Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Predicting Obstructive Sleep Apnea Based on Computed Tomography Scan Using Deep Learning Models.

RATIONALE: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population due to limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. OBJECTIVES: To predict OSA and its severity based on paranasal CT using a 3-dimensional deep learning algorithm. METHODS: One internal dataset (n=798) and two external datasets (n=135 and 85) were used in this study. In the internal dataset, 92 normal, 159 mild, 201 moderate, and 346 severe OSA participants were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a 3-dimensional convolutional neural network (CNN)-based part treating unstructured data (CT images) and a multi-layer perceptron (MLP)-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. MEASUREMENTS AND MAIN RESULTS: In four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI] 86.8-88.6) in the internal dataset and 84.0% (95% CI 83.0-85.1) and 86.3% (95% CI 85.3-87.3) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), The area under the receiver operating characteristics (AUROC), accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI 0.899-0.922), 91.0% (95% CI 90.1-91.9), 89.9% (95% CI 88.8-90.9), 93.5% (95% CI 92.7-94.3), and 93.2% (95% CI 92.5-93.9), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and AUROC for two-class classification (p<0.001). CONCLUSIONS: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis.

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to estimate the appendix orifice (AO), flexures, and "outside of the body," sequentially. Subsequently, DBSCAN algorithm is applied to identify anatomical sections. Clustering-based analysis integrates clinical knowledge and context based on the anatomical section within the model. We address challenges posed by colonoscopy images through non-informative removal preprocessing. The image data is labeled by clinicians, and the system deduces section correspondence stochastically. The model categorizes the colon into three sections: right (cecum and ascending colon), middle (transverse colon), and left (descending colon, sigmoid colon, rectum). We estimated the appearance time of anatomical boundaries with an average error of 6.31 s for AO, 9.79 s for HF, 27.69 s for SF, and 3.26 s for outside of the body. The proposed method can facilitate future advancements towards AI-based automatic reporting, offering time-saving efficacy and standardization.

Immersive virtual reality simulation training for cesarean section: a randomized controlled trial.

BACKGROUND: Caesarean section (CS) is a complex surgical procedure that involves many steps and requires careful precision. Virtual reality (VR) simulation has emerged as a promising tool for medical education and training, providing a realistic and immersive environment for learners to practice clinical skills and decision-making. This study aimed to evaluate the educational effectiveness of a VR simulation program in training the management of patients with premature rupture of membranes (PROM) and CS. MATERIALS AND METHODS: A two-arm parallel randomized controlled trial was conducted with 105 eligible participants randomly assigned to the VR group ( n =53) or the control group ( n =52) in a 1:1 ratio. The VR group received VR simulation training focused on PROM management and CS practice, while the control group watched a video presentation with narrative of clinical scenario and recording of CS. Both groups completed questionnaires assessing their prior experiences with VR, experience in managing patients with PROM and performing CS, as well as their confidence levels. These questionnaires were administered before and after the intervention, along with a mini-test quiz. RESULTS: Baseline characteristics and previous experiences were comparable between the two groups. After the intervention, the VR group had higher confidence scores in all four aspects, including managing patients with PROM, performing CS as an operator, and understanding the indications and complications of CS, compared to the control group. The VR group also achieved significantly higher scores on the mini-test quiz [median (interquartile range), 42 (37-48) in the VR group; 36 (32-40) in the control group, P <0.001]. CONCLUSION: VR simulation program can be an effective educational tool for improving participants' knowledge and confidence in managing patients with PROM and performing CS.

Digital Therapeutic Exercises Using Augmented Reality Glasses for Frailty Prevention among Older Adults.

