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1.
Adv Gerontol ; 37(1-2): 33-39, 2024.
Article in Russian | MEDLINE | ID: mdl-38944770

ABSTRACT

In recent years, complications of drug therapy are an important medical problem. Data on adverse drug reactions (ADR) in patients of older age groups were analyzed. The object of the study was notification cards for unwanted reactions received from medical organizations of the Irkutsk region for period 2009-2020 years. The Narangio scale was used to assess the causality between ADR and medicines. Of the 1021 ADR notifications in patients over 65 years of age, 2/3 (668) are presented with ADR notifications in women, 353 (34,6%) in men. The presence of background diseases was registered in 915 notifications (89,6%). There were no gender differences except for a higher incidence of chronic obstructive pulmonary disease in men (7,2 and 3,5% respectively, p<0,05) and diabetes mellitus in women (14 and 3,5% respectively, p<0,05). ADRs for antibacterial agents amounted to 31,8%, drugs for the treatment of cardiovascular diseases - 10,5%, cases of therapeutic inefficiency - 5,1%. The ADR data statement was in line with the recommended form of 76%. The most common filling defect was incomplete patient information. The validity of the Narango causation was high. The deadlines for reporting data were observed in 89,1%. For effective interaction in the pharmacovigilance system, it is necessary in each medical organization to constantly inform about the procedure for pharmacovigilance, types of ADRs, the rules for their detection and the timing of data reporting. The work should be supervised by a trained specialist.


Subject(s)
Adverse Drug Reaction Reporting Systems , Drug-Related Side Effects and Adverse Reactions , Pharmacovigilance , Humans , Female , Male , Aged , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/etiology , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Russia/epidemiology , Aged, 80 and over
2.
In Vivo ; 38(4): 2090-2096, 2024.
Article in English | MEDLINE | ID: mdl-38936887

ABSTRACT

BACKGROUND/AIM: A few case reports of central nervous system (CNS) symptoms caused by amantadine intoxication have been published, detailing various types of symptoms and differing times to onset. We encountered a patient who developed CNS symptoms with amantadine. This prompted us to investigate the types, time to onset, and outcome of CNS adverse reactions to amantadine by analyzing data from a pharmacovigilance database. PATIENTS AND METHODS: The patient was evaluated at Chutoen General Hospital, Shizuoka, Japan. Analysis was performed using the Japanese Adverse Drug Event Report (JADER) database. RESULTS: In our case, the amantadine blood concentration was 4,042 ng/ml, i.e., in the toxic range. The time to onset was 26 days for dyskinesia and 90 days for depressed level of consciousness. Symptoms resolved when amantadine was discontinued. The JADER database contained 974 cases of adverse reactions to amantadine. The most frequently reported CNS adverse reaction was hallucination, with a reporting odds ratio of 64.28 (95% confidence interval=52.67-78.46). Positive signals were detected for all CNS adverse reactions. For all CNS reactions, clinical outcomes were poor in a comparatively low percentage of cases. Most CNS reactions occurred soon after administration of amantadine, usually within approximately one month. CONCLUSION: Because most CNS adverse reactions to amantadine usually occur within approximately one month of initiating treatment, healthcare providers should exercise heightened vigilance in monitoring patients for such reactions during this period.


Subject(s)
Amantadine , Humans , Amantadine/adverse effects , Male , Adverse Drug Reaction Reporting Systems , Pharmacovigilance , Central Nervous System/drug effects , Central Nervous System/pathology , Female , Central Nervous System Diseases/chemically induced , Central Nervous System Diseases/diagnosis , Japan , Middle Aged , Aged , Drug-Related Side Effects and Adverse Reactions/diagnosis
3.
JCO Clin Cancer Inform ; 8: e2300159, 2024 May.
Article in English | MEDLINE | ID: mdl-38728613

ABSTRACT

PURPOSE: We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets. METHODS: We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI. RESULTS: A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74. CONCLUSION: The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs.


