PREDICTION OF BLADDER OUTLET OBSTRUCTION IN MEN WITH LOWER URINARY TRACT SYMPTOMS USING ARTIFICIAL NEURAL NETWORKS

Applications of machine learning in urodynamics: A narrative review

BACKGROUND

Machine learning algorithms as a research tool, including traditional machine learning and deep learning, are increasingly applied to the field of urodynamics. However, no studies have evaluated how to select appropriate algorithm models for different urodynamic research tasks.

METHODS

We undertook a narrative review evaluating how the published literature reports the applications of machine learning in urodynamics. We searched PubMed up to December 2023, limited to the English language. We selected the following search terms: artificial intelligence, machine learning, deep learning, urodynamics, and lower urinary tract symptoms. We identified three domains for assessment in advance of commencing the review. These were the applications of urodynamic studies examination, applications of diagnoses of dysfunction related to urodynamics, and applications of prognosis prediction.

RESULTS

The machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, which are urodynamic examination, diagnosis of urinary tract dysfunction and prediction of the efficacy of various treatment methods. Most of these studies were single-center retrospective studies, lacking external validation, requiring further validation of model generalization ability, and insufficient sample size. The relevant research in this field is still in the preliminary exploration stage; there are few high-quality multi-center clinical studies, and the performance of various models still needs to be further optimized, and there is still a distance from clinical application.

CONCLUSIONS

At present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics. The purpose of this review is to summarize and classify the machine learning algorithms applied in this field and to guide researchers to select the appropriate algorithm model for different task requirements to achieve the best results.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

An automatic diagnostic system for the urodynamic study applying in lower urinary tract dysfunction

OBJECTIVE

To establish an automatic diagnostic system based on machine learning for preliminarily analysis of urodynamic study applying in lower urinary tract dysfunction (LUTD).

METHODS

The eight most common conditions of LUTDs were included in the present study. A total of 527 eligible patients with complete data, from the year of 2015 to 2020, were enrolled in this study. In total, two global parameters (patients' age and sex) and 13 urodynamic parameters were considered to be the input for machine learning algorithms. Three machine learning approaches were applied and evaluated in this study, including Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM).

RESULTS

By applying machine learning algorithms into the 8 common LUTDs, the DT models achieved the AUC of 0.63-0.98, the LR models achieved the AUC of 0.73-0.99, and the SVM models achieved the AUC of 0.64-1.00. For mutually exclusive diagnoses of underactive detrusor and acontractile detrusor, we developed a classification model that classifies the patients into either of these two diseases or double-negative class. For this classification method, the DT models achieved the AUC of 0.82-0.85 and the SVM models achieved the AUC of 0.86-0.90. Among all these models, the LR and the SVM models showed better performance. The best model of these diagnostic tasks achieved an average AUC of 0.90 (0.90 ± 0.08).

CONCLUSIONS

An automatic diagnostic system was developed using three machine learning models in urodynamic studies. This automated machine learning process could lead to promising assistance and enhancements of diagnosis and provide more useful reference for LUTD treatment.

Development of a machine learning-based predictive model for prediction of success or failure of medical management for benign prostatic hyperplasia

OBJECTIVE

To develop a novel predictive model for identifying patients who will and will not respond to the medical management of benign prostatic hyperplasia (BPH).

METHODS

Using data from the Medical Therapy of Prostatic Symptoms (MTOPS) study, several models were constructed using an initial data set of 2172 patients with BPH who were treated with doxazosin (Group 1), finasteride (Group 2), and combination therapy (Group 3). K-fold stratified cross-validation was performed on each group, Within each group, feature selection and dimensionality reduction using nonnegative matrix factorization (NMF) were performed based on the training data, before several machine learning algorithms were tested; the most accurate models, boosted support vector machines (SVMs), being selected for further refinement. The area under the receiver operating curve (AUC) was calculated and used to determine the optimal operating points. Patients were classified as treatment failures or responders, based on whether they fell below or above the AUC threshold for each group and for the whole data set.

