Meta-analysis of predictive models to assess the clinical validity and utility for patient-centered medical decision making: application to the CAncer of the Prostate Risk Assessment (CAPRA)
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The Cancer of the Prostate Risk Assessment (CAPRA) score was designed and validated several times to predict the biochemical recurrence-free survival after a radical prostatectomy. Our objectives were, first, to study the clinical validity of the CAPRA score, and, second, to assess its clinical utility for stratified medicine from an original patient-centered approach.
We proposed a meta-analysis based on a literature search using MEDLINE. Observed and predicted biochemical-recurrence-free survivals were compared to assess the calibration of the CAPRA score. Discriminative capacities were evaluated by estimating the summary time-dependent ROC curve. The clinical utility of the CAPRA score was evaluated according to the following stratified decisions: active monitoring for low-risk patients, prostatectomy for intermediate-risk patients, or radio-hormonal therapy for high risk patients. For this purpose, we assessed CAPRA’s clinical utility in terms of its ability to maximize time-dependent utility functions (i.e. Quality-Adjusted Life-Years – QALYs).
We identified 683 manuscripts and finally retained 9 studies. We reported good discriminative capacities with an area under the SROCt curve at 0.73 [95%CI from 0.67 to 0.79], while graphical calibration seemed acceptable. Nevertheless, we also described that the CAPRA score was unable to discriminate between the three medical alternatives, i.e. it did not allow an increase in the number of life years in perfect health (QALYs) of patients with prostate cancer.
We confirmed the prognostic capacities of the CAPRA score. In contrast, we were not able to demonstrate its clinical usefulness for stratified medicine from a patient-centered perspective. Our results also highlighted the confusion between clinical validity and utility. This distinction should be better considered in order to develop predictive tools useful in practice.
KeywordsMeta-analysis Prostate cancer Patient-centered outcomes Stratified medicine
Area under the SROCt Curve
CAncer of the Prostate Risk Assessment
Time-dependent Summary ROC
Prostate cancer (PC) is the most common cancer in men, accounting for approximately 10% of all male cancer deaths in economically developed countries . In the long term, many patients experience biochemical recurrence (BCR), defined by an increase in prostate-specific antigen (PSA) levels , which can also lead to cancer-specific mortality . Many developments have been made in predictive scoring systems [4, 5, 6, 7] in order to avoid both over-treatment of patients at low-risk of cancer recurrence or death related to the cancer, and the under-treatment of patients at high-risk.
Among these scores, the Cancer of the Prostate Risk Assessment (CAPRA) score  was designed to predict the pre-operative risk of BCR-free survival after a radical prostatectomy (RP). RP represents the most widely performed curative therapy for patients with localized PC . The CAPRA score is calculated from five variables measured before prostatectomy: the PSA level, the Gleason score, the clinical T stage, the percentage of positive prostate biopsies and the patient age at diagnosis. Meurs et al.  performed a substantial meta-analysis of seven studies and concluded that the CAPRA rule accurately predicts BCR-free survival at 3 years.
Nevertheless, despite its good predictive capacities, the clinical usefulness of the CAPRA score may be questioned. Firstly, the results of the meta-analysis of Meurs et al.  mainly consisted in risk ratios between expected and observed numbers of events among three CAPRA-based strata (0–2 for low risk, 3–5 for intermediate risk, and 6–10 for high risk). The aim was to evaluate the calibration compared to the initial study of Cooperberg et al. . But the discriminative capacities were not evaluated. Secondly, the CAPRA thresholds used to define the three strata were arbitrarily defined, while the purpose of such a scoring system is to adapt the medical management of patients regarding the strata they belongs to. In two available online applications related to the CAPRA computation [11, 12], the following recommendations are proposed: routine surveillance for low risk patients, localized treatment (surgery or radiation alone, brachytherapy with or without external-beam therapy) for intermediate-risk patients, hormonal therapy or multimodal therapy (surgery with radiation, or radiation therapy with hormonal therapy) for high-risk patients. However, to the best of our knowledge, no study has assessed the clinical usefulness of the CAPRA score for stratified medical decision making.
