External Validation and Update of the Risk Prediction Model for Denosumab-Induced Hypocalcemia Developed From a Hospital-Based Administrative Database.

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.

Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach.

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 F1-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 F1-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.

[Evaluation of Patient Outcomes after Pharmacological Intervention in Home Health Care Utilizing Pharmaceutical Care Records].

The purpose of this study was to identify patient outcomes after pharmacist interventions in the home health care context using pharmaceutical care records accumulated during daily operations. We focused on 591 cases at Nakajima Pharmacy from April 2020 to December 2021, where dispensing fees were charged to prevent duplication of medication and unnecessary interactions of home patients (excluding those related to adjustment of ongoing medications). The study investigated the content and background of prescription changes, the follow-up rate, and patient outcomes. The most common circumstances that led to pharmacist intervention for homebound patients were symptom occurrence (uncontrolled symptom, new symptom, drug adverse event). Of the patients for whom pharmacist intervention was provided for symptoms, 72.8% received follow-up according to the pharmaceutical care records. Furthermore, 59.2% of patients with follow-up showed an improvement of their symptoms. In addition, many patients had their medications discontinued or the dosage reduced by the pharmacist despite stable symptoms. More than 90% of these patients showed no change in symptoms. Besides interventions associated with the occurrence of symptoms, many interventions related to medication adherence were found to result from the patient's physical condition, such as poor swallowing function. The results suggest that tracking pharmacy drug histories may help pharmacists to better understand the need for follow-up implementation and the changes in patient outcomes after interventions.

Concomitant use of lansoprazole and ceftriaxone is associated with an increased risk of ventricular arrhythmias and cardiac arrest in a large Japanese hospital database.

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.

Advancements in Non-Dopaminergic Treatments for Schizophrenia: A Systematic Review of Pipeline Developments.

INTRODUCTION: Conventional antipsychotic drugs that attenuate dopaminergic neural transmission are ineffective in approximately one-third of patients with schizophrenia. This necessitates the development of non-dopaminergic agents. METHODS: A systematic search was conducted for completed phase II and III trials of compounds for schizophrenia treatment using the US Clinical Trials Registry and the EU Clinical Trials Register. Compounds demonstrating significant superiority over placebo in the primary outcome measure in the latest phase II and III trials were identified. Collateral information on the included compounds was gathered through manual searches in PubMed and press releases. RESULTS: Sixteen compounds were identified; four compounds (ulotaront, xanomeline/trospium chloride, vabicaserin, and roluperidone) were investigated as monotherapy and the remaining 12 (pimavanserin, bitopertin, BI 425809, encenicline, tropisetron, pregnenolone, D-serine, estradiol, tolcapone, valacyclovir, cannabidiol, and rimonabant) were examined as add-on therapy. Compared to the placebo, ulotaront, xanomeline/trospium chloride, vabicaserin, bitopertin, estradiol, cannabidiol, rimonabant, and D-serine showed efficacy for positive symptoms; roluperidone and pimavanserin were effective for negative symptoms; and encenicline, tropisetron, pregnenolone, tolcapone, BI 425809, and valacyclovir improved cognitive function. DISCUSSION: Compounds that function differently from existing antipsychotics may offer novel symptom-specific therapeutic strategies for patients with schizophrenia.

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.

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.

Comparable Psychotropic Prescription Rates After Hospital Discharge Between Patients with COVID-19 and Those With Non-COVID-19-Related Respiratory Infection.

INTRODUCTION: Whether psychiatric symptoms after recovery from coronavirus disease 2019 (COVID-19) are specific to this illness remains unclear. METHODS: In this retrospective study, the Diagnosis Procedure Combination data and outpatient clinic data were used for patients who received inpatient treatment in Saiseikai-affiliated hospitals for COVID-19 or other respiratory tract infections (non-COVID) from 2020 to 2022. The primary outcome was new prescriptions of psychotropic drugs after discharge (i. e., prescriptions of psychotropics to patients who had not received them before or during their hospitalization). Values of interest were compared between groups using the chi-square test or Fisher's exact test. A COX proportional-hazards model was used to examine factors associated with psychotropic prescriptions after discharge in age- and sex-matched COVID-19 and non-COVID patients. RESULTS: Of 31,993 chart records, 19,613 were excluded due to a positive history with psychiatric disorders (n=2,445), prescriptions of psychotropics (n=744), and no follow-ups (n=16,424). Thus, 3,648 COVID-19 and 8,732 non-COVID patients were included (mean [range] duration of follow-up, days: 146.9 [1-727] and 239.2 [1-729], respectively). Two hundred and four (5.6%) of the 3,648 patients with COVID-19 received psychotropic prescriptions after discharge. No statistically significant differences were observed in the prescription rates of any psychotropic category between the COVID-19 and non-COVID groups. An increase in severity during hospitalization was significantly associated with more frequent psychotropic prescriptions (hazard ratio 1.83, p<0.001). DISCUSSION: The development of psychiatric symptoms should be closely observed, especially in patients who experienced increased severity during hospitalization, regardless of whether they suffered from COVID-19.

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.