RESULTS

For the entire cohort, the AUC for the boosted SVM model was 0.698. For patients in Group 1, the AUC was 0.729, for Group 2, the AUC was 0.719, and for Group 3, the AUC was 0.698.

CONCLUSION

Using MTOPS data, we were able to develop a prediction model with an acceptable rate of discrimination of medical management success for BPH.

Relationship between bladder outlet obstruction and prostatic indentation, prostatic urethral length, and bladder-prostatic urethral angle

Objective:

To evaluate the relationship between prostatic anatomical factors and pressure flow studies (PFSs).

Methods:

The study was designed with 41 patients. PFS was applied to every patient. The Qmax value and voided volume during uroflowmetry were recorded. The PdetQmax values during PFS were recorded and obstruction indices were calculated. Prostate volume, prostatic indentation, prostatic urethral length, and bladder-prostatic urethral angle were determined by transrectal ultrasonography.

Results:

The mean age of the participants was 66.2 years. The mean maximum flow value was detected as 9.91 ± 4.92 ml/s, and the mean average flow value as 4.89 ± 2.54 ml/s. The mean obstruction index of the patients was found to be 75.27 ± 46.96. There was a positive linear relationship between obstruction index and PSA, prostate volume, prostatic urethral length, and prostatic indentation ( r: 0.341, p: 0.029; r: 0.363, p: 0.020; r: 0.386, p: 0.013; and r: 0.479, p: 0.002, respectively). No significant relationship was found between obstruction index and bladder-prostatic urethral angle.

Conclusion:

Prostatic urethral length and prostatic indentation are associated with the degree of bladder outlet obstruction. Further studies involving a higher number of patients are needed to calculate the accuracy of these parameters.

Feasibility of a deep learning-based diagnostic platform to evaluate lower urinary tract disorders in men using simple uroflowmetry

Purpose

To diagnose lower urinary tract symptoms (LUTS) in a noninvasive manner, we created a prediction model for bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using simple uroflowmetry. In this study, we used deep learning to analyze simple uroflowmetry.

Materials and Methods

We performed a retrospective review of 4,835 male patients aged ≥40 years who underwent a urodynamic study at a single center. We excluded patients with a disease or a history of surgery that could affect LUTS. A total of 1,792 patients were included in the study. We extracted a simple uroflowmetry graph automatically using the ABBYY Flexicapture^® image capture program (ABBYY, Moscow, Russia). We applied a convolutional neural network (CNN), a deep learning method to predict DUA and BOO. A 5-fold cross-validation average value of the area under the receiver operating characteristic (AUROC) curve was chosen as an evaluation metric. When it comes to binary classification, this metric provides a richer measure of classification performance. Additionally, we provided the corresponding average precision-recall (PR) curves.

Results

Among the 1,792 patients, 482 (26.90%) had BOO, and 893 (49.83%) had DUA. The average AUROC scores of DUA and BOO, which were measured using 5-fold cross-validation, were 73.30% (mean average precision [mAP]=0.70) and 72.23% (mAP=0.45), respectively.

Conclusions

Our study suggests that it is possible to differentiate DUA from non-DUA and BOO from non-BOO using a simple uroflowmetry graph with a fine-tuned VGG16, which is a well-known CNN model.

The use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use?

OBJECTIVES

Artificial intelligence (AI) is increasingly used in medicine, but data on benign prostatic enlargement (BPE) management are lacking. This study aims to test the performance of several machine learning algorithms, in predicting clinical outcomes during BPE surgical management.