We hypothesized that the clinical usefulness depends on its ability to bring benefits for patients by differentiating the following alternative therapies: the combined radio-hormonal therapy (RHT) for patients at high-risk of BCR, the RP for medium-risk patients and the active monitoring (AM) for low-risk patients. In this paper, from an update of the meta-analysis proposed by Meurs et al. , our objectives were i) to study the clinical validity of the CAPRA score, i.e. its prognostic capacities both in terms of discrimination and calibration, and ii) to evaluate its clinical utility, i.e. its usefulness for stratified medical decision making from an original patient-centered methodology that considers its corresponding benefits and drawbacks .
We conducted a literature search using the MEDLINE citation database to identify eligible studies by combining the following MeSH terms and keywords: “CAPRA score”, “biochemical recurrence” and “prostate cancer”. Since our analysis was an update of the meta-analysis of Meurs et al.  published in March 2012, we restricted our search after this date. No language restriction was performed. Cross-referencing was applied to complete the identification of other studies.
PICOS table related to the selection of the papers
Patients with Prostate Cancer with available CAPRA score
Having a radical prostatectomy
With or without control group
Prospective or retrospective study presenting hazard ratio and/or survival curves
From the eligible studies, two independent investigators (H.M and Y.F) collected the data independently. For each study, the following characteristics were collected: first author, year of publication, country, period of recruitment, duration of follow-up, study setting and design, sample size, age, reported outcomes, association between CAPRA and BCR-free survival (HRs and 95%CI), number of patients in each CAPRA-based strata, presence of survival curves and the corresponding number of at-risk patients, and the Harrell’s concordance index (C-index). Any disagreement was resolved by consensus. All statistical analyses were performed using the 3.1.1 version of the R software . The survival probabilities were extracted from a digitalized picture by using the R packages ReadImages and digitize . Many papers do not provide the number of at-risk patients over time, so we applied the method proposed by Parmar et al.  in order to obtain estimations.
Assessing clinical validity
The association between the CAPRA score and BCR-free survival was evaluated by estimating pooled HRs which compared the high-risk category (CAPRA ≥6) and the intermediate-risk category (2 < CAPRA < 6) to the low-risk category of the CAPRA score (CAPRA≤2). When it was available, we used the category from 0 to 2 as the low-risk category, or otherwise we used the category 0 to 1. Collected HRs were pooled by applying a random-effects model and the DerSimonian-Laird estimator  (R package metaphor). Sensitivity analysis was conducted by eliminating a single study one-at-a-time. Publication bias was evaluated statistically by the Kendall’s tau and the Egger’s linear regression test of the intercept . Heterogeneity between studies was assessed by Cochrane Q and I2 statistics  (R package meta). We also investigated potential heterogeneity by comparing pooled HRs stratified given possible heterogeneity factors: effective size, inclusion period and the geographic origin of the study. The outcome definition appeared homogeneous between studies, so there was no reason to test it as a heterogeneity factor.
Pooled survival curves per CAPRA-based strata were estimated by the distribution-free approach with random effects proposed by Combescure et al. . The calibration was assessed graphically by comparing the pooled survival curves for each CAPRA strata to those estimated in the original paper by Cooperberg . The discrimination capacities at 5-years post-RP were evaluated using the time-dependent summary ROC (SROCt) curves .
Assessing clinical utility
We endorse a patient-centered approach to medical decision making by defining the clinical utility of a decision in terms of the expected Quality-Adjusted Life-Years (QALYs) it brings. QALYs combine into a single index, information about the length of life and health-related quality of life. They are computed by weighting each period of life (usually a year) by a utility score such that a score of zero indicates death and a score of one stands for perfect health . The utility scores represent the individuals’ preferences over health states such that a higher score indicates a more preferred health state. Although QALYs were primarily designed for the conduct of economic evaluation, their composite nature may also be useful to inform clinical decision making .
The survival probabilities were estimated from articles including patients with RP. For a stratified decision, we considered alternative therapies (AM and RHT) that will impact the survival probabilities. We studied several plausible scenarios in terms of decreasing length of life: a RMST loss of 10% at 5 years for AM versus RP  and in-between 20 to 40% for RP versus RHT [27, 28, 29].
