Fractional polynomials in a new metastatic renal carcinoma continuous prognostic index involving histology, laboratory, and clinical predictors.

Background: We analyzed the effect of histology and various clinical and laboratory predictors in a new continuous prognostic index for metastatic renal-cell carcinoma based on fractional polynomials.

Patients And Methods: We evaluated 322 metastatic renal-cell carcinoma patients treated with subcutaneous recombinant cytokine-based home therapies in consecutive trials. We evaluated papillary histology, along with age, disease localizations, C-reactive protein, and neutrophil count in a new prognostic index, which was based on the multivariable fractional polynomial (MFP) algorithm.

Results: The MFP model allowed us to divide patients into three risk groups, using seven selected significant prognostic factors: histology, neutrophils, C-reactive protein, bone metastases, liver metastases, lymph node metastases, and age. Using the multivariable fractional polynomial model, median overall survival for high-, intermediate-, and low-risk patients was 10 months (n=80), 23 months (n=162), and 41 months (n=80) (scheme A; p
Conclusions: Histology, clinical, and laboratory predictors contribute to a MFP algorithm-based prognostic index, allowing for a highly effective long-term risk discrimination in metastatic renal-cell carcinoma patients receiving recombinant immunotherapy. The flexibility in creating patient groups by using a continuous prognostic index allows to meet clinical needs in terms of improved prediction and optimized treatment selection.