Factors affecting short-term precision of musculoskeletal measures using peripheral quantitative computed tomography (pQCT) Academic Article uri icon


  • UNLABELLED:Few studies have investigated factors influencing the precision of peripheral quantitative computed tomography (pQCT) measures. This study found time between repeat scans and subject anthropometric characteristics to influence short-term precision of pQCT-derived measures in the lower leg. These findings have implications for both investigators and clinicians utilizing pQCT outcomes. INTRODUCTION:Peripheral quantitative computed tomography (pQCT) is increasingly being used to investigate musculoskeletal changes associated with age, disease and/or intervention. Precision of pQCT measures is of paramount importance in this endeavor. This study aimed to establish the short-term precision of pQCT-derived musculoskeletal measures of the lower leg and investigate factors influencing this precision. METHODS:Thirty healthy subjects had a series of six pQCT scans of the lower leg (66% of tibial length proximal from its distal end) performed on two separate days by two different testers. The influences of different testers, time between repeat scans, and subject anthropometric characteristics on precision were explored. RESULTS:Overall precision error (root mean square) increased from bone (<1%) to muscle (<1.5%) to fat (3%). The two testers were equally precise in performing pQCT measures; however, precision error increased when repeat scans were repeated 1 week apart as opposed to on the same day. Subject anthropometric characteristics influenced precision errors with the general finding being that an increase in subject size was associated with less precise pQCT measures. CONCLUSIONS:pQCT is a relatively precise technique for the assessment of bone and muscle, but precision is influenced by time between repeat scans and subject anthropometric characteristics. Investigators and clinicians need to be aware of these factors influencing pQCT outcomes as they may influence statistical power in clinical studies and the characterization of change in individual patients.

publication date

  • November 2010