Assisted reproduction

This issue of eMedRef provides information to clinicians on the pathophysiology, diagnosis, and therapeutics of assisted reproduction

What is the Impact of the Underserved Pathway program on Entering an Underserved Family Medicine Residency? A Comparison of Three Approaches for Estimating the Average Treatment Effect

Thesis (Master's)--University of Washington, 2016-06Introduction: It is well known that more primary care physicians are needed in underserved areas of the U.S. While prior research has shown that medical student experiences in underserved settings helps to increase the likelihood that they will ultimately choose to become physicians serving underserved areas, none have controlled for selection bias. In addition, none have examined intermediate steps in medical training experiences, such as how specific experiences relate to residency choices (the pipeline to eventual clinical practice). One manner of strengthening causal claims includes controlling for covariates that are correlated with treatment (program) and outcome variables using regression approaches, but this can have dimensionality or multicollinearity problems. Another approach to estimating less biased treatment effects involves propensity score (PS) methods, which are reduce the problems in the regression approach by employing a single score that captures the correlations between treatment status and all covariates. The primary aim of the present study was to use a set of methods to estimate whether the University of Washington (UW) School of Medicine’s longitudinal extracurricular experience, Underserved Pathway, impacts graduates’ choice in completing their residency in an underserved area. A secondary aim was to evaluate which of the analytic methods (and results) is most defensible. Methods: Extant data from one cohort of N = 158 UW medical students from matriculation surveys conducted between 2004 and 2011 were used for this project. Three popular approaches to estimating the Underserved Pathway program’s effect on underserved setting residency choice were employed, including multiple logistic regression, PS 1:n Matching with replacement, and inverse weighted probability (IWP) regression. Results: Average treatment (program) effects from the three approaches ranged from 12.0% of Underserved Pathway graduates choosing an underserved area for their residency (using the IPW method), 17.2% (using logistic regression), to 23.4% (using PS matching); the latter two were statistically significantly greater than zero by a Wald test. Tests of the covariate balance (i.e., the extent to which the ignorability assumption held) showed that PS matching offered better covariate balance than IPW for metrical covariates but that no other differences between methodologies on covariate balance were found. Discussion: Completion of the Underserved Pathway resulted in a significant (17.2% - 23.4%) increase in program graduates matching to a residency in an underserved setting according to the logistic regression and PS matching approaches; these methods are preferred given that neither differed in covariate balance from each other, and further, PS matching was superior in balance across groups over IPW on one covariate. Additionally, given that the logistic regression approach does not delete any cases, it seems likely that the logistic regression approach is the method that is most defensible in reducing selection bias in this situation. Selection of a method should address covariate balance with simplicity of approach with robust and transparent reporting to allow for assessment of any causal claims

Multimorbidity Profile of Urologic Patients in a Large, Integrated Health Care Delivery System

Background: Two-thirds of urologic surgeries are performed in patients over 65 years old. As the American population ages, urologists are faced with increasingly complex older adults with multimorbidity. Our objective was to describe the multimorbidity profile of urologic patients in a large, integrated health care delivery system. We hypothesized that urologic patients are older and have more chronic conditions than the general primary care population. Methods: We identified all Geisinger Health System (GHS) primary care patients from 2001 to 2015 and the subset that had at least one outpatient encounter in the urology department. The Agency for Healthcare Research and Quality’s Clinical Classifications Software tool was applied to identify prevalent conditions based on diagnosis codes attached to outpatient visits, laboratory and pharmacy orders, and procedures. Results: We identified 390,271 GHS primary care patients; 33,085 had at least one urology outpatient visit (8.5%). Compared to the general GHS population, urology patients tended to be older (mean age: 48 years vs 61 years). Urology patients had a mean of 7 chronic conditions. The 5 most common conditions were hypertension, hyperlipidemia, prostate disorders, gastroesophageal reflux disease and other. The poster will include comparisons of urology population condition profiles to the GHS primary care population. We also will include chronic condition profiles by urologic condition. Conclusion: Urologic patients were older compared to the general GHS primary care population and had significant numbers of chronic conditions. Multimorbidity profiles in the urologic population may be used to inform future efforts toward surgical prognostication and decision-making

Patient-reported quality of life recovery curves after robotic prostatectomy are similar across body mass index categories

Purpose: To assess the impact of body mass index (BMI) on postoperative recovery curve of urinary and sexual function after robotic-assisted laparoscopic prostatectomy (RALP). We hypothesized that overweight and obese men have different recovery curves than normal weight men. Materials and Methods: We reviewed preoperative and postoperative surveys from 691 men who underwent RALP from 2004– 2014 in an integrated healthcare delivery system. Survey instruments included: sexual health inventory for men (SHIM), urinary behavior, leakage, and incontinence impact questionnaire (IIQ). A repeated measures analysis with autoregressive covariance structure was employed with linear splines with 2 knots for the time factor. We fit unadjusted and adjusted models and stratified by BMI (under/normal weight, overweight, and obese). Adjusted models included age, race/ethnicity, smoking status, diabetes, operation length, prostate-specific antigen, pathologic stage, nerve-sparing status, and surgery year. Results: Mean age was 59 years. Most men were overweight (43%) and obese (42%). There were no significant differences in mean baseline SHIM, urinary behavior, leakage, and IIQ scores by BMI category. All groups had initial steep declines in urinary and sexual function in the first 3 months after RALP. There were no significant differences in postoperative urinary and sexual function score curves by BMI category. Conclusions: The pattern of urinary and sexual function recovery was similar across all BMI categories. Overweight and obese men may be counseled that urinary and sexual function recovery curves after surgery is similar to that of normal weight men

