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Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

Authors :
Susan E. Evans
Benjamin Solomon
Marliese Alexander
Ann Officer
David Ball
Rory Wolfe
Matthew Conron
Robert G Stirling
Michael MacManus
Sameer Karnam
Kate Burbury
Source :
British Journal of Cancer
Publication Year :
2017
Publisher :
Springer Science and Business Media LLC, 2017.

Abstract

Introduction: Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. Methods: Prospective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell’s c-statistic for discrimination and Cox–Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284). Results: The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score⩽9); 61, 61, and 42% (LCPI 2: score 10–13); 33, 32, and 14% (LCPI 3: score 14–16); 7, 16, and 5% (LCPI 4: score ⩾15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. Conclusions: The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses.

Details

ISSN :
15321827 and 00070920
Volume :
117
Database :
OpenAIRE
Journal :
British Journal of Cancer
Accession number :
edsair.doi.dedup.....9e6367995f3a1bacb22406ecf021d2d6
Full Text :
https://doi.org/10.1038/bjc.2017.232