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Yuta Okazawa,1 Hayato Kizaki,1 Nobuyuki Suzuki,2 Wakako Osaka,3 Satoko Hori1,2 1Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, 105-8512, Japan; 2Patient Salon Association, Tokyo, Japan; 3Keio University Faculty of Nursing and Medical Care, Tokyo, 160-8582, JapanCorrespondence: Satoko Hori, Division of Drug Informatics, Keio University Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan, Tel +81-3-5400-2650, Fax +81-3-5400-2799, Email hori-st@pha.keio.ac.jpPurpose: This study aims to investigate the impact of a Science Café (SC) dealing with medical topics on participants’ patient activation (PA), a concept that refers to patients’ involvement in managing their own health, working with their healthcare providers, and maintaining their health.Material and Methods: Semi-structured interviews were conducted with patients who had participated in a medical SC (n = 10) to identify the medical SC-associated factors that influenced PA. Through a questionnaire of medical SC participants (n = 23), the impact on PA and correlations with relevant psychological measures were quantitatively assessed.Results: The interviews revealed three factors: “Experience & acceptance of chronic conditions”, “Features of medical SC” and “Changes as a result of participation.” The questionnaire results showed a positive correlation between PA and resilience and a negative correlation with decision regret.Conclusions: Participation in a medical SC by people with illnesses can improve PA by improving knowledge and skills for self-management and increasing self-awareness of illness in a supportive environment. The study highlights the potential benefits of using medical SC as a strategy for healthcare providers to improve PA and health outcomes.Keywords: self-management, psychological measures, patient community, supportive environment

Hydrofluoric acid (HF) etching of the SiC surface assisted by Pt as a catalyst is investigated using density functional theory. Etching is initiated by the dissociative adsorption of HF on step-edge Si, forming a five-fold coordinated Si moiety as a metastable state. This is followed by breaking of the Si-C back-bond by a H-transfer process. The gross activation barrier strongly correlates with the stability of the metastable state and is reduced by the formation of Pt-O chemical bonds, leading to an enhancement of the etching reaction. [ABSTRACT FROM AUTHOR]

AMP-activated protein kinase (AMPK) is a stressresponsive enzyme involved in cell adaptation to an energy crisis. We hypothesized that hypoxia suppresses oxidative phosphorylation and ATP production, resulting in AMPK activation to protect cells. We investigated the effects of hypoxia on cell proliferation, the expression of AMPK and hypoxia-inducible factor 1α (HIF-1α), the activation of AMPK, and the relationship between AMPK and HIF-1α expression in rat dental pulp RPC-C2A cells. AMPK in the cells was composed of catalytic α1, and regulatory β1 and γ1 subunit isoforms. Cell proliferation was initially suppressed under hypoxia, but it increased thereafter, together with an increase in the expression of AMPK and HIF-1α, and the activation of AMPK. Down-regulation of AMPKα1 by siRNA inhibited cell proliferation under both normoxia and hypoxia, revealing that AMPK induction and activation were required for cell proliferation, although HIF-1α expression under hypoxia was not affected. [ABSTRACT FROM AUTHOR]

A previously unidentified 120-kDa protein was detected in Streptococcus mutans strain Z1 and was involved in the cold-agglutination of the strain. We have identified the gene, designated cnm, as being involved in the agglutination of strain Z1 following random mutagenesis. The amino acid sequence of the deduced Cnm protein exhibited high similarity to those of collagen-binding adhesins from staphylococci and other organisms. To confirm whether the protein is involved in collagen-binding, we cloned a cnm gene fragment, overexpressed it in E.coli, and prepared crude extracts. The extracts containing recombinant protein exhibited binding to immobilized collagen and laminin but not to fibronectin. Compared with the parental strain Z1, the cold-agglutination-negative mutant 05A02 exhibited reduced binding to collagen and laminin but retained that to fibronectin. This gene was detected in some strains of S. mutans. Therefore, the cnm gene encoded a new strain-specific member of the collagen-binding adhesin family. [ABSTRACT FROM AUTHOR]