Utility scores determination
Following Koerber et al. , we assumed that the baseline utility for PC patients can be approximated by an age-adjusted utility score of the general population. This baseline utility is then combined with the utilities of various health conditions (symptoms of prostate cancer and treatments) using a multiplicative model. Although there is no consensus about the appropriate method that should be used, there is recent evidence in favor of the multiplicative model for combining scores for health states compared to the alternative model [31, 32].
The event predicted by the CAPRA score is the first failure between disease progression (biochemical recurrence or distant metastasis) and death. For each treatment group (RP, AM, RHT), we calculated the expected utility before this event as the product of age-adjusted baseline utility and the utilities of symptoms associated with the treatment group weighted by their repartition. We calculated the corresponding utility after the event as the product of utility before the event and the utilities of disease progression or death weighted by their repartition. Supplementary information on the precise method of utility calculation is provided in the Additional file 1.
Sources and estimation of raw utility scoresa
Baseline utility value for age 60
Ara and Brazier 
Stewart et al. 
Sexual dysfunction and urinary incontinence
Impotence and bowel dysfunction
Impotence, urinary incontinence, and bowel dysfunction
Local disease progression
Description of the selected studies
Description of the 9 studies included in the meta-analysis
Country, Period, Follow-up
Effective size, Ethnicity, Age
Definition of BCR
Cooperberg et al., 2005 
USA, 1992–2001, median = 24 m
1439, 88% Caucasian 8.4% African-American mean = 62y (no SD)
2 consecutive PSA ≥ 0.2 ng/mL or secondary treatment for elevated postoperative PSA
Cooperberg et al., 2006 
USA, 1988–2004, mean = 42 m
1309, 59% Caucasian, mean = 61.9y (±6.6)
1 PSA > 0.2 ng/mL or 2 consecutive PSA ≥ 0.2 ng/mL or secondary treatment for elevated postoperative PSA
May et al., 2007 
Germany, 1992–2005, mean = 56 m
1296, 100% Caucasian, mean = 63.7y (±5.5)
1 PSA > 0.2 ng/mL, 2 PSA ≥ 0.2 ng/mL or secondary treatment for elevated postoperative PSA
Zhao et al., 2008 
USA, 1984–2006, median = 4y
6737, 91% Caucasian, mean = 58y (no SD)
1 PSA level > 0.2 ng/mL
Loeb et al., 2012 
USA, 2003–2009, median = 34 m
726, 93.5% Caucasian, mean = 59.3y (no SD)
Repeated PSA ≥ 0.2 ng/mL, secondary treatment for elevated postoperative PSA
Ishizaki et al., 2011 
Japan, 1999–2010, mean = 38 m
211, 100% Japanese, mean = 62.2y (±5.8)
2 PSA ≥ 0.2 ng/mL, secondary treatment for elevated postoperative PSA
Budaus et al., 2012 
Germany, 1992–2009, mean = 56 m
2937, unknown, median = 64y (48–74)
1 PSA level ≥ 0.2 ng/mL
Yoshida et al., 2012 
Japan, 1995–2008, median = 44 m
503, 100% Japanese, median = 65y (47–76)
1 PSA ≥ 0.2 ng/mL followed by a 2nd PSA higher, radiotherapy or hormonal therapy for the postoperative PSA elevation
Seo et al., 2014 
Korea, 2008–2013, median = 13 m
115, 100% Korean, mean = 66.4y (±6.5)
2 PSA ≥ 0.2 ng/mL, additional treatment more than 6 months after RP
Association between the CAPRA score and BCR-free survival
Investigation of the heterogeneity of the HRs according to CAPRA-based strata
Number of studies per subgroup
Pooled HR (High versus Low) per subgroup
Pooled HR (Intermediate versus Low) per subgroup
> 1000: 5
≤ 1000: 4
Inclusion start ≥1999: 3
Inclusion start < 1999: 6
Clinical validity of the CAPRA score up to five years post-prostatectomy
The area under the SROCt curve (AUC) at 5 years post-prostatectomy was 0.73 [95%CI from 0.67 to 0.79]. The AUCs for each study ranged from 0.67 [95%CI from 0.61 to 0.73] to 0.76 [95%CI from 0.71 to 0.81], indicating acceptable discriminative capacities of the CAPRA score.
