Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review. Issue 6 (22nd June 2019)

• Record Type: Journal Article
• Authors: Thurtle, David; Rossi, Sabrina H; Berry, Brendan; Pharoah, Paul; Gnanapragasam, Vincent J
• Is Part Of: BMJ open. Volume 9:Issue 6(2019)
• Publication Date: 2019-06-22
• Subjects: prostate cancer -- survival -- prognosis -- decision-making; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-029149
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Men diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models. Design: Systematic literature review, pre-specified and registered on PROSPERO (CRD42018086394). Data sources: MEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms. Eligibility criteria: Inclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis. Data extraction and synthesis: Title, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool. Results: Database searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available. Conclusion: Few long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.