Prevalence of opioid-induced adverse events across opioids commonly used for analgesic treatment in Japan: a multicenter prospective longitudinal study.

PURPOSE: Although opioids have been shown to be effective for cancer pain, opioid-induced adverse events (AEs) are common. To date, little is known about the differences in risks of AEs by opioid type. This study was performed to compare the prevalence of AEs across opioids commonly used for analgesic treatment in Japan. METHODS: This study was conducted as a preplanned secondary analysis of a multicenter prospective longitudinal study of inpatients with cancer pain who received specialized palliative care for cancer pain relief. We assessed daily AEs until termination of follow-up. We rated the severity of AEs based on the Common Terminology Criteria for Adverse Events version 5.0. We computed adjusted odds ratios for each AE (constipation, nausea and vomiting, delirium, and drowsiness) with the following variables: opioid, age, sex, renal dysfunction, and primary cancer site. RESULTS: In total, 465 patients were analyzed. Based on the descriptive analysis, the top four most commonly used opioids were included in the analysis: oxycodone, hydromorphone, fentanyl, and tramadol. With respect to the prevalence of AEs among all analyzed patients, delirium (n = 25, 6.3%) was the most frequent, followed by drowsiness (n = 21, 5.3%), nausea and vomiting (n = 19, 4.8%), and constipation (n = 28, 4.6%). The multivariate logistic analysis showed that no single opioid was identified as a statistically significant independent predictor of any AE. CONCLUSION: There was no significant difference in the prevalence of AEs among oxycodone, fentanyl, hydromorphone, and tramadol, which are commonly used for analgesic treatment in Japan.

Comparison of statistical models for estimating intervention effects based on time-to-recurrent-event in stepped wedge cluster randomized trial using open cohort design.

BACKGROUND: There are currently no methodological studies on the performance of the statistical models for estimating intervention effects based on the time-to-recurrent-event (TTRE) in stepped wedge cluster randomised trial (SWCRT) using an open cohort design. This study aims to address this by evaluating the performance of these statistical models using an open cohort design with the Monte Carlo simulation in various settings and their application using an actual example. METHODS: Using Monte Carlo simulations, we evaluated the performance of the existing extended Cox proportional hazard models, i.e., the Andersen-Gill (AG), Prentice-Williams-Peterson Total-Time (PWP-TT), and Prentice-Williams-Peterson Gap-time (PWP-GT) models, using the settings of several event generation models and true intervention effects, with and without stratification by clusters. Unidirectional switching in SWCRT was represented using time-dependent covariates. RESULTS: Using Monte Carlo simulations with the various described settings, in situations where inter-individual variability do not exist, the PWP-GT model with stratification by clusters showed the best performance in most settings and reasonable performance in the others. The only situation in which the performance of the PWP-TT model with stratification by clusters was not inferior to that of the PWP-GT model with stratification by clusters was when there was a certain amount of follow-up period, and the timing of the trial entry was random within the trial period, including the follow-up period. In situations where inter-individual variability existed, the PWP-GT model consistently underperformed compared to the PWP-TT model. The AG model performed well only in a specific setting. By analysing actual examples, it was found that almost all the statistical models suggested that the risk of events during the intervention condition may be somewhat higher than in the control, although the difference was not statistically significant. CONCLUSIONS: When estimating the TTRE-based intervention effects of SWCRT in various settings using an open cohort design, the PWP-GT model with stratification by clusters performed most reasonably in situations where inter-individual variability was not present. However, if inter-individual variability was present, the PWP-TT model with stratification by clusters performed best.

Clinical trial monitoring effectiveness: Remote risk-based monitoring versus on-site monitoring with 100% source data verification.

