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BACKGROUND: The guidelines to conduct and interpret conventional pulmonary function (PFT) tests are frequently reviewed and updated. However, the quality assurance and quality control (QA/QC) guidelines for respiratory oscillometry testing remain limited. QA/QC guidelines are essential for oscillometry to be used as a diagnostic pulmonary function test (PFT) in a clinical setting. METHODS: We developed a QA/QC protocol shortly after oscillometry was introduced in our laboratory as part of a clinical study. The first clinical study began after the research personnel completed 3 h of combined didactic and hands-on training and establishment of a standard operating protocol (SOP) for oscillometry testing. All oscillometry tests were conducted using the initial SOP protocol from October 17, 2017, to April 6, 2018. At this time, the first QA/QC audit took place, followed by revisions to the SOP, the addition of a QA/QC checklist, and the development of a 12-h training program. A second audit of oscillometry tests was conducted from April 9, 2018, to June 30, 2019. Both audits were completed by a registered cardiopulmonary technologist from the Toronto General Pulmonary Function Lab. RESULTS: The first audit evaluated 197 paired oscillometry-PFT tests and found 10 tests (5.08%) to be invalid, with a coefficient of variation > 15%. The second audit examined 1,930 paired oscillometry-PFT tests; only 3 tests (0.16%) were unacceptable, with a coefficient of variation > 15%. Improvement in QA/QC was significantly better compared to the first audit (P < .001). CONCLUSIONS: Although oscillometry requires minimal subject cooperation, application of the principles that govern the conduct and application of a PFT are important for ensuring that oscillometry testing is performed according to acceptability and reproducibility. Specifically, the inclusion of a SOP, a proper training program, a QA/QC checklist, and regular audits with feedback are vital to ensure that oscillometry is conducted accurately and precisely.

Sarcopenia is an important predictor of clinical outcomes in lung transplant candidates. Dual-energy Xray absorptiometry (DXA) is the gold standard to determine appendicular lean mass, a primary marker of sarcopenia which is not always feasible due to requirement of DXA equipment, cost, technical expertise, and risk of radiation exposure. Muscle ultrasound can be used to quantify muscle size and quality and may be a simpler, alternative tool for detecting sarcopenia. The objectives of this study were (i) to develop a regression-based model to predict appendicular lean mass index (ALMI) from DXA using ultrasonography and (ii) determine the most parsimonious model from ultrasound to predict ALMI.Methods: We conducted a cross-sectional study of adult lung transplant candidates from a single centre. Subjects underwent B-mode ultrasound of the dominant leg to assess muscle layer thickness of quadriceps (sum of rectus femoris (RF), vastus intermedius and lateralis), and gastrocnemius, RF cross-sectional area (CSA), tibialis anterior (TA) CSA and echogenicity (ECHO) of RF and TA. Ultrasound measures of muscle size were normalized to limb length. ALMI (kg/m 2 ) was assessed with DXA, and used to define sarcopenia using cut‐points of ≤7.26 kg/m 2 for men and ≤5.45 kg/m 2 for women. Hierarchical, stepwise multilinear regression analysis was used to predict ALMI from ultrasound measures and demographic variables (age, sex, diagnosis). Three multilinear regression analysis were developed based on the feasibility of the ultrasound imaging protocol (i.e. number of muscles, subject position and image acquisition time). Level of significance was set at p ≤ 0.05.Results: 61 lung transplant candidates were included (52% female, median (IQR) age= 63 [55][56][57][58][59][60][61][62][63][64][65][66][67]] kg/m 2 , diagnosis: 48% ILD, 34% COPD, 18% other diseases). 53% of females and 52% of males had sarcopenia based on ALMI criteria. Age, diagnosis and TA ECHO were not correlated with ALMI. All the regression models were strongly associated with ALMI: five muscle model [quadriceps thickness (sum of RF, vastus intermedius and lateralis thickness) + gastrocnemius thickness + TA CSA + sex; R 2 = 0.764, p < 0.001]; four-muscle model (quadriceps thickness + TA CSA + sex; R 2 = 0.748, p < 0.001) and two-muscle model (RF CSA + TA CSA + sex; R 2 = 0.723, p = 0.001). Conclusions: Lower limb muscle ultrasound can be used to predict ALMI in lung transplant candidates using as few as two muscles. This method may be applicable in clinical/research settings to evaluate sarcopenia.

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