Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling.

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.

Analyzing the Impact of Drug Name Similarity on Dispensing Errors: An Examination Using a Drug Name Similarity Index.

Dispensing errors pose a significant health risk, with drug name similarity being a potential contributory factor. To determine the impact of drug name similarity on dispensing errors within clinical settings, we analyzed 563 dispensing errors at an acute hospital in Japan from April 2015 to June 2018. Drug name similarity between two drugs was classified into Name-Similar and Name-Dissimilar groups using the m2-vwhtfrag index, the value of the drug name similarity. Drug efficacy similarity was categorized into Efficacy-Same, Efficacy-Close, and Efficacy-Far. The drug name similarity and drug efficacy similarity of all possible pair combinations were obtained and similarly classified. The proportion of the number of pairs with dispensing errors per the total number of drug pairs in the hospital's drug formulary in each category was calculated. The highest proportion of the number of pairs with dispensing errors was 36% for the Efficacy-Same and Name-Similar group, and the lowest proportion was 0.022% for the Efficacy-Far and Name-Dissimilar group. The proportion of the number of pairs with dispensing errors was significantly higher in the Name-Similar category than in the Name-Dissimilar category for all drug efficacy categories. Our results indicate that drug name similarity increases the risk of dispensing errors, and that m2-vwhtfrag is a useful indicator to assess dispensing errors in clinical practice. Such drug name and efficacy similarity evaluations can help identify factors causing dispensing errors, and predict the risk of dispensing errors for newly adopted drugs, considering the relationship with the whole drug formulary in the hospital dispensary.

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.

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.

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.

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.

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.