Pharmacoepidemiology and Drug Safety最新文献

Purpose: Previous research and pharmacovigilance monitoring activities have suggested a potential association between indapamide use and rhabdomyolysis. This study aims to investigate the potential causal relationship between the use of indapamide and rhabdomyolysis.

Methods: A case-control study conducted using electronic health records data, between July 1, 2016 and December 31, 2022. Patients who have rhabdomyolysis event (cases) were matched to four controls bases on age, gender, and date. We examined the odds for indapamide exposure through three risk periods: current use, recent use, and former. The study outcome was ascertained through the presence of CK level over 1000 U/L (i.e., rhabdomyolysis event). Subsequently, a multivariable conditional logistic regression analysis was utilized to assess the causal association of indapamide exposure on the likelihood of developing rhabdomyolysis, while accounting for potential confounding variables.

Results: The study population consisted of 2965 cases and 11 860 controls. The results of the conditional logistic regression analysis indicated a lack of association between exposure to indapamide for the current users with an odds ratio (OR) of 0.6 (95% confidence intervals (CI): 0.39-1.05). The odds of recent indapamide use among cases was lower than controls (OR 0.2; 95% CI: 0.14-0.47). Lastly, the OR for former use of indapamide was 0.1 (95% CI: 0.07-0.23).

Conclusions: In this study, we did not find association between indapamide use and rhabdomyolysis regardless timing of exposure.

Aim: This article provides an overview of time-to-event (TTE) analysis in pharmacoepidemiology.

Materials & methods: The key concept of censoring is reviewed, including right-, left-, interval- and informative censoring. Simple descriptive statistics are explained, including the nonparametric estimation of the TTE distribution as per Kaplan-Meier method, as well as more complex TTE regression approaches, including the parametric Accelerated Failure Time (AFT) model and the semi-parametric Cox Proportional Hazards and Restricted Mean Survival Time (RMST) models. Additional approaches and various TTE model extensions are presented as well. Finally, causal inference for TTE outcomes is addressed.

Results: A thorough review of the available concepts and methods outlines the immense variety of available and useful TTE models.

Discussion: There may be underused TTE concepts and methods, which are highlighted to raise awareness for researchers who aim to apply the most appropriate TTE approach for their study.

Conclusion: This paper constitutes a modern summary of TTE analysis concepts and methods. A curated list of references is provided.

Background: Ocular surface disorders have been reported among patients with various medical conditions, including atopic dermatitis (AD). Nonetheless, validated algorithms to identify conjunctivitis and keratitis in claims data are lacking.

Objective: Develop validated, claims-based algorithms for conjunctivitis and keratitis among patients with AD using medical records.

Methods: Patients with AD were identified in a claims database between March 2017 and November 2019. Among these patients, candidate algorithms were developed that included diagnosis codes for conjunctivitis or keratitis, alone and in combination with ophthalmic treatments. Among patients who met ≥ 1 candidate algorithms, a subset was randomly selected for medical record review. Additionally, records from a random sample of patients with AD were reviewed to assess sensitivity. Overall, 341 records were sought and 262 adjudicated by an expert ophthalmologist. The positive predictive value (PPV) of each algorithm was calculated and compared to a pre-specified threshold of ≥ 70%.

Results: For conjunctivitis, the final algorithm was ≥ 1 conjunctivitis diagnosis (PPV = 81%, 95% confidence interval [CI]: 73%-87%). For keratitis, the final algorithm combined the following 2 candidate algorithms: ≥ 1 keratitis diagnosis and ≥ 1 dispensing of a topical antibiotic or antibiotic-steroid combination (PPV = 91%); and ≥ 1 keratitis diagnosis and ≥ 1 dispensing of an ophthalmic corticosteroid, topical immune-modulator, or topical NSAID (PPV = 68%) for an overall PPV of 80% (95% CI: 55%-93%).

Conclusion: The first claims-based algorithms to identify conjunctivitis and keratitis among AD patients were developed and validated. They are available for use in future studies, particularly to better understand conjunctivitis and keratitis occurrence among patients with AD.

Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these technologies within healthcare settings. AI/ML-based tools also play a role in pharmacoepidemiology research, as we may apply them to answer our own research questions, take responsibility for evaluating medical devices with AI/ML components, or participate in interdisciplinary research to create new AI/ML algorithms. While epidemiologic expertise is essential to deploying AI/ML responsibly and ethically, the rapid advancement of these technologies in the past decade has resulted in a knowledge gap for many in the field. This article provides a brief overview of core AI/ML concepts, followed by a discussion of potential applications of AI/ML in pharmacoepidemiology research, and closes with a review of important concepts across application areas, including interpretability and fairness. This review is intended to provide an accessible, practical overview of AI/ML for pharmacoepidemiology research, with references to further, more detailed resources on fundamental topics.

Purpose: Hydrochlorothiazide (HCTZ) exposure has been linked to increased skin cancer in Caucasian (white) populations, especially squamous cell carcinoma (SCC), but not basal cell carcinoma (BCC). This study aimed to evaluate and compare skin cancer risks associated with HCTZ- and other antihypertensives use.

