Constrictive versus compressive bladder outflow obstruction in men: Does it matter?

INTRODUCTION: Bladder outflow obstruction (BOO) is a urethral resistance (UR) at a level above a clinically relevant threshold. UR is currently graded in terms of the existence and severity of the BOO based on maximum flowrate and associated detrusor pressure only. However, the pressure-flow relation throughout the course of voiding includes additional information that may be relevant to identify the type of BOO. This study introduces a new method for the distinction between the provisionally called compressive and constrictive types of BOO and relates this classification to underlying patient and urodynamic differences between those BOO types. METHODS: In total, 593 high-quality urodynamic pressure-flow studies in men were included in this study. Constrictive BOO was identified if the difference Δp between the actual minimal urethral opening pressure (pmuo) and the expected pmuo according to the linearized passive urethral resistance relation (linPURR) nomogram was >25 cmH2O. Compressive BOO is identified in the complementary case where the pressure difference Δp ≤ 25 cmH2O. Differences in urodynamic parameters, patient age, and prostate size were explored. RESULTS: In 81 (13.7%) of the cases, constrictive BOO was found. In these patients, the prostate size was significantly smaller when compared to patients diagnosed with compressive BOO, while displaying a significantly lower maximum flowrate, higher detrusor pressure at maximal flowrate and more postvoid residual (PVR). CONCLUSION: This study is an initial step in the validation of additional subtyping of BOO. We found significant differences in prostate size, severity of BOO, and PVR, between patients with compressive and constrictive BOO. Subtyping of voiding-outflow dynamics may lead to more individualized management in patients with BOO.

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023.

INTRODUCTION: A "Think Tank" at the International Consultation on Incontinence-Research Society meeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data. METHODS: Examples of the use of ML applied to data from uroflowmetry, pressure flow studies and imaging were presented. The advantages and limitations of ML were considered. Recommendations made during the subsequent debate for research studies were recorded. RESULTS: ML analysis holds great promise for the kind of data generated in urodynamic studies. To date, ML techniques have not yet achieved sufficient accuracy for routine diagnostic application. Potential approaches that can improve the use of ML were agreed and research questions were proposed. CONCLUSIONS: ML is well suited to the analysis of urodynamic data, but results to date have not achieved clinical utility. It is considered likely that further research can improve the analysis of the large, multifactorial data sets generated by urodynamic clinics, and improve to some extent data pattern recognition that is currently subject to observer error and artefactual noise.

Quantifying bladder outflow obstruction in men: A comparison of four approximation methods exploiting large data samples.

INTRODUCTION: A pressure flow study (PFS), part of the International Continence Society standard urodynamic test, is regarded gold standard for the classification and quantification of the urethral resistance (UR), expressed in the bladder outflow obstruction (BOO). For men with benign prostatic hyperplasia, the minimum urethral opening pressure (pmuo ), found at the end of the passive urethral resistance relation is considered the relevant parameter describing BOO. However, in clinical practice, direct measurements of pmuo are easily confounded by terminal dribbling. For that reason, alternative methods were developed to derive pmuo , and thereby assess BOO using the maximum urine flow rate (Qmax ) and the corresponding pressure (pdetQmax ) instead. These methods were never directly compared against a large data set. With the increasing variety of treatments becoming available more precise grading of UR may become of relevance. The current study compares four well-known methods to approximate pmuo and examines the relation between pmuo and pdetQmax . METHODS: In total, 1717 high-quality PFS of men referred with lower urinary tract symptoms between 2003 and 2020 without earlier lower urinary tract surgery were included. From these recordings, pmuo was calculated according to three one-parameter methods. In addition, a three-parameter method (3PM) was used, based on a fit through the lowest pressure flank of the pressure-flow plot. The estimated pmuo 's were compared with a precisely assessed pmuo . A difference of <10 cmH2 O between an estimate and the actual pmuo was considered accurate. A comparison between the four approximation methods and the actual pmuo was visualized using a Bland-Altman plot. The differences between the actual and the estimated slope were assessed and dependency on pmuo was analyzed. RESULTS: A total of 1717 studies were analyzed. In 55 (3.2%) PFS, 3PM analysis was impossible because all pressures after Qmax were higher than pdetQmax . The 3PM model was superior in predicting pmuo , with 75.9% of the approximations within a range of +10 or -10 cmH2 O of the actual pmuo . Moreover, pmuo according to urethral resistance A (URA) and linearized passive urethral resistance relation (linPURR) appear equally reliable. Bladder outflow obstruction index (BOOI) was significantly less accurate when compared to all others. Bland-Altman analysis showed a tendency of BOOI to overestimate pmuo in men with higher grades of UR, while URA tended to underestimate pmuo in those cases. The slope between pmuo and pdetQmax -Qmax increased with larger pmuo , as opposed to the constant relation proposed within BOOI. Although significant differences were found, the clinical relevance of those differences is not known. CONCLUSION: Of the four methods to estimate pmuo and quantify BOO, 3PM was found the most accurate and BOOI the least accurate. As 3PM is not generally available and performance in lower quality PFS is unknown, linPURR is (for now) the most physiologically accurate.