[Evaluation of Pharmacists' Active Intervention to Reduce Potentially Inappropriate Medications in Special Older Adult Nursing Home].

Currently, elderly care facilities that do not offer long-term care are not required to employ pharmacists, and duties such as the dispensing and distribution of medicines are entrusted to external pharmacies. Pharmacists seldom spend sufficient time at the facilities for elderly people requiring special care. Thus, in many cases, the pharmacists have insufficient knowledge of the residents' medication status, leading to their inability in determining whether the residents are receiving a suitable drug therapy. We previously documented various problems in the practices adopted by nursing staff (with negligible intervention by pharmacists) for assisting residents in taking their medications. In the present pilot study, we attempted to eliminate the use of potentially inappropriate medications by stationing a pharmacist at a nursing home for 24 h every week (3 d/week). We proactively collected information from nurses and other nursing staff and observed the residents' actual living conditions and medication use. As a result of this intervention, 56 prescriptions were changed. However, only two of these were changed exclusively based on the prescription information. Most prescriptions were able to change based on the information obtained by the pharmacist present at the facility. Therefore, pharmacists' presence at the facility (at least for a few hours) is necessary, as they can actively intervene and collaborate with other staff to prevent the use of potentially inappropriate medications.

Serious Bacterial Infections Associated with Eculizumab: A Pharmacovigilance Study.

Objective Molecular-targeted agents, including eculizumab and rituximab, are considered treatment options for refractory myasthenia gravis (MG), but bacterial infections can occur as serious adverse events when using these agents. The present study elucidated the relative risks of bacterial infections associated with eculizumab and rituximab using a pharmacovigilance database. Methods We analyzed eculizumab- and rituximab-associated adverse events reported between 2007 and 2021 in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and herein report a refractory MG patient who developed streptococcal toxic shock syndrome during eculizumab treatment. Patients We evaluated a 74-year-old Japanese woman with refractory MG who developed severe bacteremia after receiving eculizumab. Results A total of 44,215 and 108,485 adverse events were reported with eculizumab and rituximab, respectively, from among 13,742,321 individual case safety reports in the FAERS database after data cleaning. We found a strong association between eculizumab and Neisseria infections. In contrast, we found only one case of meningococcal meningitis treated with rituximab. Both eculizumab and rituximab were weakly associated with streptococcal infections. Two cases of streptococcal toxic shock syndrome were associated with rituximab. Conclusion Careful monitoring of serious bacterial infections associated with eculizumab treatment is warranted.

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.

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.

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.

Metabolic and toxicological considerations regarding CGRP mAbs and CGRP antagonists to treat migraine in COVID-19 patients: a narrative review.

INTRODUCTION: Migraine pharmacological therapies targeting calcitonin gene-related peptide (CGRP), including monoclonal antibodies and gepants, have shown clinical effect and optimal tolerability. Interactions between treatments of COVID-19 and CGRP-related drugs have not been reviewed. AREAS COVERED: An overview of CGRP, a description of the characteristics of each CGRP-related drug and its response predictors, COVID-19 and its treatment, the interactions between CGRP-related drugs and COVID-19 treatment, COVID-19 and vaccination-induced headache, and the neurological consequences of Covid-19. EXPERT OPINION: Clinicians should be careful about using gepants for COVID-19 patients, due to the potential drug interactions with drugs metabolized via CYP3A4 cytochrome. In particular, COVID-19 treatment (especially nirmatrelvir packaged with ritonavir, as Paxlovid) should be considered cautiously. It is advisable to stop or adjust the dose (10 mg atogepant when used for episodic migraine) of gepants when using Paxlovid (except for zavegepant). CGRP moncolconal antibodies (CGRP-mAbs) do not have drug - drug interactions, but a few days' interval between a COVID-19 vaccination and the use of CGRP mAbs is recommended to allow the accurate identification of the possible adverse effects, such as injection site reaction. Covid-19- and vaccination-related headache are known to occur. Whether CGRP-related drugs would be of benefit in these circumstances is not yet known.

Real-world evidence of fremanezumab for treating migraine in Japan: a retrospective study.

BACKGROUND: There have been very few real-world studies reported in the literature solely focusing on fremanezumab in Asia. This study aimed to evaluate the efficacy and safety of fremanezumab in a real-world setting in Japan. METHOD: This single-centered, observational, retrospective study examined patients with migraines who received four doses of fremanezumab between December 2021 and August 2022 at Keio University Hospital. We assessed the changes in monthly migraine days, responder rates, and migraine-associated symptoms, as well as injection site reactions and adverse events. RESULT: Twenty-nine patients were enrolled, wherein 79.3% were women. Compared with those at baseline, the monthly migraine days decreased by 5.9 days at 4 months. The 50% responder rate was 55.2% at 4 months. A total of 57.9%, 47.8%, and 65.0% of patients showed improvement in the severity of photophobia, phonophobia, and nausea/vomiting, respectively. Moreover, injection site reactions were the most common adverse events (55.2%). CONCLUSION: Fremanezumab is effective and safe for migraine prevention in Japan. Fremanezumab also improved migraine-associated symptoms in half of the patients.