Postoperative hydronephrosis following ureteral reimplantation: Clinical significance and importance of surgical technique and experience.

BACKGROUND: Though common, postoperative hydronephrosis (POHN) following ureteroneocystostomy raises concern for an underlying obstruction. We aimed to determine the clinical significance of POHN following open (OUR) or robotic (RALUR) ureteral reimplantation. METHODS: We retrospectively reviewed pediatric patients who underwent ureteral reimplantation for vesicoureteral reflux (VUR) from 2008 to 2019 by a single surgeon. Baseline characteristics, operative outcomes, and trends in POHN were assessed. POHN was defined as new onset hydronephrosis or exacerbation of pre existing hydronephrosis. Renal ultrasounds were performed 1, 4, and 12 months postoperatively. Voiding cystourethrograms were performed 4 months postoperatively. Surgical experience for RALUR cases was defined as number of ureters operated over time. RESULTS: Altogether, 93 patients (127 ureters) underwent RALUR and 19 patients (26 ureters) underwent OUR. POHN was found in 27.6% and 30.8% of ureters after RALUR and OUR, respectively. Rate and time to POHN resolution for RALUR (91.4%, 112 days) and OUR (75%, 211 days) were statistically similar. Odds of POHN after RALUR were directly related with preoperative VUR grade (Range OR: 2.82[2.26-3.52]) and surgical experience (Range OR: 8.88[7.16-11.02]). Surgical experience was inversely related with odds of VUR recurrence (Range OR: 0.41[0.32-0.54]). Rates of VUR resolution were comparable for OUR and RALUR patients. No patient required additional intervention for POHN. CONCLUSIONS: Incidence and resolution rate of POHN after OUR and RALUR were similar. Higher VUR grades were associated with increased odds of POHN after RALUR. Increasing RALUR experience improved VUR resolution rate but increased odds of POHN. Surgical success rates were similar for RALUR and OUR. LEVEL OF EVIDENCE: II.

Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan.

OBJECTIVES: To evaluate the performance of an engineered machine learning algorithm to identify kidney stones and measure stone characteristics without the need for human input. METHODS: We performed a cross-sectional study of 94 children and adults who had kidney stones identified on non-contrast CT. A previously developed deep learning algorithm was trained to segment renal anatomy and kidney stones and to measure stone features. The performance and speed of the algorithm to measure renal anatomy and kidney stone features were compared to the current gold standard of human measurement performed by 3 independent reviewers. RESULTS: The algorithm was 100% sensitive and 100% specific in detecting individual kidney stones. The mean stone volume segmented by the algorithm was smaller than that of human reviewers and had moderate overlap (Dice score: 0.66). There was substantial variation between human reviewers in total segmented stone volume (Jaccard score: 0.17) and volume of the single largest stone (Jaccard score: 0.33). Stone segmentations performed by the machine learning algorithm more precisely approximated stone borders than those performed by human reviewers on qualitative assessment. CONCLUSION: An engineered machine learning algorithm can identify and characterize stones more accurately and reliably than humans, which has the potential to improve the precision and efficiency of assessing kidney stone burden.

Assessing the accuracy of multiparametric MRI to predict clinically significant prostate cancer in biopsy naïve men across racial/ethnic groups.

INTRODUCTION: The Prostate Imaging Reporting and Data System (PIRADS) has shown promise in improving the detection of Gleason grade group (GG) 2-5 prostate cancer (PCa) and reducing the detection of indolent GG1 PCa. However, data on the performance of PIRADS in Black and Hispanic men is sparse. We evaluated the accuracy of PIRADS scores in detecting GG2-5 PCa in White, Black, and Hispanic men. METHODS: We performed a multicenter retrospective review of biopsy-naïve Black (n = 108), White (n = 108), and Hispanic (n = 64) men who underwent prostate biopsy (PB) following multiparametric MRI. Sensitivity and specificity of PIRADS for GG2-5 PCa were calculated. Race-stratified binary logistic regression models for GG2-5 PCa using standard clinical variables and PIRADS were used to calculate area under the receiver operating characteristics curves (AUC). RESULTS: Rates of GG2-5 PCa were statistically similar between Blacks, Whites, and Hispanics (52.8% vs 42.6% vs 37.5% respectively, p = 0.12). Sensitivity was lower in Hispanic men compared to White men (87.5% vs 97.8% respectively, p = 0.01). Specificity was similar in Black versus White men (21.6% vs 27.4%, p = 0.32) and White versus Hispanic men (27.4% vs 17.5%, p = 0.14). The AUCs of the PIRADS added to standard clinical data (age, PSA and suspicious prostate exam) were similar when comparing Black versus White men (0.75 vs 0.73, p = 0.79) and White versus Hispanic men (0.73 vs 0.59, p = 0.11). The AUCs for the Base model and PIRADS model alone were statistically similar when comparing Black versus White men and White versus Hispanic men. CONCLUSIONS: The accuracy of the PIRADS and clinical data for detecting GG2-5 PCa seems statistically similar across race. However, there is concern that PIRADS 2.0 has lower sensitivity in Hispanic men compared to White men. Prospective validation studies are needed.

Performance of prostate health index and PSA density in a diverse biopsy-naïve cohort with mpMRI for detecting significant prostate cancer.

