Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: a systematic review.

Prognostic models are developed to estimate the probability of the occurrence of future outcomes incorporating multiple variables. We aimed to identify and summarize existing multivariable prognostic models developed for predicting overall survival in patients with metastatic castration-resistant prostate cancer (mCRPC).

The protocol was prospectively registered (CRD42017064448). We systematically searched Medline and reference lists up to May 2018 and included experimental and observational studies, which developed and/or internally validated prognostic models for mCRPC patients and were further externally validated or updated. The outcome of interest was overall survival. Two authors independently performed literature screening and quality assessment.

We included 12 studies that developed models including 8750 patients aged 42-95 years. Models included 4-11 predictor variables, mostly hemoglobin, baseline PSA, alkaline phosphatase, performance status, and lactate dehydrogenase. Very few incorporated Gleason score. Two models included predictors related to docetaxel and mitoxantrone treatments. Model performance after internal validation showed similar discrimination power ranging from 0.62 to 0.73. Overall survival models were mainly constructed as nomograms or risk groups/score. Two models obtained an overall judgment of low risk of bias.

Most models were not suitable for clinical use due to methodological shortcomings and lack of external validation. Further external validation and/or model updating is required to increase prognostic accuracy and clinical applicability prior to their incorporation in clinical practice as a useful tool in patient management.

World journal of urology. 2018 Dec 15 [Epub ahead of print]

M Pinart, F Kunath, V Lieb, I Tsaur, B Wullich, Stefanie Schmidt, German Prostate Cancer Consortium (DPKK)

Department of Urology and Pediatric Urology, University Hospital Erlangen, Erlangen, Germany., Department of Urology, University Medicine Mainz, Mainz, Germany., UroEvidence@Deutsche Gesellschaft für Urologie, Berlin, Germany. .