Prediction models for prostate cancer to be used in the primary care setting: a systematic review

Abstract

ObjectiveTo identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.DesignSystematic review.Data sourcesMEDLINE and Embase databases combined from inception and up to the end of January 2019.EligibilityStudies 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 synthesisRelevant 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.ResultsAn 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.ConclusionOnly 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.

Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

“…Most CPMs in our study were rated high ROB, in line with previous systematic reviews using PROBAST. [16][17][18][19][20] This is inherent to the structure of PROBAST: one incorrectly performed item in one domain determines an overall judgment of high ROB. While the high percentage of models with high ROB might be interpreted as a reflection of the low overall quality of the literature, it might also reflect limitations of the tool.…”

Definition of Outcome-Based Prostate-Specific Antigen (PSA) Thresholds for Advanced Prostate Cancer Risk Prediction

“…The main challenge remains to use PSA results to identify patients for prostate biopsy referral who most likely will benefit from an intervention defined by a shared decision made between the patients and the care provider [ 4 , 6 ]. A wide spectrum of pre-biopsy risk nomograms has been proposed as an aid in decision with the aim to maximize the diagnostic specificity, but the overall poor predictive capability resulting from the validation phases has conditioned the recommendation of their use [ 7 , 8 , 9 ]. On the other hand, several methodological issues have been identified within studies developing pre-biopsy risk models, such as low numbers of biopsy core that were examined, presence of verification bias in studies including men from screening programs, lack of calibration of predictive models, and/or use of different PSA assays as an additional cause of miscalibration and source of intra-study heterogeneity [ 1 , 9 , 10 , 11 , 12 ].…”

“…A wide spectrum of pre-biopsy risk nomograms has been proposed as an aid in decision with the aim to maximize the diagnostic specificity, but the overall poor predictive capability resulting from the validation phases has conditioned the recommendation of their use [ 7 , 8 , 9 ]. On the other hand, several methodological issues have been identified within studies developing pre-biopsy risk models, such as low numbers of biopsy core that were examined, presence of verification bias in studies including men from screening programs, lack of calibration of predictive models, and/or use of different PSA assays as an additional cause of miscalibration and source of intra-study heterogeneity [ 1 , 9 , 10 , 11 , 12 ]. Some experts have asked how much accurate the estimate of the risk predicted at the individual level is, underlining the burden of unnecessary biopsies and the rate of missed aggressive cancer [ 13 ].…”

“…The clinical goals for the use of PSA test in decision making should be better defined. Studies in literature which have evaluated the performance of the risk models, recommended values for diagnostic sensitivity ranging between 90% and 95%, while the ratio of one cancer detected for every three performed biopsies [corresponding to a positive predictive value (PPV) of 33.3%] was considered acceptable [ 9 , 14 ]. On the other hand, the estimate and the reliability of predictive values are strongly dependent on the rate of PCa incidence in the referral population [ 15 , 16 ], so that epidemiological data should be kept in mind to ultimately transfer affordable risk thresholds into the clinical practice.…”

Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

“…Discrimination and calibration are used to measure the performance of a prediction model. Several reviews have summarized the risk prediction models for lung cancer [13], colorectal cancer [14], and prostate cancer [15]. However, the quality for liver cancer prediction models remains unknown.…”