced Oscillation technique parameters, and a high accuracy for clinical use was obtained (AUC.0.9, Se = 80%, Sp = 95%). The use of spirometric parameters did not obtain an appropriate accuracy for clinical use. The diagnostic performance of the Forced Oscillation technique parameters was significantly higher than that of spirometry. CONCLUSIONS: The results of the present study provide substantial evidence that the Forced Oscillation technique can contribute to the easy identification of initial respiratory abnormalities in rheumatoid arthritis patients that are not detectable by spirometric exams. Therefore, we believe that the Forced Oscillation technique can be used as a complementary exam that may help to improve the treatment of breathing disorders in rheumatoid arthritis patients.

  • machine learning algorithms and Forced Oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease
    Computer Methods and Programs in Biomedicine, 2012
    Co-Authors: Jorge L. M. Amaral, Alvaro Camilo Dias Faria, Agnaldo Jose Lopes, Jose Manoel Jansen, Pedro Lopes De Melo
    Abstract:

    The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using Forced Oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n=25; healthy, n=25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se>87%, Sp>94%, and AUC>0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using Forced Oscillation measurements.

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