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BackgroundMild cognitive impairment (MCI) is a high-risk condition for conversion to dementias, including Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneity in patterns of pathology, and MCI does not always convert to AD dementia. Detailed subtyping of MCI and accurate prediction of the patients in whom MCI will convert to AD dementia may support new trial designs and enable evaluation of the efficacy of drugs within small numbers of patients during clinical trials. MethodsWe constructed a decision tree model by the heterogeneous mixture learning (HML) method, integrating cerebrospinal fluid (CSF) biomarker data, structural MRI data, APOE genotype data, and a recorded age at examination. The decision tree model was applied to predict conversion to AD dementia and to identify subtypes of MCI. After the test performances of HML models were assessed, MCI subjects were classified into some subtypes based on a decision tree. Then, we characterized each MCI subtype in terms of the degree of CSF biomarker abnormalities and brain atrophy, declines of cognitive functions, and gene expression alterations derived from peripheral blood samples.ResultsWe identified five subtypes of MCI using the HML approach and categorized them into three groups: those similar to CN subjects with low conversion rates; those with intermediate conversion rates; and those similar to patients with AD with high conversion rates. Furthermore, the subtypes with intermediate conversion rates were separated into the subtype with CSF biomarker abnormalities or the subtype with brain atrophy. The results from the CSF inflammation marker and gene expression analysis suggested the occurrence of aberrant inflammatory immune responses in the CSF and blood of the subjects in the subtypes with CSF biomarker abnormalities. ConclusionThe subtypes that were identified in this study exhibited varying conversion rates to AD as well as differing levels of biological features. Focusing on specific subtypes in which conversion to AD can be predicted with the most accuracy could enable more efficient clinical trials to be conducted.

Background Medicines may cause various adverse reactions. An enormous amount of money and effort is spent investigating adverse drug events (ADEs) in clinical trials and postmarketing surveillance. Real-world data from multiple electronic medical records (EMRs) can make it easy to understand the ADEs that occur in actual patients. Objective In this study, we generated a patient medication history database from physician orders recorded in EMRs, which allowed the period of medication to be clearly identified. Methods We developed a method for detecting ADEs based on the chronological relationship between the presence of an adverse event and the medication period. To verify our method, we detected ADEs with alanine aminotransferase elevation in patients receiving aspirin, clopidogrel, and ticlopidine. The accuracy of the detection was evaluated with a chart review and by comparison with the Roussel Uclaf Causality Assessment Method (RUCAM), which is a standard method for detecting drug-induced liver injury. Results The calculated rates of ADE with ALT elevation in patients receiving aspirin, clopidogrel, and ticlopidine were 3.33% (868/26,059 patients), 3.70% (188/5076 patients), and 5.69% (226/3974 patients), respectively, which were in line with the rates of previous reports. We reviewed the medical records of the patients in whom ADEs were detected. Our method accurately predicted ADEs in 90% (27/30patients) treated with aspirin, 100% (9/9 patients) treated with clopidogrel, and 100% (4/4 patients) treated with ticlopidine. Only 3 ADEs that were detected by the RUCAM were not detected by our method. Conclusions These findings demonstrate that the present method is effective for detecting ADEs based on EMR data.

Background Electronic medical records (EMRs) are widely used, but in many cases, they are used within a network physically separated from the Internet. Multicenter clinical studies use Internet-connected electronic data capture (EDC) systems to collect data, where data entered into the EMR are manually transcribed into the EDC system. In addition, medical images for clinical research are also collected manually. Variations in EMRs and differing data structures among vendors hamper the use of data for clinical research. Methods We solved this problem by developing a network infrastructure for clinical research between Osaka University Hospital and affiliated hospitals in the Osaka area and introducing a clinical data collection system (CDCS). In each hospital's EMR network, we implemented a CRF reporter that accumulated data for clinical research using a template and then sent the data to a management server in the Osaka University Hospital Data Center. To organize the patient profile data and clinical laboratory data stored in each EMR for use in clinical research, the data are retrieved from the template by an interface module developed by each vendor, according to our common data output interface specification. The data entered into the CRF reporter template for clinical research are also recorded in the EMR progress notes and sent to the data management server. This network infrastructure can also be used as a medical image collection system that automatically collects images for research from PACS at each hospital. These systems are managed under common subject numbers issued by the CDCS. Results A network infrastructure was established among 19 hospitals, and a CRF reporter was incorporated into the EMR. A medical image transfer system was introduced in 13 hospitals. Since 2013, 28 clinical studies have been conducted using this system, and data for 9,987 cases have been collected as of December 31, 2020. Conclusion Incorporating a CRF reporter with medical image transfer system into the EMR has proven useful for collecting research data.

