Objectives: To investigate the clinical features of detrusor underactivity (DU) in elderly men without neurological disorders.
Methods: A total of 336 men aged ≥50 years without neurogenic disorders who underwent pressure flow studies and who had DU or bladder outlet obstruction (BOO) were reviewed retrospectively. According to the bladder contractility index (BCI) and the BOO index (BOOI), the subjects were classified into the following three groups: (a) pure DU group, BCI < 100 and BOOI < 40; (b) DU + BOO group, BCI < 100 and BOOI ≥ 40; and (c) pure BOO group, BCI ≥ 100 and BOOI ≥ 40. Subjective and objective parameters were compared among the three groups, and the predictors for pure DU were evaluated by multivariate analysis. Results: Of the 336 patients, 205 who met the study criteria were included in the analysis: 63 (30.7%) with pure DU, 48 (23.4%) with DU + BOO, and 94 (45.9%) with pure BOO. The proportion of the pure DU group increased with increasing age. Prostate volume was the lowest in the pure DU group. Frequency, urgency on the International Prostate Symptom Score (IPSS), and the IPSS storage subscore were the lowest in the pure DU group. Multivariate analysis showed that age (odds ratio [OR] 1.114 [95% CI, 1.032-1.203], P = .005), prostate volume (OR 0.968 [95% CI, 0.949-0.987], P = .001), and urgency (OR 0.623 [95% CI, 0.431-0.900], P = .012) were predictors of pure DU. Conclusion: Older age, smaller prostate volume, and less urgency may be clinical features of pure DU.
Faults that impair performance can occur in a heat source system because it comprises various devices and has complex controls. This article presents a novel method for fault detection and diagnosis (FDD). This study focused on a real system with a water thermal storage tank. First, system behaviors in response to faults were determined using a detailed system simulation. Then, a fault database was generated using the simulation results with fault labels. We preprocessed the database and converted the data into images. Then, convolutional neural networks (CNNs) were trained using the database, and the trained CNNs were used for diagnosing real data. The accuracy of the CNNs was 98.7% in training, and real data were diagnosed with probabilities. We analyzed the real data, where the probability indicated the likely presence of a fault and reviewed how the real data were similar to the fault assumed in the simulation. We concluded that the proposed FDD method will help in analyzing real data, as it indicates faults emerging in the real data with probability, whereas conventional data analysis requires checking the data using expert knowledge.
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