BACKGROUND/AIMS: Traditional on-site monitoring of clinical trials via frequent site visits and 100% source data verification is cost-consuming, and it still cannot guarantee data quality effectively. Depending on the types and designs of clinical trials, an alternative would be combining several monitoring methods, such as risk-based monitoring and remote monitoring. However, there is insufficient evidence of its effectiveness. This research compared the effectiveness of risk-based monitoring with a remote monitoring system with that of traditional on-site monitoring. METHODS: With a cloud-based remote monitoring system called beagle View®, we created a remote risk-based monitoring methodology that focused only on critical data and processes. We selected a randomized controlled trial conducted at Tohoku University Hospital and randomly sampled 11 subjects whose case report forms had already been reviewed by data managers. Critical data and processes were verified retrospectively by remote risk-based monitoring; later, all data and processes were confirmed by on-site monitoring. We compared the ability of remote risk-based monitoring to detect critical data and process errors with that of on-site monitoring with 100% source data verification, including an examination of clinical trial staff workload and potential cost savings. RESULTS: Of the total data points (n = 5617), 19.7% (n = 1105, 95% confidence interval = 18.7-20.7) were identified as critical. The error rates of critical data detected by on-site monitoring, remote risk-based monitoring, and data review by data managers were 7.6% (n = 84, 95% CI = 6.2-9.3), 7.6% (n = 84, 95% confidence interval = 6.2-9.3), and 3.9% (n = 43, 95% confidence interval = 2.9-5.2), respectively. The total number of critical process errors detected by on-site monitoring was 14. Of these 14, 92.9% (n = 13, 95% confidence interval = 68.5-98.7) and 42.9% (n = 6, 95% confidence interval = 21.4-67.4) of critical process errors were detected by remote risk-based monitoring and data review by data managers, respectively. The mean time clinical trial staff spent dealing with remote risk-based monitoring was 9.9 ± 5.3 (mean ± SD) min per visit per subject. Our calculations show that remote risk-based monitoring saved between 9 and 41 on-site monitoring visits, corresponding to a cost of between US$13,500 and US$61,500 per trial site. CONCLUSION: Remote risk-based monitoring was able to detect critical data and process errors as well as on-site monitoring with 100% source data verification, saving travel time and monitoring costs. Remote risk-based monitoring offers an effective alternative to traditional on-site monitoring of clinical trials.

Multicenter double-blind randomized controlled trial to evaluate the effectiveness and safety of bortezomib as a treatment for refractory systemic lupus erythematosus.

OBJECTIVES: The objective of this study is to evaluate the efficacy and safety of bortezomib for treating systemic lupus erythematosus (SLE), in patients whose disease activity could not be controlled. METHODS: Fourteen SLE patients with persistent disease activity were selected, who required prednisolone doses of >10 mg/d despite concomitant immunosuppressive therapy. Patients were randomly administered either bortezomib or a placebo, eight times. The primary and secondary end-points were a change in anti-dsDNA antibody titer at week 24 and the SLE Responder Index (SRI), respectively. RESULTS: In the bortezomib group, four out of eight patients discontinued the trial; three others failed to complete the minimum protocol treatment due to adverse reactions. The changes in anti-dsDNA antibody titers at week 24 were 4.24% and -1.96%, for the bortezomib and placebo groups, respectively, disconfirming bortezomib's efficacy. In contrast, the corresponding SRI at week 12 was 75% and 40%. CONCLUSIONS: As bortezomib therapy for SLE is associated with many adverse reactions, treatment indications should be selected carefully, and protocols should aim to prevent these occurrences. Although the change in anti-dsDNA antibody titer did not support the efficacy of bortezomib as a treatment for SLE, high SRI in the treatment group suggests bortezomib may utilize mechanisms other than inhibition of anti-dsDNA antibody production.

Burden of narcolepsy in Japan: A health claims database study evaluating direct medical costs and comorbidities.