Objective: To compare Prostate Health Index (PHI) and prostate-specific antigen (PSA) density as secondary tests after multiparametric magnetic resonance imaging (mpMRI) in improving the detection accuracy of Gleason grade group (GG) 2-5 prostate cancer (PCa) and in decreasing unnecessary biopsies in a multiethnic biopsy-naïve population. Methods: From February 2017 to February 2020, we recruited consecutive biopsy-naïve men in participating urology clinics for elevated PSA levels. They all had a PHI score, mpMRI, and prostate biopsy. Experienced genitourinary radiologists read all mpMRI studies based on PIRADS version 2.0. Logistic regression models were used to generate receiver operating characteristic curves. Models were tested for effect modification between Race (Black vs White) and both PHI and PSA density, and Race and PIRADS to determine if race impacted their prediction accuracy. Sensitivity, specificity, and predictive values of PHI and PSA density thresholds were calculated by PIRADS scores. The primary outcome was GG2-5 PCa, that is, Gleason score ≥3 + 4. Results: The study included 143 men, of which 65 (45.5%) were self-reported Black. Median age was 62.0 years and 55 men (38.4%) had GG2-5 PCa. Overall, 18.1% had PIRADS 1-2, 32.9% had PIRADS 3, and 49.0% had PIRADS 4-5. For the binary logistic regressions, the interactions between PIRADS and Race (P = .08), Log (PHI) and Race (P = .17), and Log (PSA density) and Race (P = .42) were not statistically significant. Within PIRADS 3 lesions, a PHI ≥49 prevented unnecessary biopsies in 55% of men and missed no GG2-5 PCa, yielding a negative predictive value of 100%. There was no reliable PHI or PSA density threshold to avoid PCa biopsies in PIRADS 1-2 or 4-5. Conclusions: PHI and PSA density can be used after mpMRI to improve the detection of GG2-5 PCa in a biopsy-naïve cohort. PHI may be superior to PSA density in PIRADS 3 lesions by avoiding 55% of unnecessary biopsies. Using both PHI and PSA density in series may further increase specificity and lead to fewer unnecessary biopsies, but further larger studies are warranted to determine the optimal threshold of each biomarker.

Performance of Prostate Health Index in Biopsy Naïve Black Men.

PURPOSE: The Prostate Health Index is validated for prostate cancer detection but has not been well validated for Gleason grade group 2-5 prostate cancer detection in Black men. We hypothesize that the Prostate Health Index has greater accuracy than prostate specific antigen for detection of Gleason grade group 2-5 prostate cancer. We estimated probability of overall and Gleason grade group 2-5 prostate cancer across previously established Prostate Health Index ranges and identified Prostate Health Index cutoffs that maximize specificity for Gleason grade group 2-5 prostate cancer with sensitivity >90%. MATERIALS AND METHODS: We recruited a "cancer-free" Black control cohort (135 patients) and a cohort of biopsy naïve Black men (158) biopsied for elevated prostate specific antigen. Descriptive statistics compared the prostate cancer cases and controls and the frequency of Gleason grade group 2-5 prostate cancer across Prostate Health Index scores. Receiver operating characteristics compared the discrimination of prostate specific antigen, Prostate Health Index and other prostate specific antigen related biomarkers. Sensitivity and specificity for Gleason grade group 2-5 prostate cancer detection were assessed at prostate specific antigen and Prostate Health Index thresholds alone and in series. RESULTS: Of biopsied subjects 32.9% had Gleason grade group 2-5 prostate cancer. In Blacks with prostate specific antigen from 4.0-10.0 ng/ml, Prostate Health Index and prostate specific antigen had similar discrimination for Gleason grade group 2-5 prostate cancer (0.63 vs 0.57, p=0.27). In Blacks with prostate specific antigen ≤10.0, a threshold of prostate specific antigen ≥4.0 had 90.4% sensitivity for Gleason grade group 2-5 prostate cancer; a threshold of prostate specific antigen ≥4.0 with Prostate Health Index ≥35.0 in series avoided unnecessary biopsy in 33.0% of men but missed 17.3% of Gleason grade group 2-5 prostate cancer. Prostate specific antigen ≥4.0 with Prostate Health Index ≥28.0 in series spared biopsy in 17.9%, while maintaining 90.4% sensitivity of Gleason grade group 2-5 prostate cancer. CONCLUSIONS: The Prostate Health Index has moderate accuracy in detecting Gleason grade group 2-5 prostate cancer in Blacks, but Prostate Health Index ≥28.0 can be safely used to avoid some unnecessary biopsies in Blacks.

The effect of red blood cell transfusion on fatigability after hospital discharge.

In patients with anemia, there is interest in understanding the impact of red blood cell (RBC) transfusion on patient-reported outcomes such as fatigue. However, data from previous studies are mixed as to whether transfusion improves fatigue. One explanation for this is that prior studies have not examined whether changes in fatigue from transfusion may also affect patient activity levels. This is important because if transfusion reduces fatigue, patients may become more active, which could increase their fatigue. Thus, testing whether transfusion affects patients' fatigability, a measure of fatigue in the context of activity, may be more useful than testing the effect of transfusion on fatigue alone. The objective of this study was to test the effect of transfusion during hospitalization on patients' fatigability 7 days postdischarge. This prospective observational study included hospitalized general medicine patients with hemoglobin levels <10 g/dL. Patient-reported fatigability was collected during hospitalization and by telephone 7 days after discharge. Multivariable linear regression was used to test the association between receipt of a transfusion and fatigability 7 days postdischarge. Among the 350 patients participating, larger reductions in fatigability were observed with more transfused RBCs. Receipt of 1 U of RBCs was associated with a smaller reduction in fatigability, whereas receipt of 2 to 3 U of RBCs was associated with reductions in fatigability nearly 1 standard deviation from baseline and 3 times greater than patients receiving 1 U of RBCs. In hospitalized patients with anemia, receipt of a transfusion is associated with reductions in fatigability 7 days after hospital discharge.