Long-Term Effectiveness of a Nonsurgical BPH Treatment System in Prostate Radiation Patients with Medication-Refractory Lower Urinary Tract Symptoms

PURPOSE/OBJECTIVE(S)

To evaluate the long-term outcomes and toxicity of a nonsurgical prostatic urethral lift system in patients with prior prostate cancer radiation therapy (RT).

MATERIALS/METHODS

From 2018 to 2020, nineteen patients with a history of prior prostate RT underwent a nonsurgical prostatic urethral lift (PUL) procedure for lower urinary track symptoms (LUTS) refractory to alpha blocker medications. Prostate cancer RT was completed a median of 17.8 months (IQR 13.4-27.4) prior to PUL procedure. All patients were assessed with IPSS/quality of life (QOL) scores and urinary post void residual (PVR) ultrasound before and after PUL. Patients were also assessed for ability to discontinue alpha blockade medications for their LUTS and any post PUL toxicity based on CTCAE v5.0 criteria.

RESULTS

All patients underwent successful placement of the PUL implants. Median pre-PUL IPSS score was 20 (IOR 17.5-22.5) and the median prostate volume was 41cc (IQR 33-49). Median follow up was 22 months (IQR 5-30). The average improvement in IPSS score was 8.5 points (IRQ 5.5-11) at 3 months and 7.5 points (IQR 6-10) at last follow-up. Two patients (10%) initially had improvement in IPSS score at 3 months with subsequent significant increase in IPSS (>5 points) at last follow-up. Overall, there was minimal change in reported QOL scores (median 0, IQR 0-1.75). The average reduction in PVR was 90cc. The percentage of patients able to discontinue alpha blockade at last follow-up was 68%. There was no grade 3 or higher GU toxicity in either group.

CONCLUSION

Use of a nonsurgical prostatic urethral lift system in patients with prior prostate RT refractory to alpha blocker medications results in significant long-term improvement of LUTS after radiation therapy for the majority of patients with minimal toxicity.

Machine Learning Models to Predict Kidney Stone Recurrence Using 24 Hour Urine Testing and Electronic Health Record-Derived Features

Objective

To assess the accuracy of machine learning models in predicting kidney stone recurrence using variables extracted from the electronic health record (EHR).

Methods

We trained three separate machine learning (ML) models (least absolute shrinkage and selection operator regression [LASSO], random forest [RF], and gradient boosted decision tree [XGBoost] to predict 2-year and 5-year symptomatic kidney stone recurrence from electronic health-record (EHR) derived features and 24H urine data (n = 1231). ML models were compared to logistic regression [LR]. A manual, retrospective review was performed to evaluate for a symptomatic stone event, defined as pain, acute kidney injury or recurrent infections attributed to a kidney stone identified in the clinic or the emergency department, or for any stone requiring surgical treatment. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified important features for each model.

Results

The 2- and 5- year symptomatic stone recurrence rates were 25% and 31%, respectively. The LASSO model performed best for symptomatic stone recurrence prediction (2-yr AUC: 0.62, 5-yr AUC: 0.63). Other models demonstrated modest overall performance at 2- and 5-years: LR (0.585, 0.618), RF (0.570, 0.608), and XGBoost (0.580, 0.621). Patient age was the only feature in the top 5 features of every model. Additionally, the LASSO model prioritized BMI and history of gout for prediction.

Conclusions

Throughout our cohorts, ML models demonstrated comparable results to that of LR, with the LASSO model outperforming all other models. Further model testing should evaluate the utility of 24H urine features in model structure.

Quick Sequential Organ Failure Assessment (qSOFA) Score is a Better Predictor of Septic Shock after Percutaneous Nephrolithotomy: A secondary analysis of Two Multi-Center Prospective Trials

INTRODUCTION

Recent retrospective literature suggests that the quick sequential organ failure assessment scoring (qSOFA) tool is a potentially superior tool over use of the systemic inflammatory response syndrome criteria (SIRS) to predict septic shock after percutaneous nephrolithotomy (PCNL) surgery. Here we examine use of qSOFA and SIRS to predict septic shock within data series collected prospectively on PCNL patients as part of a greater study of infectious complications.

