Prediction models for prostate cancer to be used in the primary care setting: a systematic review
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 bas...
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BMJ Publishing Group
2020-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/7/e034661.full |
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| author | Artitaya Lophatananon Mohammad Aladwani William Ollier Kenneth Muir |
| author_facet | Artitaya Lophatananon Mohammad Aladwani William Ollier Kenneth Muir |
| author_sort | Artitaya Lophatananon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-188d6f3833dc4aad88749f49b9eb48f5 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2020-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-188d6f3833dc4aad88749f49b9eb48f52024-12-04T17:05:08ZengBMJ Publishing GroupBMJ Open2044-60552020-07-0110710.1136/bmjopen-2019-034661Prediction models for prostate cancer to be used in the primary care setting: a systematic reviewArtitaya Lophatananon0Mohammad Aladwani1William Ollier2Kenneth Muir3Division of Population Health, Health Services Research and Primary Care,School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UKDivision of Population Health, Health Services Research and Primary Care School of Health Sciences Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK12 Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UKDivision of Population Health, Health Services Research and Primary Care,School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UKObjective 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.https://bmjopen.bmj.com/content/10/7/e034661.full |
| spellingShingle | Artitaya Lophatananon Mohammad Aladwani William Ollier Kenneth Muir Prediction models for prostate cancer to be used in the primary care setting: a systematic review BMJ Open |
| title | Prediction models for prostate cancer to be used in the primary care setting: a systematic review |
| title_full | Prediction models for prostate cancer to be used in the primary care setting: a systematic review |
| title_fullStr | Prediction models for prostate cancer to be used in the primary care setting: a systematic review |
| title_full_unstemmed | Prediction models for prostate cancer to be used in the primary care setting: a systematic review |
| title_short | Prediction models for prostate cancer to be used in the primary care setting: a systematic review |
| title_sort | prediction models for prostate cancer to be used in the primary care setting a systematic review |
| url | https://bmjopen.bmj.com/content/10/7/e034661.full |
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