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Data quality assurance

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Data quality assurance

  1. 1. Data quality assurance Richard Baker Professor of Clinical Gait Analysis Blog: wwRichard.net 1
  2. 2. Gait analysis is based on measurement … … if we can’t make good measurements there is no point us being here. 2
  3. 3. 3 14 chapters on how to make measurements. 1 chapter on what to do with them.
  4. 4. Measuring walking • Both a science and an art We need to • understand the science • practice the art Need training in both and there is very little available (www.CMAster.eu) 4
  5. 5. Quality assurance • Staff training and education • Vigilance for errors in data
  6. 6. Staff training Before the analysis 6
  7. 7. Normative datasets For too long we have used normative datasets as an excuse for doing things differently. Normative data should be compared between centres to show we are doing the same things 7
  8. 8. Normative datasets 8 Differences in average traces suggest systematic differences in how markers are applied Differences in standard deviations suggest one lab has more repeatable practices than the other.
  9. 9. Repeatability studies Measurement science can be quite simple. All we need to know is the standard error of measurement (SEM - Standard deviation of repeat measurements made on the same subject). Two measurements need to differ by 3xSEM for there to be evidence of difference. 9
  10. 10. Other repeatability measure • Never use a repeatability measure you don’t understand. • Never use a repeatability measure that is not expressed in the original units of measurement. • Never trust someone else’s definition of “acceptable repeatability (particularly a psychologist) • “For many clinical measurements ICC should exceed 0.9 to ensure reasonable validity” (Portney and Watkins, 2009) 10
  11. 11. Repeatability studies 11 McGinley, J. L., Baker, R., Wolfe, R., & Morris, M. E. (2009). The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait and Posture, 29(3), 360-369. SEM<2° “acceptable” don’t need to consider measurement variability explicitly in interpretation 2°<SEM<5° “reasonable” need to consider measurement variability in interpretation. SEM>5° “concerning” measurement variability may mis- lead interpretation.
  12. 12. Physical examination McDowell et al. Gait & Posture, 2000Fosang et al. Dev Med Child Neurol, 2003
  13. 13. Repeatability studies Gait analysis measures can be more repeatable than physical exam measures … … but may not be in your laboratory 13
  14. 14. Repeatability studies Require one or more analyst to make repeat measurements on same person. If repeat testing of single analyst space measurements out. If comparison of multiple analysts have them close together. 14
  15. 15. Informal repeatability study 15 Measurements from three therapists (different colours) each measuring the same person on two different days
  16. 16. Formal repeatability study 16
  17. 17. Formal repeatability study • Considerable undertaking • Extremely difficult on children with cerebral palsy • Considerable uncertainty in SEM estimates 17
  18. 18. Quality assurance • Protocols written by team making measurements – Process more important than result • Regular review • Repeatability studies • Critical self-appraisal – by individuals – within teams – within community (peer review) • Open and honest culture 18
  19. 19. Vigilance for errors During and after the analysis 19
  20. 20. Vigilance for errors • Check data before the patient leaves • Requires processed data to be available before then (preferably before markers removed) • Keep assessments short and focussed so that both patient and analyst are prepared to repeat tests if necessary. 20
  21. 21. Is the data likely to be representative for the patient? • General health • Pain • Fatigue • Behaviour • No way of telling this from data
  22. 22. Agreement with data from other sources – Clinical exam0 Pst Hip Flexion 70 -20 Flex Ext deg Knee Flexion 75 -30 Dwn Hip 30 -30 Add Abd deg Kne 30Bilateral hip flexion contracture
  23. 23. Agreement with data from other sources – Video. -20 Flex Ext deg Knee Flexion 75 -15 Flx Ext deg Dorsiflexion 30 Dor deg -30 Add Abd deg Knee Adduction 30 -30 Var Val deg Ankle Rotation 30 Int deg -30 Int Ext deg Kne 30 -30 Int Ext deg Foot 30 Int deg Gait data may help explain the video data but it should not contradict it
  24. 24. Agreement with data from other sources – Video.
  25. 25. Smooth data Be very suspicious of jerky data If one kinetic graph is wrong you should be highly suspicious of all of them even if artefact is less obvious.
  26. 26. Smooth data Gait data is almost always smooth (it has been filtered to be so)
  27. 27. Consistent data • I can’t see all the detail • Should you be interpreting detail you can’t see?
  28. 28. Consistent data • Be particularly careful if traces fall into groups. • If this occurs in kinetics but not in kinematics then check force plates Picture from J Stebbins with permission
  29. 29. Swing phase ankle moments
  30. 30. Learn consequences of marker placement error 30
  31. 31. Hip rotation offsets 5° offsets of KAD
  32. 32. Consequences of marker placement error • Play! • Place markers erroneously on a colleague and predict changes in gait graphs. • If you can’t then you shouldn’t be placing markers on patients at all. 32
  33. 33. Professional competencies • Excellent data quality can only be provided by excellent gait analysts • Requires combination of biomechanical and clinical competencies • In many centres these are provided by different people
  34. 34. Professional competencies • Gait analysis requires: – Patient (and parent) management skills – Physical examination skills – Biomechanical measurement skills – Biomechanical analysis skills • Recruit staff with some of these skills • Train them in the others • Longer term training • Assessed competencies
  35. 35. Thanks for listening Richard Baker Professor of Clinical Gait Analysis Blog: wwRichard.net 35