Radical prostatectomy accounted for half of primary treatment selection among all patients enrolled in CaPSURE between 1990 and 2008.19
Population-based data estimate that approximately one-third of prostate cancer patients in the United States undergo the procedure.20
A subset of these will experience biochemical recurrence, of whom a fraction will progress to clinical recurrence and/or metastases and face disease-specific mortality. Postoperative PSA kinetics may help identify which patients are at greatest risk,21
but require multiple PSA assessments, potentially delaying interventions such as radiation or androgen ablation which have greater benefit with earlier administration for selected patients.22–24
Conversely, many patients with limited adverse pathologic features will not progress following surgery and could be spared the additional morbidity of further treatment.25
A recent review identified eight previously published instruments for prediction of biochemical outcomes following prostatectomy.11
A set of lookup tables, not included in this review has also been published.26
Of these, the only externally validated models based on standard clinical and pathological variables with published accuracy estimates are a prediction formula by Bauer et al.,27
the postoperative nomogram originally developed by Kattan et al28
and the updated version published by Stephenson et al..12
The latter, while based on a recurrence definition using a PSA threshold of 0.4 ng/ml rather than 0.2 ng/ml, incorporates similar variables as the CAPRA-S: preoperative PSA, pGS, SM status, ECE, SVI, LNI, and adds year of surgery. This instrument had a c-index of 0.86 for the development set, and 0.81 and 0.77–0.78 for validation studies in the same institution and another academic institution, respectively, and tended to overestimate somewhat the likelihood of progression-free probability for patients at the higher end of the risk spectrum.12
The bootstrap-corrected c-index for the CAPRA-S score in this study is 0.77, which indicates good discriminatory accuracy. Moreover, the scoring system requires no paper tables or software, and with practice can be determined rapidly from memory. An individual patient’s likelihood of recurrence 3 and 5 years after surgery can be estimated from the figures given in . However, the absolute risks will vary across cohorts and surgical series, for which reason the CAPRA-S score in meant to be used primarily as a measure of relative risk. Additional validation studies will be required to determine how consistently the absolute risk predictions are calibrated across different clinical contexts.
Of note, two previous papers have performed direct head-to-head comparisons of the pre-treatment CAPRA score to popular nomograms: a U.S. study compared the CAPRA score to the original Kattan preoperative nomogram 3
, and a European study compared it to the updated preoperative nomogram published by Stephenson et al..4
In both analyses the accuracy of the CAPRA score was similar to that of the nomograms, while the European study found that the CAPRA score performed better both in terms of calibration and in decision curve analysis.4
Likewise, in the present analysis, the CAPRA-S score and postoperative nomogram have similar discrimination as calculated by the c-index, but the CAPRA-S score performs better in calibration and decision curve analyses. This finding may reflect the application of a nomogram derived from a high-volume surgeon’s experience to broader community practice. We previously observed this phenomenon in analyzing the performance of the Kattan preoperative nomogram in CaPSURE29
; in that study the preoperative nomogram was also somewhat over-optimistic when applied to the community-based data, though the miscalibration was not as great as we observe with the postoperative nomogram in this current analysis.
The CAPRA-S scores, while still concentrated among the lower scores, are more broadly distributed than the original pre-treatment CAPRA scores. The CAPRA-S score thus should be quite useful in practice for helping patients understand their risk of recurrence and the possible utility of adjuvant therapy. The score should also be applicable as a composite measure of disease risk in the research setting, both for consistent identification of eligible patients, and for risk-based subgroup classification of trial results. The CaPSURE data are multi-institutional and largely community-based, so should be robust in terms of external applicability.
Several caveats should be noted. First, completeness of pathologic data is variable in CaPSURE, reflecting the nature of the registry, with multiple clinicians contributing data, including pathology reports written to varying standards by an unknown number of pathologists. However, the sensitivity analysis is reassuring that the model is robust, and bootstrap correction of the confidence intervals on the parameter estimates supports the credibility of the results. We expected LNI to be more strongly predictive of recurrence; its relatively minor contribution to the CAPRA-S score likely reflects the very low prevalence of LNI in the dataset. This finding is typical of U.S. surgical series in which a relatively limited lymphadenectomy is usually performed; in series including higher risk patients, in which extended template lymph node dissection is employed, the prevalence of LNI is substantially higher.30–32
Many patients did not have a lymphadenectomy performed, so excluding those with unknown LNI status would be problematic. Of note, in the postoperative nomogram, LNI also contributes relatively few points—comparable to SM but less important than ECE or SVI.12
There is a degree of overlap between adjacent scores, particularly CAPRA-S scores 6 and 7. However, while the incremental increase in risk with increasing CAPRA-S score is not entirely smooth, the analysis of the score as a continuous variable and the pairwise comparisons presented confirm that in general each two point increase in CAPRA-S score indicates at least a doubling of risk of recurrence. The a priori establishment of thresholds for dividing the CAPRA-S scores among low-risk (0–2), intermediate-risk (3–5), and high-risk (6–10) should facilitate use of the score as a risk stratification tool in the clinical research setting. Like other U.S. surgical cohorts, CaPSURE includes mostly men at relatively low risk of progression, so the interpretation of the CAPRA-S score at higher risk levels may be less reliable.
Our definition of recurrence included the one favored by the American Urological Association Prostate Guidelines for Localized Prostate Cancer.15
However, biochemical recurrence does not necessarily correlate with ultimate mortality from prostate cancer.33
This analysis does indicate that the CAPRA-S score predicts prostate cancer-specific mortality. However, important future directions will include both external validation and assessment of the CAPRA-S score’s performance in predicting cause-specific and overall mortality with larger numbers of patients ultimately reaching these endpoint—and additional head-to-head comparisons of CAPRA-S against other postoperative risk models in terms of accuracy, discrimination, and calibration. These studies are planned in the near future, and will include cohorts with greater representation of men with relatively high-risk disease.
Incorporating pathological information, the CAPRA-S score in predicting disease recurrence after prostatectomy, yet remains straightforward to calculate. No nomogram or scoring system can replace individualized clinician-patient decision-making, which must consider life expectancy, utilities for quality of life outcomes, and treatment preferences. However, we believe that given the accuracy and ease of use of the CAPRA-S score, this instrument will prove a useful tool both to inform decision-making after prostatectomy and to classify patients for future studies of adjuvant therapy.