MATERIALS AND METHODS

We performed a secondary analysis of two prospective multicenter studies including PCNL patients across nine institutions. Clinical signs informing SIRS and qSOFA scores were collected no later than post-operative day one. The primary outcome was sensitivity and specificity of SIRS and qSOFA (high risk score of greater-or-equal to 2 points) in predicting admission to the intensive care unit (ICU) for vasopressor support.

RESULTS

A total of 218 cases at nine institutions were analyzed. One patient required vasopressor support in the ICU. The sensitivity/specificity was 100%/72.4% (McNemar's test p < 0.001) for SIRS and was 100%/90.8% (McNemar's test p < 0.001) for qSOFA.

CONCLUSION

Although PPV for both qSOFA and SIRS in prediction of post-PCNL septic shock is low, prospectively collected data demonstrates use of qSOFA may offer greater specificity than SIRS criteria when predicting post-PCNL septic shock.

The Fate of Residual Fragments After Percutaneous Nephrolithotomy: Results from the Endourologic Disease Group for Excellence Research Consortium

Background: Residual fragments (RFs) after percutaneous nephrolithotomy (PCNL) have a significant impact on patients' quality of life and clinical course. There is a paucity of studies that evaluate the natural history of RFs after PCNL. The objective of this study is to compare rates of reintervention, complications, stone growth, and passage in patients with RFs >4, ≤4, and ≤2 mm after PCNL. Methods: Sites from the Endourologic Disease Group for Excellence (EDGE) research consortium examined data of PCNL patients from 2015 to 2019 with at least 1-year follow-up. RF passage, regrowth, reintervention, and complications were recorded and RFs were stratified into >4 and ≤4 mm groups, as well as >2 and ≤2 mm groups. Potential predictors for stone-related events after PCNL were determined using multivariable logistic regression analysis. It was hypothesized that larger RF thresholds would result in lower passage rates, faster regrowth, and greater clinically significant events (complications and reinterventions) than smaller RF thresholds. Results: A total of 439 patients with RFs >1 mm on CT postoperative day 1 were included in this study. For RFs >4 mm, rates of reintervention were found to be significantly higher and Kaplan-Meier curve analysis showed significantly higher rates of stone-related events. Passage and RF regrowth were not found to be significantly different compared with RFs ≤4 mm. However, RFs ≤2 mm had significantly higher rates of passage, and significantly lower rates of fragment regrowth (>1 mm), complications, and reintervention compared with RFs >2 mm. On multivariable analysis, older age, body mass index, and RF size were found to be predictive of stone-related events. Conclusions: With the largest cohort to date, this study by the EDGE research consortium further confirms that clinically insignificant residual fragment is problematic for patients after PCNL, particularly in older more obese patients with larger RFs. Our study underscores the importance of complete stone clearance post-PCNL and challenges the use of Clinically insignificant residual fragment (CIFR).

Enhancing sizing accuracy in ultrasound images with an alternative ADMIRE model and dynamic range considerations

Ultrasound imaging can struggle with sizing accuracy, especially when the targets have a significantly different amplitude compared to the surrounding background. In this work, we consider the challenging task of accurately sizing hyperechoic structures, and specifically kidney stones, where accurate sizing is critical for determining medical intervention. AD-Ex, an extended alternative model of our aperture domain model image reconstruction (ADMIRE) pre-processing method, is introduced and is designed to improve clutter removal and improve sizing accuracy. We compare this method against other resolution enhancing methods such as minimum variance (MV) and generalized coherence factor (GCF), and against those methods using AD-Ex as a pre-processing tool. These methods are evaluated among patients with kidney stone disease, with the task of accurately sizing the stones against the gold standard, computed tomography (CT). Stone ROI's were selected using contour maps as reference from which the lateral stone size was estimated. Among the in vivo kidney stone cases we processed, AD-Ex+MV had the overall lowest sizing error among the methods, with an average error of 10.8% compared to the next best method AD-Ex which had an average error of 23.4%. For reference, DAS had an average error of 82.4%. Though dynamic range was evaluated to determine optimal thresholding for sizing applications, variability between stone cases was too high for any conclusions to be drawn at this time.

Machine Learning Models to Predict 24 Hour Urinary Abnormalities for Kidney Stone Disease

OBJECTIVES

To help guide empiric therapy for kidney stone disease, we sought to demonstrate the feasibility of predicting 24-hour urine abnormalities using machine learning methods.