METHODS

Clinical data were extracted from a prospectively collected database for 153 men with BPE, treated with transurethral resection (monopolar or bipolar) or vaporization of the prostate. Due to small sample size, we applied a method for increasing our dataset, Synthetic Minority Oversampling Technique (SMOTE). The new dataset created with SMOTE has been expanded by 453 synthetic instances, in addition to the original 153. The WEKA Data Mining Software was used for constructing predictive models, while several appropriate statistical measures, like Correlation coefficient (R), Mean Absolute Error (MAE), Root Mean-Squared Error (RMSE), were calculated with several supervised regression algorithms - techniques (Linear Regression, Multilayer Perceptron, SMOreg, k-Nearest Neighbors, Bagging, M5Rules, M5P - Pruned Model Tree, and Random forest).

RESULTS

The baseline characteristics of patients were extracted, with age, prostate volume, method of operation, baseline Qmax and baseline IPSS being used as independent variables. Using the Random Forest algorithm resulted in values of R, MAE, RMSE that indicate the ability of these models to better predict % Qmax increase. The Random Forest model also demonstrated the best results in R, MAE, RMSE for predicting % IPSS reduction.

CONCLUSIONS

Machine Learning techniques can be used for making predictions regarding clinical outcomes of surgical BPRE management. Wider-scale validation studies are necessary to strengthen our results in choosing the best model.

Complementarity of Clinician Judgment and Evidence Based Models in Medical Decision Making: Antecedents, Prospects, and Challenges

Early accounts of the development of modern medicine suggest that the clinical skills, scientific competence, and doctors' judgment were the main impetus for treatment decision, diagnosis, prognosis, therapy assessment, and medical progress. Yet, clinician judgment has its own critics and is sometimes harshly described as notoriously fallacious and an irrational and unfathomable black box with little transparency. With the rise of contemporary medical research, the reputation of clinician judgment has undergone significant reformation in the last century as its fallacious aspects are increasingly emphasized relative to the evidence based options. Within the last decade, however, medical forecasting literature has seen tremendous change and new understanding is emerging on best ways of sharing medical information to complement the evidence based medicine practices. This review revisits and highlights the core debate on clinical judgments and its interrelations with evidence based medicine. It outlines the key empirical results of clinician judgments relative to evidence based models and identifies its key strengths and prospects, the key limitations and conditions for the effective use of clinician judgment, and the extent to which it can be optimized and professionalized for medical use.

Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction: analysis using causal Bayesian networks

PURPOSE

To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO) in patients with benign prostatic hyperplasia (BPH) using causal Bayesian networks (CBN).

SUBJECTS AND METHODS

From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV), transition zone volume (TZV), prostate specific antigen (PSA), maximum flow rate (Qmax), and post-void residual volume (PVR) on uroflowmetry, and International Prostate Symptom Score (IPSS). Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR) model with the same dataset.

RESULTS

Mean age, TPV, and IPSS were 6.2 (±7.3, SD) years, 48.5 (±25.9) ml, and 17.9 (±7.9), respectively. The mean BOO index was 35.1 (±25.2) and 477 patients (34.5%) had urodynamic BOO (BOO index ≥40). By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%). However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020).

CONCLUSIONS

Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

Non-invasive parameters predicting bladder outlet obstruction in Korean men with lower urinary tract symptoms

The goal of this study was to evaluate the clinical and urodynamic features in Korean men with lower urinary tract symptoms (LUTS) and to determine non-invasive parameters for predicting bladder outlet obstruction (BOO). Four hundred twenty nine Korean men with LUTS over 50 yr of age underwent clinical evaluations for LUTS including urodynamic study. The patients were divided into two groups according to the presence of BOO. These two groups were compared with regard to age, the results of the uroflowmetry, serum prostate-specific antigen (PSA) level, prostate volume, International Prostate Symptom Score (I-PSS), and the results of the urodynamic study. Patients with BOO had a lower maximal flow rate (Q(max)), lower voided volume, higher serum PSA level and larger prostate volume (P<0.05). BOO group had a significantly higher rate of involuntary detrusor contraction and poor compliance compared to the patients without BOO (P<0.05). The multivariate analysis showed that Q(max) and poor compliance were significant factors for predicting BOO. Our results show that Q(max) plays a significant role in predicting BOO in Korean men with LUTS. In addition, BOO is significantly associated with detrusor dysfunction, therefore, secondary bladder dysfunction must be emphasized in the management of male patients with LUTS.