Subject(s)
Algorithms , Immune Checkpoint Inhibitors , Liver Neoplasms , Humans , Immune Checkpoint Inhibitors/adverse effects , Male , Female , Middle Aged , Aged , Liver Neoplasms/drug therapy , Liver Neoplasms/immunology , Retrospective Studies , Phenotype , Chemical and Drug Induced Liver Injury/etiology , Chemical and Drug Induced Liver Injury/diagnosis , Carcinoma, Hepatocellular/drug therapy , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/etiology , Liver/pathology , Liver/drug effects , Liver/immunology
4.
Zhonghua Yi Xue Za Zhi ; 104(20): 1790-1803, 2024 May 28.
Article in Chinese | MEDLINE | ID: mdl-38782747

ABSTRACT

Immune checkpoint inhibitors (ICIs) have emerged as crucial therapeutic agents for various malignancies by activating the host immune system against tumor cells. However, many different types of skin adverse reactions may occur during its use, including eruption, pruritus, blistering, hypopigmentation, alopecia, and even severe cases, Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN). These cutaneous immune-related adverse events (cirAEs) had a high incidence, which seriously affected patients' quality of life and antitumor treatment decisions. Some severe cutaneous adverse reactions (SCARs) even endanger patients' lives. Therefore, the Chinese Society of Dermatology, the Chinese Dermatologist Association of the Chinese Medical Doctor Association, the Dermatology Division of the Chinese Geriatrics Society, and other relevant experts jointly discussed and formulated the 'Chinese Expert Consensus on the Diagnosis and Treatment of Immune Checkpoint Inhibitor-Related Cutaneous Adverse Reactions'. This consensus covers the name, epidemiology, pathogenesis, clinical features, classification and grading of cirAEs, principles of management and the re-initiation of ICIs. It aims to provide a more scientific and authoritative reference for the diagnosis and treatment of cirAEs in China in the future.


Subject(s)
Consensus , Immune Checkpoint Inhibitors , Humans , Immune Checkpoint Inhibitors/adverse effects , China , Stevens-Johnson Syndrome/therapy , Stevens-Johnson Syndrome/diagnosis , Stevens-Johnson Syndrome/etiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/therapy , Quality of Life , Skin/pathology , Neoplasms/drug therapy , Drug Eruptions/diagnosis , Drug Eruptions/therapy , Drug Eruptions/etiology
5.
J Pharmacol Toxicol Methods ; 127: 107514, 2024.
Article in English | MEDLINE | ID: mdl-38768933

ABSTRACT

Determining the causality of Adverse Drug Reactions (ADRs) is essential for management and prevention of future occurrences. The WHO-Uppsala Monitoring Centre (UMC) system is recommended under the Pharmacovigilance Program of India whereas Naranjo's algorithm is commonly utilized by clinicians, but their agreement remains a subject of investigation. This study aims to compare the inter-rater agreement between these two scales for causality assessment of ADRs. In this cross-sectional study, two groups of pharmacovigilance experts were given a set of total 399 anonymized individual case safety reports, collected over six months. The raters were blinded to each other's assessments and applied the WHO-UMC system and Naranjo algorithm to each case independently. Inter-rater agreement was then evaluated utilizing Cohen's kappa. The suspected ADRs were also comprehensively analysed on parameters like age, sex, route of administration, speciality, organ system affected, most common drug categories and individual drugs, outcome of ADRs. Analysis of 399 suspected ADRs revealed that mean age of patients was 36.8 ± 18.0 years, females were more frequently affected, highest proportion of reports were from psychiatry inpatients, seen with antipsychotic drugs, involved the central nervous system, with oral administration, and 91% resolved. On causality assessment by the WHO-UMC system, 53.3% were "Certain" whereas Naranjo's algorithm categorized 96.74% of ADRs as "Probable". Cohen's kappa showed a "Minimal" agreement (0.22) between WHO-UMC and Naranjo system of causality assessment. The considerable lack of agreement between the two commonly employed systems of causality assessment of ADRs warrants further investigation into specific factors influencing the disagreement to improve the accuracy of causality assessments.


