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

Epidemiology of trauma in patients with mental disorders.

Objectives: We aimed to explore the influence of mental disorders on the risk of developing complications and in-hospital mortality after trauma. Methods: We conducted an institutional review board-approved cohort study of 23 500 adult patients admitted to the Puerto Rico Trauma Hospital from 2002 to 2019. Participants were divided into 2 groups according to the presence or absence of psychiatric illnesses. Logistic regressions were employed to investigate the effect of mental illness on study outcomes. Results: Psychiatric illness was associated with higher risk of complications; this risk increased with age. The pattern was accentuated for those with substance use disorders (SUD) and attenuated for those with non-substance-related diagnoses (NSRD). Psychiatric patients with Glasgow Coma Scale (GCS) scores of 15 had a 42% higher risk of dying, while the opposite was seen for those with scores <15. SUD was associated with a 51% higher risk of death in patients with GCS scores of 15, while NSRD was linked to a 49% lower odds of death among subjects with scores <15. Conclusions: Our results suggest that trauma patients with SUD are at increased risk of developing complications and those with SUD and GCS scores of 15 are at increased risk of death. Mental health screening is an essential component of the management of trauma patients. Stratifying based on mental health disorders may be helpful during the clinical management of trauma patients, as those with SUD may benefit from more aggressive management. Level of evidence: Level 4, prognostic and epidemiological study. Study type: Original retrospective cohort study

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 &gt; 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

Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

BACKGROUND Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. METHODS We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. RESULTS High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). CONCLUSIONS We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools

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

A contemporary prostate biopsy risk calculator based on multiple heterogeneous cohorts

BACKGROUND: Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems. OBJECTIVE: We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool. DESIGN, SETTING, AND PARTICIPANTS: We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts. RESULTS AND LIMITATIONS: Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies. CONCLUSIONS: The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome. PATIENT SUMMARY: A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making