Forced Oscillation Technique. Prof. Raffaele Dellacà

Transcription

1 Forced Oscillation Technique Prof. Raffaele Dellacà

2 Measuring mechanical properties of the respiratory system 2 Respiratory mechanics: the study of the relationships between pressures (forces) and flows (displacements) of the respiratory system V aw lung & chest wall mechanics resp. muscles pressure Atmospheric pressure

3 Inspiration Flow Expiration Spirometry: principles 3 Maximal Expiratory Flow Volume (MEFV) curve Increasing muscular efforts Volume Vital capacity

4 Assessment of lung function 4 The gold standard for the assessment of lung function is spirometry As a forced maneuver is required, patients must be trained and the maneuver must be performed under supervision of trained personnel. Moreover, young children and elderly patients may fail or take long time to complete a spirometry with the desired quality standards; Flow-volume loops are often of difficult interpretation long training is needed. Not suitable for GPs; FEV1 is mainly representative airway obstruction over a wide range of lung volumes, making it difficult to assess the degree of obstruction at the operating lung volume of a given patient (low sensitivity and specificity).

5 Measuring mechanical properties of the respiratory system 5 V aw Pressure generator Paw lung & chest wall mechanics resp. muscles pressure Atmospheric pressure

6 Measuring mechanical properties of the respiratory system 6 V aw Pressure generator Paw lung & chest wall mechanics resp. muscles pressure Atmospheric pressure

7 Forced oscillation technique 7 The Forced Oscillation Technique (FOT) FOT allows the study of structural and mechanical properties of the respiratory system deduced from its mechanical response to small time-varying forces DuBois AB, Brody AW, Lewis DH, Burgess BF. Oscillation mechanics of lungs and chest in man. J Appl Physiol 1956; 8:

8 Forced Oscillation Technique (FOT) 8 1 Pressure (cmh 2 O) 0-1 Flow (l/s) ins s

9 Forced Oscillation Technique (FOT) 9 1 Power spectral density of the pressure signal Pressure (cmh 2 O) Flow (l/s) ins Power spectrum ( (cmh 2 O) 2 ) Spontaneous breathing Forced oscillations Frequency (Hz) s Z(f ) P(f ) V(f )

10 P [cmh 2 O] [l s -1 ] P [cmh 2 O] Flow [l s -1 ] 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5-2,0 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5-2,0-0,5-1,0 1,0 0,5 0,0 1,0 0,5 0,0 Forced Oscillation Technique (FOT) Pressure at the mouth Filtro 0,00,51,01,52,02,53,03,54,04,55,05,5 Butterworth passa-basso time [s] Filtro Butterworth passa-banda time [s] Airflow at the mouth Filtro Butterworth passa-banda time [s] Filtro Butterworth passa-basso P resp [cmh 2 O] P stim [cmh 2 O] Flow Flow stim resp [l [l s -1 s ] -1 ] Flow Flow resp stim [l [l s -1 s ] -1 ] ,5 1,0 0,5 0,0-0,5-1,0-1,5 1,0 1,0 0,5 0,5 0,0 0,0-0,5-0,5-1,0-1,0 1,0 1,0 0,5 0,5 0,0 0,0-0,5-0,5-1,0-1, time [s] time [s] 0,00,51,01,52,02,53,03,54,04,55,05, time [s] 0,00,51,01,52,02,53,03,54,04,55,05, time [s] 10 Lung function independent from breathing

11 FLOW (L/s) PRESSURE (cmh 2 O) P R V R rs = P R V s R rs = 2.32 cmh 2 O s/l

12 FLOW (L/s) PRESSURE (cmh 2 O) P X V X rs = P X V -0.2 X rs = 2.04 cmh 2 O s/l 0.5 s E 2 f X rs = 25.6 cmh 2 O/L

13 Experimental Set-up 13 High inertance tube Vacuum generator Loudspeaker Bias flow Pneumotach. Power Amplifier A V ao Pao LP Filter Forcing signal A/D - D/A board Personal computer

14 DuBois AB, Brody AW, Lewis DH, Burgess BF. Oscillation mechanics of lungs and chest in man. J Appl Physiol 1956; 8:

15 Main Features: Multiple waveforms Different protocol implemented (drugs effects) Clinical report Possibility to export data for personal analysis Stand alone device (no external PC is needed) Large touchscreen display Intuitive User Interface Internal database Multiple user account

16 Different approaches to compute and interpret Zin Multifrequency PRN 3 Impedance 16 Amplitude [cmh 2 O] 1,0 0,5 0,0-0,5-1,0 0,0 0,5 1,0 1,5 2,0 Z [cmh 2 O s l -1 ] R X t [s] Frequency [Hz]

17 17 The use of mathematical models allows to partition airway resistance (Raw) and inertance (Iaw) from tissue damping (G) and elastance (H) by using a constant-phase tissue compartment:

18 18 Hidden assumptions: 1) The respiratory system behaves as a stationary linear dynamic system; 2) The mathematical models used describes appropriately the complexity of the system being under analysis.

19 Airway dynamic during inspiration and expiration 19 End expiratory Expiration Inspiration

20 Expiratory Flow Limitation and the waterfall concept 20

21 Is this the most appropriate approach to use FOT? 21

22 Different approaches to compute and interpret Zin Multifrequency PRN 3 Impedance 22 Amplitude [cmh 2 O] 1,0 0,5 0,0-0,5-1,0 0,0 0,5 1,0 1,5 2,0 Z [cmh 2 O s l -1 ] R X t [s] Frequency [Hz] Single sinusoidal 3 Amplitude [cmh 2 O] 1,0 0,5 0,0-0,5-1,0 0,0 0,5 1,0 1,5 2,0 Z [cmh 2 O s l -1 ] R X t [s] t [s]

23 Detection of EFL by FOT Inspiration Inspiration Inspiration Expiration Expiration Expiration Volume (l) Z in = Rrs - j Rrs Caw Ca Rrs at 5Hz cmh2osl -1 Xrs at 5Hz cmh2osl -1 1 ( Caw + Ca)ω Xinsp DXrs Xexp Time (s) Z in = Rrs - EFL j Rrs Caw Ca 1 ( Caw + Ca)ω (4) Dellacà et al. Eur Resp J, 2004 EFL DXrs>2.8 (4) Ca>>Caw

24 Detection of bronchodilator response by FOT in COPD 24 The PRN signal (5 Hz, 11 Hz, 19 Hz) made possible the simultaneous assessment of the presence of EFL and estimation the frequency spectrum of Rrs NFL 7 Rtot (cmh Rinsp 2 O L (cmh20 s-1) L s-1) 20 FL NS NFL Pre BD NFL Post BD FL Pre BD FL Post BD R insp (cmh 2 O L s -1 ) P< Stimulus frequency (Hz) Stimulus frequency (Hz) The non stationarity introduced by the presence of EFL reduces the sensitivity of Rrs to the effects of BD when the whole breathing cycle is used. Within breath analysis improved significantly the sensitivity of Rrs to BD in COPD Dellacà et al. Eur Respir J. 33: ,2009

25 V NAT (%) Physiological meaning of Xrs 25 Left lung Baseline After RM After lavage atelectasis After RM, after lavage V NAT C X /X 5 (ml/cmh 2 O) Dellaca et al, Intensive Care Med Dec;35(12):

26 Physiological meaning of Xrs r 2 = C X5 (ml cmh 2 O -1 ) VtissNA (%) Dellaca et al, Intensive Care Med Dec;35(12):

27 Rrs and Xrs Normal - both resistance (R) and reactance (X) are within the normal range Peripheral disease Resistance is normal and reactance more negative (I.e. possible airway obstruction, excluded alveoli, disomogeneity of ventilation, or possible restriction) Central obstruction Resistance is increased and reactance is within the normal range (i.e diseases affecting central airways) Severe Obstructive disease both resistance and reactance are outside the normal ranges and resistance is frequency dependent (i.e severe asthma, severe COPD) 27

28 PRE POST

29 Development of FOT 29 The advancements in technology, data processing, understanding of the physiology and on how diseases impact the oscillatory response of the lung lead to new interest and applications on FOT in the last decade Scientific papers per publication year (search: FOT and RESPIRATORY) 50 Nr. of scientific publications Publication year

30 Lung function testing outside the lab: applications 30 Point of care diagnostics: screening and tailoring treatments on the field Integration in mechanical ventilators to monitor the patient and optimize parameters FOT Improving knowledge on respiratory disease and identifying more specific phenotypes Home monitoring of lung function in patients with chronic respiratory conditions

31 Variability in chronic respiratory diseases Asthma and COPD are characterized by a high variability of symptoms and high fluctuations of the related respiratory parameters (Lancet, 2008) A strategy with more than one observed index and including statistical measures of objective parameters with time should provide a more comprehensive picture of the pathology and of its progression.