OBJECTIVE: This study aimed to determine the burden of narcolepsy in terms of direct medical costs and comorbidities and compare it with the respective burden of schizophrenia, epilepsy, and ulcerative colitis as controls. METHODS: Patients diagnosed with narcolepsy (at least once based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, code G47.4) between April 2017 and March 2022 were identified on the health insurance claims database compiled by JMDC Inc. Patients with schizophrenia (F20), epilepsy (G40), and ulcerative colitis (K51) were matched as controls. Direct medical costs (including inpatient, outpatient, and medication costs) and comorbidities were analyzed. RESULTS: We identified 4,594 patients with narcolepsy (≥18 years), 18,376 with schizophrenia, 18,376 with epilepsy, and 4,594 with ulcerative colitis. The total annual direct medical cost per person with narcolepsy was 349,188 JPY. The cost for narcolepsy was less than that for schizophrenia, epilepsy, and ulcerative colitis. Several comorbidities, such as sleep apnea, attention deficit hyperactivity disorder (ADHD), and obesity were more prevalent in the narcolepsy group. CONCLUSIONS: The total direct cost for narcolepsy was approximately three times higher than the national medical expense for people aged 15-44 years (122,000 JPY in 2020), but lower than the total cost for all control diseases. The patients with narcolepsy were also likely to have comorbidities that affected their burden. These findings can contribute to future discussions on medical expense assistance programs for patients with narcolepsy.

The development and analysis of a Japanese modern comprehensive clinical data management training program.

Background: Clinical data management (CDM) collects, integrates, and makes data available. It plays a vital role in clinical research. However, there are few opportunities for Japanese clinical data managers to learn about its systematic framework, particularly in academic research organizations. While Japanese-language CDM training exists, its effectiveness in a Japanese context requires clarification. Objectives: We aimed to develop an advanced program of instruction for professionals to understand CDM and to determine the effectiveness of the training program. Methods and results: We developed an advanced program including risk-based monitoring and the Clinical Data Interchange Standards Consortium on a trial basis for clinical data managers to provide them with a comprehensive understanding of CDM. Fifty-two people attended the program and reported that they were highly satisfied with it. Conclusions: To provide comprehensive CDM training in Japan, it is imperative to continue improving the content and develop an advanced program. Due to the recent tightening of clinical research regulations and the development and dissemination of various systems for conducting clinical research, the competency-based educational program requires further development.

Prescription trends in Japanese advanced Parkinson's disease patients with non-motor symptoms: J-FIRST.

BACKGROUND: Non-motor symptoms (NMS) are important factors when selecting treatments for patients with advanced Parkinson's disease (PD). We sought to elucidate the prescribing practices for advanced PD patients with NMS in Japanese clinical practice. METHODS: We examined the prescription rates and doses of anti-PD drugs, and the use of non-steroidal anti-inflammatory drugs (NSAIDs) in post hoc analyses of a 52-week observational study of 996 PD patients with wearing-off on levodopa-containing therapy and ≥1 NMS. RESULTS: Dopamine agonists were the most frequently prescribed drugs combined with levodopa-containing drugs, followed by entacapone, zonisamide, istradefylline, selegiline, and amantadine. The daily dose of levodopa-containing drugs, rotigotine, entacapone, istradefylline, and droxidopa, and the levodopa-equivalent dose increased during the observation period. In a subgroup analysis of patients stratified by NMS status (improved/unchanged/deteriorated), the deteriorated group had higher prescription rates of entacapone and istradefylline, whereas the improved group had higher prescription rates of NSAIDs and zonisamide at Week 52. Prescriptions varied by geographical region for anti-PD drugs and by NMS status for NSAIDs. CONCLUSIONS: There were significant changes in the prescriptions and dosing of selected anti-PD drugs, especially newer drugs. Anti-PD drug and NSAID prescriptions also varied by changes in NMS status and geographic region.

Effectiveness of remote risk-based monitoring and potential benefits for combination with direct data capture.

BACKGROUND: In recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC. METHODS: R2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection. RESULTS: Of the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8-22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1-7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2-47.9) of uploaded photographs for R2BM were reduced. CONCLUSIONS: R2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.

Efficiency of eSource Direct Data Capture in Investigator-Initiated Clinical Trials in Oncology.

