Faculty Bibliography

INTRODUCTION/BACKGROUND:In 2014, the Canadian Task Force on Preventive Health Care (CTFPHC) recommended against routine prostate cancer screening with the prostate-specific antigen (PSA) blood test. We surveyed Canadian primary care physicians (PCPs) to understand their opinions and attitudes towards prostate cancer screening in 2016. METHODS:Twenty PCPs piloted the survey to assess its accessibility. We distributed a flyer to 19 633 PCPs as an insert in a large mailed package inviting them to attend a national meeting, and later promoted the survey at the meeting. Multinomial logistic regression models examined factors associated with agreement of key guideline statements and the overall benefit of PSA screening. RESULTS:A total of 1254 PCPs responded (rate of 6.4%); 54.7% of physicians aware of the CTFPHC recommendations report screening less often as a result. Overall, 55.6% of PCPs feel that the risks of PSA screening outweigh the benefits. On multivariable analysis, physicians who did not read the guidelines, did not have an academic appointment, or were in practice for over 20 years were significantly more likely to disagree with the statement that men 55-69 years old should not be screened for prostate cancer with PSA. CONCLUSIONS:Our national survey found that the prostate cancer screening practices of Canadian PCPs varies widely across physician demographic groups, with almost equal numbers for or against. This has significant ethical, medical, and legal implications. The poor response rate to highly incentivized survey request may suggest a reluctance or general apathy towards this subject because of the Task Force recommendations. Future efforts should provide physicians with objective guidance around PSA screening, incorporating input from all stakeholders, including PCPs, urologists, and patients.

OBJECTIVES:To determine whether studies that used propensity score (PS) methods in the urology literature provide sufficient detail to allow scientific reproducibility and whether appropriate statistical tests were used to obtain valid measures of effect. MATERIALS AND METHODS:We searched OVID Medline and the Science Citation Index from inception to November 2016 to identify studies that used PS methods in five general urology journals. From each included article, we extracted pertinent information related to the PS methodology, such as estimation of the PS, whether balance diagnostics were performed, and the statistical analysis performed. RESULTS:We identified 114 articles for inclusion. Matching on the PS was the most common method used (62 studies, 54.4%). Of all studies, 103 (90.4%) described which covariates were used to estimate the PS; however, only 24 provided justification for the selected covariates. Although the majority of studies (70.2%) performed some sort of diagnostic evaluation to assess balance, few studies (24.6%) used appropriate methods for balance assessment. Only four (6.4%) studies that used PS matching provided sufficient detail to replicate the matching strategy. Finally, the majority (77.4%) of studies that used PS matching explicitly used inappropriate statistical methods to estimate the effect of an exposure on an outcome. CONCLUSIONS:In the urology literature PS methods were poorly described and implemented. We provide recommendations for improvement to allow scientific reproducibility and obtain valid measures of effect from their use.

Importance:Despite 3 decades of study, there remains ongoing debate regarding whether vasectomy is associated with prostate cancer. Objective:To determine if vasectomy is associated with prostate cancer. Data Sources:The MEDLINE, EMBASE, Web of Science, and Scopus databases were searched for studies indexed from database inception to March 21, 2017, without language restriction. Study Selection:Cohort, case-control, and cross-sectional studies reporting relative effect estimates for the association between vasectomy and prostate cancer were included. Data Extraction and Synthesis:Two investigators performed study selection independently. Data were pooled separately by study design type using random-effects models. The Newcastle-Ottawa Scale was used to assess risk of bias. Main Outcomes and Measures:The primary outcome was any diagnosis of prostate cancer. Secondary outcomes were high-grade, advanced, and fatal prostate cancer. Results:Fifty-three studies (16 cohort studies including 2 563 519 participants, 33 case-control studies including 44 536 participants, and 4 cross-sectional studies including 12 098 221 participants) were included. Of these, 7 cohort studies (44%), 26 case-control studies (79%), and all 4 cross-sectional studies were deemed to have a moderate to high risk of bias. Among studies deemed to have a low risk of bias, a weak association was found among cohort studies (7 studies; adjusted rate ratio, 1.05; 95% CI, 1.02-1.09; P < .001; I2 = 9%) and a similar but nonsignificant association was found among case-control studies (6 studies; adjusted odds ratio, 1.06; 95% CI, 0.88-1.29; P = .54; I2 = 37%). Effect estimates were further from the null when studies with a moderate to high risk of bias were included. Associations between vasectomy and high-grade prostate cancer (6 studies; adjusted rate ratio, 1.03; 95% CI, 0.89-1.21; P = .67; I2 = 55%), advanced prostate cancer (6 studies; adjusted rate ratio, 1.08; 95% CI, 0.98-1.20; P = .11; I2 = 18%), and fatal prostate cancer (5 studies; adjusted rate ratio, 1.02; 95% CI, 0.92-1.14; P = .68; I2 = 26%) were not significant (all cohort studies). Based on these data, a 0.6% (95% CI, 0.3%-1.2%) absolute increase in lifetime risk of prostate cancer associated with vasectomy and a population-attributable fraction of 0.5% (95% CI, 0.2%-0.9%) were calculated. Conclusions and Relevance:This review found no association between vasectomy and high-grade, advanced-stage, or fatal prostate cancer. There was a weak association between vasectomy and any prostate cancer that was closer to the null with increasingly robust study design. This association is unlikely to be causal and should not preclude the use of vasectomy as a long-term contraceptive option.