OBJECTIVES: The objective of this study was to investigate the effects of a digital therapeutic exercise platform for pre-frail or frail elderly individuals using augmented reality (AR) technology accessed through glasses. A tablet-based exercise program was utilized for the control group, and a non-inferiority assessment was employed. METHODS: The participants included older adult women aged 65 years and older residing in Incheon, South Korea. A digital therapeutic exercise program involving AR glasses or tablet-based exercise was administered twice a week for 12 weeks, with gradually increasing exercise duration. Statistical analysis was conducted using the t-test and Wilcoxon rank sum test for non-inferiority assessment. RESULTS: In the primary efficacy assessment, regarding the change in lower limb strength, a non-inferior result was observed for the intervention group (mean change, 5.46) relative to the control group (mean change, 4.83), with a mean difference of 0.63 between groups (95% confidence interval, -2.33 to 3.58). Changes in body composition and physical fitness-related variables differed non-significantly between the groups. However, the intervention group demonstrated a significantly greater increase in cardiorespiratory endurance (p < 0.005) and a significantly larger decrease in the frailty index (p < 0.001). CONCLUSIONS: An AR-based digital therapeutic program significantly and positively contributed to the improvement of cardiovascular endurance and the reduction of indicators of aging among older adults. These findings underscore the value of digital therapeutics in mitigating the effects of aging.

Deep Learning-Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates.

Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve during speech, is a typical poor outcome that should be evaluated after cleft palate repair. The interpretation of VPI considering both imaging analysis and perceptual evaluation is essential for further management. The authors retrospectively reviewed patients with repaired cleft palates who underwent assessment for velopharyngeal function, including both videofluoroscopic imaging and perceptual speech evaluation. The final diagnosis of VPI was made by plastic surgeons based on both assessment modalities. Deep learning techniques were applied for the diagnosis of VPI and compared with the human experts' diagnostic results of videofluoroscopic imaging. In addition, the results of the deep learning techniques were compared with a speech pathologist's diagnosis of perceptual evaluation to assess consistency with clinical symptoms. A total of 714 cases from January 2010 to June 2019 were reviewed. Six deep learning algorithms (VGGNet, ResNet, Xception, ResNext, DenseNet, and SENet) were trained using the obtained dataset. The area under the receiver operating characteristic curve of the algorithms ranged between 0.8758 and 0.9468 in the hold-out method and between 0.7992 and 0.8574 in the 5-fold cross-validation. Our findings demonstrated the deep learning algorithms performed comparable to experienced plastic surgeons in the diagnosis of VPI based on videofluoroscopic velopharyngeal imaging.

Deep Learning of Speech Data for Early Detection of Alzheimer's Disease in the Elderly.

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. OBJECTIVE: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. MATERIALS AND METHODS: The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results. RESULTS: Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group. CONCLUSIONS: The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.

Exploring the Category and Use Cases on Digital Therapeutic Methodologies.

OBJECTIVES: As the Fourth Industrial Revolution advances, there is a growing interest in digital technology. In particular, the use of digital therapeutics (DTx) in healthcare is anticipated to reduce medical expenses. However, analytical research on DTx is still insufficient to fuel momentum for future DTx development. The purpose of this article is to analyze representative cases of different types of DTx from around the world and to propose a classification system. METHODS: In this exploratory study examining DTx interaction types and representative cases, we conducted a literature review and selected seven interaction types that were utilized in a large number of cases. Then, we evaluated the specific characteristics of each DTx mechanism by reviewing the relevant literature, analyzing their indications and treatment components. A representative case for each mechanism was provided. RESULTS: Cognitive behavioral therapy, distraction therapy, graded exposure therapy, reminiscence therapy, art therapy, therapeutic exercise, and gamification are the seven categories of DTx interaction types. Illustrative examples of each variety are provided. CONCLUSIONS: Efforts from both the government and private sector are crucial for success, as standardization can decrease both the expense and the time required for government-led DTx development. The private sector should partner with medical facilities to stimulate potential demand, carry out clinical research, and produce scholarly evidence.

Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists.

BACKGROUND: Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. OBJECTIVES: We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. METHODS: A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. RESULTS: In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. CONCLUSIONS: Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.

User Experience of Augmented Reality Glasses-based Tele-Exercise in Elderly Women.