The Streptococcus mutans gbpC gene encoding cell wall-anchoring glucan-binding protein C is involved in the dextran(alpha-l,6 glucan)-dependent aggregation (ddag) of this organism. Unlike cells of other strains of S. mutans, strain GS-5 cells did not exhibit dextran(alpha-1,6 glucan)-dependent aggregation under any conditions. We therefore hypothesized that the gbpC gene may be mutated in strain GS-5. Sequencing analysis of the 1752-nucleotide GS-5 gbpC gene revealed a point mutation that switched codon 65 to a TAA termination codon. Strain GS-5 was previously reported also to have a mutation in the pac gene encoding the cell wall-anchored major protein antigen. The laboratory-maintained strain GS-5 is regarded as having lower cariogenicity than the original isolate. The decreased cariogenicity developed during the laboratory culture of strain GS-5 may have been caused by mutations in an environment lacking appropriate selective pressures. [ABSTRACT FROM AUTHOR]

CPT-11 is a promising new anticancer drug in which 4-piperidinopiperidine is a side-chain structure. In the present studies, we examined the role played by 4-piperidinopiperidine in the pharmacological activity of CPT-11. When T-cell lymphoma RVC cells were incubated with 4-piperidinopiperidine at concentrations higher than 50 micrograms/ml, the cells underwent apoptosis with a nucleosomal ladder of chromosomal DNA on agarose gels in a dose-dependent manner. We then established a cell line resistant to 4-piperidinopiperidine (4-pp-R), which was about 20-fold more resistant to 4-piperidinopiperidine than the parent RVC cells. Moreover, 4-pp-R cells showed coresistance to CPT-11. However, the growth rate and cell cycle population of 4-pp-R cells were not different from those of the parent RVC cells, and there were no differences between the two cells lines with regard to their drug transport system. CPT-11-metabolizing activity, their activity and amount of topoisomerase I, or their sensitivity to either SN-38 or etoposide, suggesting that the cytotoxicity of CPT-11 is not a consequence of the activity of its metabolite SN-38. The present studies suggested that resistance to CPT-11 is in part due to insensitivity to 4-piperidinopiperidine and its metabolites, since 4-piperidinopiperidine was cytotoxic and 4-pp-R cells were less sensitive to CPT-11. [ABSTRACT FROM AUTHOR]

Cyclosporin A (CsA) is reported to inhibit programmed cell death. We confirmed this by using T-celI hybridomas which are inducible to programmed cell death by activation with immobilized anti-CD3 antibody or with anti-Thy 1.2 antibody. Cell death and DNA fragmentation, characteristic features of programmed cell death, were almost completely blocked by CsA or FK506. To investigate whether CsA inhibits only the cell death through the signals via the TCR/CD3 complex or all of the programmed cell death induced by various reagents, we further established CD4+8+ thymic lymphomas which result in programmed cell death after activation with calcium ionophore, dexamethasone, cyclic AMP or anti-CD3 antibody. It was revealed that CsA could block only the cell death mediated by the TCR/CD3 complex. For the clarification of the site of action of CsA, Ca2+ influx and endocytosis of receptors after stimulation with anti-CD3 antibody were monitored in the presence of CsA, and no significant effects of CsA were observed. Furthermore, prevention of cell death was examined by adding CsA at various periods of time after initiation of culture. CsA was found to exert its effect even when added after 4 h of cultivation, and the kinetic pattern of suppression was similar to that of the suppressive effect on IL-2 production. These observations indicate that in the events of programmed cell death, the major site of action of CsA will not be the inhibition of the immediate membrane events after activation of the TCR/CD3 complex but rather the interference in the function of molecules that transmit signals between membrane events and the activation of genes in the nucleus. [ABSTRACT FROM AUTHOR]

