Faculty Bibliography

Background/UNASSIGNED:Radical cystectomy for bladder cancer has one of the highest rates of morbidity among urologic surgery, but the ability to predict postoperative complications remains poor. Our study objective was to create machine learning models to predict complications and factors leading to extended length of hospital stay and discharge to a higher level of care after radical cystectomy. Methods/UNASSIGNED:Using the American College of Surgeons National Surgical Quality Improvement Program, peri-operative adverse outcome variables for patients undergoing elective radical cystectomy for bladder cancer from 2005 to 2016 were extracted. Variables assessed include occurrence of minor, infectious, serious, or any adverse events, extended length of hospital stay, and discharge to higher-level care. To develop predictive models of radical cystectomy complications, we fit generalized additive model (GAM), least absolute shrinkage and selection operator (LASSO) logistic, neural network, and random forest models to training data using various candidate predictor variables. Each model was evaluated on the test data using receiver operating characteristic curves. Results/UNASSIGNED:A total of 7557 patients were identified who met the inclusion criteria, and 2221 complications occurred. LASSO logistic models demonstrated the highest area under curve for predicting any complications (0.63), discharge to a higher level of care (0.75), extended length of stay (0.68), and infectious (0.62) adverse events. This was comparable with random forest in predicting minor (0.60) and serious (0.63) adverse events. Conclusions/UNASSIGNED:Our models perform modestly in predicting radical cystectomy complications, highlighting both the complex cystectomy process and the limitations of large healthcare datasets. Identifying the most important variable leading to each type of adverse event may allow for further strategies to model cystectomy complications and target optimization of modifiable variables pre-operative to reduce postoperative adverse events.

PURPOSE/OBJECTIVE:While MRI-Ultrasound Fusion-targeted biopsy (MRF-TB) allows for improved detection of clinically significant prostate cancer (csPCa), concerning numbers of clinically significant disease are still missed. We hypothesize that a number of these are due to the learning curve associated with MRF-TB. We report results of repeat MRF-TB in men with continued suspicion for cancer and the institutional learning curve in detection of csPCa over time. MATERIALS AND METHODS/METHODS:Analysis of 1813 prostate biopsies in a prospectively acquired cohort of men presenting for prostate biopsy over a 4-year period. All men were offered pre-biopsy MRI and assigned a maximum Prostate Imaging - Reporting and Data System version 2 (PI-RADS) score. Biopsy outcomes of men with suspicious region of interest (ROI) were compared. The relationship between time and csPCa detection was analyzed. RESULTS:csPCa detection rate increased 26% over time in men with PI-RADS 4 and 5 (4/5) ROI. On repeat MRF-TB in men with continued suspicion for cancer, 53% of men with PI-RADS 4/5 ROI demonstrated clinically significant discordance from initial MRF-TB, compared to only 23% of men with PI-RADS 1/2 ROI. Significantly less csPCa were missed or under-graded in the most recent biopsies as compared to the earliest biopsies. CONCLUSION/CONCLUSIONS:High upgrade rates on repeat MRF-TB and increasing cancer detection rate over time demonstrate the significant learning curve associated with MRF-TB. Men with low risk or negative biopsies with persistent concerning ROI should be promptly re-biopsied. Improved targeting accuracy with operator experience can help decrease the number of missed csPCa.

BACKGROUND:The number of prostate biopsy cores that need to be taken from each magnetic resonance imaging (MRI) region of interest (ROI) to optimize sampling while minimizing overdetection has not yet been clearly elucidated. OBJECTIVE:To characterize the incremental value of additional MRI-ultrasound (US) fusion targeted biopsy cores in defining the optimal number when planning biopsy and to predict men who might benefit from more than two targeted cores. DESIGN, SETTING, AND PARTICIPANTS/METHODS:This was a retrospective cohort study of MRI-US fusion targeted biopsies between 2015 and 2017. INTERVENTION/METHODS:MRI-US fusion targeted biopsy in which four biopsy cores were directed to each MRI-targeted ROI. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS/UNASSIGNED:The MRI-targeted cores representing the first highest Gleason core (FHGC) and first clinically significant cancer core (FCSC; GS≥3+4) were evaluated. We analyzed the frequency of FHGC and FCSC among cores 1-4 and created a logistic regression model to predict FHGC >2. The number of unnecessary cores avoided and the number of malignancies missed for each Gleason grade were calculated via clinical utility analysis. The level of agreement between biopsy and prostatectomy Gleason scores was evaluated using Cohen's κ. RESULTS AND LIMITATIONS/CONCLUSIONS:A total of 479 patients underwent fusion targeted biopsy with four individual cores, with 615 ROIs biopsied. Among those, FHGC was core 1 in 477 (76.8%), core 2 in 69 (11.6%), core 3 in 48 (7.6%), and core 4 in 24 men (4.0%) with any cancer. Among men with clinically significant cancer, FCSC was core 1 in 191 (77.8%), core 2 in 26 (11.1%), core 3 in 17 (6.2%), and core 4 in 11 samples (4.9%). In comparison to men with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 5, patients were significantly less likely to have FHGS >2 if they had PI-RADS 4 (odds ratio [OR] 0.287; p=0.006), PI-RADS 3 (OR 0.284; p=0.006), or PI-RADS 2 (OR 0.343; p=0.015). Study limitations include a single-institution experience and the retrospective nature. CONCLUSIONS:Cores 1-2 represented FHGC 88.4% and FCSC 88.9% of the time. A PI-RADS score of 5 independently predicted FHGC >2. Although the majority of cancers in our study were appropriately characterized in the first two biopsy cores, there remains a proportion of men who would benefit from additional cores. PATIENT SUMMARY/UNASSIGNED:In men who undergo magnetic resonance imaging-ultrasound fusion targeted biopsy, the first two biopsy cores diagnose the majority of clinically significant cancers. However, there remains a proportion of men who would benefit from additional cores.