Subject(s)
Adverse Drug Reaction Reporting Systems , Algorithms , Drug-Related Side Effects and Adverse Reactions , Pharmacovigilance , Humans , Female , Male , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/epidemiology , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Adult , Cross-Sectional Studies , Middle Aged , India , Young Adult , World Health Organization , Observer Variation , Aged , Adolescent
6.
Br J Clin Pharmacol ; 90(7): 1688-1698, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38604986

ABSTRACT

AIMS: While diagnostic codes from administrative health data might be a valuable source to identify adverse drug events (ADEs), their ability to identify unintended harms remains unclear. We validated claims-based diagnosis codes for ADEs based on events identified in a prospective cohort study and assessed whether key attributes predicted their documentation in administrative data. METHODS: This was a retrospective analysis of 3 prospective cohorts in British Columbia, from 2008 to 2015 (n = 13 969). We linked prospectively identified ADEs to administrative insurance data to examine the sensitivity and specificity of different diagnostic code schemes. We used logistic regression to assess which key attributes (e.g., type of event, symptoms and culprit medications) were associated with better documentation of ADEs in administrative data. RESULTS: Among 1178 diagnosed events, the sensitivity of the diagnostic codes in administrative data ranged from 3.4 to 52.6%, depending on the database and codes used. We found that documentation was worse for certain types of ADEs (dose-related: odds ratio [OR]: 0.32, 95% confidence interval [CI]: 0.15, 0.69; nonadherence events (OR: 0.35, 95% CI: 0.20, 0.62), and better for those experiencing arrhythmias (OR: 4.19, 95% CI: 0.96, 18.28). CONCLUSION: ADEs were not well documented in administrative data. Alternative methods should be explored to capture ADEs for health research.


Subject(s)
Databases, Factual , Drug-Related Side Effects and Adverse Reactions , Humans , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Female , British Columbia/epidemiology , Male , Databases, Factual/statistics & numerical data , Middle Aged , Retrospective Studies , Adult , Aged , International Classification of Diseases , Prospective Studies , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Adverse Drug Reaction Reporting Systems/standards , Clinical Coding/standards , Documentation/standards , Documentation/statistics & numerical data , Sensitivity and Specificity
7.
Asia Pac J Clin Oncol ; 20(4): 463-471, 2024 Aug.
Article in English | MEDLINE | ID: mdl-38608154

ABSTRACT

BACKGROUND: Immune-checkpoint inhibitors (ICIs) often cause immune-related adverse events (irAEs). The spectrum of irAEs and their managements has been partially clarified, however the knowledge on time-course of irAEs is not well understood. METHODS: A retrospective study based on the medical record was performed. The study subjects were consisting of patients with various types of solid tumors for whom ICIs (nivolumab, pembrolizumab, durvalumab, atezolizumab, nivolumab plus ipilimumab) were used between April 2016 and October 2021. We focused on irAEs developed more than 1-year after commencement ICIs (delayed irAE group) and compared with irAEs developed within 1-year (non-delayed irAE group) in terms of types and severity of irAEs. RESULTS: A total of 336 patients were enrolled in the study. Eighty-eight patients (26.2%) developed irAEs and 248 did not. Most of the patients developing irAEs were treated using PD-L1/PD-1 inhibitors. Eighty-one patients (24.1%) in non-delayed irAE group and 7 patients (2.1%) in delayed irAE group developed irAEs. The median onset of irAEs in the delayed irAE group was 18.6 months (range: 13.5-24.3). The types of irAEs observed in delayed irAE group were dermatitis (2 cases), pneumonitis (2 cases), nephritis (1 case), arthritis (1 case), and gastritis (1 case). The severity of irAEs was almost mild (≤G2), but one patient (.3%) developed G3 nephritis. CONCLUSION: PD-L1/PD-1 inhibitors frequently caused various irAEs but their severities were mostly tolerable. Few patients developed delayed irAE with mild toxities.


Subject(s)
Neoplasms , Humans , Male , Female , Retrospective Studies , Middle Aged , Aged , Neoplasms/drug therapy , Neoplasms/immunology , Immune Checkpoint Inhibitors/adverse effects , Adult , Immunotherapy/adverse effects , Immunotherapy/methods , Aged, 80 and over , Drug-Related Side Effects and Adverse Reactions/etiology , Drug-Related Side Effects and Adverse Reactions/diagnosis
8.
J Pharmacol Toxicol Methods ; 127: 107507, 2024.
Article in English | MEDLINE | ID: mdl-38636673

