Pretreatment prognostic factors for survival in small-cell lung cancer: A new prognostic index and validation of three known prognostic indices on 341 patients

Aims: a) To identify which pretreatment clinical or blood parameters were predictive of patient survival in small-cell lung cancer (SCLC) in a retrospective analysis. b) To validate three known prognostic indices: Royal Marsden Model (index 1), London Group (index 2) and Manchester Score (index 3). Patients and methods. From 1951 to 1993, 341 SCLC patients were treated with chemotherapy with or without surgery or radiotherapy. Univariate and multiple regression analyses of survival were performed and the feasibility of these models was explored, index 1: Karnofsky index, albumin: sodium and alkaline phosphatase; index 2: ECOG performance status (PS), albumin and alanine transaminase; and index 3: lactate dehydrogenase (LDH), disease extent, sodium, Karnofsky index, alkaline phosphatase and bicarbonate. Results. Significant prognostic factors for survival after univariate and multiple regression analysis were: disease extent, PS, creatine kinase, neutrophilia, LDH, hypoalbumine-mia, hyperglycemia and bicarbonate. 4 new prognostic index was performed that included LDH, hypoalbuminemia, neutrophilia, disease extent and PS. It defined three prognostic groups (PG). Median survival and two-year survival for these PG were 12.3, 8 and 3.4 months and 16.5%, 2.3% and 0%, respectively. The following PG were identified after application of the three models proposed: Index 1 identified two PG with 0% and 16.6% two-year survival (P < 0.001); index 2 detected three PG with 0%, 5% and 15.7% two-year survival (P < 0.001) and index 3 detected three PG with 0%, 2.5% and 16.2% two-year survivals, respectively (P < 0.001). Conclusion: A new prognostic index is proposed allowing identification of three different PG. The feasibility of three known prognostic models was validated and demonstrated, Variables other than disease extent or PS (albumin or LDH) should be taken into account in designing future clinical trials.