32 Remote locations lung function testing: network architecture 32 Home monitoring device Mobile network GSM Web access to data server INTERNET WEB BROWSER CLIENT Request Request Response Servlet Redirect JSP JDOM/JDBC Landline or broadband connection Data server XML files MySQL SERVER Data transmission, storage and access

33 Applications to home monitoring 33

34 CHROMED 34 Clinical trials for elderly patients with MultiplE Diseases Large scale RTC for the evaluation of impact in adopting a telemonitoring platform for health management in elderly patients affected by COPD with co-morbidities Patients: 300 COPD studied for 9 months; Duration: 36 months; Total Budget:

35 CHROMED consortium 35 Participant organisation Tesan S.p.A. Restech s.r.l. University of Liverpool Institute of Clinical Medicine, Tallinn University of Technology University of Barcelona University of Uppsala Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway University of Lincoln Sezana Hospital Country IT IT UK EE ES SE N UK SI

36 Study platform 36 INTERNET 1

37 Management of CHROMED patients 37 Low priority Alarm No action required NEW ALARM High priority Alarm Access the web platform Contact the patient and ask for symptoms and conditions Decide if an action is needed IN PROGRESS Report the taken action (with description) Update the symptoms (if any) on the platform CLOSED

38 CROMED CONSORT diagram 38 Enrollment Assessed for eligibility (n = 326) Excluded (n = 14): Declined to participate (n=14) Randomized (n = 312) Allocation Allocated to intervention (n = 154) Allocated to control (n = 158) Follow-Up Lost to follow-up (patients with less than 9 months of monitoring, n = 45): Death (n=3) Hospitalized (n=8) Difficulties in using the equip. (n=3) Technical problem (n=4) Personal problem/no reason provided (n=27) Lost to follow-up (patients with less than 9 months of monitoring, n = 36) Death (n=4) Hospitalized (n=7) Difficulties in using the equip. (n=0) Technical problem (n=0) Personal problem/no reason provided (n=24) Analysis Analysed (n = 151) Excluded from analysis, n=0 Analysed (n = 155) Excluded from analysis, n=0

39 Study population: baseline characteristics 39 Interventional Control p-value # of patients (M/F) 100/51 102/53 Age (yrs) 71,41 ± 6,76 71,14 ± 6,83 0,72 BMI (Kg/m2) 28,03 ± 5,78 28,32 ± 6,93 0,68 Smoking History (pack/years) 41,61 ± 26,78 44,69 ± 20,87 0,26 FEV1 postbd (L) 1,29 ± 0,50 1,34 ± 0,52 0,40 FEV1 postbd (%pred) 47,56 ± 16,23 48,84 ± 17,80 0,51 FVC postbd (L) 2,54 ± 0,80 2,62 ± 0,83 0,43 FVC postbd (%pred) 73,75 ± 19,87 74,06 ± 22,95 0,90 FEV1/FVC postbd 66,29 ± 20,89 66,38 ± 19,65 0,97 SGRQ Total score 47,48 ± 18,57 47,02 ± 19,79 0,84 Exacerbation last year: 2,33 ± 1,73 2,50 ± 2,16 0, ,00 57,00 2+ Hospitalization last year 0 90,00 88,00 98,00 90, ,00 65,00

40 Results: adherence and alarms 40 Adherence to the daily test Type of data Monitored Expected Received days measurements (% of expected) Lung function by FOT % Blood pressure, SpO2, temperature and ECG % COPD daily questionnaire % CHF daily questionnaire % Numbers of suspected worsening events RESPIRATORY CARDIAC Worsening events, nr Rate of worsening events [events/patient/month], Median (IRQ)) 0.53 ( ) 1.06 ( )

41 Results: Number and duration of hospital admissions 41 Total number of nights at the hospital Number of hospital admissions during 9 months Telemonitored patients Standard care patients Total number of nights at the hospital Number of nights at the hospital during 9 months n=329 Telemonitored patients n=650 Standard care patients

42 42 Healthcare system utilisation Difference (9 months) Mean (SD) Mean (SD) mean (95% CI) Hospital Usage Cost ( ) Usage Cost ( ) Hos pi tal i za tions 0.48 (1.40) (0.82) 1, (-300, 2947) ED pres entations 0.29 (0.78) (0.81) (-19, 41) Early discharge 0.03 (0.26) (0.12) 14 1 (-27, 30) Hos pi tal-a t-home 0.08 (0.49) (0.08) 1 12 (-3, 27) Outpa tient vi s i ts 2.05 (2.95) (3.79) (-173, 74) Ambul a nce 0.26 (0.95) (1.51) (-87, 42) Primary care GP offi ce 5.97 (7.02) (6.48) (-124, 212) Di s tri ct nurs e 1.17 (3.07) (4.62) (-187, 207) Specialist nurse 2.63 (8.21) (4.36) (-20, 74) Phys i othera pi s t 1.64 (5.97) (3.49) (-30, 110) Other 0.71 (2.69) (3.05) 38-4 (-35, 28) Management of medical alarms Respiratory alarms (n/a) 21 (n/a) 21 (21, 21) Respiratory alarms with symptoms (n/a) 29 (n/a) 29 (29, 29) Cardiac alarms (n/a) 53 (n/a) 53 (53, 53) Cardiac alarms with symptoms 0.48 (n/a) 7 (n/a) 7 (7, 7) TOTAL * NOT INCLUDING device cost and management Control group Telemonitoring group Sub-group analysis: Patients with 1+ hospitalization in the previous year Average savings on healthcare costs over 1year: (5 757AUD)