ABSTRACT

The Health and Environmental Sciences Institute (HESI) Cardiac Safety Committee designed and created a publicly accessible database with an initial set of 128 pharmacologically defined pharmaceutical agents, many with known cardiotoxic properties. The database includes specific information about each compound that could be useful in evaluating hypotheses around mechanisms of drug-induced cardiac toxicity or for development of novel cardiovascular safety assays. Data on each of the compounds was obtained from published literature and online sources (e.g., DrugBank.ca and International Union of Basic and Clinical Pharmacology (IUPHAR) / British Pharmacological Society (BPS) Guide to PHARMACOLOGY) and was curated by 10 subject matter experts. The database includes information such as compound name, pharmacological mode of action, characterized cardiac mode of action, type of cardiac toxicity, known clinical cardiac toxicity profile, animal models used to evaluate the cardiotoxicity profile, routes of administration, and toxicokinetic parameters (i.e., Cmax). Data from both nonclinical and clinical studies are included for each compound. The user-friendly web interface allows for multiple approaches to search the database and is also intended to provide a means for the submission of new data/compounds from relevant users. This will ensure that the database is constantly updated and remains current. Such a data repository will not only aid the HESI working groups in defining drugs for use in any future studies, but safety scientists can also use the database as a vehicle of support for broader cardiovascular safety studies or exploring mechanisms of toxicity associated with certain pharmacological modes of action.


Subject(s)
Cardiotoxicity , Databases, Pharmaceutical , Drug-Related Side Effects and Adverse Reactions , Animals , Humans , Cardiotoxicity/etiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug Evaluation, Preclinical/methods , Databases, Factual , Pharmaceutical Preparations
9.
Clin Cancer Res ; 30(8): 1685-1695, 2024 Apr 15.
Article in English | MEDLINE | ID: mdl-38597991

ABSTRACT

PURPOSE: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting. EXPERIMENTAL DESIGN: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing. RESULTS: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. CONCLUSIONS: Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Humans , Drug Interactions , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/etiology
10.
Rheum Dis Clin North Am ; 50(2): 229-239, 2024 May.
Article in English | MEDLINE | ID: mdl-38670722

ABSTRACT

Cancer immunotherapy is revolutionary for survival but has complications due to immunogenicity with unpredictable and potentially long-lasting autoimmune side effects known as immune-related adverse events (irAEs). Currently, treatment beyond corticosteroids can be complicated by the diversity of providers who are needed across a variety of clinical settings to manage irAEs. We outline the role of critical players in the management of irAEs, discuss the current limitations that exist, and propose various methodologies that can be adapted across clinical settings to tackle these needs. We aim to better understand who can be affected by irAEs and tailor diagnostics and therapeutics appropriately.


Subject(s)
Immunotherapy , Humans , Immunotherapy/adverse effects , Immunotherapy/methods , Neoplasms/therapy , Neoplasms/immunology , Drug-Related Side Effects and Adverse Reactions/diagnosis
11.
Acta Oncol ; 63: 213-219, 2024 Apr 21.
Article in English | MEDLINE | ID: mdl-38647024

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes in various cancers. ICI treatment is associated with the incidence of immune-related adverse events (irAEs) which can affect any organ. Data on irAEs occurrence in relation to sex- differentiation and their association with gender-specific factors are limited. AIMS: The primary objective of the G-DEFINER study is to compare the irAEs incidence in female and male patients who undergo ICI treatment. Secondary objectives are: to compare the irAEs incidence in pre- and postmenopausal female patients; to compare the irAEs incidence in female and male patients according to different clinical and gender-related factors (lifestyle, psychosocial, and behavioral factors). Exploratory objectives of the study are to compare and contrast hormonal, gene-expression, SNPs, cytokines, and gut microbiota profiles in relation to irAEs incidence in female and male patients. METHODS AND RESULTS: The patients are recruited from Fondazione IRCCS Istituto Nazionale dei Tumori, Italy, St Vincent's University Hospital, Ireland, Oslo University Hospital, Norway, and Karolinska Insitutet/Karolinska University Hospital, Sweden. The inclusion of patients was delayed due to the Covid pandemic, leading to a total of 250 patients recruited versus a planned number of 400 patients. Clinical and translational data will be analyzed. INTERPRETATION: The expected outcomes are to improve the management of cancer patients treated with ICIs, leading to more personalized clinical approaches that consider potential toxicity profiles. The real world nature of the trial makes it highly applicable for timely irAEs diagnosis.