Patient-reported quality of life recovery curves after robotic prostatectomy are similar across body mass index categories

Purpose: To assess the impact of body mass index (BMI) on postoperative recovery curve of urinary and sexual function after robotic-assisted laparoscopic prostatectomy (RALP). We hypothesized that overweight and obese men have different recovery curves than normal weight men. Materials and Methods: We reviewed preoperative and postoperative surveys from 691 men who underwent RALP from 2004– 2014 in an integrated healthcare delivery system. Survey instruments included: sexual health inventory for men (SHIM), urinary behavior, leakage, and incontinence impact questionnaire (IIQ). A repeated measures analysis with autoregressive covariance structure was employed with linear splines with 2 knots for the time factor. We fit unadjusted and adjusted models and stratified by BMI (under/normal weight, overweight, and obese). Adjusted models included age, race/ethnicity, smoking status, diabetes, operation length, prostate-specific antigen, pathologic stage, nerve-sparing status, and surgery year. Results: Mean age was 59 years. Most men were overweight (43%) and obese (42%). There were no significant differences in mean baseline SHIM, urinary behavior, leakage, and IIQ scores by BMI category. All groups had initial steep declines in urinary and sexual function in the first 3 months after RALP. There were no significant differences in postoperative urinary and sexual function score curves by BMI category. Conclusions: The pattern of urinary and sexual function recovery was similar across all BMI categories. Overweight and obese men may be counseled that urinary and sexual function recovery curves after surgery is similar to that of normal weight men

Burden of Multiple Chronic Conditions among Patients with Urological Cancer

Purpose We describe age, multiple chronic condition profiles and health system contact in patients with urological cancer. Materials and Methods Using Geisinger Health System electronic health records we identified adult primary care patients and a subset with at least 1 urology encounter between 2001 and 2015. The Agency for Health Care Research and Quality Chronic Condition Indicator and Clinical Classifications Software tools were applied to ICD-9 codes to identify chronic conditions. Multiple chronic conditions were defined as 2 or more chronic conditions. Patients with urological cancer were identified using ICD-9 codes for prostate, bladder, kidney, testis and penile cancer. Inpatient and outpatient visits in the year prior to the most recent encounter were counted to document health system contact. Results We identified 357,100 primary care and 33,079 urology patients, of whom 4,023 had urological cancer. Patients with urological cancer were older than primary care patients (71 vs 46 years) and they had more median chronic conditions (7 vs 4). Kidney and bladder cancer were the most common chronic conditions (median 8 patients each). Coronary artery disease and chronic kidney disease were common in urological cancer cases compared to mental health conditions in primary care cases. Patients with urological cancer who had multiple chronic conditions had the most health system contact, including 32% with at least 1 hospitalization and 68% with more than 5 outpatient visits during 1 year. Conclusions Urology patients are older and more medically complex, especially those with urological cancer than primary care patients. These data may inform care redesign to reduce the treatment burden and improve care coordination in urological cancer cases

Risk Factors for Postoperative Fever and Systemic Inflammatory Response Syndrome after Ureteroscopy for Stone Disease

Introduction: Infectious complications after ureteroscopy (URS) for stone disease lead to emergency department visits, hospitalizations, and other costly health care utilization. The objective of our study was to identify risk factors for postoperative fever (POF) and systemic inflammatory response syndrome (SIRS) after URS for stone disease. Materials and Methods: We performed a retrospective cohort study on 2746 patients who underwent 3298 URS for stone disease at Geisinger from 2008 to 2016. A univariate analysis tested the associations between candidate demographic, preoperative, and intraoperative predictors and the primary outcome of POF (temperature \u3e100.4°F) or SIRS. Variables with a p-value of \u3c0.05 on univariate comparisons were entered into a random-effects logistic regression model. The final model used backward elimination random-effects logistic regression to identify predictors most predictive of POF/SIRS. Results: Overall, 229 (6.9%) of 3298 URS had POF/SIRS. On univariate analysis, individuals with POF/SIRS were older, had higher mean body mass index, higher Charlson Comorbidity Index (CCI), bilateral and larger stones, stone location in the kidney, positive preoperative urine culture, pre-stented, and longer surgical times. In the final model, female gender (adjusted odds ratio [OR] 1.6, 95% confidence interval [CI] 1.19-2.15), surgical time (adjusted OR 1.01, 95% CI 1.0-1.01), CCI ≥2 (adjusted OR 1.86, 95% CI 1.29-2.67), and positive preoperative urine culture (adjusted OR 1.53, 95% CI 1.06-2.22) were the most significant predictors of POF/SIRS. Conclusions: Female gender, longer surgical time, medical complexity, and positive preoperative urine culture are associated with POF/SIRS after URS. These data may be used to identify and counsel high-risk individuals