OBJECTIVE:To define the rates of common Hospital Acquired Conditions (HACs) in patients undergoing major urological surgery over a period of time encompassing the implementation of the Hospital Acquired Condition Reduction program, and to evaluate whether implementation of the HAC reimbursement penalties in 2008 was associated with a change in the rate of HACs. METHODS:Using American College of Surgeons National Surgical Quality Improvement Program (NSQIP) data, we determined rates of HACs in patients undergoing major inpatient urological surgery from 2005 to 2012. Rates were stratified by procedure type and approach (open vs. laparoscopic/robotic). Multivariable logistic regression was used to determine the association between year of surgery and HACs. RESULTS:We identified 39,257 patients undergoing major urological surgery, of whom 2300 (5.9%) had at least one hospital acquired condition. Urinary tract infection (UTI, 2.6%) was the most common, followed by surgical site infection (SSI, 2.5%) and venous thrombotic events (VTE, 0.7%). Multivariable logistic regression analysis demonstrated that open surgical approach, diabetes, congestive heart failure, chronic obstructive pulmonary disease, weight loss, and ASA class were among the variables associated with higher likelihood of HAC. We observed a non-significant secular trend of decreasing rates of HAC from 7.4% to 5.8% HACs during the study period, which encompassed the implementation of the Hospital Acquired Condition Reduction Program. CONCLUSIONS:HACs occurred at a rate of 5.9% after major urological surgery, and are significantly affected by procedure type and patient health status. The rate of HAC appeared unaffected by national reduction program in this cohort. Better understanding of the factors associated with HACs is critical in developing effective reduction programs.

INTRODUCTION/BACKGROUND:Nutritional status has been increasingly recognized as an important predictor of prognosis and surgical outcomes for cancer patients. We evaluated the effect of preoperative malnutrition on the development of surgical complications and mortality after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). MATERIALS AND METHODS/METHODS:Using data from the American College of Surgeons National Surgical Quality Improvement Program, we evaluated the association of poor nutritional status with 30-day postoperative complications and overall mortality after RNU from 2005 to 2015. The preoperative variables suggestive of poor nutritional status included hypoalbuminemia (< 3.5 g/dL), weight loss within 6 months before surgery (> 10%), and a low body mass index. RESULTS:A total of 1200 patients were identified who had undergone RNU for UTUC. The overall complication rate was 20.5% (n = 246), and mortality rate was 1.75% (n = 21). On univariate analysis, patients who experienced a postoperative complication were more likely to have hypoalbuminemia (25.0% vs. 11.4%; P < .001) and weight loss (3.7% vs. 1.0%; P = .003). After controlling for baseline characteristics and comorbidities, hypoalbuminemia was found to be a significant independent predictor of postoperative complications (odds ratio, 2.09; 95% confidence interval, 1.29-3.38; P = .003). Hypoalbuminemia was also a significant independent predictor of mortality (odds ratio, 4.31; 95% confidence interval, 1.45-12.79; P = .008) on multivariable regression analysis. CONCLUSION/CONCLUSIONS:Our results have shown that hypoalbuminemia is a significant predictor of surgical complications and mortality after RNU for UTUC. This finding supports the importance of patients' preoperative nutritional status in this population and suggests that effective nutritional interventions in the preoperative setting could improve patient outcomes.