Subject(s)
Immune Checkpoint Inhibitors , Neoplasms , Humans , Female , Male , Neoplasms/drug therapy , Prospective Studies , Immune Checkpoint Inhibitors/adverse effects , Sex Factors , Incidence , Immunotherapy/adverse effects , Immunotherapy/methods , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/etiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Observational Studies as Topic
12.
JCO Clin Cancer Inform ; 8: e2300151, 2024 04.
Article in English | MEDLINE | ID: mdl-38687915

ABSTRACT

PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records. METHODS: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. RESULTS: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. CONCLUSION: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Electronic Health Records , Immune Checkpoint Inhibitors , Natural Language Processing , Humans , Immune Checkpoint Inhibitors/adverse effects , Female , Male , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/etiology , Neoplasms/drug therapy , Middle Aged , Aged
13.
Clin Pharmacol Ther ; 115(6): 1391-1399, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38459719

ABSTRACT

Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT) have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event (AE) detection. We adapted a new clinical LLM, University of California - San Francisco (UCSF)-BERT, to identify serious AEs (SAEs) occurring after treatment with a non-steroid immunosuppressant for inflammatory bowel disease (IBD). We compared this model to other language models that have previously been applied to AE detection. We annotated 928 outpatient IBD notes corresponding to 928 individual patients with IBD for all SAE-associated hospitalizations occurring after treatment with a non-steroid immunosuppressant. These notes contained 703 SAEs in total, the most common of which was failure of intended efficacy. Out of eight candidate models, UCSF-BERT achieved the highest numerical performance on identifying drug-SAE pairs from this corpus (accuracy 88-92%, macro F1 61-68%), with 5-10% greater accuracy than previously published models. UCSF-BERT was significantly superior at identifying hospitalization events emergent to medication use (P < 0.01). LLMs like UCSF-BERT achieve numerically superior accuracy on the challenging task of SAE detection from clinical notes compared with prior methods. Future work is needed to adapt this methodology to improve model performance and evaluation using multicenter data and newer architectures like Generative pre-trained transformer (GPT). Our findings support the potential value of using large language models to enhance pharmacovigilance.


Subject(s)
Algorithms , Immunosuppressive Agents , Inflammatory Bowel Diseases , Natural Language Processing , Pharmacovigilance , Humans , Pilot Projects , Inflammatory Bowel Diseases/drug therapy , Immunosuppressive Agents/adverse effects , Data Mining/methods , Drug-Related Side Effects and Adverse Reactions/diagnosis , Adverse Drug Reaction Reporting Systems , Electronic Health Records , Female , Male , Hospitalization/statistics & numerical data
14.
Res Social Adm Pharm ; 20(7): 576-589, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38538516

ABSTRACT

OBJECTIVE: To identify trigger tools applied to detect adverse drug events (ADEs) in older people and describe their utility and performance. METHODS: A systematic review was conducted in the PubMed, Lilacs, and Scopus databases (January 2024). Studies that developed, applied, or validated trigger tools and evaluated their utility and/or performance for detecting ADEs in older people were considered. Direct proportion meta-analyses using the inverse-variance method were performed for prevalence of ADEs and positive predictive value (PPV). RESULTS: Twenty-four studies (25 publications) were included. Twelve trigger tools were identified, of which six were developed for detecting ADEs in older population, four developed for general population and modified for older people, and two developed for general population. No tools for detecting ADEs in older people receiving palliative care or hospitalized in intensive or surgical care units were found. The performance of triggers was presented through PPV (11.5-71%), negative predictive values (83.3%), and sensitivity (30-94.8%). The overall PPV was 33.3% (95%CI: 32.5-34.2%). Triggers with good performance were changes in plasma levels of digoxin, glucose, and potassium; changes in international normalized ratio; abrupt medication stop; hypotension; and constipation. The prevalence of ADEs ranged from 2.8 to 66%, with overall prevalence of ADEs of 20% (95%CI: 19.3-20.8%). Preventability ranged from 8.4 to 94.4%. Metabolic or electrolyte disturbances induced by diuretics, constipation induced by opioids, and falls and delirium induced by benzodiazepines were the most prevalent ADEs. CONCLUSION: The trigger tools are flexible and easy to apply, and they can contribute to the detection of ADEs, their associated risk factors, the level of harm, and preventability in different health settings. However, there is no consensus on good or poor values of PPV, which indicate the performance of triggers. Furthermore, there is limited evidence regarding the evaluation of performance through negative predictive value, sensitivity, and specificity. PROSPERO: CRD42022379893.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Aged , Humans , Drug-Related Side Effects and Adverse Reactions/prevention & control , Drug-Related Side Effects and Adverse Reactions/diagnosis
15.
Intern Med J ; 54(7): 1183-1189, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38482918