Modeling the clinical assessment of men with suspected obstructed voiding using Bayes' Theorem

AIMS

Pressure-flow studies (PFS) are the only reliable way to diagnose bladder outlet obstruction (BOO) in men with lower urinary tract symptoms (LUTS). However, in routine clinical practice, BOO is usually inferred by any of a number of tests (symptoms, flow rate, prostate size...). Bayes' Theorem provides a mathematical method, which may be similar to the process used by clinicians, for combining the results of multiple tests to reach a diagnosis. We have applied Bayes' Theorem to the results of several tests known weakly to predict BOO in men with LUTS to assess if they improve the diagnostic accuracy of a flow rate test which alone is known to predict obstruction moderately well.

METHODS

We applied Bayes' Theorem to data from 50 patients using Q(max) alone and with the inclusion of additional variables (IPSS, PSA, and residual urine), to establish individual probabilities of BOO. The chi-squared statistic (with trend) was used to compare the relative diagnostic values, against the BOO index calculated from the results of subsequent PFS.

RESULTS

The diagnostic value of Q(max) alone (chi-squared = 9.2, P = 0.002), was superior than that for the Bayesian model using the combination of tests available (chi-squared = 4.9, P = 0.026).

CONCLUSIONS

Although in our sample relevant additional tests do not improve the diagnostic power of Q(max) as a predictor of BOO, we believe the Bayesian approach is conceptually suited to modeling clinical decision making but may be better tested for a more clinically relevant outcome such as treatment response.

Application of artificial neural network in prediction of bladder outlet obstruction: a model based on objective, noninvasive parameters

OBJECTIVES

An artificial neural network model previously described by us that was based on lower urinary tract symptoms yielded a modest prediction of bladder outlet obstruction. The aim of this study was to establish another model, using more objective parameters, that could better predict for bladder outlet obstruction.

METHODS

The records of 457 patients were used in the construction of the model. Of the 457 records, 300 were allocated to the training phase and 157 to the testing phase. All patients had the average flow rate, maximal flow rate, postvoid residual urine volume (PVR), and total prostate volume recorded. The results of the pressure flow study of those patients were considered the reference standard against which the artificial neural network was tested.

RESULTS

Three models were tested. Models 1 and 2 were based on a three-output design (ie, nonobstructed, equivocal, and obstructed). The only difference was the number of iterations. The accuracy of the first model was 60.5% compared with 46.5% for the second. For a third model, in which the equivocal pressure flow study results were added to the nonobstructed group, the accuracy rose to 72%. Deletion of equivocal cases (around 19% of the total) was associated with an accuracy of 76% in the prediction of obstruction.

CONCLUSIONS

An artificial neural network based on objective and noninvasive parameters could replace the pressure flow study in only 72% of cases. An accuracy of 76% in the detection of bladder outlet obstruction is rather impractical, because an equivocal zone has always been available on pressure flow study nomograms.

Noninvasive methods of diagnosing bladder outlet obstruction in men. Part 2: Noninvasive urodynamics and combination of measures

PURPOSE

Many methods have been suggested to diagnose bladder outlet obstruction, as defined by the gold standard of pressure flow studies. Difficulty arises when comparing completely different methods of diagnosing bladder outlet obstruction. A comprehensive review of the literature on the different methods used to diagnose bladder outlet obstruction by noninvasive means was performed with a view to allow such a comparison.

MATERIALS AND METHODS

A MEDLINE search was done of the published literature covering until the end of 2004 on noninvasive methods, including single measure and combinations of measures, to diagnose bladder outlet obstruction. A direct comparison of all of the different methods was made using the sensitivity, specificity, likelihood ratio, and pretest and posttest probability of diagnosing bladder outlet obstruction for each test. For many techniques these values were calculated from the data presented in the article.