Clinical utility of the CAPRA score for deciding AM instead of RP
Only patients with a CAPRA score strictly lower than 6 were included since AM is not proposed to high-risk patients (i.e. CAPRA≥6). The maximum number of QALYs was obtained when RP is proposed to all patients. In other words, for all the CAPRA thresholds in-between 1 and 5, the expected gain in terms of QoL related to AM was insufficient to balance the associated decrease in the BCR-free survival. More precisely, the mean number of QALYs, i.e. the expected equivalent of years lived in perfect health, was 42.4 months if all patients are treated by RP. In contrast, when patients with a CAPRA score lower than 2 (the usual threshold separating low and intermediate-risk strata) are treated by AM and the other ones by RP, the mean number of QALYs was 41.7 months. Therefore, for a cohort of 100 patients, this stratification was associated with an expected loss of 70 months in perfect health compared to treating all patients by RP.
Clinical utility of the CAPRA score for deciding RHT instead of RP
Only patients with a CAPRA score strictly higher than 2 were included since treatment by RHT is not proposed to low-risk patients (i.e. CAPRA≤2). The maximum number of QALYs was obtained by proposing RHT to all patients: 41.6 months in perfect health state. Regardless of the CAPRA threshold, all the stratified medical decision making resulted in lower numbers of QALYs. For instance, by using the usual CAPRA-based stratification (CAPRA< 6 for intermediate risk strata versus CAPRA≥6 for high risk strata), we estimated that the mean number of life-years in perfect health was 39.6 months. Therefore, for a cohort of 100 patients, the use of CAPRA threshold at 6 for stratifying the medical decision between RP and RHT would have been associated with a loss of 200 months in perfect health state compared to treating all patients by RHT.
Prognostic tools are essential in PC to adapt the medical management regarding the risk-level of patients [49, 50, 51]. In this context, our meta-analysis aimed to precisely describe the usefulness of the CAPRA score for stratified medicine.
Our results were obtained by analyzing 9 studies. In agreement with the literature and by considering the three usual CAPRA-based strata, we validated the association between the CAPRA score and the BCR-free survival (HR intermediate versus low = 3.29 [95%CI from 2.18 to 4.96] and HR high versus low = 10.65 [95%CI from 6.63 to 17.10]). We additionally described the CAPRA score as a marker with a robust capacity to discriminate BCR or dead patients at 5-year post-RP from alive patients without BCR at this time (AUC = 0.73 [95%CI from 0.67 to 0.79]). In terms of calibration, we also reported acceptable properties up to 5 years post-RP. However, despite this validity, we were unable to demonstrate the clinical utility of the CAPRA score in regards to the following stratified medical decision making: AM for low-risk patients, RP for intermediate-risk patients and RHT for high-risk patients.
Our study highlights the important confusion between clinical validity and utility. Indeed, even if the relevance of stratified medical decision making partially depends on the prognostic capacities of a biomarker or a scoring system, other dimensions are also important to consider, in particular the consequences of both quantity and quality of life, and on trade-offs between them. Several recent studies discussed the importance of considering these two dimensions [23, 52, 53]. We also believe that better understanding of these dimensions in studies related to predictive biomarkers or scoring systems for stratified medicine can improve the transfer of such tools into clinical practice.
From a methodological point of view, the large majority of papers in stratified medicine have focused on the evaluation of prognostic capacities, mainly by estimating area under ROC curves. In contrast, our results illustrate the necessity for a paradigm shift towards the evaluation of clinical usefulness, by estimating time-dependent utility function for instance .
