Phenotyping Hepatic Immune-Related Adverse Events in the Setting of Immune Checkpoint Inhibitor Therapy.

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.

A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions.

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.

Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

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.

Immunotherapy Toxicity Management in Clinical Practice: Building the Clinical Infrastructure for Immune-related Adverse Event Evaluation and Care.

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.

Gender Difference in sidE eFfects of ImmuNotherapy: a possible clue to optimize cancEr tReatment (G-DEFINER): study protocol of an observational prospective multicenter study.

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.

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

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.

Adverse drug reaction signal detection methods in spontaneous reporting system: A systematic review.

BACKGROUND: A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system. METHODS: We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method. RESULTS: A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods. CONCLUSION: Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events.

Validating methods used to identify non-adherence adverse drug events in Canadian administrative health data.

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.

Establishing a trigger tool based on global trigger tools to identify adverse drug events in obstetric inpatients in China.

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.

A Bayesian generating function approach to adverse drug reaction screening.

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.

Current Scenario and Future Prospects of Adverse Drug Reactions (ADRs) Monitoring and Reporting Mechanisms in the Rural Areas of India.

BACKGROUND: Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The objective of PV is to ensure the safety of the medicines and patients by monitoring and reporting all adverse drug reactions (ADRs) associated with prescribed medicine usage. Findings have indicated that about 0.2- 24% of hospitalization cases are due to ADRs, of which 3.7% of patients have lethal ADRs. The reasons include the number of prescribed drugs, an increased number of new medicines in the market, an inadequate PV system for ADR monitoring, and a need for more awareness and knowledge about ADR reporting. Severe ADRs lead to enhanced hospital stays, increased treatment costs, risk of death, and many medical and economic consequences. Therefore, ADR reporting at its first instance is essential to avoid further harmful effects of the prescribed drugs. In India, the rate of ADR reporting is less than 1%, whereas worldwide, it is 5% due to a need for more awareness about PV and ADR monitoring among healthcare providers and patients. The main objective of this review is to highlight the current scenario and possible futuristic ways of ADR reporting methods in rural areas of India. We have searched the literature using PubMed, Google scholar, Indian citation index to retrieve the resources related to ADR monitoring and reporting in India's urban and rural areas. Spontaneous reporting is the most commonly used PV method to report ADRs in India's urban and rural areas. Evidence revealed that no effective ADR reporting mechanisms developed in rural areas causing underreporting of ADR, thus increasing the threat to the rural population. Hence, PV and ADR reporting awareness among healthcare professionals and patients, telecommunication, telemedicine, use of social media and electronic medical records, and artificial intelligence are the potential approaches for prevention, monitoring, and reporting of ADRs in rural areas.

Drug-Induced Acute Pancreatitis: A Real-World Pharmacovigilance Study Using the FDA Adverse Event Reporting System Database.

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.

Assessing and further developing age-appropriate information for young people about reporting suspected adverse drug reactions.

AIMS: The Medicines and Healthcare products Regulatory Agency Yellow Card scheme (YCS) is the UK's system that collects spontaneous reports about suspected adverse drug reactions (ADRs). Reporting of suspected ADRs by young people (age <19 years) in the UK is extremely uncommon, driving efforts to improve awareness and reporting. METHODS: Quality improvement project, using an anonymous online survey about updated information for young people, distributed through school pupils (age 13-18 years) across the UK through the Alder Hey Research Ambassador programme. RESULTS: Research Ambassadors were recruited in 21 schools and colleges, generating 2933 responses (15 November 2022-08 April 2023); 6.3% of respondents had heard of the YCS, and 0.8% had previously reported a Yellow Card. There were 307 suspected drug-event combinations reported, 36 of which required attendance at hospital. The updated YCS reporting guide was understood by 92.8% of young people, and 90.8% reported knowing more about ADRs after reading the guide. The percentage of young people 'Not comfortable' reporting a suspected ADR decreased from 13.3% (before reading) to 4.1% after reading (P < .000001), and 84.5% of young people reported willingness to report a side effect in the future. The most common comments regarding further improvement of the information were content, or length of the text could be altered in some way (n = 543, 26.1%) and graphic design could be improved (n = 357, 17.2%). CONCLUSIONS: The age-appropriate information provided met many of their needs, increasing willingness to report. Integration into existing education curricula in the UK would facilitate knowledge transfer and improve reporting.

Antibiotic-induced neurological adverse drug reactions.