BACKGROUND: Clinical trials have become larger and more complex. Thus, eSource should be used to enhance efficiency. This study aimed to evaluate the impact of the multisite implementation of eSource direct data capture (DDC), which we define as eCRFs for direct data entry in this study, on efficiency by analyzing data from a single investigator-initiated clinical trial in oncology. METHODS: Operational data associated with the targeted study conducted in Japan was used to analyze time from data occurrence to data entry and data finalization, and number of visits to the site and time spent at the site by clinical research associates (CRAs). Additionally, simulations were performed on the change in hours at the clinical sites during the implementation of eSource DDC. RESULTS: No difference in time from data occurrence to data entry was observed between the DDC and the transcribed data fields. However, the DDC fields could be finalized 4 days earlier than the non-DDC fields. Additionally, although no difference was observed in the number of visits for source data verification (SDV) by CRAs, a comparison among sites that introduced eSource DDC and those that did not showed that the time spent at the site for SDV was reduced. Furthermore, the simulation results indicated that even a small amount of data to be collected or a small percentage of DDC-capable items may lead to greater efficiency when the number of subjects per site is significant. CONCLUSIONS: The implementation of eSource DDC may enhance efficiency depending on the study framework and type and number of items to be collected.

Cancer Pain Management in Patients Receiving Inpatient Specialized Palliative Care Services.

CONTEXT: Cancer pain is a common complication that is frequently undertreated in patients with cancer. OBJECTIVES: This study is aimed at assessing the time needed to achieve cancer pain management goals through specialized palliative care (SPC). METHODS: This was a multicenter, prospective, longitudinal study of inpatients with cancer pain who received SPC. Patients were continuously followed up until they considered cancer pain management successful, and we estimated this duration using the Kaplan-Meier method. We investigated the effectiveness of pain management using multiple patient-reported outcomes (PROs) and quantitative measures, including pain intensity change in the Brief Pain Inventory. A paired-sample t-test was used to compare the pain intensity at the beginning and end of the observation period. RESULTS: Cancer pain management based on the PROs was achieved in 87.9% (385/438) of all cases. In 94.5% (364/385) of these cases, cancer pain management was achieved within 1 week, and the median time to pain management was 3 days (95% confidence interval [CI], 2-3). The mean worst pain intensity in the last 24 h at the start and end of observation were 6.9 ± 2.2 and 4.0 ± 2.3, respectively, with a difference of -2.9 (95% CI, -3.2 to -2.6; p < 0.01). Overall, 81.6% of the patients reported satisfaction with cancer pain management, and 62 adverse events occurred. CONCLUSION: SPC achieved cancer pain management over a short period with a high level of patient satisfaction resulting in significant pain reduction and few documented adverse events.

Risk factors for developing dyskinesia among Parkinson's disease patients with wearing-off: J-FIRST.

BACKGROUND: Dyskinesia frequently occurs during long-term treatment with levodopa in patients with Parkinson's disease (PD) and impacts quality of life. Few studies have examined risk factors for developing dyskinesia in PD patients exhibiting wearing-off. Therefore, we investigated the risk factors and impact of dyskinesia in PD patients exhibiting wearing-off. METHODS: We investigated the risk factors and impact of dyskinesia in a 1-year observational study of Japanese PD patients exhibiting wearing-off (J-FIRST). Risk factors were assessed by logistic regression analyses in patients without dyskinesia at study entry. Mixed-effect models were used to evaluate the impact of dyskinesia on changes in Movement Disorder Society-Unified PD Rating Scale (MDS-UPDRS) Part I and PD Questionnaire (PDQ)-8 scores from one timepoint before dyskinesia was observed. RESULTS: Of 996 patients analyzed, 450 had dyskinesia at baseline, 133 developed dyskinesia within 1 year, and 413 did not develop dyskinesia. Female sex (odds ratio [95% confidence interval]: 2.636 [1.645-4.223]) and administration of a dopamine agonist (1.840 [1.083-3.126]), a catechol-O-methyltransferase inhibitor (2.044 [1.285-3.250]), or zonisamide (1.869 [1.184-2.950]) were independent risk factors for dyskinesia onset. MDS-UPDRS Part I and PDQ-8 scores increased significantly after the onset of dyskinesia (least-squares mean change [standard error] at 52 weeks: 1.11 [0.52], P = 0.0336; 1.53 [0.48], P = 0.0014; respectively). CONCLUSION: Female sex and administration of a dopamine agonist, a catechol-O-methyltransferase inhibitor, or zonisamide were risk factors for dyskinesia onset within 1 year in PD patients exhibiting wearing-off. Nonmotor symptoms and quality of life deteriorated after dyskinesia onset.

