title: Can we better estimate resting oxygen consumption by incorporating arterial blood gases and spirometric determinations?
abstract: BACKGROUND
  We hypothesize that oxygen consumption (V̇o2) estimation in patients with respiratory symptoms is inaccurate and can be improved by considering arterial blood gases or spirometric variables.
  
  
  METHODS
  For this retrospective study, we included consecutive subjects who underwent cardiopulmonary exercise testing. Resting V̇o2 was determined using breath-by-breath testing methodology. Using a training cohort (n = 336), we developed 3 models to predict V̇o2. In a validation group (n = 114), we compared our models with 7 available formulae.
  
  
  RESULTS
  Our first model (V̇o2 = -184.99 + 189.64 × body surface area [BSA, m(2)] + 1.49 × heart rate [beats/min] + 51.51 × FIO2 [21% = 0; 30% = 1] + 30.62 × gender [male = 1; female = 0]) showed an R(2) of 0.5. Our second model (V̇o2 = -208.06 + 188.67 × BSA + 1.38 × heart rate + 35.6 × gender + 2.06 × breathing frequency [breaths/min]) showed an R(2) of 0.49. The best R(2) (0.68) was obtained with our last model, which included minute ventilation (V̇o2 = -142.92 + 0.52 × heart rate + 126.84 × BSA + 14.68 × minute ventilation [L]). In the validation cohort, these 3 models performed better than other available equations, but had wide limits of agreement, particularly in older individuals with shorter stature, higher heart rate, and lower maximum voluntary ventilation.
  
  
  CONCLUSIONS
  We developed more accurate formulae to predict resting V̇o2 in subjects with respiratory symptoms; however, equations had wide limits of agreement, particularly in certain groups of subjects. Arterial blood gases and spirometric variables did not significantly improve the predictive equations.
authors.affiliation: Department of Pulmonary, Allergy and Critical Care Medicine, Respiratory Institute, Cleveland Clinic tonella@ccf.org.
authors.forename: Adriano R
authors.initials: AR
authors.lastname: Tonelli
authors.affiliation: Respiratory Institute Biostatistics Core, Quantitative Health Sciences, Cleveland Clinic.
authors.forename: Xiao-Feng
authors.initials: XF
authors.lastname: Wang
authors.affiliation: Medicine Institute, Cleveland Clinic, Cleveland, Ohio.
authors.forename: Anara
authors.initials: A
authors.lastname: Abbay
authors.affiliation: Respiratory Institute Biostatistics Core, Quantitative Health Sciences, Cleveland Clinic.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Pulmonary, Allergy and Critical Care Medicine, Respiratory Institute, Cleveland Clinic.
authors.forename: José
authors.initials: J
authors.lastname: Ramos
authors.affiliation: Department of Pulmonary, Allergy and Critical Care Medicine, Respiratory Institute, Cleveland Clinic.
authors.forename: Kevin
authors.initials: K
authors.lastname: McCarthy
mesh_terms: D000328:Adult; D000368:Aged; D001784:Blood Gas Analysis; D005080:Exercise Test / Q000379:methods*; D005260:Female; D006339:Heart Rate; D006801:Humans; D008173:Lung Diseases, Obstructive / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D010101:Oxygen Consumption / Q000502:physiology*; D011237:Predictive Value of Tests; D017534:Radial Artery; D012119:Respiration; D012146:Rest / Q000502:physiology*; D012189:Retrospective Studies; D013147:Spirometry
keywords: cardiopulmonary exercise; formula; obstructive lung disease; oxygen consumption; restrictive lung disease