RESULTS

A multitude of methods has been applied to diagnose bladder outlet obstruction. Broadly the methods were divided into nonurodynamic and noninvasive urodynamic methods. Nonurodynamic methods were considered in part 1 of the review. Part 2 considered noninvasive urodynamic techniques, such as uroflowmetry, the penile cuff, the condom method and Doppler urodynamics. A combination of single measures was also considered and the relative merits of these approaches were discussed.

CONCLUSIONS

A combination of noninvasive urodynamics and ultrasound derived measures provide promising methods of diagnosing bladder outlet obstruction. However, pressure flow studies still remain the gold standard for assessing bladder outlet obstruction.

Factors predicting for urinary morbidity following 125iodine transperineal prostate brachytherapy

PURPOSE

To assess factors related to the risk of acute urinary retention and other morbidity indices in patients undergoing transperineal seed implantation of the prostate.

MATERIALS AND METHODS

One hundred and seventy-three consecutive patients treated with (125)Iodine transperineal interstitial permanent prostate brachytherapy (TIPPB) were evaluated. Various demographic, pathological, symptomatic, urodynamic and dosimetric values were assessed in relation to the incidence of acute urinary retention as well as the International Prostate Symptom Score (IPSS) dynamics. Patients were routinely placed on alpha-blockade postimplant. Dosimetry was based on CT scan one month postimplant.

RESULTS

Acute urinary retention developed in thirty-four patients (19.7%), at a median time of four days. Peak urinary flow rate was the only independent factor which varied significantly between those suffering retention and those not (median of 16 and 19.5 ml/s respectively, P=0.005). Median preimplant IPSS was 4.0, with a median peak of 16 at 3 months. Actuarial median time to return to baseline IPSS was at 15 months. The peak IPSS above preimplant levels was correlated significantly in multivariate analysis with the number of seeds implanted superior to the physician-nominated anatomical base level of the prostate (P<0.009), as well as lower preimplant IPSS values.

CONCLUSIONS

In our series, preimplant urinary flow rate was the most important factor predictive of postimplant acute urinary retention. The patients' risk of having heightened IPSS change following implantation was correlated to a lower preimplant IPSS and an increased number of seeds implanted above the level of the prostatic base, possibly reflecting bladder base rather than urethral irritation in the development of acute urinary morbidity.

Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method

OBJECTIVE

To use information from genetic polymorphisms and from patients (drinking/exercise habits) to identify their association with stone disease, the main analytical and predictive tools being discriminant analysis (DA) and artificial neural networks (ANNs).

PATIENTS, SUBJECTS AND METHODS

Urinary stone disease is common in Taiwan; the formation of calcium oxalate stone is reportedly associated with genetic polymorphisms but there are many of these. Genotyping requires many individuals and markers because of the complexity of gene-gene and gene-environmental factor interactions. With the development of artificial intelligence, data-mining tools like ANNs can be used to derive more from patient data in predicting disease. Thus we compared 151 patients with calcium oxalate stones and 105 healthy controls for the presence of four genetic polymorphisms; cytochrome p450c17, E-cadherin, urokinase and vascular endothelial growth factor (VEGF). Information about environmental factors, e.g. water, milk and coffee consumption, and outdoor activities, was also collected. Stepwise DA and ANNs were used as classification methods to obtain an effective discriminant model.

RESULTS

With only the genetic variables, DA successfully classified 64% of the participants, but when all related factors (gene and environmental factors) were considered simultaneously, stepwise DA was successful in classifying 74%. The results for DA were best when six variables (sex, VEGF, stone number, coffee, milk, outdoor activities), found by iterative selection, were used. The ANN successfully classified 89% of participants and was better than DA when considering all factors in the model. A sensitivity analysis of the input parameters for ANN was conducted after the ANN program was trained; the most important inputs affecting stone disease were genetic (VEGF), while the second and third were water and milk consumption.

