Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Objective: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Study Design and Setting: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group greater or equal 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. Results: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history

Diabetes and Prostate Cancer Outcomes in Men with Nonmetastatic Castration-Resistant Prostate Cancer: Results from the SEARCH Cohort.

BACKGROUND: The prognosis of diabetic men with advanced prostate cancer is poorly understood and understudied. Hence, we studied associations between diabetes and progression to metastases, prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM) in men with nonmetastatic castration-resistant prostate cancer (nmCRPC). METHODS: Data from men diagnosed with nmCRPC between 2000 and 2017 at 8 Veterans Affairs Health Care Centers were analyzed using Cox regression to determine HRs and 95% confidence intervals (CI) for associations between diabetes and outcomes. Men with diabetes were classified according to: (i) ICD-9/10 codes only, (ii) two HbA1c values > 6.4% (missing ICD-9/10 codes), and (iii) all diabetic men [(i) and (ii) combined]. RESULTS: Of 976 men (median age: 76 years), 304 (31%) had diabetes at nmCRPC diagnosis, of whom 51% had ICD-9/10 codes. During a median follow-up of 6.5 years, 613 men were diagnosed with metastases, and 482 PCSM and 741 ACM events occurred. In multivariable-adjusted models, ICD-9/10 code-identified diabetes was inversely associated with PCSM (HR, 0.67; 95% CI, 0.48-0.92) while diabetes identified by high HbA1c values (no ICD-9/10 codes) was associated with an increase in ACM (HR, 1.41; 95% CI, 1.16-1.72). Duration of diabetes, prior to CRPC diagnosis was inversely associated with PCSM among men identified by ICD-9/10 codes and/or HbA1c values (HR, 0.93; 95% CI, 0.88-0.98). CONCLUSIONS: In men with late-stage prostate cancer, ICD-9/10 code-identified diabetes is associated with better overall survival than undiagnosed diabetes identified by high HbA1c values only. IMPACT: Our data suggest that better diabetes detection and management may improve survival in late-stage prostate cancer

Do Hispanic Puerto Rican men have worse outcomes after radical prostatectomy? Results from SEARCH

Abstract Background We previously reported that outcomes after radical prostatectomy (RP) were similar among non‐Hispanic Black, non‐Hispanic White, and Hispanic White Veterans Affairs (VA) patients. However, prostate cancer (PC) mortality in Puerto Rican Hispanics (PRH) may be higher than in other Hispanic groups. Data focused on PRH patients is sparse; thus, we tested the association between PR ethnicity and outcomes after RP. Methods Analysis included men in SEARCH cohort who underwent RP (1988–2020, n = 8311). PRH patients (n = 642) were treated at the PR VA, and outcomes were compared to patients treated in the Continental US regardless of race. Logistic regression was used to test the associations between PRH and PC aggressiveness, adjusting for demographic and clinicopathological features. Multivariable Cox models were used to investigate PRH versus Continental differences in biochemical recurrence (BCR), metastases, castration‐resistant PC (CRPC), and PC‐specific mortality (PCSM). Results Compared to Continental patients, PRH patients had lower adjusted odds of pathological grade group ≥2 (p < 0.001), lymph node metastasis (p < 0.001), and positive margins (p < 0.001). In contrast, PRH patients had higher odds of extracapsular extension (p < 0.001). In Cox models, PRH patients had a higher risk for BCR (HR = 1.27, p < 0.001), metastases (HR = 1.49, p = 0.014), CRPC (HR = 1.80, p = 0.001), and PCSM (HR = 1.74, p = 0.011). Further adjustment for extracapsular extension and other pathological variables strengthened these findings. Conclusions In an equal access setting, PRH RP patients generally had better pathological features, but despite this, they had significantly worse post‐treatment outcomes than men from the Continental US, regardless of race. The reasons for the poorer prognosis among PRH men require further research

Defining the Impact of Family History on Detection of High-grade Prostate Cancer in a Large Multi-institutional Cohort

BACKGROUND The risk of high-grade prostate cancer, given a family history of cancer, has been described in the general population, but not among men selected for prostate biopsy in an international cohort. OBJECTIVE To estimate the risk of high-grade prostate cancer on biopsy based on a family history of cancer. DESIGN, SETTING, AND PARTICIPANTS This is a multicenter study of men undergoing prostate biopsy from 2006 to 2019, including 12 sites in North America and Europe. All sites recorded first-degree prostate cancer family histories; four included more detailed data on the number of affected relatives, second-degree relatives with prostate cancer, and breast cancer family history. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS Multivariable logistic regressions evaluated odds of high-grade (Gleason grade group ≥2) prostate cancer. Separate models were fit for family history definitions, including first- and second-degree prostate cancer and breast cancer family histories. RESULTS AND LIMITATIONS A first-degree prostate cancer family history was available for 15 799 men, with a more detailed family history for 4617 (median age 65 yr, both cohorts). Adjusted odds of high-grade prostate cancer were 1.77 times greater (95% confidence interval [CI] 1.57-2.00, p < 0.001, risk ratio [RR] = 1.40) with first-degree prostate cancer, 1.38 (95% CI 1.07-1.77, p = 0.011, RR = 1.22) for second-degree prostate cancer, and 1.30 (95% CI 1.01-1.67, p = 0.040, RR = 1.18) for first-degree breast cancer family histories. Interaction terms revealed that the effect of a family history did not differ based on prostate-specific antigen but differed based on age. This study is limited by missing data on race and prior negative biopsy. CONCLUSIONS Men with indications for biopsy and a family history of prostate or breast cancer can be counseled that they have a moderately increased risk of high-grade prostate cancer, independent of other risk factors. PATIENT SUMMARY In a large international series of men selected for prostate biopsy, finding a high-grade prostate cancer was more likely in men with a family history of prostate or breast cancer

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.MethodsTen North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.ResultsAmong 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.ConclusionDevelopers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

BACKGROUND We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors