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.
KeywordsMetastasis Castration-resistant prostate cancer Survival Prognostic models Prognosis
Androgen deprivation therapy
Androgen receptor signaling inhibitors
Checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies
Castration-resistant prostate cancer
Dialogue for reverse engineering assessments and methods
Eastern Cooperative Oncology Group
Metastatic castration-resistant prostate cancer
Preferred reporting items for systematic reviews and meta-analyses statement
Prediction model study risk of bias assessment tool
Randomized clinical trials
Reporting recommendations for tumor marker prognostic studies
Time-dependent area under the curve
We would like to thank Dr. Robert Wolff for his comments and suggestions to the manuscript. B. Wullich, University Hospital Erlangen, Erlangen, Germany. C. Becker, Deutsche Gesellschaft für Urologie, Düsseldorf, Germany. G. Kristiansen, University Hospital Bonn, Bonn, Germany. G. Seitz, Hospital Bamberg, Bamberg, Germany. J. Linxweiler, University Hospital Saarland, Homburg, Germany. S. Füssel, University Hospital Dresden, Dresden, Germany. S. Wach, University Hospital Erlangen, Erlangen, Germany.
M. Pinart: protocol development, data collection and management, data analysis, manuscript writing and editing. F. Kunath: protocol development, manuscript writing and editing. V. Lieb: manuscript writing and editing. I. Tsaur: manuscript writing and editing. B. Wullich: manuscript writing and editing. S. Schmidt: project development, protocol development, data collection and management, data analysis, manuscript writing and editing.
This research received no specific funding.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
Research involving human and/or animal participants
This article does not contain any studies with human participants or animals performed by any of the authors.
For this type of study, formal consent is not required.
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