Results of a simple exercise test performed routinely to predict postoperative morbidity after anatomical lung resection.

Abstract

Exercise tests are considered the most reliable ones for the preoperative workup of lung resection candidates but frequently are indicated only in cases with low predicted postoperative forced expiratory volume in 1s (FEV1) and carbon monoxide diffusing capacity (DLCO). The aim of this investigation is to evaluate if a simple, standardised incremental bicycle exercise test routinely performed in patients considered operable is predictive of postoperative cardio-respiratory complication and if its performance is comparable to a logistic regression model including frequently cited clinical predictive variables. A series of 103 lung resection candidates were included in a prospective observational study. All patients underwent a standardised, incremental exercise test on a bicycle up to exhaustion. The analysed outcome was the occurrence of postoperative cardio-respiratory complications prospectively recorded and codified. The correlation of distance reached at the end of the test and the outcome was estimated by non-parametric tests. A logistic regression model including age, BMI, predicted postoperative (ppoFEV1%) and predicted postoperative DLCO (ppoDLCO) was adjusted and the individual probability of complication calculated and set as a new variable. Finally, two receiver operating characteristic (ROC) curves were constructed and compared: one with distance at the exercise test and the other with logistic regression probability of complication. Mortality of the series was nil. Outcome prevalence was 14%. Distance reached at the end of the exercise test was lower in cases with a positive outcome (3498.6m vs 4543.5m, p=0.001). On logistic regression analysis, age of the patient (p=0.016) and ppoDLCO (p=0.000) were predictive for the outcome. On ROC analysis, C-indices were 0.77 (distance) and 0.78 (logistic model, p=0.95). Reached distance in a simple standardised exercise test is related to postoperative morbidity after lung resection; and the accuracy of prediction using this variable alone is comparable to a logistic regression model including age and ppoDLCO.