METHODS

We trained a machine learning model (XGBoost [XG]) to predict 24-hour urine abnormalities from electronic health record-derived data (n=1,314). The machine learning model was compared to a logistic regression model [LR]. Additionally, an ensemble (EN) model combining both XG and LR models was evaluated as well. Models predicted binary 24-hour urine values for volume, sodium, oxalate, calcium, uric acid and citrate; as well as a multiclass prediction of pH. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified predictors for each model.

RESULTS

The XG model was able to discriminate 24-hour urine abnormalities with fair performance, comparable to LR. The XG model most accurately predicted abnormalities of urine volume (accuracy=98%, AUC-ROC=0.59), uric acid (69%, 0.73) and elevated urine sodium (71%, 0.79). The LR model outperformed the XG model alone in prediction of abnormalities of urinary pH (AUC-ROC of 0.66 vs 0.57) and citrate (0.69 vs.0.64). The EN model most accurately predicted abnormalities of oxalate (accuracy=65%, ROC-AUC=0.70) and citrate (65%, 0.69) with overall similar predictive performance to either XG or LR alone. Body mass index, age, and gender were the three most important features for training the models for all outcomes.

CONCLUSIONS

Urine chemistry prediction for kidney stone disease appears to be feasible with machine learning methods. Further optimization of the performance could facilitate dietary or pharmacologic prevention.

Machine Learning Prediction of Kidney Stone Composition Using Electronic Health Record-Derived Features

Objectives: To assess the accuracy of machine learning models in predicting kidney stone composition using variables extracted from the electronic health record (EHR). Materials and Methods: We identified kidney stone patients (n = 1296) with both stone composition and 24-hour (24H) urine testing. We trained machine learning models (XGBoost [XG] and logistic regression [LR]) to predict stone composition using 24H urine data and EHR-derived demographic and comorbidity data. Models predicted either binary (calcium vs noncalcium stone) or multiclass (calcium oxalate, uric acid, hydroxyapatite, or other) stone types. We evaluated performance using area under the receiver operating curve (ROC-AUC) and accuracy and identified predictors for each task. Results: For discriminating binary stone composition, XG outperformed LR with higher accuracy (91% vs 71%) with ROC-AUC of 0.80 for both models. Top predictors used by these models were supersaturations of uric acid and calcium phosphate, and urinary ammonium. For multiclass classification, LR outperformed XG with higher accuracy (0.64 vs 0.56) and ROC-AUC (0.79 vs 0.59), and urine pH had the highest predictive utility. Overall, 24H urine analyte data contributed more to the models' predictions of stone composition than EHR-derived variables. Conclusion: Machine learning models can predict calcium stone composition. LR outperforms XG in multiclass stone classification. Demographic and comorbidity data are predictive of stone composition; however, including 24H urine data improves performance. Further optimization of performance could lead to earlier directed medical therapy for kidney stone patients.

On the Effects of Constitutive Properties and Roughness of a Hard Inclusion in Soft Tissue on B-mode Images

We perform finite element modeling of pulse-echo ultrasound of a hard inclusion in a soft tissue to gain a better understanding of B-mode image brightness characteristics. We simulate a pressure wave emitted by an ultrasound transducer through the inclusion-tissue medium by prescribing suitable boundary conditions, and collect the scattered wave response to simulate the behavior of the transducer array used for pulse-echo ultrasound. We form B-mode images from simulated channel data using standard delay and sum beamforming. We establish the accuracy of the finite element model by comparing the point spread function with that obtained from Field II ultrasound simulation program. We also demonstrate qualitative validation by comparing the brightness characteristics of rough and smooth surfaced circular inclusions with experimental images of a cylindrical metal tool immersed in a water tank. We next conduct simulation studies to evaluate changes in B-mode image brightness intensity and contrast related to different constitutive properties, namely, compressibility of the inclusion, impedance contrast between the host and inclusion, and surface roughness of the inclusion. We find that the intensity observed behind a hard inclusion in the axial direction is strongly affected by the compressibility and roughness of the inclusion. Also, the perceived width of the stone based on the intensity is greater for rougher stones. Our study indicates that imaging of compressible inclusions may benefit from targeted B-mode image forming algorithms. Our modeling framework can potentially be useful in differentiating hard inclusions from surrounding parenchyma, and for classifying kidney stones or gallstones.