The accuracy of navigated versus freehand curettage in bone tumors: a cadaveric model study.

PURPOSE: Navigation has been suggested to guide complex benign bone tumor curettage procedures, but the contribution of navigation to the accuracy of curettage has never been quantified. We explored the accuracy of navigated curettage in a cadaveric observational pilot study, comparing navigated to freehand curettage, performed independently by an expert and a novice user. METHODS: The expert performed curettage on 20 cadaveric bones prepared with a paraffin wax mixture tumor, 10 freehand and 10 navigated. We re-used 12 bones for the novice experiments, 6 freehand and 6 navigated. Tumor and curettage cavity volumes were segmented on pre- and post-cone-beam CT scans. Accuracy was quantified using the Dice Similarity Coefficient (DSC), and with remaining tumor volume, bone curettage volume, maximal remaining width and procedure times compared between navigation and freehand groups for both users. RESULTS: There were little differences in curettage accuracy between a navigated (DSC 0.59[0.17]) and freehand (DSC 0.64[0.10]) approach for an expert user, but there were for a novice user with DSC 0.67(0.14) and 0.83(0.06), respectively. All navigated and freehand procedures had some amount of remaining tumor, generally located in a few isolated spots with means of 2.2(2.6) cm3 (mean 20% of the tumor volume) and 1.5(1.4) cm3 (18%), respectively, for the expert and more diffusely spaced with means of 5.1(2.8) cm3 (33%) and 3.0(2.2) cm3 (17%), respectively, for the novice. CONCLUSIONS: In an explorative study on 20 cadaveric bone tumor models, navigated curettage in its current setup was not more accurate than freehand curettage. The amount of remaining tumor, however, confirms that curettage could be further improved. The novice user was less accurate using navigation than freehand, which could be explained by the learning curve. Furthermore, the expert used a different surgical approach than the novice, focusing more on removing the entire tumor than sparing surrounding bone.

Feasibility study of intraoperative cone-beam CT navigation for benign bone tumour surgery.

BACKGROUND: Intraoperative cone-beam computed tomography (CBCT) offers the advantage of navigation on the current anatomical situation and the possibility to take a control scan. We assessed the feasibility of using intraoperative CBCT for navigated intralesional curettage. METHODS: Nine benign bone tumour patients were studied. Feasibility was assessed by describing the workflow and indications for navigation, scoring CBCT image quality and registration accuracy, and measuring scan and navigation set-up times. Short-term follow-up was described. RESULTS: CBCT navigation was successful in all patients. Median tumour visibility, tumour delineation, and vital structure visibility scores were good. Median registration accuracy score was very good. Median scan and verification times were 5 and 3 minutes, respectively. One patient had a tumour recurrence after 6 months. CONCLUSIONS: Intraoperative CBCT navigation is feasible and safe. Indications for use of navigation in clinical practice are closeness to vital structures, complexly shaped tumours or bone, minimally invasive surgery, and repeated surgery.