A cross-sectional survey of hepatitis B virus screening in patients who received immunosuppressive therapy for rheumatoid arthritis in Japan.

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.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

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.

Factor Analysis of Patients Who Find Tablets or Capsules Difficult to Swallow Due to Their Large Size: Using the Personal Health Record Infrastructure of Electronic Medication Notebooks.

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.

Medication incidents associated with the provision of medication assistance by non-medical care staff in residential care facilities.

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.

Detection of Adverse Event Signals with Severity Grade Classification from Cancer Patient Narrative.

Adverse event (AE) management is crucial to improve anti-cancer treatment outcomes, but it is reported that some AE signals can be missed in clinical visits. Thus, monitoring AE signals seamlessly, including events outside hospitals, would be helpful for early intervention. Here we investigated how to detect AE signals from texts written by cancer patients themselves by developing deep-learning (DL) models to classify posts mentioning AEs according to severity grade, in order to focus on those that might need immediate treatment interventions. Using patient blogs written in Japanese by cancer patients as a data source, we built DL models based on three approaches, BERT, ELECTRA, and T5. Among these models, T5 showed the best F1 scores for both Grade ≥ 1 and ≥ 2 article classification tasks (0.85 and 0.53, respectively). This model might benefit patients by enabling earlier AE signal detection, thereby improving quality of life.

Comparison of different sustained-release opioids and acute respiratory conditions in patients with cancer and chronic kidney disease.

STUDY OBJECTIVE: Few data are available on the association between the use of oxycodone in patients with chronic kidney disease (CKD) and acute respiratory conditions. The aim of this study was to investigate whether oxycodone is associated with an increased risk of acute respiratory conditions in patients with cancer and CKD compared with other opioids. DESIGN AND SETTING: The data were obtained from a claims database in Japan. Patients with cancer and CKD who had received sustained-release opioids, including oral oxycodone, oral morphine, or transdermal fentanyl, between April 2014 and May 2021 were selected. The primary outcome was defined as an acute respiratory condition. Data for age and sex, morphine equivalent daily dose, concomitant use of specified medications, comorbidities defined based on the modified Charlson comorbidity index, substance use disorder, and lung cancer or metastatic lung cancer were investigated as covariates. Distribution of acute respiratory conditions was compared among the three sustained-release opioid groups using the log-rank test. Estimates of the incidence of acute respiratory conditions were compared among the groups using a Cox proportional hazards model with time-varying variables. MAIN RESULTS: A significant difference in the distribution of acute respiratory conditions was found among the three groups (p < 0.01). Cox regression analysis showed a significantly higher risk of acute respiratory conditions with morphine (hazard ratio [HR]: 3.04, 95% confidence interval [CI]: 1.07-8.65, p = 0.04) compared with oxycodone but no significant difference in risk with oxycodone (HR 0.67, 95% CI: 0.32-1.38, p = 0.27) compared with fentanyl. CONCLUSIONS: The findings suggest that the risk of acute respiratory conditions may be lower in patients with CKD who use oxycodone for cancer pain than in those who use morphine. Additionally, no difference in the risk of acute respiratory conditions was found between oxycodone and fentanyl use.

A Survey of the Status of Methadone Switching in Japan Using a Hospital-Based Administrative Claims Database.

Methadone is generally used for the management of cancer pain in patients who cannot obtain adequate analgesia from other strong opioids; however, it has a complicated and inconsistent conversion ratio from pre-switching opioid dosage to methadone. This issue may be pronounced in Japan because only oral tablets are commercially available. We aimed to elucidate the status of methadone switching in Japan, focusing on its dosage. Using a Japanese hospital-based administrative claims database, we included patients who switched to methadone between April 2008 and January 2021. The proportion of methadone switching completion that required more than the defined conversion ratio in the Japanese package insert (called "high-dose methadone switching") was evaluated as a primary endpoint. Other endpoints included "the duration from initiation to completion of methadone switching" and "factors affecting high-dose methadone switching by using multivariate logistic regression analysis". Of 1585 patients who received methadone, 370 were enrolled. Among those, 130 (35.1%) received high-dose methadone switching. The median duration of methadone switching completion (12 days) was longer in the high-dose methadone switching group than in other patients. Four variables were identified as factors affecting high-dose methadone switching. Younger age and outpatient status increased the risk of requiring high-dose methadone switching, whereas the concomitant use of nonsteroidal anti-inflammatory drugs and fentanyl as a pre-switching opioid decreased the risk. In conclusion, more than 30% of the patients underwent high-dose methadone switching and required long completion periods, suggesting that methadone switching remains challenging in Japan.