Antibiotics are drugs widely used all around the world. Central nervous system adverse drug reactions (CNS ADRs) are mostly under-suspected with antibiotics. Nevertheless, these ADRs could lead to severe complications such as encephalopathy. To illustrate the clinical patterns of these off-target ADRs, we here present data from pharmacovigilance system, through different populations and points of view (worldwide, French population, vulnerable population and individual). These data could help clinicians to better know about CNS ADRs with antibiotics, to better identify risk factors and vulnerable patients and to highlight the importance to set up the right diagnostic explorations in the best timing to avoid complications. Clinicians should request a pharmacological opinion from pharmacologist (biologists and pharmacovigilance clinicians) in front of vulnerable population before or during antibiotics. Pharmacovigilance advice could help clinicians in the diagnosis and the management of an ADR. Therapeutic drug monitoring is particularly contributive to adjust doses of antibiotics administered in vulnerable patients. Pharmacovigilance advice and TDM are essential to perform personalized medicine, and contribute to the proper use of drugs.

No action is without its side effects: Adverse drug reactions and missed doses of antituberculosis therapy, a scoping review.

AIMS: A key reason for the failure of antituberculosis (anti-TB) treatment is missed doses (instances where medication is not taken). Adverse drug reactions (ADRs) are 1 cause of missed doses, but the global evidence, their relative contribution to missed doses vs. other causes, the patterns of missed doses due to ADRs and the specific ADRs associated with missed doses have not been appraised. We sought to address these questions through a scoping review. METHODS: MEDLINE, Embase and Web of Science were searched on 3 November 2021 using terms around active TB, missed doses and treatment challenges. Studies reporting both ADR and missed dose data were examined (PROSPERO: CRD42022295209). RESULTS: Searches identified 108 eligible studies: 88/108 (81%) studies associated ADRs with an increase in missed doses; 33/61 (54%) studies documenting the reasons for missed doses gave ADRs as a primary reason. No studies examined patterns of missed doses due to ADRs; 41/108 (38%) studies examined associations between 68 types of ADR (across 15 organ systems) and missed doses. Nuance around ADR-missed doses relations regarding drug susceptibility testing profile and whether the missed doses originated from the patient, healthcare professionals, or both were found. CONCLUSION: There is extensive evidence that ADRs are a key driver for missed doses of anti-TB treatment. Some papers examined specific ADRs and none evaluated the patterns of missed doses due to ADRs, demonstrating a knowledge deficit. Knowing why doses both are and are not missed is essential in providing targeted interventions to improve treatment outcomes.

The expanding role of HLA gene tests for predicting drug side effects.

Adverse drug reactions can be either dose-dependent (Type A) or idiosyncratic (Type B). Type B adverse drug reactions tend to be extremely rare and difficult to predict. They are usually immune-mediated. Examples include severe skin reactions and drug-induced liver injury. For many commonly prescribed drugs (such as antibiotics), the risk of developing an idiosyncratic adverse drug reaction is influenced by variability in the human leukocyte antigen (HLA) genes. Because these HLA-mediated adverse drug reactions can be lethal, there is growing interest in defining which specific drug-gene relationships might benefit from pre-emptive HLA genotyping and automated clinical decision support. This review summarizes the literature for HLA-mediated adverse reactions linked to common drugs.

Comparing patient reported and medical record data of adverse drug reactions to anti-seizure drugs.

BACKGROUND: Anti-seizure drugs (ASDs) can potentially cause serious adverse drug reactions (ADRs). Patient self-reporting can increase the rate of ADR detection, but studies examining patient self-reporting of ADRs caused by ASDs are lacking. AIM: To determine the characteristics of ADRs reported by patients receiving ASDs, assess laboratory data and medical record confirmation of patient-reported ADRs, and explore factors associated with laboratory data and medical record confirmation. METHOD: A self-reporting questionnaire was distributed to patients prescribed ASDs at outpatient clinics. Patients assessed the causality of suspected ADRs using Causality Assessment Tool. Naranjo's algorithm was used by researchers for causality assessment. Medical records were used to gather information on ADR symptoms, ASD medication, and abnormal laboratory data. RESULTS: From 478 distributed questionnaires, 93.1% completed the questionnaire and 67.4% of respondents reported at least one ADR. The most common ADRs were drowsiness (50.7%), dizziness (9.7%), and ataxia (4.3%). For causality, suspected ADRs were classified as possible in 52.3% of cases and probable in 46.3% of cases by patients, and possible in 64.7% of cases and probable in 25.7% of cases by researchers. Only 12.7% of patients had laboratory data and/or medical record confirmation of suspected ADRs. The psychiatry clinic was less likely to confirm suspected ADRs compared to the epilepsy clinic (OR = 0.412, p = 0.022). CONCLUSION: Confirmation of patient-reported ADRs with either laboratory data or medical records was uncommon. Recording patient-reported ADRs in patients' medical history and monitoring laboratory tests related to patient-reported symptoms should be promoted to increase the safety of ASD treatment.

Incidence of Severe Adverse Drug Reactions to Ultrasound Enhancement Agents in a Contemporary Echocardiography Practice.

