Article Text
Abstract
Objective To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.
Design Systematic review.
Data sources MEDLINE and Embase databases combined from inception and up to the end of January 2019.
Eligibility Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
Data extraction and synthesis Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.
Results An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.
Conclusion Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.
- epidemiology
- urology
- prostate disease
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Footnotes
Contributors MA, AL and KM were involved in study conception, idea and design. Data acquisition and extraction was obtained by MA. Data synthesis and interpretation was carried out by MA, AL and WO. MA and AL drafted the manuscript. All authors approved the final version of manuscript. KM is the study guarantor.
Funding KM and AL are supported by the NIHR Manchester Biomedical Research Centre and by the ICEP (This work was also supported by CRUK (grant number C18281/A19169)). KM is also supported by the Allan Turing Institute.
Competing interests All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request. Data extraction sheet and the PROBAST assessment form for risk of bias is available upon request.