ABSTRACT

BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data and transfer learning. AIMS: The aims of this study were to investigate the use of additional training data and novel machine learning strategies, namely synthetic data and transfer learning, to improve the performance of penicillin adverse drug reaction (ADR) machine learning classification. METHODS: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system electronic health record (EHR). The models were developed by training on various labelled data sets. ADR entries were split into training and testing data sets and used to develop and test a variety of machine learning models. The effect of training on additional data and synthetic data versus the use of transfer learning was analysed. RESULTS: Following the application of these techniques, the area under the receiver operator curve of best-performing models for the classification of penicillin allergy (vs intolerance) and high-risk allergy (vs low-risk allergy) improved to 0.984 (using the artificial neural network model) and 0.995 (with the transfer learning approach) respectively. CONCLUSIONS: Machine learning models demonstrate high levels of accuracy in the classification and risk stratification of penicillin ADR labels using the reaction documented in the EHR. The model can be further optimised by incorporating additional training data and using transfer learning. Practical applications include automating case detection for penicillin allergy delabelling programmes.


Subject(s)
Electronic Health Records , Machine Learning , Natural Language Processing , Penicillins , Humans , Penicillins/adverse effects , Drug Hypersensitivity/diagnosis , Drug Hypersensitivity/classification , Drug Hypersensitivity/etiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Neural Networks, Computer , Anti-Bacterial Agents/adverse effects , Adverse Drug Reaction Reporting Systems/standards
16.
Curr Drug Saf ; 19(3): 382-394, 2024.
Article in English | MEDLINE | ID: mdl-38310553

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs) used in immunotherapy have revolutionized cancer management. However, ICI therapy can come with serious neurologic risks. OBJECTIVE: The objective of our study is to analyze the occurrence of neurologic events with ICIs. METHODS: We referred to EudraVigilance (EV) and VigiAccess to evaluate the frequency of individual case safety reports (ICSRs), including neurologic events with ICIs. Data was gathered for a period from the date of ICI's marketing authorization till 30 January 2023. The computational assessment was conducted with the help of reporting odds ratio (ROR) and its 95% confidence interval (CI). RESULTS: Overall, 8181 ICSRs in EV and 15905 ICSRs from VigiAccess were retrieved for neurologic events, with at least one ICI as the suspected drug. The majority of the ICSRs were reported for nivolumab, pembrolizumab, and ipilimumab, whereas frequently reported events were neuropathy peripheral, myasthenia gravis, seizure, Guillain-Barre syndrome, paraesthesia, syncope, encephalopathy, somnolence. Under EV, 92% of ICSRs were reported as serious, 10% included fatal outcomes, and nearly 61% cited patient recovery. Atezolizumab (ROR 1.64, 95% CI 1.75- 1.52), cemiplimab (ROR 1.61, 95% CI 1.98-1.3), and nivolumab (ROR 1.38, 95% CI 1.44-1.31) had a considerable increase in the frequency of ICSR reporting. Cerebrovascular accident, posterior reversible encephalopathy syndrome, tremor, and somnolence were identified as potential signals. CONCLUSION: ICIs were significantly associated with neurologic risks, which cannot be generalized. A considerable increase in ICSR reporting frequency was observed with atezolizumab, cemiplimab, and nivolumab, while avelumab, pembrolizumab, durvalumab, and cemiplimab were linked with four potential signals. These findings suggest the consideration of a revision of the neurologic safety profile of ICIs. Furthermore, the necessity for additional ad-hoc research is emphasized.