Influence of Participation in a Medical-Themed Science Café on Patient Activation.

Purpose: 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.

Influence of Voiced and Semi-voiced Sounds on the Subjective Similarity of Different Drug Names: A Cognitive Psychological Experiment.

The similarity of drug names is one of the common causes of medication error. In Japan, similarity evaluation is performed prior to approval of new drugs in order to avoid potential confusion. However, existing indices do not take account of the difference between characters that contain voiced or semi-voiced and unvoiced sounds, so it is not clear whether such sounds influence the subjective similarity of drug names. Thus, we performed a cognitive psychological experiment to investigate this issue, using participants who had not received any education in medicine, nursing, or pharmacy. An analogue scale questionnaire was used to evaluate the subjective similarity of the names of drug pairs. Drug pairs for the main analysis were prepared by matching the first 0 to 3 characters, and then varying the difference in the number of voiced and semi-voiced characters from 0 to 3 in these matched characters. By means of this procedure, the drug pairs were classified into a total of 10 groups. Then, a total of 60 drug pairs were created by assigning 6 drugs to each group. The subjective similarity tended to increase with increasing number of common characters among the first three characters. When classified according to the number of these common characters, the subjective similarity was significantly decreased when voiced or semi-voiced sounds were present, as compared with when they were absent. These results indicate that a new drug name similarity index that takes account of voiced and semi-voiced sound differences should be developed to minimize medication errors.

Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities.

Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients' activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients' narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were "pain or numbness", "fatigue" and "nausea". Our results suggest that this AE monitoring scheme focusing on patients' ADL has potential to reinforce current AE management provided by medical staff.

Detection of Vaccine Adverse Events Before Package Insert Revisions Using a Japanese Spontaneous Reporting System.

The usefulness of disproportionality analysis for the pharmacovigilance of vaccines in the Japanese Adverse Drug Event Report (JADER) database is yet to be proven. This study aimed to verify whether significant disproportionality could be detected before adding new vaccine adverse event information to package inserts. Information on package insert revisions related to vaccine adverse drug events from January 2013 to March 2023 was extracted from the Pharmaceuticals and Medical Devices Agency website. This period was set as the maximum period for which early disproportionalities could be detected by the latest JADER database (April 2004 to December 2022). From JADER data, 15 revision histories (10 types of vaccines) of package inserts were identified, and 823,662 cases were obtained. Of the 15, 12 (80%) adverse events were identified as significant disproportionalities before package insert revisions were made. Nine of the 15 (60%) events were identified as significant disproportionalities earlier than at least 12 months. These findings suggest that the JADER database may detect vaccine adverse events earlier than package insert revisions, indicating its usefulness for the safety surveillance of vaccines.

Exploring Factors Affecting the Occurrence of Hypersensitivity Reactions Induced by Nonionic Iodine Contrast Media.