OBJECTIVES: The purpose of this study was to identify any difference in user experience between tablet- and augmented reality (AR) glasses-based tele-exercise programs in elderly women. METHODS: Participants in the AR group (n = 14) connected Nreal glasses with smartphones to display a pre-recorded exercise program, while each member of the tablet group (n = 13) participated in the same exercise program using an all-in-one personal computer. The program included sitting or standing on a chair, bare-handed calisthenics, and muscle strengthening using an elastic band. The exercise movements were presented first for the upper and then the lower extremities, and the total exercise time was 40 minutes (5 minutes of warm-up exercises, 30 minutes of main exercises, and 5 minutes of cool-down exercises). To evaluate the user experience, a questionnaire consisting of a 7-point Likert scale was used as a measurement tool. In addition, the Wilcoxon rank-sum test was used to assess differences between the two groups. RESULTS: Of the six user experience scales, attractiveness (p = 0.114), stimulation (p = 0.534), and novelty (p = 0.916) did not differ significantly between the groups. However, efficiency (p = 0.006), perspicuity (p = 0.008), and dependability (p = 0.049) did vary significantly between groups. CONCLUSIONS: When developing an AR glasses-based exercise program for the elderly, the efficiency, clarity, and stability of the program must be considered to meet the participants' needs.

Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults.

Teeth are known to be the most accurate age indicators of human body and are frequently applied in forensic age estimation. We aimed to validate data mining-based dental age estimation, by comparing the accuracy of the estimation and classification performance of 18-year thresholds with conventional methods and with data mining-based age estimation. A total of 2657 panoramic radiographs were collected from Koreans and Japanese populations aged 15 to 23 years. They were subdivided into a training and internal test set of 900 radiographs each from Koreans, and an external test set of 857 radiographs from Japanese. We compared the accuracy and classification performance of the test sets from conventional methods with those from the data mining models. The accuracy of the conventional method with the internal test set was slightly higher than that of the data mining models, with a slight difference (mean absolute error < 0.21 years, root mean square error < 0.24 years). The classification performance of the 18-year threshold was also similar between the conventional method and the data mining models. Thus, conventional methods can be replaced by data mining models in forensic age estimation using second and third molar maturity of Korean juveniles and young adults.

Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis.

Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false positive). Here we showed that transfer learning was more effective than scratch learning in terms of area under curve, sensitivity, specificity and negative/positive predictive value, that about 3900 images were minimally required to demonstrate an acceptable performance, and that algorithm performance can be customized according to the population characteristics by adjusting threshold value.

Usage of the Internet of Things in Medical Institutions and its Implications.

OBJECTIVES: The purpose of this study was to explore new ways of creating value in the medical field and to derive recommendations for the role of medical institutions and the government. METHODS: In this paper, based on expert discussion, we classified Internet of Things (IoT) technologies into four categories according to the type of information they collect (location, environmental parameters, energy consumption, and biometrics), and investigated examples of application. RESULTS: Biometric IoT diagnoses diseases accurately and offers appropriate and effective treatment. Environmental parameter measurement plays an important role in accurately identifying and controlling environmental factors that could be harmful to patients. The use of energy measurement and location tracking technology enabled optimal allocation of limited hospital resources and increased the efficiency of energy consumption. The resulting economic value has returned to patients, improving hospitals' cost-effectiveness. CONCLUSIONS: Introducing IoT-based technology to clinical sites, including medical institutions, will enhance the quality of medical services, increase patient safety, improve management efficiency, and promote patient-centered medical services. Moreover, the IoT is expected to play an active role in the five major tasks of facility hygiene in medical fields, which are all required to deal with the COVID-19 pandemic: social distancing, contact tracking, bed occupancy control, and air quality management. Ultimately, the IoT is expected to serve as a key element for hospitals to perform their original functions more effectively. Continuing investments, deregulation policies, information protection, and IT standardization activities should be carried out more actively for the IoT to fulfill its expectations.

Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging.