Enhanced Recovery After Surgery Protocol for Patients Undergoing Ureteroscopy: Prospective Evaluation of an Opioid-Free Protocol

Purpose: A large part of the national opioid epidemic has been tied to prescription opioids, leading to a push to reduce or eliminate their use when feasible. The objective of this study was to evaluate outcomes of implementing an Enhanced Recovery After Surgery (ERAS) protocol for patients undergoing ureteroscopic stone treatment with stent placement geared toward minimizing opioid use. Materials and Methods: We performed a pre-post study concerning a process improvement project of consecutive patients undergoing ureteroscopic stone treatment with stent placement utilizing a novel ERAS protocol. A lead-in period with patients managed conventionally with opioids was performed before implementation of the ERAS protocol. Data regarding opioid utilization, postoperative outcomes, and patient-reported outcomes, including Patient-Reported Outcomes Measurement Information System (PROMIS), were compared between groups. Results: There were 28 pre-ERAS patients and 52 ERAS-managed patients. Patients discharged with an opioid prescription decreased from 93% to 0% (p < 0.05). Mean total morphine milligram equivalent decreased from 60.1 ± 41 to 7.7 ± 26 (p < 0.05). There was no significant difference noted for postoperative calls for pain in the pre-ERAS vs ERAS groups (25% vs 19%, p = 0.9) or in unscheduled provider encounters (0% vs 4%, p = 0.46). There were no clinically significant differences between groups on patient-reported measures. Conclusions: Implementation of an ERAS protocol for ureteroscopic stone treatment resulted in a significant reduction in perioperative opioids, a total reduction in discharge opioid prescriptions, and ∼90% reduction in total 30-day postoperative opioid prescribing with no adverse effects on recovery or increase in postoperative clinical encounters.

The Effect of Operative Field Instrument Clutter During Intraoperative Fluoroscopy on Radiation Exposure

Introduction and Objectives: Fluoroscopy units are routinely operated in the automatic brightness control (ABC) mode to optimize image quality. During ureteroscopy, objects may be placed within the fluoroscopy beam and the effect upon radiation exposure is unknown. The purpose of this study is to investigate the effects of equipment within the fluoroscopy beam during simulated ureteroscopy. Methods: ABC fluoroscopy of a cadaver was performed in eight clinical scenarios, including a control (no equipment), and seven groups with different equipment within the fluoroscopy beam. Equipment tested included electrocardiogram (EKG) leads, a Kelly clamp, camera and light cords (straight and coiled configurations), flexible ureteroscope, rigid cystoscope, and the lateral table support beam. Ten 145-second fluoroscopy trials were performed for each arm. The primary outcome was radiation dose (mGy) compared using the Mann-Whitney test with p < 0.05 considered significant. Results: Compared with control (18.5 mGy), radiation exposure was significantly increased with the presence of a straight camera and light cords (19.3 mGy), Kelly clamp (19.4 mGy), coiled camera and light cords (20.2 mGy), a flexible ureteroscope (21.0 mGy), a rigid cystoscope (21.2 mGy), and when the lateral table support beam was in the path of the X-ray (25.0 mGy; a 35% increase; p < 0.007 for all). The EKG leads did not affect the radiation dose. Conclusions: Avoiding equipment within the fluoroscopy beam using ABC mode can reduce radiation exposure. Adjusting the table and patient position to exclude the lateral table support beam will reduce radiation exposure by 35%.

Improved Detection of Kidney Stones Using an Optimized Doppler Imaging Sequence

Kidney stones have been shown to exhibit a "twinkling artifact" (TA) under Color-Doppler ultrasound. Although this technique has better specificity than conventional Bmode imaging, it has lower sensitivity. To improve the overall performance of using TA as a diagnostic tool, Doppler output parameters were optimized in-vitro. The collected data supports a previous hypothesis that TA is caused by random oscillations of micron sized bubbles trapped in the cracks and crevices of kidney stones. A set of optimized parameters were implemented such that that the MI & TI remained within the FDA approved limits. Several clinical kidney scans were performed with the optimized settings and were able to detect stones with improved SNR relative to the default settings.