Objectives: Factors affecting denosumab-induced hypocalcemia in male patients with osteoporosis remain unclear because of the small patient population. Nevertheless, it is important to explore male-specific risk factors. This study aimed to identify the factors affecting the development of denosumab-induced hypocalcemia in male patients with osteoporosis and compare them with those in female patients with osteoporosis., Study Design: This retrospective, observational study used data from a hospital-based administrative claims database. Patients with osteoporosis undergoing denosumab treatment between April 2008 and May 2020 were enrolled. Data on age, inpatient/outpatient status, laboratory data, drug use, and disease status were collected., Main Outcome Measures: Patients were classified as those with hypocalcemia and without hypocalcemia, and variables with P < 0.2 and missing proportions <30 % on univariate analysis were entered into multivariate logistic regression analysis. The multiple imputation method was used to compensate for missing data., Results: Overall, 175 males and 1335 females were included in the analysis, and hypocalcemia developed in 52 and 247 patients, respectively. In the multivariate analysis, prior use of preventive medicine (calcium supplements or activated vitamin D) decreased the risk of osteoporosis only in male patients. Low corrected levels of calcium and albumin and low estimated glomerular filtration rates increased the risk in both sexes., Conclusions: Prior use of preventive medicine may be a factor that decreases the risk of denosumab-induced hypocalcemia in male patients. Low corrected levels of calcium and albumin, and low estimated glomerular filtration rates are common risk factors in both male and female patients., Competing Interests: Declaration of competing interest The authors declare that they have no competing interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psychological, behavioral, and demographic factors. When analyzing these factors, challenges such as multicollinearity and variable selection often arise, complicating the interpretation of results. To address the issue of multicollinearity and better analyze the importance of each factor, machine learning methods are considered to be useful., Objective: This study aimed to identify key factors influencing medication compliance by applying regularized logistic regression and LightGBM., Methods: A questionnaire survey was conducted among 638 adult patients in Japan who had been continuously taking medications for at least 3 months. The survey collected data on demographics, medication habits, psychological adherence factors, and compliance. Logistic regression with regularization was used to handle multicollinearity, while LightGBM was used to calculate feature importance., Results: The regularized logistic regression model identified significant predictors, including "using the drug at approximately the same time each day" (coefficient 0.479; P=.02), "taking meals at approximately the same time each day" (coefficient 0.407; P=.02), and "I would like to have my medication reduced" (coefficient -0.410; P=.01). The top 5 variables with the highest feature importance scores in the LightGBM results were "Age" (feature importance 179.1), "Using the drug at approximately the same time each day" (feature importance 148.4), "Taking meals at approximately the same time each day" (feature importance 109.0), "I would like to have my medication reduced" (feature importance 77.48), and "I think I want to take my medicine" (feature importance 70.85). Additionally, the feature importance scores for the groups of medication adherence-related factors were 77.92 for lifestyle-related items, 52.04 for awareness of medication, 20.30 for relationships with health care professionals, and 5.05 for others., Conclusions: The most significant factors for medication compliance were the consistency of medication and meal timing (mean of feature importance), followed by the number of medications and patient attitudes toward their treatment. This study is the first to use a machine learning model to calculate and compare the relative importance of factors affecting medication adherence. Our findings demonstrate that, in terms of relative importance, lifestyle habits are the most significant contributors to medication compliance among the general patient population. The findings suggest that regularization and machine learning methods, such as LightGBM, are useful for better understanding the numerous adherence factors affected by multicollinearity., (©Haru Iino, Hayato Kizaki, Shungo Imai, Satoko Hori. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.12.2024.)