Methods: This retrospective cohort study utilized Taiwan's National Health Insurance Research Database. We identified patients aged 20 years and older, newly receiving antihypertensive medications between 2004 and 2015. We calculated the medication possession ratio (MPR) for the first 2 years of treatment to determine patient eligibility and treatment classification, whereby only patients with MPR above 80% were included. These were subsequently categorized by the type of antihypertensives they received, namely HCTZ, other thiazide diuretics, non-thiazide diuretics or non-diuretic antihypertensives. Cox proportional hazards model was used to evaluate skin cancer risks, and these were then classified as SCC or BCC.

Results: Our study included 41 086, 27 402, 19 613, and 856 782 patients receiving HCTZ, other thiazide diuretics, non-thiazide diuretics, and non-diuretic antihypertensives, respectively. We found BCC risks were similar when comparing HCTZ with other thiazides (adjusted hazard ratio: 0.84; 95% CI: 0.54-1.33), non-thiazide diuretics (0.93; 0.51-1.67), and non-diuretic antihypertensives (0.91; 0.66-1.26). We observed a higher SCC risk in the HCTZ group, compared to other thiazides (1.24; 0.74-2.08), non-thiazide diuretics (1.32; 0.70-2.51), and non-diuretic antihypertensives (1.23; 0.87-1.73), although the confidence intervals (CIs) were wide and crossed the null.

Conclusions: We concluded that skin cancer need not be of major concern to physicians when prescribing antihypertensives for an Asian population.

Purpose: To assess the impact of the 2018 European additional risk minimisation measures (aRMMs) regarding the use of valproate in women of childbearing potential (WCBP) and during pregnancy.

Methods: A cross-sectional, non-interventional survey conducted in six European countries among 1982 healthcare professionals (HCPs) (July-October 2020) and 779 WCBP treated with valproate for epilepsy, bipolar disorder or other indications (August 2020-February 2021). HCPs were prescribing physicians (neurologists, psychiatrists, paediatricians and GPs), gynaecologists and pharmacists. Prespecified criteria were defined for success in the dimensions of awareness, knowledge and behaviour (correct answers to ≥ 80% of questions at individual level) and overall success (≥ 90%/80% successful HCPs/patients respectively, in the behaviour dimension and one of the two other dimensions).

Results: HCPs and patients did not meet the success criteria either overall or in any dimension. Highest success rates were in the behaviour dimension for gynaecologists (71.7%), pharmacists (49.7%) and patients (51.2%), and in the awareness dimension for prescribing physicians (23.6%). HCPs reported being unfamiliar with some educational materials and lacked knowledge of detailed prescribing conditions for valproate and the need for contraception regardless of sexual activity. More than 50% of patients were aware of the relevant patient materials and knew about the teratogenic risks of valproate.

Conclusion: Self-reported levels of awareness, knowledge and behaviour varied considerably by HCP type and among patient respondents. Further investigation is needed into why certain measures of the pregnancy prevention programme are not well known and followed, to improve their effectiveness. This will be addressed in a qualitative study which will be based on interviews with HCPs and patients.

Purpose: We validated claims-based algorithms using the International Classification of Diseases, Tenth Revision (ICD-10) to identify patients with the first-ever coronavirus disease (COVID-19) onset between May 2020 and August 2022.

Methods: The study cohort was comprised of residents of one municipality enrolled in a public insurance program. This study used data provided by the municipality, including residents' insurer-based medical claims data linked to the Health Center Real-time Information-Sharing System (HER-SYS). The HER-SYS data included positive results from COVID-19 tests and were used as reference standards. Claims-based algorithms #1 and #2 were U07.1, B34.2, with and without suspicious diagnoses, respectively. Claims-based algorithms #3 and #4 were U07.1 with and without suspicious diagnoses, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm.

Results: The study cohort included 165 038 residents, including 13 402 residents were the reference standard. For the entire period, the sensitivity, specificity, PPV, and NPV were 55.7% (95% confidence interval: 54.8%-56.5%), 65.4% (65.2%-65.6%), 11.5% (11.3%-11.8%), and 98.9% (98.8%-99.0%) for Algorithm #1, and 67.0% (66.2%-67.8%), 88.1% (87.9%-88.3%), 31.6% (31.1%-32.2%), and 97.8% (97.7%-97.8%) for Algorithm #2, and 52.9% (52.0%-53.7%), 67.1% (66.9%-67.3%), 11.5% (11.2%-11.8%), and 98.3% (98.3%-98.4%) for Algorithm #3, 62.6% (61.8%-63.4%), 88.5% (88.3%-88.7%), 30.9% (30.3%-31.4%), and 97.3% (97.2%-97.4%) for Algorithm #4, respectively.

Conclusions: Our study showed that the validity of claims-based algorithms consisting of COVID-19-related ICD-10 codes to identify patients with first-onset COVID-19 is limited.