An alternative method for predicting the risk of postoperative complications in lung resection

The aims of this study were to design a best fit linear regression model to estimate VO2max (estimated VO2) and to compare the ability of VO2 values (measured and estimated) predicting cardiorespiratory complications in a series of patients undergoing lung resection for lung cancer.

This was a prospective, observational study performed in 83 patients. Variables analyzed were: demographic characteristics, comorbidity, body mass index (BMI), FEV1%, FVC%, diffusion capacity (DLCO%), mean daily distance walked in kilometers, VO2max measured by cardio-pulmonary exercise test (CPET) and postoperative complications. Descriptive and comparative statistical analysis of the variables was performed using the Mann-Whitney test for categorical variables and the Student's t-test for continuous variables. A new linear regression model was designed, where the dependent variable (measured VO2max) was estimated by the distance, DLCO% and age, resulting in the estimated VO2. The predictive power of the measured and estimated consumption was analyzed using the Student's t-test, grouping by the occurrence or absence of cardiorespiratory complications.

Both groups were homogeneous for age, sex, BMI, FEV1%, DLCO%, comorbidity, type of resection performed and mean distance walked per day. Estimated VO2 and measured VO2 were normally distributed (K-Smirnov test, P>.32). VO2 means estimated by the model (age, DLCO% and mean distance walked per day) were significantly different between patients with and without complications (Student's t test, P=.037) compared with measured VO2 values, which did not differentiate groups (Student's t test, P=.42).

The VO2max estimated by the model is more predictive in this case series than the VO2max measured during a standard exercise test.