Association between varicocele and hypogonadism, or erectile dysfunction: A systematic review and meta-analysis.

OBJECTIVE: To assess the association between varicocele and hypogonadism, or erectile dysfunction. METHODS: We searched MEDLINE, EMBASE, LILACS, CENTRAL, and other sources. We included cohort, case-control, and cross-sectional studies. The primary outcome was the association between varicocele and hypogonadism, or erectile dysfunction, and the secondary outcome included semen analysis. We assessed the risk of bias with the Newcastle-Ottawa Scale. We performed statistical analysis in Review Manager 5.3 and reported information about the Odds Ratio (OR) with a 95% confidence interval. We produced a forest plot for the primary outcome. RESULTS: We included ten studies in qualitative analysis and six studies in quantitative analysis. Most of the cross-sectional studies showed a low risk of bias, not so for the two case-control studies, which represented a high risk of bias. Most of the reports described a correlation between having varicocele and presenting low testosterone levels: the meta-analysis showed that there is a significant association between varicocele and hypogonadism (OR 3.27 95% CI 1.23 to 8.68). Regarding varicocele and erectile, only one study showed a significant difference in erectile function in comparison to varicocele patients and men without varicocele. CONCLUSION: There is an association between varicocele presence and hypogonadism, although more studies are needed. Besides, not much is reported about an association between varicocele and erectile dysfunction, but impairment can occur through hormone disturbances.

Recent metformin adherence and the risk of hypoglycaemia in the year following intensification with a sulfonylurea.

AIM: To evaluate whether recent low adherence to metformin monotherapy is associated with hypoglycaemia after addition of a sulfonylurea. METHODS: We assembled a retrospective cohort of veterans who filled a new prescription for metformin between 2001 and 2011 and intensified treatment with a sulfonylurea after ≥1 year of metformin use. We calculated metformin adherence from pharmacy data using the proportion of days covered in the 180-day period before intensification. The primary outcome was hypoglycaemia, defined as a hospitalization or emergency department visit for hypoglycaemia or an outpatient blood glucose measurement <3.3 mmol/l in the year following intensification. Cox proportional hazards models were used to compare the risk of hypoglycaemia between participants with low (<80%) and high (≥80%) adherence. Adherence was also modelled as a continuous variable using restricted cubic splines. RESULTS: Of 187 267 participants who initiated metformin monotherapy, 49 424 added a sulfonylurea after ≥1 year. The median (interquartile range) rate of treatment adherence was 87 (50-100)% and 43% had adherence <80%. Hypoglycaemia rates per 1000 person-years were 23.1 (95% CI 21.1-25.4) and 24.5 (95% CI 22.7-26.4) in participants with low and high adherence, respectively (adjusted hazard ratio 0.95, 95% CI 0.84-1.08). The risk of hypoglycaemia was similar across all levels of adherence when adherence was modelled as a continuous variable. CONCLUSIONS: We found no evidence that past low adherence to metformin monotherapy was associated with hypoglycaemia after intensification with a sulfonylurea.

Perioperative mortality and associated factors in patients undergoing radical nephrectomy.

OBJECTIVE: To determine the incidence and related factors of perioperative mortality associated with radical nephrectomy in patients with renal tumours in a tertiary hospital. MATERIAL AND METHODS: We conducted a cross-sectional study that reviewed the medical records of patients undergoing radical nephrectomy between January 1, 2007 and December 31, 2011 in a tertiary university hospital (Cali, Colombia). We measured sociodemographic variables and factors that may be associated with perioperative mortality. The statistical analysis was performed using STATA. RESULTS: We analysed 57 patients who underwent radical nephrectomy, 54.4% of whom were male, whose ages ranged from 14 to 81 years. All tumours had a unilateral presentation; 96.5% of the tumours were solid renal lesions, and 3.5% were cystic lesions. The most frequent histological findings were clear cell (63.2%), papillary (8.7%) and chromophobe cell (5.2%) renal carcinoma. There were no complications in 27 (47.3%) of the patients. According to the Clavien-Dindo classification of surgical complications, 16 (28%) patients had minor (grades i and ii) complications and 9 (15.6%) had major (grades iii and iv) complications, with an overall perioperative mortality (grade v) of 8.7% (5 patients). CONCLUSIONS: The perioperative mortality at 30 days for patients with nonmetastatic renal carcinoma who underwent radical nephrectomy at a tertiary university hospital in Cali, Colombia, was 4.1% (2 patients).

A new Bayesian method to forecast and fine tune individual hemodialysis dose.

BACKGROUND: The most commonly used formulas for hemodialysis dose are based on single-pool urea kinetics; i.e., they consider the body as a single compartment and use an ad hoc adjustment for postdialysis urea rebound. We present a new urea kinetic modeling approach, individualized Bayesian urea kinetic modeling (IBKM), which incorporates prior knowledge. This method uses measurements made during previous treatments to forecast a patient's postdialysis urea rebound and clearance and provides a choice of possible dialysis parameters to achieve a desired clearance goal. METHODS: We used data from 18 patients (a total of 38 hemodialysis sessions) to build the model. All patients had been on thrice-weekly hemodialysis for at least 1 year before enrollment, and their dialysis prescription remained unchanged during the study period. Recorded variables included blood urea nitrogen (BUN) measurements and dialysis prescription parameters (dialyzer size, KoA, treatment time, blood and dialysis flow). The population distribution of urea kinetic parameters-derived from the 18 patients' data-and individual urea kinetic data (i.e., pre- and postdialysis BUN) are used in the IBKM method to make individual predictions. RESULTS: Estimates (mean+/-SE) of population urea kinetic parameters are generation rate 0.17+/-0.01 mmol/min, clearance between extracellular and intracellular compartments 646+/-60 mL/min, and total volume of distribution 31.5+/-1.5 L, of which the extracellular volume is 36+/-4%. The effective dialysis clearance is estimated to be 9.0+/-1.7%, less than the expected dialyzer clearance. IBKM predictions of postdialysis equilibrated BUN concentrations are accurate: a root mean squared error of 3.4% of the "postrebound" BUN concentration at 30 min, a value in the range of urea measurement error itself. CONCLUSIONS: IBKM can estimate not only the urea kinetics of an actual hemodialysis, but it can also predict a patient's target hemodialysis dose for any desired, flexible hemodialysis treatment. The method should prove useful for bedside monitoring, forecasting, and fine tuning of hemodialysis dose.