CONCLUSIONS

While data-mining tools such as DA and ANN both provide accurate results for assessing genetic markers of calcium stone disease, the ANN provides a better prediction than the DA, especially when considering all (genetic and environmental) related factors simultaneously. This model provides a new way to study stone disease in combination with genetic polymorphisms and environmental factors.

Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks

Artificial neural networks are intelligent systems that have been successfully used for prediction in different medical fields. In this study, efficiency of neural networks for prediction of lupus nephritis in patients with systemic lupus erythematosus (SLE) was compared with a logistic regression model and clinicians' diagnosis. Overall accuracy, sensitivity and specificity of the optimal neural network were 68.69, 73.77 and 62.96%, respectively. Overall accuracy of neural network was greater than the other two methods (P-value < 0.05). The neural network was more specific in predicting lupus nephritis (P-value < 0.01), but there was no significant difference between sensitivities of the three methods. Sensitivities of all three methods were greater than their specificities. We concluded that neural networks are efficient in predicting lupus nephritis in SLE patients.

A review of evidence of health benefit from artificial neural networks in medical intervention

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

Aspirin treatment improves bladder function after outlet obstruction in rabbits

OBJECTIVES

To examine whether bladder smooth muscle dysfunction after outlet obstruction could be altered by treatment with aspirin. Long-term outlet obstruction causes contractile and metabolic dysfunction of the bladder in vivo and in vitro. The evidence is growing that a decrease in bladder perfusion is an important cause of this phenomenon. The platelet aggregation inhibitor, acetylsalicylic acid (aspirin), has been used to improve perfusion of the heart for decades.

METHODS

Ten male New Zealand white rabbits were obstructed for 4 weeks. Five rabbits received no further treatment (Obs), and 5 rabbits received 2 mg/kg/day aspirin (Obs+aspirin), administered by an osmotic pump implanted subcutaneously 1 week before the surgical obstruction. The bleeding time was measured to confirm the effectiveness of the aspirin treatment. Three different control groups were created: sham-operated rabbits, unobstructed rabbits with pumps containing DMSO (vehicle), and unobstructed rabbits with pumps containing aspirin. The contractile responses of bladder strips to field stimulation, adenosine triphosphate, carbachol, and KCl were determined. A section of each detrusor tissue was fixed in formalin and used to determine the smooth muscle and collagen (connective tissue) volume fraction.

RESULTS

No differences were found in the bladder weights or responses to stimuli in the different control groups, which were therefore combined. Partial bladder outlet obstruction caused significant increases in the bladder weight of the obstructed animals (Obs+aspirin, 10.15 +/- 0.87 g; Obs, 10.17 +/- 0.88 g; and controls, 2.87 +/- 0.21 g). The aspirin treatment increased the bleeding time from 1.7 +/- 0.3 minutes to 3.3 +/- 0.1 minutes. The responses to field stimulation were significantly reduced in all of the obstructed rabbits. However, the responses of the bladder strips from the Obs rabbits to field stimulation were impaired to a significantly greater degree than were those from the Obs+aspirin rabbits. The response to 32-Hz stimulation was reduced by 86% in the Obs group but by only 64% in the Obs+aspirin group. The responses to carbachol were significantly reduced by 62% in the strips from the Obs rabbits, but the responses of the strips from the Obs+aspirin rabbits were similar to the responses of the strips from the controls. The responses to KCl and adenosine triphosphate were reduced, although they just failed to achieve statistical significance using Bonferroni's analysis. The ratio of smooth muscle and connective tissue shifted slightly toward smooth muscle after 4 weeks of obstruction, but no difference was found with or without aspirin treatment.

CONCLUSIONS

Low-dose aspirin has a small but significant protective effect on the contractile dysfunction induced by bladder outlet obstruction in rabbits, although the increase in bladder mass was not altered. Bladders of the same weight showed improved responses to all forms of stimulation after pretreatment with aspirin. Already used by millions of patients with heart diseases, aspirin could be a useful protection against contractile dysfunction of the obstructed bladder.

