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P107 Predictors of mortality in patients undergoing lung cancer surgery
  1. H Law1,
  2. P Foden1,
  3. M Evison1,
  4. F Wallace2,
  5. A Ashworth2,
  6. R Shah3,
  7. P Crosbie1,
  8. R Booton1
  1. 1North West Lung Centre, UHSM, Manchester, UK
  2. 2Cardiothoracic ICU, UHSM, Manchester, UK
  3. 3Department of Cardiothoracic Surgery, UHSM, Manchester, UK


Introduction Surgical resection is a treatment of choice for patients with early stage lung cancer and physiological measurements are routinely used to help predict post-operative risk, particularly in the ‘high-risk’ patient group.

At present, there is a lack of concordance between current available guidelines incorporating the use of such parameters aimed to guide decisions on surgery for high-risk patients with lung cancer. As a result, the decision to operate will differ for a particular patient depending on which guidelines are consulted.

We aim to identify which parameters best predicts post-operative mortality and whether this information can be used to construct a more encompassing pre-operative risk prediction model to help guide these difficult decision processes.

Methods Retrospective analysis of all patients undergoing CPET (cardio-pulmonary exercise testing) prior to lung cancer surgery between 01/01/2012 and 31/12/2015 was carried out. Age, BMI along with pre-operative and post-operative predicted physiological parameters were reviewed and statistical analysis performed. We also looked at survival based on type of surgery (sub-lobar, lobar, pneumonectomy), histology and cancer staging.

Results Single variable analysis of the 178 patients identified that low BMI (p = 0.005) and PPO DLCO% (p = 0.004) were associated with greater post-operative mortality risk.

There was a statistically significant difference between different cancer stage and type of surgery as expected.

Using the probabilities from the logistical regression model to predict one-year mortality gives an AUC of 0.764. A probability cut-off of 0.167 used to predict whether a patient will die within one year of surgery provides a sensitivity of 76.5%, specificity 66.4%, PPV 35.1% and NPV 92.2%.

Conclusions Contrary to current guidelines, CPET data did not seem to carry statistically significant weighting in determining post-operative mortality outcomes in our patient group with BMI and PPO DLCO% showing a stronger, statistically significant association.

Absolute% change between pre and PPO FEV1 values appears to be a good predictor of one-year mortality following surgery.

Further work is required but early analysis suggested that parameters such as BMI, PPO DLCO% and absolute post-operative change in FEV1% can be used to construct a pre-surgical prediction model for ‘high-risk’ patients undergoing surgery for lung cancer.

Abstract P107 Table 1

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