Exploring factors related to difficulties in extracting tablets or capsules from press-through-packages is essential for optimizing the dosage form. To achieve this, the involvement of patient insight is important. In the present study, the preferences of patients regarding drugs that are difficult to extract from their packaging were collected using an electronic medication notebook ('harmo®') based on dispensing data. We found that approximately 30% of respondents had difficulty removing tablets or capsules from their packaging, with 125 specific drugs identified as 'hard to push out'. Independent factors related to 'hard-to-push-out' drugs were female sex and feeling of weakness in the hands or fingers. Furthermore, several 'hard-to-push-out' drugs had characteristics such as a spherical shape or small major axis or small major axis drug-to-pocket size ratio. The findings of this study may help improve the quality of drug packaging, thus enhancing the patients' medication experience., (© 2024 The Author(s). British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Background: Wearable devices (WDs) have evolved beyond simple fitness trackers to sophisticated health monitors capable of measuring vital signs, such as heart rate and blood oxygen levels. Their application in health care, particularly medication management, is an emerging field poised to significantly enhance patient adherence to treatment regimens. Despite their widespread use and increasing incorporation into clinical trials, a comprehensive review of WDs in terms of medication adherence has not been conducted., Objective: This study aimed to conduct a comprehensive scoping review to evaluate the impact of WDs on medication adherence across a variety of diseases, summarizing key research findings, outcomes, and challenges encountered., Methods: Adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a structured search was conducted across MEDLINE, Web of Science, and Embase databases, covering the literature from January 1, 2010, to September 30, 2022. The search strategy was based on terms related to WDs and medication adherence, specifically focusing on empirical studies to ensure the inclusion of original research findings. Studies were selected based on their relevance to medication adherence, usage of WDs in detecting medication-taking actions, and their role in integrated medication management systems., Results: We screened 657 articles and identified 18 articles. The identified studies demonstrated the diverse applications of WDs in enhancing medication adherence across diseases such as Parkinson disease, diabetes, and cardiovascular conditions. The geographical distribution and publication years of these studies indicate a growing interest in this research area. The studies were divided into three types: (1) studies reporting a correlation between data from WDs or their usage and medication adherence or drug usage as outcomes, (2) studies using WDs to detect the act of medication-taking itself, and (3) studies proposing an integrated medication management system that uses WDs in managing medication., Conclusions: WDs are increasingly being recognized for their potential to enhance medication management and adherence. This review underscores the need for further empirical research to validate the effectiveness of WDs in real-life settings and explore their use in predicting adherence based on activity rhythms and activities. Despite technological advancements, challenges remain regarding the integration of WDs into routine clinical practice. Future research should focus on leveraging the comprehensive data provided by WDs to develop personalized medication management strategies that can improve patient outcomes., (© Haru Iino, Hayato Kizaki, Shungo Imai, Satoko Hori. Originally published in JMIR Human Factors (https://humanfactors.jmir.org).)

Introduction: The concomitant use of non-steroidal anti-inflammatory drugs (NSAIDs) and proton pump inhibitors (PPIs) potentially increases the risk of acute kidney injury (AKI). However, the risk of AKI has not been comprehensively assessed for the concomitant use of NSAIDs with gastroprotective agents such as misoprostol and PPIs. The objective of this study was to evaluate whether the use of various gastroprotective agents affects the risk of AKI in patients receiving NSAIDs., Methods: The data analyzed were obtained from the JMDC hospital-based administrative claims database between April 2014 and August 2022. Histamine-2 receptor antagonists (H2RAs) were compared with PPIs or misoprostol in patients receiving NSAIDs. The primary outcome was the incidence of AKI. The covariates considered were age and sex, admission to intensive care unit, presence of comorbidities based on the modified Charlson Comorbidity Index, and use of renin-angiotensin system inhibitors, loop diuretics, other diuretics, and lithium. AKI was identified by changes in serum creatinine. The distribution of AKI was analyzed using the log-rank test, and estimates of the incidence of AKI were compared among the groups using a Cox proportional hazards model with time-varying variables. Models were adjusted using a doubly robust method that accounts for the inverse probability of treatment weighting at baseline while adjusting for covariates., Results: After screening, 11,688 patients were eligible for inclusion (1729 for H2RAs, 368 for misoprostol, and 9591 for PPIs). AKI occurred in 0.5% of H2RA recipients and 1.1% of PPI recipients; no AKI was observed in the misoprostol group. Compared with H2RAs, the risk of AKI tended to be higher with PPIs (adjusted hazard ratio 1.83, 95% confidence interval 0.92-3.63, p = 0.08)., Conclusion: Compared with H2RAs, PPIs may increase the risk of AKI in patients receiving NSAIDs, although no statistically significant difference was observed. Further research is required to assess the risk trade-off with consideration of both peptic ulcer prevention and the increased risk of AKI in patients concurrently treated with NSAIDs and H2RAs, misoprostol, or PPIs., (© 2024 The Author(s). Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy published by Wiley Periodicals LLC on behalf of Pharmacotherapy Publications, Inc.)