Thus far, there have been no reported specific rules for systematically determining the appropriate augmented sample size to optimize model performance when conducting data augmentation. In this paper, we report on the feasibility of synthetic data augmentation using generative adversarial networks (GAN) by proposing an automation pipeline to find the optimal multiple of data augmentation to achieve the best deep learning-based diagnostic performance in a limited dataset. We used Waters' view radiographs for patients diagnosed with chronic sinusitis to demonstrate the method developed herein. We demonstrate that our approach produces significantly better diagnostic performance parameters than models trained using conventional data augmentation. The deep learning method proposed in this study could be implemented to assist radiologists in improving their diagnosis. Researchers and industry workers could overcome the lack of training data by employing our proposed automation pipeline approach in GAN-based synthetic data augmentation. This is anticipated to provide new means to overcome the shortage of graphic data for algorithm training.

Tele-consent using mixed reality glasses (NREAL) in pediatric inguinal herniorrhaphy: a preliminary study.

There is an increasing demand and need for patients and caregivers to actively participate in the treatment process. However, when there are unexpected findings during pediatrics surgery, access restrictions in the operating room may lead to a lack of understanding of the medical condition, as the caregivers are forced to indirectly hear about it. To overcome this, we designed a tele-consent system that operates through a specially constructed mixed reality (MR) environment during surgery. We enrolled 11 patients with unilateral inguinal hernia and their caregivers among the patients undergoing laparoscopic inguinal herniorrhaphy between January through February 2021. The caregivers were informed of the intraoperative findings in real-time through MR glasses outside the operating room. After surgery, we conducted questionnaire surveys to evaluate the satisfaction and usefulness of tele-consent. We identified contralateral patent processus vaginalis in seven out of 11 patients, and then additionally performed surgery on the contralateral side with tele-consent from their caregivers. Most caregivers and surgeons answered positively about the satisfaction and usefulness of tele-consent. This study found that tele-consent with caregivers using MR glasses not only increased the satisfaction of caregivers and surgeons, but also helped to accommodate real-time findings by adapting surgical plan through the tele-consent.

Review of Smart Hospital Services in Real Healthcare Environments.

OBJECTIVE: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. METHODS: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group interview with experts in hospital information systems, and ultimately derived eight smart hospital service types. RESULTS: Smart hospital services can be classified into the following types: services based on location recognition and tracking technology that measures and monitors the location information of an object based on short-range communication technology; high-speed communication network-based services based on new wireless communication technology; Internet of Things-based services that connect objects embedded with sensors and communication functions to the internet; mobile health services such as mobile phones, tablets, and wearables; artificial intelligence-based services for the diagnosis and prediction of diseases; robot services provided on behalf of humans in various medical fields; extended reality services that apply hyper-realistic immersive technology to medical practice; and telehealth using ICT. CONCLUSIONS: Smart hospitals can influence health and medical policies and create new medical value by defining and quantitatively measuring detailed indicators based on data collected from existing hospitals. Simultaneously, appropriate government incentives, consolidated interdisciplinary research, and active participation by industry are required to foster and facilitate smart hospitals.

Effects of smartphone mirroring-based telepresence exercise on body composition and physical function in obese older women.

AIMS: This study aimed to develop a smartphone mirroring-based telepresence exercise program that can be performed at home while allowing for real-time feedback by instructors. METHODS: For this randomized controlled trial, 29 obese older women aged 66-87 years with ≥ 30% body fat were recruited at a senior citizen center. The intervention group was provided with the smartphone mirroring-based telepresence exercise program, in which participants exercised in their homes for 20-40 min three times a week for 12 weeks. Participants in the control group performed the same exercise program at the senior citizen center. Body composition and functional abilities were measured before and after the program. RESULTS: Women in the intervention group showed a decrease in their body fat percentage (P = 0.026) and an increase in grip strength (P = 0.008). In the control group, women demonstrated a decrease in their weight (P = 0.006) and body fat percentage (P = 0.001) and an increase in skeletal muscle (P = 0.044) and grip strength (P = 0.006). CONCLUSION: Smartphone mirroring-based telepresence exercises at home lower body fat percentage and increase muscle strength similar to traditional group exercises. They present an innovative way for obese older women to improve and maintain their health. TRIAL REGISTRATION: Clinical Research Information Service KCT0006147.

Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

Augmented Reality-Based Visual Cue for Guiding Central Catheter Insertion in Pediatric Oncologic Patients.

BACKGROUND: Pediatric hemato-oncologic patients require central catheters for chemotherapy, and the junction of the superior vena cava and right atrium is considered the ideal location for catheter tips. Skin landmarks or fluoroscopic supports have been applied to identify the cavoatrial junction; however, none has been recognized as the gold standard. Therefore, we aim to develop a safe and accurate technique using augmented reality technology for the location of the cavoatrial junction in pediatric hemato-oncologic patients. METHODS: Fifteen oncology patients who underwent chest computed tomography were enrolled for Hickman catheter or chemoport insertion. With the aid of augmented reality technology, three-dimensional models of the internal jugular veins, external jugular veins, subclavian veins, superior vena cava, and right atrium were constructed. On inserting the central vein catheters, the cavoatrial junction identified using the three-dimensional models were marked on the body surface, the tip was positioned at the corresponding location, and the actual insertion location was confirmed using a portable x-ray machine. The proposed method was evaluated by comparing the distance from the cavoatrial junction to the augmented reality location with that to the conventional location on x-ray. RESULTS: The mean distance between the cavoatrial junction and augmented reality location on x-ray was 1.2 cm, which was significantly shorter than that between the cavoatrial junction and conventional location (1.9 cm; P = 0.027). CONCLUSIONS: Central catheter insertion using augmented reality technology is more safe and accurate than that using conventional methods and can be performed at no additional cost in oncology patients.

Maximum Voided Volume Is a Better Clinical Parameter for Bladder Capacity Than Maximum Cystometric Capacity in Patients With Lower Urinary Tract Symptoms/Benign Prostatic Hyperplasia: A Prospective Cohort Study.

PURPOSE: Bladder capacity is an important parameter in the diagnosis of lower urinary tract dysfunction. We aimed to determine whether the maximum bladder capacity (MCC) measured during a urodynamic study was affected by involuntary detrusor contraction (IDC) in patients with Lower Urinary Tract Symptoms (LUTS)/Benign Prostatic Hyperplasia (BPH). METHODS: Between March 2020 and April 2021, we obtained maximum voided volume (MVV) from a 3-day frequency-volume chart, MCC during filling cystometry, and maximum anesthetic bladder capacity (MABC) during holmium laser enucleation of the prostate under spinal or general anesthesia in 139 men with LUTS/BPH aged >50 years. Patients were divided according to the presence of IDC during filling cystometry. We assumed that the MABC is close to the true value of the MCC, as it is measured under the condition of minimizing neural influence over the bladder. RESULTS: There was no difference in demographic and clinical characteristics between the non-IDC (n=20) and IDC groups (n=119) (mean age, 71.5±7.4) (P>0.05). The non-IDC group had greater bladder volume to feel the first sensation, first desire, and strong desire than the IDC group (P<0.001). In all patients, MABC and MVV were correlated (r=0.41, P<0.001); however, there was no correlation between MCC and MABC (r=0.19, P=0.02). There was no significant difference in MABC between the non-IDC and IDC groups (P=0.19), but MVV and MCC were significantly greater in the non-IDC group (P<0.001). There was no significant difference between MABC and MVV (MABC-MVV, P=0.54; MVV/MABC, P=0.07), but there was a significant difference between MABC and MCC between the non-IDC and IDC groups (MABC-MCC, P<0.001; MCC/MABC, P<0.001). CONCLUSION: Maximum bladder capacity from a urodynamic study does not represent true bladder capacity because of involuntary contractions.