43 Day by day variability of airway obstruction in Asthma 5 months PEF measured daily in the morning and in the afternoon Frey, Suki et al. Nature 2005, dati da un crossover clinical trial Taylor et al. Thorax, 1994

44 Gulotta C, Gobbi A, Pedotti A, Suki B, Brusasco V, Pellegrino R, Dellaca R American Journal of Respiratory and Critical Care Journal, Vol 185, 2012

45 Is increased day by day variability a specific feature of asthma? 45 Increased day-to-day variability of respiratory resistance is a specific feature of asthma. A. Gobbi, C. Gulotta, B. Suki, E. Mellano, M. Vitacca, F. Colombo, R. Pellegrino, V. Brusasco and R. L. Dellacà. Submitted

46 SD 14 R insp, cmh 2 O s L -1 Is increased day by day variability a specific feature of asthma? Asthma Healthy COPD m 14 R insp, cmh 2 O s L -1 Increased day-to-day variability of respiratory resistance is a specific feature of asthma. Gobbi et al, submitted

47 Sensitivity Sensitivity / Specificity A B C Sensitivity Specificity J-index CV 14 R insp, AUC = 0.91 PEF 14, AUC = Specificity CV 14 R insp PEF 14, % Increased day-to-day variability of respiratory resistance is a specific feature of asthma. Gobbi et al, submitted

48 Home monitoring clinical study Prediction of future extreme values of Rrs 48 An extreme value of Rrs is defined at least equal to twice the age, sex and weight-predicted value, ρ (Eur. Resp. Review, 1994) We calculate the conditional probability π of having an extreme value of Rrs in a future prediction window given a variability window. Variability window φ t 0 Prediction window τ 2-day time 8-day 4-day 4-day 7-day Conditional probability: π( R rs > ρ in τ φ) 15-day

49 Home monitoring clinical study Prediction of future extreme values of Rrs 49 Prediction window τ: 4 days Variability window φ: 2, 4, 8 days Variability window φ: 8 days Prediction window τ: 4, 7, 15, 30 days A φ = 8 days generates a predictor close to the ideal shape, i.e. a step function Gulotta et al., AJRCCM Vol 185, 2012 The predictors with τ up to 15 days maintain a good ability to discriminate extreme events

50 Variability in asthma: therapeutic applications Adjustment of therapy Assessment of ICP

51 Adapting mechanical ventilation to the patient's need 51 AUTOMATIC SYSTEM VENTILATOR PATIENT MEASURES AUTOMATIC INTERVENTION CLINICIAN

52 Dynamic hyperinflation and intrinsic PEEP 52 Patients with COPD requiring NIV often develop dynamic hyperinflation; As a consequence, they develop intrinsic PEEP (PEEPi); PEEPi determines an increased work of breathing, poor patient-ventilator triggering and patient-ventilator dyssynchrony, impairing the efficacy of the treatment; The appropriate setting of an externally applied PEEP able to counterbalance PEEPi improves the efficacy of the therapy.

53 53 Automatic Tailoring of Positive End-expiratory Pressure (PEEP) by Forced Oscillation Technique (FOT) During Non-invasive Ventilation: Effects of Posture and Exertion in COPD Pao (cmh 2 O) Xrs cm H 2 O / l sec Time (s) Automatic Tailoring of Positive End-expiratory Pressure (PEEP) by Forced Oscillation Technique (FOT) During Non-invasive Ventilation: Effects of Posture and Exertion in COPD. ERS 2012 Raffaele Dellaca, Bob Romano, Joe Garuccio, Cherian John, Ramesh Thimmiah, Melvin Saludes, and Charles Cain,

54 p=0.004 vs. seated PEEPopt (cmh 2 O) Seated Supine Walking Automatic Tailoring of Positive End-expiratory Pressure (PEEP) by Forced Oscillation Technique (FOT) During Noninvasive Ventilation: Effects of Posture and Exertion in COPD. ERS 2012 Raffaele Dellaca, Bob Romano, Joe Garuccio, Cherian John, Ramesh Thimmiah, Melvin Saludes, and Charles Cain,

55 Automatic tailoring of PEEP on COPD: night trial

56 Conclusions 56 New approaches to lung function measurements and data analyses based on FOT are now opening new opportunities for improving our understanding of the complexity of the act of breathing and for developing new interventions to restore impaired conditions. The exploitation of these approaches to their full potential will be object of future research

57 Acknowledgments 57

58 58