Achieving Bright Organic Light-Emitting Field-Effect Transistors with Sustained Efficiency through Hybrid Contact Design.

Organic light-emitting field-effect transistors (OLEFETs) with bilayer structures have been widely studied due to their potential to integrate high-mobility organic transistors and efficient organic light-emitting diodes. However, these devices face a major challenge of imbalance charge transport, leading to a severe efficiency roll-off at high brightness. Here, we propose a solution to this challenge by introducing a transparent organic/inorganic hybrid contact with specially designed electronic structures. Our design aims to steadily accumulate the electrons injected into the emissive polymer, allowing the light-emitting interface to effectively capture more holes even when the hole current increases. Our numerical simulations show that the capture efficiency of these steady electrons will dominate charge recombination and lead to a sustained external quantum efficiency of 0.23% over 3 orders of magnitude of brightness (4 to 7700 cd/m2) and current density (1.2 to 2700 mA/cm2) from -4 to -100 V. The same enhancement is retained even after increasing the external quantum efficiency (EQE) to 0.51%. The high and tunable brightness with stable efficiency offered by hybrid-contact OLEFETs makes them ideal light-emitting devices for various applications. These devices have the potential to revolutionize the field of organic electronics by overcoming the fundamental challenge of imbalance charge transport.

Conventional and Kampo Medicine Treatment for Mild-to-moderate COVID-19: A Multicenter, Retrospective, Observational Study by the Integrative Management in Japan for Epidemic Disease (IMJEDI Study-observation).

Objective Patients in whom coronavirus disease 2019 (COVID-19) was suspected or confirmed between January 1, 2020, and October 31, 2021, were enrolled from Japanese hospitals in this multicenter, retrospective, observational study. Methods Data on the treatment administered (including conventional and Kampo medicine) and changes in common cold-like symptoms (such as fever, cough, sputum, dyspnea, fatigue, and diarrhea) were collected from their medical records. The primary outcome was the number of days without a fever (with a body temperature <37°C). The secondary outcomes were symptomatic relief and the worsening of illness, defined as the presence of a condition requiring oxygen inhalation. The outcomes of patients treated with and without Kampo medicine were compared. Patients We enrolled 962 patients, among whom 528 received conventional and Kampo treatment (Kampo group) and 434 received conventional treatment (non-Kampo group). Results Overall, after adjusting for the staging of COVID-19 and risk factors, there were no significant between-group differences in the symptoms or number of days being afebrile. After performing propensity score matching and restricting the included cases to those with confirmed COVID-19 who did not receive steroid administration and initiated treatment within 4 days from the onset, the risk of illness worsening was significantly lower in the Kampo group than in the non-Kampo group (odds ratio=0.113, 95% confidence interval: 0.014-0.928, p=0.0424). Conclusion Early Kampo treatment may suppress illness worsening risk in COVID-19 cases without steroid use. Further randomized controlled studies are needed to confirm the clinical benefit of Kampo medicine for COVID-19.

A Multi-institutional Study to Diagnose the Risk of Lymph Node Metastasis Using a CRP Genetic Polymorphism Test Kit in pT1, cN0 Thoracic Esophageal Squamous Cell Carcinoma.

