Background High‐fat dairy, particularly whole milk, in healthy men may increase risk of aggressive prostate cancer. However, data are limited regarding dairy after prostate cancer diagnosis. Method We conducted a prospective study among 1334 men with non‐metastatic prostate cancer in the Cancer of the Prostate Strategic Urologic Research Endeavor. Men answered a food frequency questionnaire in 2004‐2005 (median 2 years after diagnosis) and were followed until 2016 for recurrence, defined as: prostate cancer death, bone metastases, biochemical recurrence, or secondary treatment. Multivariate Cox proportional hazards regression was used to calculate hazards ratios (HR) and 95% confidence intervals (CI) for associations between whole and low‐fat milk; total, high‐fat, and low‐fat dairy; and other dairy items and risk of recurrence. Results During a median follow‐up of 8 years, we observed 137 events. Men who consumed >4 servings/week versus 0‐3 servings/month of whole milk had an 73% increased risk of recurrence (HR: 1.73; 95%CI: 1.00, 2.98; P‐value = 0.04). Body mass index (BMI) modified the association (P‐interaction = 0.01). Among men with a BMI ≥27 kg/m2, >4 servings/week versus 0‐3 servings/month of whole milk was associated with a 3‐fold higher risk of recurrence (HR: 2.96; 95%CI: 1.58, 5.54; P‐value < 0.001). No association was seen in men with BMI <27 kg/m2. Low‐fat milk and other dairy foods were not associated with recurrence. Conclusion In conclusion, whole milk consumption after prostate cancer diagnosis was associated with increased risk of recurrence, particularly among very overweight or obese men. Men with prostate cancer who choose to drink milk should select non‐fat or low‐fat options.

168 Background: Recent research suggests a positive relationship between intake of high-fat dairy, particularly whole milk, and prostate cancer (PC) mortality. However, data are limited in men after PC diagnosis. Methods: We conducted a prospective cohort study among 1336 men with non-metastatic PC in CaPSURE. The men answered a food frequency questionnaire (FFQ) in 2004-2005 (median time from diagnosis to the FFQ: 2 y) and were followed for PC progression until April 2016. PC progression was defined as: prostate cancer death, bone metastasis from PC, biochemical recurrence, or secondary treatment. Multivariate Cox Proportional Hazards regression was used to calculate hazards ratios (HR) and 95% confidence intervals (CI) for associations between total, whole fat, and low-fat milk; total, high-fat, and low-fat dairy; and specific dairy items and PC progression. We adjusted for time from diagnosis to FFQ, calories, age at diagnosis, CAPRA score, smoking, BMI, walking pace, and primary PC treatment. Results: 314 events were observed (mean follow-up: 7.2 y). Whole milk was associated with an increased risk of PC progression when adjusting for age, calories, and time since diagnosis (HR ≥1 vs. <1 serving/wk: 1.37; 95% CI: 1.03, 1.84; p-value: 0.03). This association was slightly attenuated, and not statistically significant, when adjusting for clinical and other lifestyle factors (HR: 1.27; 95% CI: 0.91, 1.77; p-value: 0.15). High-fat dairy intake also appeared associated with an increased risk of PC progression, but the association was not statistically significant (adjusted HR ≥4 vs. <1 servings/day: 1.40; 95% CI: 0.92, 2.13; p-trend: 0.18). Post-diagnostic intakes of low-fat milk and other dairy foods were not associated with PC progression. Conclusions: Post-diagnostic intake of milk and other dairy foods was not associated with PC progression. Research in populations with greater intake of whole milk is warranted to further investigate whether post-diagnostic whole milk intake increases risk of PC progression. Funding: This work was funded by the DOD Prostate Cancer Research Program (W81XWH-13-2-0074) and the NIH (K07CA197077).

A total of 2486 clinic appointments were compiled for four providers in the adult urology clinic over six months. Of the total, 408 were actual missed clinic visits at an overall no-show rate of 16.4%. The calculated number of patients missing their appointments was 488. Of the predicted 488 missed visits, the calculated number of patients was over by 130 with an average of 1.19 patients over per day, and under by 50 with an average of 0.46 patients under per day. The number of perfect days where the predicted number matched the actual number was 26/109 (23.9%), within +/-1 patients 61/109 (56.0%), and within +/-2 patients 87/109 (79.8%). Conversely, the model over predicted 4 or greater patient no-shows on 6/109 (5.5%) of days. Overpredicted patients per day ranged from 0.01-6.5 with a mean of 1.58.CONCLUSIONS: This review further characterizes the predictable patient characteristics associated with missed clinic visits for an under-served academic urology patient population. This model works well over a large number of patients with a 79.8% efficacy within 2 patients. Applying this to a clinical setting would be limited by overestimating the number of patients that would be scheduled. The model still will require validation when put to test on data from different practice settings and larger patient data sets. Additionally, we predict there may be confounding factors (type of insurance, distance to appointment, previous missed appointments) that we plan to study in order to add to the accuracy of the model.

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