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

Marliese Alexander, Rory Wolfe, David Lee Ball, Matthew Conron, Robert G. Stirling, Benjamin Solomon, Michael MacManus, Ann Officer, Sameer Karnam, Kate L Burbury, Sue M. Evans

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)

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: Scoreâ(c) 1/29); 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 â(c) 3/415). 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.

Original languageEnglish
Pages (from-to)744-751
Number of pages8
JournalBritish Journal of Cancer
Volume117
Issue number5
DOIs
Publication statusPublished - 22 Aug 2017

Keywords

  • Lung cancer
  • risk score
  • prognostic model

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