Increased blood flow after catheterization and drainage in the chronically obstructed rabbit urinary bladder

OBJECTIVES

To determine the effect of drainage on rabbit bladder blood flow after 4 weeks of partial outlet obstruction. Previous studies have shown that catheterization and drainage of the urinary bladder in control rabbits resulted in a significant nitric oxide-induced increase of blood flow to the bladder. It was also shown that 4 weeks' partial outlet obstruction caused a significant decrease in blood flow to the bladder.

METHODS

Male New Zealand White rabbits underwent partial outlet obstruction by standard methods. After 4 weeks, the blood flow to the bladder muscle and mucosa was determined by a microsphere technique. Within 1 to 2 minutes after transurethral catheterization and complete drainage of the bladder, the blood flow was again determined. Unobstructed animals served as controls. Four other control animals underwent a repetitive blood flow study during 10 minutes to determine the time frame of blood flow changes after drainage. Blood flow was also measured in 2 control rabbits after transurethral catheterization without drainage and in 2 control rabbits after drainage by suprapubic puncture. To exclude the possibility that increased intravesical pressure alters the blood flow measurements, the relationship between the intravesical volume and the bladder pressure was examined in the obstructed rabbits.

RESULTS

After drainage of the bladder, the blood flow to the bladder muscle increased 4.5-fold in the decompensated obstructed group (bladder weights greater than 15 g) and 2.5-fold in the compensated animals (bladder weights less than 5 g) and control animals. Blood flow to the mucosa followed the same pattern but without reaching significance. Blood flow returned to near baseline values within 5 minutes. Catheterization without drainage did not alter the blood flow. In contrast, drainage by puncture increased the blood flow significantly. Higher intravesical volumes increased the intravesical pressure slightly, but after opening the abdominal fascia, the intravesical pressure did not change with increasing volumes.

CONCLUSIONS

Although the previously shown decreased blood flow to the bladder smooth muscle may be an etiologic factor in bladder contractile dysfunction secondary to partial outlet obstruction, the bladder does have the ability to increase the blood flow after drainage. This ability could be a compensatory and possibly protective mechanism after outlet obstruction.

Effect of chronic bladder outlet obstruction on blood flow of the rabbit bladder

PURPOSE

Previous studies have shown that the initial reaction of the rabbit bladder to partial bladder outlet obstruction is increased blood flow at day 1 and a return to baseline blood flow at 1 week. Mucosal and muscle blood flow followed this pattern but mucosal blood flow was always 4 to 5-fold greater. In this study we examined the effect of 4 weeks of outlet obstruction on bladder blood flow and correlated it with the severity of bladder contractile dysfunction.

MATERIALS AND METHODS

A total of 14 male New Zealand White rabbits underwent partial outlet obstruction creation by standard methods. After 4 weeks the rabbits were anesthetized, and blood flow to the muscle and mucosa was determined by standard fluorescent microsphere technique. A section of each detrusor was used for in vitro contractility studies. Contractile responses to field stimulation, carbachol and potassium chloride were determined. A section of each detrusor tissue was fixed in formalin and used to determine the smooth muscle volume fraction.

RESULTS

Four weeks of partial bladder outlet obstruction caused a significant and variable increase in bladder weight and a decrease in blood flow to bladder muscle without changes in the blood flow to mucosa. There was a clear correlation between the severity of contractile dysfunction, bladder weight and the magnitude of the decrease in blood flow in muscle. The smooth muscle volume fraction remained stable at approximately 40%.

CONCLUSIONS

Bladder decompensation was associated with decreased blood flow to bladder smooth muscle. Because compensated obstructed bladders with relatively normal contractile function are also hypertrophied but have normal blood flow, decreased blood flow in decompensated bladders is not simply a response to bladder hypertrophy. From this study we hypothesize that decreased blood flow to bladder smooth muscle is an etiological factor in bladder contractile dysfunction (bladder decompensation) secondary to partial outlet obstruction.