BACKGROUND:Exposure to commonly prescribed medications may be associated with cancer risk. However, there is limited data in kidney cancer. Furthermore, methods of classifying cumulative medication exposure in previous studies may be prone to bias. METHODS:We conducted a population-based case-control study of 10,377 incident kidney cancer cases aged ≥66 years matched with 35,939 controls on age, sex, history of hypertension, comorbidity score, and geographic location. Cumulative exposure to commonly prescribed medications hypothesised to modulate cancer risk was obtained using prescription claims data. We modelled exposure in four different fashions: (1) as continuous exposures using (a) fractional polynomials (which allow for a non-linear relationship between an exposure and outcome) or (b) assuming linear relationships; and 2) as dichotomous exposures denoting (a) ≥3 years versus <3 years exposure; or (b) "ever" versus "never" exposure. We used conditional logistic regression to estimate the association of medication exposure on incident kidney cancer. RESULTS:The directions of association were relatively consistent across analyses; however, the magnitudes were sensitive to the method of analysis. When utilising fractional polynomials, increasing cumulative exposure to acetylsalicylic acid, selective serotonin reuptake inhibitors, and proton-pump inhibitors was associated with significantly reduced risk of kidney cancer, while increasing exposure to antihypertensive drugs was associated with significantly increased risk. CONCLUSIONS:Our study provides impetus to further explore the effect of commonly prescribed medications on carcinogenesis to identify modifiable pharmacological interventions to reduce the risk of kidney cancer.

INTRODUCTION/BACKGROUND:Variables, such as smoking and obesity, are rarely available in administrative databases. We explored the added value of including these data in an administrative database study evaluating the association of statin use with survival in kidney cancer. METHODS:We linked administrative data with chart-abstracted data on smoking and obesity for 808 patients undergoing nephrectomy for kidney cancer. Base models consisted of variables from administrative databases (age, sex, year of surgery, and different measures of comorbidity [to compare their sensitivity to smoking and obesity data]); extended models added chart-abstracted data. We compared coefficients for statin use with overall (OS) and cancer-specific survival (CSS), and used the c-statistic and net reclassification improvement (NRI) to compare predications of five-year survival obtained from Cox proportional hazard models. RESULTS:The coefficient for statin use changed minimally following addition of abstracted data (<6% for OS, <2% for CSS). Base models performed similarly for OS, with c-statistics of 0.75 (95% confidence interval [CI] 0.72-0.79) for Charlson score and 0.73 (95% CI 0.69-0.78) for John Hopkins Aggregated Diagnosis Groups score. After including abstracted data, c-statistics modestly improved (change <0.02); CSS demonstrated similar findings. NRIs were 0.210 (95% CI 0.062-0.297) and 0.186 (-0.031-0.387) when using the Charlson score, and 0.207 (0.068-0.287) and 0.197 (0.007-0.399) when using the Aggregated Diagnosis Groups score, for OS and CSS, respectively. CONCLUSIONS:The inclusion of data on smoking and obesity marginally influences survival models in kidney cancer studies using administrative data.

