Abstract
Background and Objective: The treatment landscape of metastatic prostate cancer (mPCa) has evolved significantly over the last two decades. Despite this, the optimal therapy for patients with mPCa has not been determined. This systematic review identifies available predictive models which assess mPCa patient’s response to treatment.
Materials and methods: We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. Only quantitative studies in English were included with no time restrictions. Quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic
Reviews (CHARMS) criteria.
Key findings: The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were internally or externally validated. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to adequately detail model performance resulting in a high risk of bias. Where reported, the models indicated good or excellent performance.
Conclusion: Most predictive models identified require additional evaluation and validation in properly designed studies before they can be implemented in clinical practice to assist with treatment decision-making for men with mPCa.
Advancing Practice: To our knowledge, this is the first systematic review to identify predictive models assessing mPCa patient’s response to treatment. We describe how existing models can be improved upon and consequently used to assist with treatment decision-making by clinicians.
Patient Summary: In this paper, we review studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before they can be used by health care professionals.
Materials and methods: We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. Only quantitative studies in English were included with no time restrictions. Quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic
Reviews (CHARMS) criteria.
Key findings: The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were internally or externally validated. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to adequately detail model performance resulting in a high risk of bias. Where reported, the models indicated good or excellent performance.
Conclusion: Most predictive models identified require additional evaluation and validation in properly designed studies before they can be implemented in clinical practice to assist with treatment decision-making for men with mPCa.
Advancing Practice: To our knowledge, this is the first systematic review to identify predictive models assessing mPCa patient’s response to treatment. We describe how existing models can be improved upon and consequently used to assist with treatment decision-making by clinicians.
Patient Summary: In this paper, we review studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before they can be used by health care professionals.
| Original language | English |
|---|---|
| Journal | European Urology Open Science |
| Publication status | Accepted/In press - 26 Mar 2024 |
Bibliographical note
Funding: This research is part of PIONEER – an international Big Data Consortium led by the European Association of Urology to answer key questions for men with PCa, funded through the IMI2 Joint Undertaking under grant agreement No.777492.Keywords
- adverse events
- disease progression
- metastatic prostate cancer
- overall survival
- predictive models
- treatment discontinuation
- Treatment selection
- Toxicity
