Introduction
Lung cancer is the leading cause of cancer death in Europe. Surgical resection is
often the preferred treatment but is associated with morbidity and mortality. Survival
with a meaningful quality of life is important; however, the prediction of post-operative
dyspnoea (POD) is often difficult and innaccurate.1 The European Society of Thoracic
Surgeons (ESTS) and the (UK) National Institute of Clinical Excellence (NICE) advocate
studies concerning operative risk for surgical resection. Conventional prediction
uses pulmonary function; predicted post-operative FEV1%(ppoFEV1%) and predicted post-operative
DLCO%(ppoDLCO%) with <40% in either domain being ‘high risk’. The aim is to improve
conventional prediction of the risk of POD and identify a sub-population for targeted
recruitment (prognostic enrichment) to interventional studies seeking to mitigate
the risk of breathlessness
Methods
With informed consent and ethics approval, we prospectively recruited 250 patients
undergoing lung resection in four UK centres. Dyspnoea was measured pre-operatively
and 3 months post-operatively using the Medical Research Council (MRC) score. The
primary outcome was patients with a post-operative MRC>2, excluding those with an
MRC>2 pre-operatively. Two conventional models were derived (n=93, 1 site), before
external validation (n=85, 3 sites) using the variables age, gender and ppoFEV1%/ppoDLCO%.
Model 1(M1) incorporates ppoFEV1%/ ppoDLCO% with conventional cut offs and Model 2(M2)
treats them continuously. Using similar internal derivation and external validation,
two new models were explored. Univariate analysis identified risk predictors (p<0.1)
for candidates with and without the primary outcome. Variables with significance were
then used in logistic regression to create Model 3(M3) (M2 with the next-best additional
variable- pre-operative EQ-5DL index score) & Model 4(M4) (not pre-defined and selected
from all significant variables- ppoFEV1%, BMI, Diabetes status and pre-operative brief
pain inventory score). Models were compared using sensitivity, specificity, positive
predicted value (PPV), negative predictive value (NPV) and Net Reclassification Indexing
(NRI)
Results
New models improved prediction within the internal dataset: M2 Vs M4 (AUROC comparison,
p=0.03, NRI 0.26). (Fig.1) The best conventional and new models (M2 & M4) performed
similarly within the external population: Sensitivity (55% vs 50%), Specificity (68%
Vs 73%), PPV (38% Vs 39%), NPV (81% Vs 81%), respectively.
Discussion
This study demonstrates conventional risk prediction for POD using pulmonary function
is poor. It also highlights challenges in creating new scoring tools: at external
validation conventional models performed equally to new models with similar sensitivity/specificity/NPV
and PPV. Using ppoFEV1%/ ppoDLCO% as continuous variables rather than dichotomised
at 40%, may increase predictive strength. Future work should explore new variables
to predict POD, such as pre-operative quality of life and biomarkers. For prognostic
enrichment, models should have high sensitivity & high NPV, targeting those who would
benefit most from low-risk interventions
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© 2021 Published by Elsevier Inc.