Because drug‐induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X‐ray reports, KL‐6, and SP‐D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language‐processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X‐ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS‐1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS‐1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.

Drug-induced interstitial pneumonia (DIP) is a serious adverse drug reaction. The occurrence rete of DIP was evaluated by clinical trial before available in the market. However, due to limited number of cases in clinical trials, it may be inapplicable to the real market. We aimed to seek a method to evaluate the occurrence rate of DIP using clinical data warehouse at a hospital. Initially we developed a method that assesses whether presence of IP was written in reports by natural language processing. Next we detected DIP by estimating IP before, during and after the drug administration. Presence of IP was determined according to the reports of CT if CT was performed, otherwise it was determined based on the changes in the results of chest X-ray, level of KL-6 or SP-D. DIP was determined according to the pattern of presence of IP in each phase. In this study we chose amiodarone as a target drug. The number of patients who suffered from IP caused by amiodarone was 16 (3.9 %), including one definitively diagnosed and 15 strong doubt cases. Most of them could be validated by medical record chart. Using this method, we were able to successfully detect occurrence of DIP from accumulated data in a hospital information system.

Summary Background: The International Classification of Functioning, Disability and Health (ICF) has been available as a means of coding life functions but the coding process is cumbersome due to the large number of ICF codes. In the current study, we developed ICF code selection tools to support the coding of activity and participation data recorded in domiciliary mental health care reports. Methods: We first developed a search system to facilitate the selection of ICF codes by tracking back through codes’ conceptual trees using a directory tool. We performed a morphological analysis on the training data set to correlate nouns with the ICF codes and obtained an analysis corpus to which numerical scores representing the frequencies of associated ICF codes for each noun were assigned. Based on the obtained corpus we developed a full-text search tool, which could simplify ICF coding relative to that performed using the directory tool. We then evaluated the usefulness of the former tool on the test data set. Results: Using the full-text search tool, correct ICF codes were recorded in the first candidate list for only 54.2% of sentences. However, correct ICF codes appeared on the combined candidate lists for 90.1% of sentences and on the top three candidate lists for 71.7%. In a specific case (General Tasks and Demands), 100% of the correct codes were included on the combined candidate lists. Conclusion: We developed selection tools that effectively supported ICF coding, although it was impossible to fully automate ICF coding. This indicated that ICF codes could more effectively be applied to mental health care.

This system is designed as a tool which solves some challenges imposed in community mental health care. Using a computer network, the electronic-bulletin-board-like system which uses ICF-based data input is applied, for the purpose of sharing community mental health care consumer information. Such system is expected to facilitate health care consumer rehabilitation and in the prevention of a recurrence of an illness. It will become a vital necessity in the future of community mental health care.

The cyclic micro-deformation of the arterial wall with pulsatile flow was measured to get fundamental data for estimation of the mechanical stress in endothelial cells. The descending aorta (1-2 mm diameter) of an anesthetized rat was exposed under thoracotomy. The displacement measuring system was assembled with the charge coupled devise (CCD) of laser sensor. The movement of laser beam (670 nm wave length) reflected at the vessel wall was calibrated to the movement of the arterial wall. The fluctuating movement was also measured at four points marked on the vessel wall with CCD camera to distinguish the circumferential movement from the longitudinal one. The results showed that the present designed system has enough resolution to measure the arterial vessel wall cyclic-micro-fluctuation, which is 10 percent of diameter in the circumferential direction without deformation in the longitudinal direction with the cardiac beating in vivo.