OBJECTIVES: Prior data indicate a very rare risk of serious adverse drug reaction (ADR) to ultrasound enhancement agents (UEAs). We sought to evaluate the frequency of ADR to UEA administration in contemporary practice. METHODS: We retrospectively reviewed 4 US health systems to characterize the frequency and severity of ADR to UEA. Adverse drug reactions were considered severe when cardiopulmonary involvement was present and critical when there was loss of consciousness, loss of pulse, or ST-segment elevation. Rates of isolated back pain and headache were derived from the Mayo Clinic Rochester stress echocardiography database where systematic prospective reporting of ADR was performed. RESULTS: Among 26,539 Definity and 11,579 Lumason administrations in the Mayo Clinic Rochester stress echocardiography database, isolated back pain or headache was more frequent with Definity (0.49% vs 0.04%, P < .0001) but less common with Definity infusion versus bolus (0.08% vs 0.53%, P = .007). Among all sites there were 201,834 Definity and 84,943 Lumason administrations. Severe and critical ADR were more frequent with Lumason than with Definity (0.0848% vs 0.0114% and 0.0330% vs 0.0010%, respectively; P < .001 for each). Among the 3 health systems with >2,000 Lumason administrations, the frequency of severe ADR with Lumason ranged from 0.0755% to 0.1093% and the frequency of critical ADR ranged from 0.0293% to 0.0525%. Severe ADR rates with Definity were stable over time but increased in more recent years with Lumason (P = .02). Patients with an ADR to Lumason since the beginning of 2021 were more likely to have received a COVID-19 vaccination compared with matched controls (88% vs 75%; P = .05) and more likely to have received Moderna than Pfizer-Biotech (71% vs 26%, P < .001). CONCLUSION: Severe and critical ADR, while rare, were more frequent with Lumason, and the frequency has increased in more recent years. Additional work is needed to better understand factors, including associations with recently developed mRNA vaccines, which may be contributing to the increased rates of ADR to UEA since 2021.

Development and validation of a predictive tool for adverse drug reactions in neonates under intensive care.

AIMS: Neonates hospitalized in neonatal intensive care units (NICUs) commonly experience adverse drug reactions (ADRs). Thus, we aimed to develop and validate a tool for predicting ADRs in neonates hospitalized in NICUs. METHODS: A nested case-control study in an open cohort with neonates admitted to the NICU of a maternity hospital in Natal, Brazil was conducted from January 2019 to January 2022 [Correction added on 4 December 2023, after first online publication: 2023 has been changed to 2019 in the preceding sentence.]. Neonates with ADR were randomly paired with 2 controls. For the development of the tool, a multivariate logistic regression was applied on 2/3 of the sample (cases with respective controls). The model's fit was evaluated using the Hosmer-Lemeshow test for calibration and the Brier score for performance assessment. Validation of the tool was performed by determining the area under the receiver operating characteristic curve with bootstrap adjusted c-statistics. RESULTS: In all, 450 neonates (150 cases and 300 controls) were included in the study. We identified 5 independent risk factors for ADR, 4 related to the neonate (current mechanical ventilation, heart rate ≥178 beats/min, intravenous medications, ≥5 prescription medications) and 1 to the mother (gestational hypertension). The tool had a classification cut-off point of ≥15, and its total score ranged from 0 to 34. In validation, the tool had an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI] 0.66-0.81) with sensitivity of 52.02% (95% CI 47.40-56.64) and specificity of 81.35% (95% CI 77.75-84.95). CONCLUSION: The tool demonstrated adequate discriminative ability and utilized 5 commonly monitored variables in the NICU.

Accuracy in patient-reported adverse drug reactions and their recognition: a mixed-methods study.

BACKGROUND: The causality assessment tool can be utilized to assist patients in identifying adverse drug reactions (ADRs). AIM: To evaluate the accuracy of the causality assessment tool for patients identifying ADRs compared to assessments made by pharmacists, and to explore how patients recall and recognize symptoms as ADRs. METHOD: Mixed methods study consisting of self-administered questionnaires (phase 1) and semi-structured, face-to-face interviews (phase 2) with patients who had experienced ADRs in the past year at a tertiary care hospital in Thailand. RESULTS: Out of 769 questionnaires, 716 were returned and 622 of these were both valid and had at least one ADR (86.8%). Classification of patient-reported symptoms using the causality assessment tool found 12 (1.9%) highly-probable ADRs, 399 (64.1%) probable ADRs, 207 (33.3%) possible ADRs, and 4 (0.6%) that were not classified as ADRs. There was fair agreement between patient-assessed and pharmacist-assessed causality classifications using the Naranjo algorithm (K = 0.268) and the World Health Organization Uppsala Monitoring Centre (WHO-UMC) criteria (K = 0.373). The timing relationship between the occurrence of symptoms and administration of a suspected drug was the most frequently mentioned reason that patients gave for recalling and recognizing suspected ADRs. CONCLUSION: Promoting the causality assessment tool for use by patients in collaboration with healthcare professionals is likely to enhance patients' ability to correctly identify ADRs and ultimately contribute to increased medication safety.