BACKGROUND:Exposure to metformin, a medication used to treat diabetes, has been associated with improved survival outcomes in various malignancies. However, studies evaluating the association between metformin use and kidney cancer survival outcomes have demonstrated conflicting results. PATIENTS AND METHODS:We performed a retrospective review of diabetic patients undergoing nephrectomy for M0 renal cell carcinoma from 2000 to 2014 at a tertiary academic center. Medication use at the time of surgery was determined by medical record review. Inverse probability of treatment weights (IPTWs) were derived from a propensity score model that included various clinical, surgical, and pathologic characteristics. Cox proportional hazard models were used to evaluate the association between metformin use and disease-free, cancer-specific, and overall survival in the sample weighted by the IPTWs. RESULTS:A total of 158 diabetic patients were identified, 82 (52%) of whom were taking metformin at the time of surgery. Before the application of the propensity score methods, the metformin users were significantly older and were more likely to undergo surgery between 2009 and 2014. After applying the IPTWs, the clinical, surgical, and pathologic characteristics were well balanced between the 2 groups. The median follow-up period was 43 months. Metformin use was not significantly associated with disease-free (hazard ratio [HR], 0.99; 95% confidence interval [CI], 0.36-2.74), cause-specific (HR, 0.38; 95% CI, 0.08-1.86), or overall (HR, 0.86; 95% CI, 0.40-1.85) survival. CONCLUSION:We found no significant association between metformin use and kidney cancer outcomes. Population-based studies are needed to further evaluate the role of metformin in kidney cancer therapy.

There is conflicting evidence whether diabetes is associated with survival outcomes in patients undergoing a nephrectomy for renal cell carcinoma. We performed a retrospective review of 1034 patients undergoing nephrectomy for unilateral, M0, renal cell carcinoma between 2000 and 2016 at a tertiary academic center. Inverse probability of treatment weights were derived from a propensity score model based on various clinical, surgical, and pathological characteristics. We used Cox proportional hazard models to evaluate the association between diabetes and disease-free survival, cancer-specific survival, and overall survival in the sample weighted by the inverse probability of treatment weights. Furthermore, to evaluate whether severity of diabetes was associated with survival outcomes, we performed separate analyses where inverse probability of treatment weights were computed based on the probability of having diabetes that was controlled by medication. Of the 1034 patients, 180 (17 %) had diabetes. Of these, 139 (77 %) patients required medications for diabetes control while the remaining 41 (23 %) had diet controlled diabetes. Median follow-up was 50 months (IQR 17-86). Diabetes at the time of surgery was not significantly associated with disease-free survival (HR 1.11, 95 % CI 0.64 -1.91), cancer-specific survival (HR 0.96, 95 % CI 0.49-1.91), or overall survival (HR 1.28, 95 % CI 0.84-1.95). We found similar results when we compared diabetics controlled with medication vs. non-diabetics or diet controlled diabetics. In summary, we found no significant association between diabetes and survival outcomes in patients undergoing nephrectomy for M0 renal cell carcinoma. These results suggest that diabetics should be treated and followed in a similar manner to non-diabetics.

BACKGROUND:Patients on surveillance for clinical stage I (CSI) testicular cancer are counseled regarding their baseline risk of relapse. The conditional risk of relapse (cRR), which provides prognostic information on patients who have survived for a period of time without relapse, have not been determined for CSI testicular cancer. OBJECTIVE:To determine cRR in CSI testicular cancer. DESIGN, SETTING, AND PARTICIPANTS:We reviewed 1239 patients with CSI testicular cancer managed with surveillance at a tertiary academic centre between 1980 and 2014. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: cRR estimates were calculated using the Kaplan-Meier method. We stratified patients according to validated risk factors for relapse. We used linear regression to determine cRR trends over time. RESULTS AND LIMITATIONS:At orchiectomy, the risk of relapse within 5 yr was 42.4%, 17.3%, 20.3%, and 12.2% among patients with high-risk nonseminomatous germ cell tumor (NSGCT), low-risk NSGCT, seminoma with tumor size ≥3cm, and seminoma with tumor size <3cm, respectively. However, for patients without relapse within the first 2 yr of follow-up, the corresponding risk of relapse within the next 5 yr in the groups was 0.0%, 1.0% (95% confidence interval [CI] 0.3-1.7%), 5.6% (95% CI 3.1-8.2%), and 3.9% (95% CI 1.4-6.4%). Over time, cRR decreased (p≤0.021) in all models. Limitations include changes to surveillance protocols over time and few late relapses. CONCLUSIONS:After 2 yr, the risk of relapse on surveillance for CSI testicular cancer is very low. Consideration should be given to adapting surveillance protocols to individualized risk of relapse based on cRR as opposed to static protocols based on baseline factors. This strategy could reduce the intensity of follow-up for the majority of patients. PATIENT SUMMARY:Our study is the first to provide data on the future risk of relapse during surveillance for clinical stage I testicular cancer, given a patient has been without relapse for a specified period of time.