BackgroundDecrease of dual-task (DT) ability is known to be one of the risk factors for falls. We developed a new game concept, Dual-Task Tai Chi (DTTC), using Microsoft’s motion-capture device Kinect, and demonstrated that the DTTC test can quantitatively evaluate various functions that are known risk factors for falling in elderly adults. Moreover, DT training has been attracting attention as a way to improve balance and DT ability. However, only a few studies have reported that it improves cognitive performance.ObjectiveThe purpose of this study was to demonstrate whether or not a 12-week program of DTTC training would effectively improve cognitive functions.MethodsThis study examined cognitive functions in community-dwelling older adults before and after 12 weeks of DTTC training (training group [TG]) or standardized training (control group [CG]). Primary end points were based on the difference in cognitive functions between the TG and the CG. Cognitive functions were evaluated using the trail-making test (part A and part B) and verbal fluency test.ResultsA total of 41 elderly individuals (TG: n=26, CG: n=15) participated in this study and their cognitive functions were assessed before and after DTTC training. Significant differences were observed between the two groups with significant group × time interactions for the executive cognitive function measure, the delta-trail-making test (part B−part A; F 1,36=4.94, P=.03; TG: pre mean 48.8 [SD 43.9], post mean 42.2 [SD 29.0]; CG: pre mean 49.5 [SD 51.8], post mean 64.9 [SD 54.7]).ConclusionsThe results suggest that DTTC training is effective for improving executive cognitive functions.Trial RegistrationJapan Medical Association Clinical Trial Registration Number: JMA-IIA00092; https://dbcentre3.jmacct.med.or.jp/jmactr/App/JMACTRS06/JMACTRS06.aspx?seqno=2682 (Archived by WebCite at http://www.webcitation.org/6NRtOkZFh).

We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

BackgroundAcetaminophen (APAP) is frequently used for analgesia and is considered safer than nonsteroidal anti-inflammatory drugs (NSAIDs) for the kidneys. However, there is little epidemiological evidence of the association between APAP and acute kidney injury (AKI).ObjectivesTo examine the association between APAP and AKI using the self-controlled case series (SCCS) method, which is a novel strategy to control between-person confounders by comparing the risk and reference periods in each patient.MethodsWe performed SCCS in 1,871 patients (39.9% female) who were administered APAP and subsequently developed AKI, by reviewing electronically stored hospital information system data from May 2011 to July 2016. We used conditional Poisson regression to compare each patient’s risk and reference period. As a time-varying confounder, we adjusted the status of liver and kidney functions, systemic inflammation, and exposure to NSAIDs.ResultsWe identified 5,650 AKI events during the 260,549 person-day observation period. The unadjusted incidences during the reference and exposure periods were 2.01/100 and 3.12/100 person-days, respectively. The incidence rate ratio adjusted with SCCS was 1.03 (95% confidence interval [CI]: 0.95–1.12). When we restricted endpoints as stage 2 AKI- and stage 3 AKI-level creatinine elevations, the incidence rate ratios were 1.20 (95% CI 0.91–1.58) and 1.20 (95% CI 0.62–2.31), respectively, neither of which was statistically significant.ConclusionOur findings added epidemiological information for the relationship between APAP administration and AKI development. The results indicated scarce association between APAP and AKI, presumably supporting the general physicians’ impression that APAP is safer for kidney.

As Japan becomes a super-aging society, presentation of the best ways to provide medical care for the elderly, and the direction of that care, are important national issues. Elderly people have multi-morbidity with numerous medical conditions and use many medical resources for complex treatment patterns. This increases the likelihood of inappropriate medical practices and an evidence-practice gap. The present study aimed to: derive findings that are applicable to policy from an elucidation of the actual state of medical care for the elderly; establish a foundation for the utilization of National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), and present measures for the utilization of existing databases in parallel with NDB validation.Cross-sectional and retrospective cohort studies were conducted using the NDB built by the Ministry of Health, Labor and Welfare of Japan, private health insurance claims databases, and the Kyoto University Hospital database (including related hospitals). Medical practices (drug prescription, interventional procedures, testing) related to four issues—potential inappropriate medication, cancer therapy, chronic kidney disease treatment, and end-of-life care—will be described. The relationships between these issues and clinical outcomes (death, initiation of dialysis and other adverse events) will be evaluated, if possible.

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Background: There are limited data on the prevalence and burden of severe eosinophilic asthma (SEA) both in Japan and globally. This study aimed to assess the prevalence and burden of SEA in Japan. Methods: This study was a retrospective, observational cohort analysis using health records or health insurance claims from patients with severe asthma treated at Kyoto University Hospital. The primary outcome was the prevalence of SEA, defined as a baseline blood eosinophil count !300 cells/lL. Secondary outcomes included frequency and risk factors of asthma exacerbations, and asthma-related healthcare resource utilization and costs. Results: Overall, 217 patients with severe asthma were included; 160 (74%) had eosinophil assessments. Of these, 97cases (61%), 54cases (34%), and 33cases (21%) had a blood eosinophil count !150, !300, and !500 cells/lL, respectively. Proportion of SEA was 34%. Blood eosinophil count was not associated with a significantly increased frequency of exacerbations. In the eosinophilic group, lower % forced expiratory volume in 1 second and higher fractional exhaled nitric oxide were predictive risk factors, while the existence of exacerbation history was a predictive risk factor for asthma exacerbations in the non-eosinophilic group. Severe asthma management cost was estimated as ¥357,958/patient-year, and asthma exacerbations as ¥26,124/patient-year. Conclusions: Approximately, one-third of patients with severe asthma in Japan have SEA. While risk factors for exacerbations differed between SEA and severe noneosinophilic asthma, both subgroups were associated with substantial disease and economic burden. From subgroup analysis, blood eosinophil counts could be an important consideration in severe asthma management.

Aims/Introduction Although the epidemiological relationship between hypoglycemia and increased risk of acute coronary syndrome (ACS) has been well established, the time period for increased risk of ACS after a severe hypoglycemic episode remains unknown. The present study aimed to determine the ACS risk after a severe hypoglycemic episode. Materials and Methods We carried out a retrospective population‐based cohort study based on national claims data in Japan. We retrieved data of diabetes patients aged ≥35 years collected from April 2014 to March 2016. The absolute risk of ACS was defined as the occurrence of an emergency percutaneous coronary intervention after a severe hypoglycemic episode. Results In total, data of 7,909,626 patients were included in the analysis. The absolute risk of ACS was 2.9 out of 1,000 person‐years in all patients. ACS risk in patients with severe hypoglycemic episodes was 3.0 out of 1,000 person‐years. Severe hypoglycemic episodes increased the absolute risk of ACS in patients aged ≥70 years, but not in patients aged <70 years. The absolute risk of ACS was 10.6 out of 1,000 person‐years within 10 days of a severe hypoglycemic episode. There was a significant trend between shorter duration after an episode and higher ACS risk. Conclusions Severe hypoglycemia was associated with an increased risk of ACS in elderly diabetes patients. ACS risk increased with a shorter period after a severe hypoglycemic episode, suggesting that severe hypoglycemia leads to an increased risk of ACS in diabetes patients. These findings show that it is important to avoid severe hypoglycemia while treating diabetes, particularly in elderly patients.