Hypersensitivity reactions induced by nonionic iodine contrast media sometimes occur and can be life threatening. However, independent factors affecting their occurrence remain to be fully established. Therefore, the purpose of this study was to clarify independent factors affecting the occurrence of hypersensitivity reactions induced by nonionic iodine contrast media. Patients who received nonionic iodine contrast media at Keiyu Hospital from April 2014 to December 2019 were included. The adjusted odds ratio (OR) and 95% confidence interval (CI) for factors affecting hypersensitivity reactions induced by contrast media were calculated by logistic regression analysis. The multiple imputation method was used to impute missing data. Hypersensitivity reactions occurred in 0.72% (163 cases) of 22,695 cases enrolled in this study. In univariate analysis, 10 variables met the criteria of P < .05 and proportion of missing data <50%. In multivariate analysis, age (OR, 0.98; 95% CI, 0.97-0.99), outpatient status (OR, 2.08; 95% CI, 1.20-3.60), contrast medium iodine content (OR, 1.02; 95% CI, 1.01-1.04), history of drug allergy (OR, 2.41; 95% CI, 1.50-3.88), and asthma (OR, 17.4; 95% CI, 7.53-40.1) were identified as independent factors affecting contrast media-induced hypersensitivity reactions. Among these factors, history of drug allergy and asthma appear to be clinically relevant and reliable due to their high OR and plausible biological mechanisms, but the other three factors require further validation.

Investigation of Prescription Status and Exploration of Risk Factors Related to Denosumab-Induced Hypocalcemia in Combination Therapy with 1α,25-Dihydroxy-vitamin D3.

To prevent denosumab-induced hypocalcemia in patients with renal dysfunction, combination therapy with 1α,25-dihydroxy-vitamin D3 (active vitamin D) is recommended. We previously developed a risk prediction model for hypocalcemia in patients with cholecalciferol/calcium (natural vitamin D). However, the prescription status and the risk factors of patients with active vitamin D have not been identified, so we designed this retrospective observational study using a large practice database covering June 2013 to May 2020 to analyze prescription status and risk factors. Patients were classified according to vitamin D type. After that, factors associated with development of hypocalcemia in patients with active vitamin D were explored. Univariate analysis was conducted to compare patient backgrounds between the hypocalcemia and non-hypocalcemia groups. Receiver operating characteristic analysis was conducted to evaluate the predictive potential of the extracted factors. Of the 33442 patients who received denosumab, 22347 and 3560 patients were co-administered natural and active vitamin D, respectively. Patients with active vitamin D had significantly lower renal function (estimated glomerular filtration rate (eGFR) median: 74.0 vs. 69.7 mL/min/1.73 m2), but some patients (23.6%) with sufficient renal function (eGFR ≥90) were also receiving active vitamin D. Of the 3560 patients with active vitamin D, non-hypocalcemia (n = 166) and hypocalcemia (n = 17) groups who met the study criteria were analyzed. Renal function was lower in the hypocalcemia group, and alkaline phosphatase gave the best discrimination. High aspartate aminotransferase (AST), renal dysfunction, high alkaline phosphatase (ALP), and low hemoglobin may be significant factors in risk prediction for hypocalcemia in patients with active vitamin D.

Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs.

BACKGROUND: Patients with breast cancer have a variety of worries and need multifaceted information support. Their accumulated posts on social media contain rich descriptions of their daily worries concerning issues such as treatment, family, and finances. It is important to identify these issues to help patients with breast cancer to resolve their worries and obtain reliable information. OBJECTIVE: This study aimed to extract and classify multiple worries from text generated by patients with breast cancer using Bidirectional Encoder Representations From Transformers (BERT), a context-aware natural language processing model. METHODS: A total of 2272 blog posts by patients with breast cancer in Japan were collected. Five worry labels, "treatment," "physical," "psychological," "work/financial," and "family/friends," were defined and assigned to each post. Multiple labels were allowed. To assess the label criteria, 50 blog posts were randomly selected and annotated by two researchers with medical knowledge. After the interannotator agreement had been assessed by means of Cohen kappa, one researcher annotated all the blogs. A multilabel classifier that simultaneously predicts five worries in a text was developed using BERT. This classifier was fine-tuned by using the posts as input and adding a classification layer to the pretrained BERT. The performance was evaluated for precision using the average of 5-fold cross-validation results. RESULTS: Among the blog posts, 477 included "treatment," 1138 included "physical," 673 included "psychological," 312 included "work/financial," and 283 included "family/friends." The interannotator agreement values were 0.67 for "treatment," 0.76 for "physical," 0.56 for "psychological," 0.73 for "work/financial," and 0.73 for "family/friends," indicating a high degree of agreement. Among all blog posts, 544 contained no label, 892 contained one label, and 836 contained multiple labels. It was found that the worries varied from user to user, and the worries posted by the same user changed over time. The model performed well, though prediction performance differed for each label. The values of precision were 0.59 for "treatment," 0.82 for "physical," 0.64 for "psychological," 0.67 for "work/financial," and 0.58 for "family/friends." The higher the interannotator agreement and the greater the number of posts, the higher the precision tended to be. CONCLUSIONS: This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients' worries and give them timely social support.