Narratives posted on the internet by patients contain a vast amount of information about various concerns. This study aimed to extract multiple concerns from interviews with breast cancer patients using the natural language processing (NLP) model bidirectional encoder representations from transformers (BERT). A total of 508 interview transcriptions of breast cancer patients written in Japanese were labeled with five types of concern labels: "treatment," "physical," "psychological," "work/financial," and "family/friends." The labeled texts were used to create a multi-label classifier by fine-tuning a pre-trained BERT model. Prior to fine-tuning, we also created several classifiers with domain adaptation using (1) breast cancer patients' blog articles and (2) breast cancer patients' interview transcriptions. The performance of the classifiers was evaluated in terms of precision through 5-fold cross-validation. The multi-label classifiers with only fine-tuning had precision values of over 0.80 for "physical" and "work/financial" out of the five concerns. On the other hand, precision for "treatment" was low at approximately 0.25. However, for the classifiers using domain adaptation, the precision of this label took a range of 0.40-0.51, with some cases improving by more than 0.2. This study showed combining domain adaptation with a multi-label classifier on target data made it possible to efficiently extract multiple concerns from interviews., Competing Interests: HY is the CEO of Mediaid Corporation, which operates LifePalette. The other authors declare no competing. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Watabe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: To determine whether concomitant use of ceftriaxone and oral or intravenous lansoprazole increases the risk of ventricular arrhythmia and cardiac arrest in the real-world setting in Japan., Methods: The data analyzed were obtained from the JMDC hospital-based administrative claims database for the period April 2014 to August 2022. Patients who received a proton pump inhibitor (PPI) while receiving ceftriaxone or sulbactam/ampicillin were identified. The frequency of ventricular arrhythmia and cardiac arrest was analyzed according to whether oral or intravenous PPI was concomitant with ceftriaxone or sulbactam/ampicillin. Estimates of the incidence of ventricular arrhythmia and cardiac arrest were then compared among the groups, using the Fine-Gray competing risk regression model., Results: The results showed that the risk of ventricular arrhythmia and cardiac arrest was significantly higher with concomitant ceftriaxone and oral lansoprazole (hazard ratio 2.92, 95% confidence interval 1.99-4.29, P < 0.01) or intravenous lansoprazole (hazard ratio 4.57, 95% confidence interval 1.24-16.80, P = 0.02) than with concomitant sulbactam/ampicillin and oral or intravenous lansoprazole., Conclusions: Oral and intravenous lansoprazole may increase the risk of ventricular arrhythmia and cardiac arrest in patients who are receiving ceftriaxone., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare that are relevant to the content of this article., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Medication safety in residential care facilities is a critical concern, particularly when nonmedical staff provide medication assistance. The complex nature of medication-related incidents in these settings, coupled with the psychological impact on health care providers, underscores the need for effective incident analysis and preventive strategies. A thorough understanding of the root causes, typically through incident-report analysis, is essential for mitigating medication-related incidents., Objective: We aimed to develop and evaluate a multilabel classifier using natural language processing to identify factors contributing to medication-related incidents using incident report descriptions from residential care facilities, with a focus on incidents involving nonmedical staff., Methods: We analyzed 2143 incident reports, comprising 7121 sentences, from residential care facilities in Japan between April 1, 2015, and March 31, 2016. The incident factors were annotated using sentences based on an established organizational factor model and previous research findings. The following 9 factors were defined: procedure adherence, medicine, resident, resident family, nonmedical staff, medical staff, team, environment, and organizational management. To assess the label criteria, 2 researchers with relevant medical knowledge annotated a subset of 50 reports; the interannotator agreement was measured using Cohen κ. The entire data set was subsequently annotated by 1 researcher. Multiple labels were assigned to each sentence. A multilabel classifier was developed using deep learning models, including 2 Bidirectional Encoder Representations From Transformers (BERT)-type models (Tohoku-BERT and a University of Tokyo Hospital BERT pretrained with Japanese clinical text: UTH-BERT) and an Efficiently Learning Encoder That Classifies Token Replacements Accurately (ELECTRA), pretrained on Japanese text. Both sentence- and report-level training were performed; the performance was evaluated by the F 1 -score and exact match accuracy through 5-fold cross-validation., Results: Among all 7121 sentences, 1167, 694, 2455, 23, 1905, 46, 195, 1104, and 195 included "procedure adherence," "medicine," "resident," "resident family," "nonmedical staff," "medical staff," "team," "environment," and "organizational management," respectively. Owing to limited labels, "resident family" and "medical staff" were omitted from the model development process. The interannotator agreement values were higher than 0.6 for each label. A total of 10, 278, and 1855 reports contained no, 1, and multiple labels, respectively. The models trained using the report data outperformed those trained using sentences, with macro F 1 -scores of 0.744, 0.675, and 0.735 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. The report-trained models also demonstrated better exact match accuracy, with 0.411, 0.389, and 0.399 for Tohoku-BERT, UTH-BERT, and ELECTRA, respectively. Notably, the accuracy was consistent even when the analysis was confined to reports containing multiple labels., Conclusions: The multilabel classifier developed in our study demonstrated potential for identifying various factors associated with medication-related incidents using incident reports from residential care facilities. Thus, this classifier can facilitate prompt analysis of incident factors, thereby contributing to risk management and the development of preventive strategies., (©Hayato Kizaki, Hiroki Satoh, Sayaka Ebara, Satoshi Watabe, Yasufumi Sawada, Shungo Imai, Satoko Hori. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 23.07.2024.)