BACKGROUND/AIM: For patients with T1a muscularis mucosae (MM) esophageal squamous cell carcinoma (ESCC) with lymphovascular invasion (LVI) or T1b submucosal (SM) ESCC, endoscopic resection is non-curative, and adjuvant treatment entailing esophagectomy or definitive chemoradiotherapy is necessary. This is because about 30% of these cases have lymph node (LN) metastasis. The purpose of this study was to test the utility of a CRP genetic polymorphism test kit for determining the risk of LN metastasis with the aim of eliminating additional invasive adjuvant therapy. PATIENTS AND METHODS: This is a retrospective, multi-institutional, observational study. The CRP 1846C>T genetic polymorphisms were identified using a fully automated genotyping system. The primary end points were an 85% negative predictive value (NPV) for diagnosis of LN metastasis in pT1a (MM) and 80% NPV in pT1b (SM1) patients. RESULTS: A total of 742 ESCC (105 pMM, 166 pSM1 and 471 pSM2-3) patients who had received esophagectomy with 2- or 3-field LN dissection at 65 institutions were enrolled. According to this test, patients with the C/C and C/T genotypes were considered to be low risk. The NPVs using this test were 82.8% in pMM and 71.7% in pSM1 patients. CONCLUSION: CRP 1846C>T genetic polymorphism is not a useful diagnostic indicator for determining the risk of LN metastasis; however, the possibility that CRP gene polymorphisms are involved in the mechanism of lymph node metastasis in solid tumors still remains.

Semi-automated Conversion of Clinical Trial Legacy Data into CDISC SDTM Standards Format Using Supervised Machine Learning.

OBJECTIVE: This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods: data normalization; feature extraction by distributed representation of dataset names, variable names, and variable labels; and supervised machine learning. MATERIALS AND METHODS: Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation: Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms-namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms-to identify the one that could generate the best prediction model. RESULTS: The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format. CONCLUSION: By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.

Influence of istradefylline on non-motor symptoms of Parkinson's disease: A subanalysis of a 1-year observational study in Japan (J-FIRST).

INTRODUCTION: The non-motor symptoms (NMSs) of Parkinson's disease (PD) significantly impact the patient's health-related quality of life. This subanalysis of the J-FIRST study evaluated the effect of istradefylline, a selective adenosine A2A receptor antagonist, on NMSs in istradefylline-naïve Japanese patients with PD. METHODS: Patients with PD and ≥1 NMS and 'wearing-off' with their current antiparkinsonian treatment were observed for up to 52 weeks. The effect of istradefylline on NMSs was measured in terms of changes in the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part 1 total, individual sub-items scores and the 8 item PD questionnaire (PDQ-8) estimated by the marginal structural model. RESULTS: Overall, 732 patients were istradefylline-naïve prior to the study, of whom 171 were treated with istradefylline for ≥8 weeks during the observation period (istradefylline-treated patients). At baseline, istradefylline-treated patients were more likely to have a dyskinesia (49.7% vs 40.8%) and received a significantly higher daily dose of levodopa (462.8 mg vs 413.0 mg) than those who did not receive istradefylline (n = 561). MDS-UPDRS Part 1 total score at the end of the 52-week observational period slightly increased in patients who received istradefylline and those who did not (0.49 ± 0.41 vs 0.07 ± 0.20; P = 0.36). There were no statistically significant differences between the two groups of patients in terms of changes in the MDS-UPDRS Part 1 total score or any sub-items, or in the PDQ-8 total score. CONCLUSION: NMSs remained generally controlled in istradefylline-treated Japanese patients with PD who exhibited wearing-off with their current antiparkinsonian treatment. Istradefylline could be a feasible treatment option for patients with advanced PD, without worsening existing NMSs.

Real-World Nonmotor Changes in Patients with Parkinson's Disease and Motor Fluctuations: J-FIRST.