Latent class analysis of patients' background factors affecting the risk of specific adverse drug reactions to dipeptidyl peptidase 4 inhibitors.

BACKGROUND AND PURPOSE: Spontaneous reporting is widely used to identify adverse drug reactions (ADRs), but relatively little is known about the relationships between specific ADRs and background factors of affected patients. Here, we applied latent class analysis (LCA) to identify background factors associated with different ADRs in type 2 diabetes patients treated with dipeptidyl peptidase 4 (DPP-4) inhibitors, using the Japanese Adverse Drug Event Report (JADER) database. MATERIALS AND METHODS: Patients using only a DPP-4 inhibitor who encountered ADRs were selected from the JADER database up to April 2019 (N = 3,577). LCA was employed to classify these cases based on underlying diseases and lifestyle factors (alcohol, tobacco, diet, and exercise) and to identify characteristic ADRs in each class. The optimum number of classes was determined by selecting the model with the lowest value of the Bayesian information criterion (BIC). RESULTS: A six-class model had the lowest BIC, and these classes were characterized by specific background factors and ADRs. For example, one class included diabetes complications, while another class included exercise and diet as background factors. Increased risk of a specific ADR(s), such as pancreatitis or pemphigoid, was found in each class. The nine DPP-4 inhibitors were not uniformly distributed among the classes, though individual classes included patients receiving different inhibitors. CONCLUSION: Our findings indicate that characteristic background factors of patients experiencing specific DPP-4 inhibitor-induced ADRs reported in the JADER database are different and can be classified by LCA. This methodology may be useful for predicting ADRs not detected during drug development.

Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms.

Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for cancer patients. Recently, an increasing number of patients post their daily experiences to internet community, for example in blogs, where potential ADR signals not captured through routine clinic visits can be described. Therefore, this study aimed to identify patients with potential ADRs, focusing on HFS, from internet blogs by using natural language processing (NLP) deep-learning methods. From 10,646 blog posts, written in Japanese by cancer patients, 149 HFS-positive sentences were extracted after pre-processing, annotation and scrutiny by a certified oncology pharmacist. The HFS-positive sentences described not only HFS typical expressions like "pain" or "spoon nail", but also patient-derived unique expressions like onomatopoeic ones. The dataset was divided at a 4 to 1 ratio and used to train and evaluate three NLP deep-learning models: long short-term memory (LSTM), bidirectional LSTM and bidirectional encoder representations from transformers (BERT). The BERT model gave the best performance with precision 0.63, recall 0.82 and f1 score 0.71 in the HFS user identification task. Our results demonstrate that this NLP deep-learning model can successfully identify patients with potential HFS from blog posts, where patients' real wordings on symptoms or impacts on their daily lives are described. Thus, it should be feasible to utilize patient-generated text data to improve ADR management for individual patients.

Analysis of contributory factors to incidents related to medication assistance for residents taking medicines in residential care homes for the elderly: a qualitative interview survey with care home staff.