Purpose: Denosumab is used to treat patients with bone metastasis from solid tumors, but sometimes causes severe hypocalcemia, so careful clinical management is important. This study aims to externally validate our previously developed risk prediction model for denosumab-induced hypocalcemia by using data from two facilities with different characteristics in Japan and to develop an updated model with improved performance and generalizability., Methods: In the external validation, retrospective data of Kameda General Hospital (KGH) and Miyagi Cancer Center (MCC) between June 2013 and June 2022 were used and receiver operating characteristic (ROC)-AUC was mainly evaluated. A scoring-based updated model was developed using the same data set from a hospital-based administrative database as previously employed. Selection of variables related to prediction of hypocalcemia was based on the results of external validation., Results: For the external validation, data from 235 KGH patients and 224 MCC patients were collected. ROC-AUC values in the original model were 0.879 and 0.774, respectively. The updated model consisting of clinical laboratory tests (calcium, albumin, and alkaline phosphatase) afforded similar ROC-AUC values in the two facilities (KGH, 0.837; MCC, 0.856)., Conclusion: We developed an updated risk prediction model for denosumab-induced hypocalcemia with small interfacility differences. Our results indicate the importance of using data from plural facilities with different characteristics in the external validation of generalized prediction models and may be generally relevant to the clinical application of risk prediction models. Our findings are expected to contribute to improved management of bone metastasis treatment.