BACKGROUND: Nonmotor symptoms (NMSs) of Parkinson's disease (PD) impair health-related quality of life. OBJECTIVES: To identify changes in NMSs during 52 weeks in Japanese PD patients exhibiting motor fluctuations. METHODS: In PD patients with ≥1 NMS and wearing-off, changes in total/subscore of the Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) Part I and 8-item PD Questionnaire were assessed. Group-based trajectory models were used to characterize longitudinal patterns of MDS-UPDRS Part I. RESULTS: Data from 996 patients were analyzed. MDS-UPDRS Part I subscores for cognitive function decreased linearly over time. Total and subscores for apathy and lightheadedness on standing significantly deteriorated with fluctuations, whereas other subscores fluctuated without significant deterioration. Changes in the MDS-UPDRS Part I total score correlated with changes in the 8-item PD Questionnaire total score. Based on group-based trajectory models, longitudinal pattern analysis of MDS-UPDRS Part I scores yielded the following 3 separate groups: unchanged (63.8%), deteriorated (20.1%), and improved (16.2%). The improved group had significantly more NMSs at baseline, significantly higher MDS-UPDRS Part I/8-item PD Questionnaire total scores, and modified Hoehn and Yahr scores, and had received treatment for NMSs. The multivariate analysis revealed significant associations between severe motor disability and receiving any treatment for NMSs at baseline and improvement of MDS-UPDRS Part I total scores. CONCLUSIONS: Changes in MDS-UPDRS Part I scores were variable and related to changes in health-related quality of life in PD patients with motor fluctuations.

Computer-aided diagnosis system for breast ultrasound images using deep learning.

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for the classification of malignant and benign masses in the breast using ultrasonography based on a convolutional neural network (CNN), a state-of-the-art deep learning technique. We explored the regions for the correct classification by generating a heat map that presented the important regions used by the CNN for human malignancy/benign classification. Clinical data was obtained from a large-scale clinical trial previously conducted by the Japan Association of Breast and Thyroid Sonology. Images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations were collected, with each breast mass captured from various angles using an ultrasound (US) imaging probe. We constructed an ensemble network by combining two CNN models (VGG19 and ResNet152) fine-tuned on balanced training data with augmentation and used the mass-level classification method to enable the CNN to classify a given mass using all views. For an independent test set consisting of 154 masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9% (95% confidence interval 84.5-97.3), a specificity of 87.0% (79.5-94.5), and area under the curve (AUC) of 0.951 (0.916-0.987) compared to that of the two CNN models. In addition, our study indicated that the breast masses themselves were not detected by the CNN as important regions for correct mass classification. Collectively, this CNN-based CAD system is expected to assist doctors by improving the diagnosis of breast cancer in clinical practice.

Clinical manifestations of nonmotor symptoms in 1021 Japanese Parkinson's disease patients from 35 medical centers.

INTRODUCTION: We aimed to investigate the prevalence and severity of nonmotor symptoms (NMSs) and to identify factors affecting NMSs and the health-related quality of life of Japanese patients with Parkinson's disease (PD). METHODS: A total of 1021 patients with PD who had one or more NMS and showed wearing-off under anti-parkinsonian treatment were enrolled from 35 medical centers in Japan for this observational study. The primary measurements were the Movement Disorder Society unified Parkinson's disease rating scale (MDS-UPDRS) part I and the Parkinson's Disease Questionnaire (PDQ-8). The relationships of MDS-UPDRS and PDQ-8 with the patient's clinical background and undertaken medical interventions were determined. Here, we report baseline data of our 52-week ongoing study. RESULTS: The mean MDS-UPDRS part I and PDQ-8 scores were 10.9 and 7.3, respectively. The most common NMSs were constipation problems (85.4%), sleep problems (73.7%), pain and other sensations (72.7%) and daytime sleepiness (72.0%). Fatigue was an NMS that affected 79.6% of females but only 72.6% of males, whereas features of dopamine dysregulation syndrome affected only 5.6% of females and 10.8% of males. Positive correlations were found between the MDS-UPDRS part I and the PDQ-8 (p < 0.0001, r = 0.56) and between the number of NMSs and the PDQ-8 score (p < 0.0001, r = 0.47). CONCLUSIONS: This study revealed distinctive patterns of NMSs in Japanese patients with PD and suggested that the prevalence and severity of NMSs vary between sexes, and that the NMSs are important factors affecting the long-term quality of life of PD patients.