BACKGROUND:The American Society of Anesthesiologists physical status classification system, modified Charlson Comorbidity Index (mCCI), and modified Frailty Index have been associated with complications after urologic surgery. No study has compared the predictive performance of these indexes for postoperative complications after radical cystectomy (RC) for bladder cancer. MATERIALS AND METHODS/METHODS:Data from 1516 patients undergoing elective RC for bladder cancer were extracted from the 2005 to 2011 American College of Surgeons National Surgical Quality Improvement Program for a retrospective review. The perioperative outcome variables assessed were occurrence of minor adverse events, severe adverse events, infectious adverse events, any adverse event, extended length of hospital stay, discharge to a higher level of care, and mortality. Patient comorbidity indexes and demographic data were assessed for their discriminative ability in predicting perioperative adverse outcomes using an area under the curve (AUC) analysis from the receiver operating characteristic curves. RESULTS:The most predictive comorbidity index for any adverse event was the mCCI (AUC, 0.511). The demographic factors were the body mass index (BMI; AUC, 0.519) and sex (AUC, 0.519). However, the overall performance for all predictive indexes was poor for any adverse event (AUC < 0.52). Combining the most predictive demographic factor (BMI) and comorbidity index (mCCI) resulted in incremental improvements in discriminative ability compared with that for the individual outcome variables. CONCLUSION/CONCLUSIONS:For RC, easily obtained patient mCCI, BMI, and sex have overall similar discriminative abilities for perioperative adverse outcomes compared with the tabulated indexes, which are more difficult to implement in clinical practice. However, both the demographic factors and the comorbidity indexes had poor discriminative ability for adverse events.

BACKGROUND:Diffusion-weighted imaging (DWI) provides insight into the pathophysiology underlying renal dysfunction. Variants of DWI include intravoxel incoherent motion (IVIM), which differentiates between microstructural diffusion and vascular or tubular flow, and diffusion tensor imaging (DTI), which quantifies diffusion directionality. PURPOSE/OBJECTIVE:To investigate the reproducibility of joint IVIM-DTI and compare controls to presurgical renal mass patients. STUDY TYPE/METHODS:Prospective cross-sectional. SUBJECTS/METHODS:Thirteen healthy controls and ten presurgical renal mass patients were scanned. Ten controls were scanned twice to investigate reproducibility. FIELD STRENGTH/SEQUENCE/UNASSIGNED:Subjects were scanned on a 3T system using 10 b-values and 20 diffusion directions for IVIM-DTI in a study approved by the local Institutional Review Board. ASSESSMENT/RESULTS:Retrospective coregistration and measurement of joint IVIM-DTI parameters were performed. STATISTICAL ANALYSIS/METHODS:Parameter reproducibility was defined as intraclass correlation coefficient (ICC) >0.7 and coefficient of variation (CV) <30%. Patient data were stratified by lesion side (contralateral/ipsilateral) for comparison with controls. Corticomedullary differentiation was evaluated. RESULTS:In controls, the reproducible subset of REnal Flow and Microstructure AnisotroPy (REFMAP) parameters had average ICC = 0.82 and CV = 7.5%. In renal mass patients, medullary fractional anisotropy (FA) was significantly lower than in controls (0.227 ± 0.072 vs. 0.291 ± 0.044, P = 0.016 for the kidney contralateral to the mass and 0.228 ± 0.070 vs. 0.291 ± 0.044, P = 0.018 for the kidney ipsilateral). In the kidney ipsilateral to the mass, cortical Dp,radial was significantly higher than in controls (P = 0.012). Conversely, medullary Dp,axial was significantly lower in contralateral than ipsilateral kidneys (P = 0.027) and normal controls (P = 0.044). DATA CONCLUSION/UNASSIGNED:REFMAP-MRI parameters provide unique information regarding renal dysfunction. In presurgical renal mass patients, directional flow changes were noted that were not identified with IVIM analysis alone. Both contralateral and ipsilateral kidneys in patients show reductions in structural diffusivities and anisotropy, while flow metrics showed opposing changes in contralateral vs. ipsilateral kidneys. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

BACKGROUND:The accumulation of data through a prospective, multi-center coordinated registry network (CRN) is a practical way to gather real world evidence on the performance of novel prostate ablation technologies. Urologic oncologists, targeted biopsy experts, industry representatives, and the Food and Drug Administration (FDA) convened to discuss the role, feasibility, and important data elements of a CRN to assess new and existing prostate ablation technologies. METHODS:A multi-round Delphi consensus approach was performed which included the opinion of 15 expert urologists, representatives of the FDA and leadership from HIFU device manufacturers. Stakeholders provided input in three consecutive rounds, with conference calls following each round to obtain consensus on remaining items. Participants agreed that these elements initially developed for HIFU are compatible with other prostate ablation technologies. CRN elements were reviewed and supplemented with data elements from FDA's common study metrics. RESULTS:The working group reached consensus on capturing specific patient demographics, treatment details, oncologic outcomes, functional outcomes, and complications. Validated health-related quality of life questionnaires were selected to capture patient-reported outcomes, which include IIEF-5, IPSS, EPIC-26, and MSHQ-EjD. Group consensus was to obtain a follow up multi-parametric MRI and prostate biopsy at approximately 12 months after ablation, with additional imaging or biopsy performed as clinically indicated. CONCLUSIONS:A national prostate ablation CRN brings forth vital practice pattern and outcomes data for this emerging treatment paradigm in the United States. Our multi-stakeholder consensus identifies critical elements to evaluate new and existing energy modalities and devices.