BACKGROUND: In Japan, staff who are not doctors or nurses can assist the elderly in residential care facilities to take their pre-packaged medicines. Therefore, there is a potential risk of incidents specific to staffs. The aim of this study was to clarify the causes of incidents related to medication assistance by staff in residential care facilities. METHOD: Semi-structured interviews with staff involved in medication incidents in long-term care facilities, focusing on how and why each incident happened, were conducted. The interview covered basic information about the subject and resident, the circumstances under which the incident had occurred, contributing factors, and countermeasures put in place. Verbatim transcripts of the interviews were prepared. Based on thematic analysis, codes and themes were created. RESULTS: Twelve subjects participated in this study. All subjects were staffs (not doctors or nurses) in long-term care facilities. All incidents covered in this study were incidents in which the wrong resident was given the medication. The incidents arose because of "not following procedures", such as lack of "self-check of residents' faces/residents' names/residents' medicine envelopes" or "double-check with other staff" or "using a device for medication intake". Contributory factors were grouped into four categories: individual resident factor items such as "decreased ability to understand their medication" or "refusal to take medicines", individual staff factor items such as "lack of knowledge related to medication" or "mental burden" or "experience in medication assistance", team factor items such as "failure to communicate with other staff", work environment factor items such as "presence of other residents" or "other work besides medication assistance" or "not enough time" or "little understanding of fostering a safety culture at the facility". CONCLUSION: This study identified four categories of contributory factors that may lead to incidents during medication assistance by caregivers for residents of care homes. These findings should be helpful for risk management in residential care facilities where staff usually provide medication assistance. Separation of meal times and medication assistance, and professional review to stagger the timing of administration of residents' medication may be effective in reducing incidents.

Development of Risk Prediction Model for Grade 2 or Higher Hypocalcemia in Patients With Bone Metastasis Treated With Denosumab Plus Cholecalciferol (Vitamin D3 )/Calcium Supplement.

Denosumab-induced hypocalcemia is sometimes severe, and although a natural vitamin D/calcium combination is used to prevent hypocalcemia, some patients rapidly develop severe hypocalcemia even under supplementation. It is clinically important to predict this risk. This study aimed to develop a risk prediction model for grade ≥2 hypocalcemia within 28 days after the first denosumab dose under natural vitamin D/calcium supplementation. Using a large database containing multicenter practice data, 2399 patients with bone metastasis who were treated with denosumab between June 2013 and May 2020 were retrospectively analyzed. Background factors in patients who developed grade ≥2 hypocalcemia within 28 days after the first denosumab dose and those who did not were compared by univariate analysis. Multivariate analysis was conducted to develop a risk prediction model. The model was evaluated for discriminant performance (receiver operating characteristic-area under the curve, sensitivity, specificity) and predictive performance (calibration slope). A total of 124 patients in the hypocalcemia group and 1191 patients in the nonhypocalcemia group were extracted. A risk prediction model consisting of sex, calcium, albumin, alkaline phosphatase, osteoporosis, breast cancer, gastric cancer, proton pump inhibitor combination, and pretreatment with zoledronic acid was developed. The receiver operating characteristic-area under the curve was 0.87. Sensitivity and specificity were 83% and 81%, respectively, and the calibration slope indicated acceptable agreement between observed and predicted risk. This model appears to be useful to predict the risk of denosumab-induced hypocalcemia and thus should be helpful for risk management of denosumab treatment in patients with bone metastases.

Telepharmacy in mountainous depopulated areas of Japan: an exploratory interview study of patients' perspectives.

Japan has an ageing population and geographical impediments to healthcare access, so an experimental trial of telepharmacy has recently been implemented in remote islands or remote areas of Japan prior to the formal implementation. This exploratory study was conducted to understand patients' perspectives on telepharmacy in a mountainous depopulated area away from urban areas of Japan. Semi-structured interviews were conducted with four elderly patients, who were all of the patients receiving telepharmacy in Toyone village, Japan, at the time of the survey. The transcribed interview data were qualitatively analyzed by coding and categorization. The subjects thought telepharmacy would be advantageous to overcome poor access to a clinic and to improve convenience in processes ranging from medical examination to obtaining prescribed medicines. However, they pointed out the low digital literacy of the elderly. Also, they had low expectations for pharmacists, because they had previously had no relationship with pharmacists due to lack of pharmacies in the area. To promote telepharmacy, efforts to eliminate resistance to smartphones and to provide support for smartphone operations are needed among the elderly. Work is also needed to establish how pharmacists should best be involved in patient care and health support in remote areas. Our findings suggest that telepharmacy is useful in remote areas of Japan, but in locations where there is no existing relationship with pharmacists, it would be desirable for pharmacists to be actively involved with the community to maximize its effectiveness.