Subject(s)
Adverse Drug Reaction Reporting Systems , Immune Checkpoint Inhibitors , Humans , Immune Checkpoint Inhibitors/adverse effects , Adverse Drug Reaction Reporting Systems/statistics & numerical data , Nervous System Diseases/chemically induced , Nervous System Diseases/epidemiology , Neoplasms/drug therapy , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/diagnosis
17.
Br J Clin Pharmacol ; 90(5): 1240-1246, 2024 May.
Article in English | MEDLINE | ID: mdl-38320955

ABSTRACT

AIMS: Medication non-adherence is a type of adverse drug event that can lead to untreated and exacerbated chronic illness, and that drives healthcare utilization. Research using medication claims data has attempted to identify instances of medication non-adherence using the proportion of days covered or by examining gaps between medication refills. We sought to validate these measures compared to a gold standard diagnosis of non-adherence made in hospital. METHODS: This was a retrospective analysis of adverse drug events diagnosed during three prospective cohorts in British Columbia between 2008 and 2015 (n = 976). We linked prospectively identified adverse drug events to medication claims data to examine the sensitivity and specificity of typical non-adherence measures. RESULTS: The sensitivity of the non-adherence measures ranged from 22.4% to 37.5%, with a proportion of days covered threshold of 95% performing the best; the non-persistence measures had sensitivities ranging from 10.4% to 58.3%. While a 7-day gap was most sensitive, it classified 61.2% of the sample as non-adherent, whereas only 19.6% were diagnosed as such in hospital. CONCLUSIONS: The methods used to identify non-adherence in administrative databases are not accurate when compared to a gold standard diagnosis by healthcare providers. Research that has relied on administrative data to identify non-adherent patients both underestimates the magnitude of the problem and may label patients as non-adherent who were in fact adherent.


Subject(s)
Databases, Factual , Drug-Related Side Effects and Adverse Reactions , Medication Adherence , Humans , Medication Adherence/statistics & numerical data , Drug-Related Side Effects and Adverse Reactions/epidemiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , British Columbia , Female , Retrospective Studies , Male , Databases, Factual/statistics & numerical data , Middle Aged , Aged , Adult , Sensitivity and Specificity , Prospective Studies , Administrative Claims, Healthcare/statistics & numerical data , Young Adult
18.
PLoS One ; 19(1): e0297189, 2024.
Article in English | MEDLINE | ID: mdl-38241386

ABSTRACT

Determining causality of an adverse drug reaction (ADR) requires a multifactor assessment. The classic Naranjo algorithm is still the dominant assessment tool used to determine causality. But, in spite of its effectiveness, the Naranjo algorithm is manually intensive and impractical for assessing very many ADRs and drug combinations. Thus, over the years, many "automated" algorithms have been developed in an attempt to determine causality. By-and-large, these algorithms are either regression-based or Bayesian. In general, the automatic algorithms have several major drawbacks that preclude fully automated causality assessment. Therefore, signal detection (or causality screening) plays a role in a "first pass" of large ADR databases to limit the number of ADR/drug combinations a skilled human further assesses. In this work a Bayesian signal detector based on analytic combinatorics is developed from a point of view commonly adopted by engineers in the field of radar and sonar signal processing. The algorithm developed herein addresses the commonly encountered issues of misreported data and unreported data. In the framework of signal processing, misreported ADRs are identified as "clutter" (unwanted data) and unreported ADRs are identified as "missed detections". Including the aforementioned parameters provides a more complete probabilistic description of ADR data.


Subject(s)
Adverse Drug Reaction Reporting Systems , Drug-Related Side Effects and Adverse Reactions , Humans , Bayes Theorem , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/epidemiology , Causality , Algorithms , Drug Combinations
19.
BMC Health Serv Res ; 24(1): 72, 2024 Jan 15.
Article in English | MEDLINE | ID: mdl-38225629

