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

• Published in: British journal of cancer 2017-08, Vol.117 (5), p.744-751
• Main Authors: Alexander, Marliese, Wolfe, Rory, Ball, David, Conron, Matthew, Stirling, Robert G, Solomon, Benjamin, MacManus, Michael, Officer, Ann, Karnam, Sameer, Burbury, Kate, Evans, Sue M
• Format: Article
• Language: English
• Subjects: 692/308/409; 692/4028/67/1612/1350; 692/499; Adenocarcinoma - genetics; Adenocarcinoma - mortality; Adenocarcinoma - pathology; Adenocarcinoma - secondary; Adult; Age Factors; Aged; Aged, 80 and over; Anaplastic Lymphoma Kinase; Biomedical and Life Sciences; Biomedicine; Cancer Research; Carcinoma, Non-Small-Cell Lung - genetics; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Non-Small-Cell Lung - secondary; Comorbidity; Data processing; Design standards; Diagnosis; Discrimination; Drug Resistance; Epidemiology; ErbB Receptors - genetics; Female; Follow-Up Studies; Health risks; Health Status; Histology; Humans; Lung cancer; Lung Neoplasms - genetics; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Lungs; Male; Medical diagnosis; Medical prognosis; Middle Aged; Molecular Medicine; Mutation; Neoplasm Staging; Non-small cell lung carcinoma; Oncology; Prognosis; Proportional Hazards Models; Receptor Protein-Tyrosine Kinases - genetics; Respiratory Tract Diseases - epidemiology; Risk Factors; Sex Factors; Smoking; Survival; Survival Rate; Weight Loss; Young Adult
• ISSN: 0007-0920; 1532-1827
• DOI: 10.1038/bjc.2017.232

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.