Alternative decision-making approaches have been proposed that incorporate the consequences of clinical choices when assessing the usefulness of diagnostic tests or of prediction models. For instance, the widely used Decision Curve Analysis (DCA) computes the net benefit of a medical decision as the difference between its benefits and harms . In DCA, the benefits and harms are obtained by asking the physicians for the probability threshold, i.e. the rate at which the patients are willing to trade-off between the harms and the benefits of a medical decision. A decision curve then plots the net benefit of a decision against the net benefits of “treating all” or “treating none” for the various possible values of the probability threshold. Although DCA may be easier to implement compared to our QALY-based approach, the two approaches may not lead to similar conclusions as they differ importantly. DCA relies on the physicians’ prediction about the value of outcome , whereas patients’ utility scores come from those experiencing (of having experienced) the health conditions under consideration. There is no guarantee that the physicians’ judgments accurately reflect the views of the patients. Several studies found differences between the preferences of the patients and physicians for various conditions , especially in prostate cancer . Although our QALY-based approach may be more in line with patients’ preferences than DCA, the assessment of utility scores raises practical difficulties and published utility values are quite heterogeneous. Nevertheless, we believe that addressing these difficulties in future research may offer a more promising way to develop patient-centered approaches of medical decision making rather than relying solely on physicians’ judgements.
It appears important to underline that the concept of clinical utility is multidimensional. It cannot be summarized into a single measure, whether it is QALYs or DCA. As explained by Smart , one can judge the concept of utility by using four components: appropriate, accessible, practicable, and acceptable. QALYs or DCA mainly concern the first one.
Our study nevertheless has some limitations. First, we identified a significant heterogeneity between the studies retained in our meta-analysis. We explored the possible causes of this heterogeneity, but we were not able to identify any significant reason. Second, the estimations of the SROCt curve  and the time-dependent utility function  both depend on many parametric assumptions. As with any meta-analysis on aggregated data, the corresponding limitation being listed by Lyman and Kuderer , it would have been preferable to collect the individual data necessary for such an analysis. To limit this issue, we have also non-parametrically adapted the two estimators after having reconstructed individual data (data not shown). The corresponding results were similar to the ones described in the main text. Third, numerous hypotheses are required to define the potential consequences of each alternative treatment (AM, RP and RHT) in terms of both quantity and quality of life. We attempted to be as realistic as possible in defining these hypotheses on data recently published in this field. Fourth, we did not study RP (for medium-risk patients) and RP + RHT (for high-risk patients). The reason is that it would require information about the removed tumor that is not included in the CAPRA score. Cooperberg et al. proposed the CAPRA-S score , as an extension of the CAPRA score that consider information about the tumor (surgical margin, seminal vesicle invasion, extracapsular extension, lymph node invasion). However, an analysis based on the CAPRA-S would be beyond the scope of our paper. Fifth, we separately investigated the discriminative capacities and the calibration of the CAPRA score. It would have been relevant to also estimate the global prognostic performance of the score, for instance by estimating the Index of Prognostic Accuracy, as recently proposed by Kattan and Gerds . Nevertheless, the corresponding pooled estimation from a published study does not exist to our knowledge and constitutes interesting perspective for future methodological developments.
Our study validated the prognostic capacities of the CAPRA score. Nevertheless, based on a QALYs maximization approach, our results challenged its clinical usefulness to beneficially stratify patients between three common therapeutic strategies: RHT for high-risk, RP for intermediate-risk, and AM for low-risk patients. Our results highlight the confusion between prognostic capacities and clinical usefulness. It calls for a better consideration of this distinction and for a paradigm shift from clinical validity to clinical utility in studies related to stratified medicine.
This work was supported by the Cancer National Institute (INCa, Project MAP-MARKER, Decision N°2013–137). The INCa had no role in designing or editing this study.
Availability of data and materials
The datasets extracted from survival published survival curves and used during the current study are available from the corresponding author on reasonable request or online at the following address: https://www.labcom-risca.com/capra
ML participated in the statistical analyses and writing the manuscript. HM participated in data extraction and statistical analyses. PT participated in the utility collection and writing the paper. SS provided clinical expertise and participated in writing the paper. ED participated writing the paper and analyzing the data. YF supervised this work, participated writing the paper and analyzing the data. All authors read and approved the final manuscript.
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Consent for publication
The authors declare that they have no competing interests.
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