ABSTRACT

BACKGROUND: Pregnant women belong to the special population of drug therapy, and their physiological state, pharmacokinetics and pharmacodynamics are significantly different from the general population. Drug safety during pregnancy involves two generations, which is a hot issue widely concerned in the whole society. Global Trigger Tool (GTT) of the Institute for Healthcare Improvement (IHI) has been wildly used as a patient safety measurement strategy by several institutions and national programs, and the effectiveness had been demonstrated. But only one study reports the use of GTT in obstetric delivery until now. The aim of the study is to establish triggers detecting adverse drug events (ADEs) suitable for obstetric inpatients on the basis of the GTT, to examine the performance of the obstetric triggers in detecting ADEs experienced by obstetric units compared with the spontaneous reporting system and GTT, and to assess the utility and value of the obstetric trigger tool in identifying ADEs of obstetric inpatients. METHODS: Based on a literature review searched in PubMed and CNKI from January of 1997 to October of 2023, retrospective local obstetric ADEs investigations, relevant obstetric guidelines and the common adverse reactions of obstetric therapeutic drugs were involved to establish the initial obstetric triggers. According to the Delphi method, two rounds of expert questionnaire survey were conducted among 16 obstetric and neonatological physicians and pharmacists until an agreement was reached. A retrospective study was conducted to identity ADEs in 300 obstetric inpatient records at the Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital from June 1 to September 30, 2018. Two trained junior pharmacists analyzed the first eligible records independently, and the included records reviewed by trained pharmacist and physician to identify ADEs. Sensitivity and specificity of the established obstetric triggers were assessed by the number of ADEs/100 patients and positive predictive value with the spontaneous reporting system (SRS) and GTT. Excel 2010 and SPSS22 were used for data analysis. RESULTS: Through two rounds of expert investigation, 39 preliminary triggers were established that comprised four modules (12 laboratory tests, 9 medications, 14 symptoms, and 4 outcomes). A total of 300 medical records were reviewed through the obstetric triggers, of which 48 cases of ADEs were detected, with an incidence of ADEs of 16%. Among the 39 obstetric triggers, 22 (56.41%) were positive and 11 of them detected ADEs. The positive predictive value (PPV) was 36.36%, and the number of ADEs/100 patients was 16.33 (95% CI, 4.19-17.81). The ADE detection rate, positive trigger rate, and PPV for the obstetric triggers were significantly augmented, confirming that the obstetric triggers were more specific and sensitive than SRS and GTT. CONCLUSION: The obstetric triggers were proven to be sensitive and specific in the active monitoring of ADE for obstetric inpatients, which might serve as a reference for ADE detection of obstetric inpatients at medical institutions.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Inpatients , Pregnancy , Humans , Female , Retrospective Studies , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/epidemiology , Patient Safety , China/epidemiology
20.
Clin Pharmacol Ther ; 115(3): 535-544, 2024 03.
Article in English | MEDLINE | ID: mdl-38069538

ABSTRACT

Timely identification and discontinuation of culprit-drug is the cornerstone of clinical management of drug-induced acute pancreatitis (AP), but the comprehensive landscape of AP culprit-drugs is still lacking. To provide the current overview of AP culprit-drugs to guide clinical practice, we reviewed the adverse event (AE) reports associated with AP in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database from 2004 to 2022, and summarized a potential AP culprit-drug list and its corresponding AE report quantity proportion. The disproportionality analysis was used to detect adverse drug reaction (ADR) signals for each drug in the drug list, and the ADR signal distribution was integrated to show the risk characteristic of drugs according to the ADR signal detection results. In the FAERS database, a total of 62,206 AE reports were AP-related, in which 1,175 drugs were reported as culprit-drug. On the whole, metformin was the drug with the greatest number of AE reports, followed by quetiapine, liraglutide, exenatide, and sitagliptin. Drugs used in diabetes was the drug class with the greatest number of AE reports, followed by immunosuppressants, psycholeptics, drugs for acid-related disorders, and analgesics. In disproportionality analysis, 595 drugs showed potential AP risk, whereas 580 drugs did not show any positive ADR signal. According to the positive-negative distribution of the ADR signal for drug classes, the drug class with the greatest number of positive drugs was antineoplastic agents. In this study, we provided the current comprehensive landscape of AP culprit-drugs from the pharmacovigilance perspective, which can provide reference information for clinical practice.


Subject(s)
Drug-Related Side Effects and Adverse Reactions , Pancreatitis , United States/epidemiology , Humans , Pharmacovigilance , Adverse Drug Reaction Reporting Systems , United States Food and Drug Administration , Acute Disease , Pancreatitis/chemically induced , Pancreatitis/epidemiology , Drug-Related Side Effects and Adverse Reactions/diagnosis , Drug-Related Side Effects and Adverse Reactions/epidemiology
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