Background: Understanding patient preference regarding taking tablet or capsule formulations plays a pivotal role in treatment efficacy and adherence. Therefore, these preferences should be taken into account when designing formulations and prescriptions., Objective: This study investigates the factors affecting patient preference in patients who have difficulties swallowing large tablets or capsules and aims to identify appropriate sizes for tablets and capsules., Methods: A robust data set was developed based on a questionnaire survey conducted from December 1, 2022, to December 7, 2022, using the harmo smartphone app operated by harmo Co, Ltd. The data set included patient input regarding their tablet and capsule preferences, personal health records (including dispensing history), and drug formulation information (available from package inserts). Based on the medication formulation information, 6 indices were set for each of the tablets or capsules that were considered difficult to swallow owing to their large size and concomitant tablets or capsules (used as controls). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of each index. The index demonstrating the highest area under the curve of the ROC was selected as the best index to determine the tablet or capsule size that leads to swallowing difficulties. From the generated ROCs, the point with the highest discriminative performance that maximized the Youden index was identified, and the optimal threshold for each index was calculated. Multivariate logistic regression analysis was performed to identify the risk factors contributing to difficulty in swallowing oversized tablets or capsules. Additionally, decision tree analysis was performed to estimate the combined risk from several factors, using risk factors that were significant in the multivariate logistic regression analysis., Results: This study analyzed 147 large tablets or capsules and 624 control tablets or capsules. The "long diameter + short diameter + thickness" index (with a 21.5 mm threshold) was identified as the best indicator for causing swallowing difficulties in patients. The multivariate logistic regression analysis (including 132 patients with swallowing difficulties and 1283 patients without) results identified the following contributory risk factors: aged <50 years (odds ratio [OR] 1.59, 95% CI 1.03-2.44), female (OR 2.54, 95% CI 1.70-3.78), dysphagia (OR 3.54, 95% CI 2.22-5.65), and taking large tablets or capsules (OR 9.74, 95% CI 5.19-18.29). The decision tree analysis results suggested an elevated risk of swallowing difficulties for patients with taking large tablets or capsules., Conclusions: This study identified the most appropriate index and threshold for indicating that a given tablet or capsule size will cause swallowing difficulties, as well as the contributory risk factors. Although some sampling biases (eg, only including smartphone users) may exist, our results can guide the design of patient-friendly formulations and prescriptions, promoting better medication adherence., (©Masaki Asano, Shungo Imai, Yuri Shimizu, Hayato Kizaki, Yukiko Ito, Makoto Tsuchiya, Ryoko Kuriyama, Nao Yoshida, Masanori Shimada, Takanori Sando, Tomo Ishijima, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.04.2024.)

Background: Patients with a history of hepatitis B virus (HBV) infection who are receiving immunosuppressive therapy are at risk of HBV reactivation and disease. Therefore, HBV screening is required prior to administering antirheumatic drugs with immunosuppressive effects. This study aimed to determine the status of hepatitis B surface antigen (HBsAg), hepatitis B core antibody (HBcAb), and hepatitis B surface antibody (HBsAb) screening prior to the initiation of drug therapy, including new antirheumatic drugs, in patients with rheumatoid arthritis., Methods: This retrospective cross-sectional study used data from April 2014 to August 2022 from the Japanese hospital-based administrative claims database. The inclusion criteria were rheumatoid arthritis and first prescription date of antirheumatic drugs., Results: A total of 82,282 patients with rheumatoid arthritis who were first prescribed antirheumatic drugs between April 2016 and August 2022 were included. Of the eligible patients, 9.7% (n=7,959) were screened for all HBV (HBsAg, HBsAb, and HbcAb) within 12 months prior to the date of initial prescription. The HBsAg test was performed in 30.0% (n=24,700), HBsAb test in 11.8% (n=9,717), and HBcAb test in 13.1% (n=10,824) of patients. The proportion of patients screened for HBV infection has been increasing since 2018; however, the proportion of patients screened for rheumatoid arthritis remains low., Conclusions: Our findings suggest that HBV screening may be insufficient in patients who received antirheumatic drugs. With the increasing use of new immunosuppressive antirheumatic drugs, including biological agents, healthcare providers should understand the risk of HBV reactivation and conduct appropriate screening., (© 2024. The Author(s).)

Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients., Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed., Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs., Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives., Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work., (©Satoshi Nishioka, Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Hayato Kizaki, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.)

The shift towards community-based care in Japan has led to increased medication assistance for older people by non-medical care staff. These staff members help take pre-packaged medications, apply patches, and administer eye drops. This study assessed the risks associated with such assistance by reviewing medication-related incidents across 106 residential care facilities between April 1, 2015, and March 31, 2016. An analysis of incident reports showed that all incidents were minor, with no serious outcomes. The incidents were categorized into four types: dropped drugs, misdelivery/misuse of medicines, forgetting to take medicines, and loss of medicines, with dropped drugs being the most frequent. Most incidents occurred in the morning and primarily involved residents with intermediate nursing care needs. These findings indicate a low risk of serious incidents because of medication assistance from non-medical staff. However, the frequency and nature of the incidents were influenced by the timing of medication administration and the care needs of the residents. These insights highlight the need for customized approaches to medication assistance, considering the residents' care levels and potentially optimizing medication administration times to improve safety in residential care settings.