Pharmacoepidemiology and Drug Safety最新文献

Introduction: Use of three-drug combinations is increasingly prevalent and associated with an increased risk of adverse drug events (ADEs). While real-world data-based pharmacovigilance and pharmacoepidemiology studies have derived knowledge on potential adverse three-drug combinations, the relationship between doses of each drug in three-drug combination exposure and the risk of ADEs remains unclear.

Methods: We derived matched case-control datasets from US nationwide health insurance claims data for potential ADEs including acute kidney injury, acute myocardial infarction, gastrointestinal bleeding, hypoglycemia, and opioid-related ADE. We used the conditional logistic regression model to investigate the relationship between the dose of three-drug combination exposure and the risk of ADE. We used Benjamini and Hochberg's procedure to control the false discovery rate (FDR). We explored the relationship between the reduction of drug dose and the risk of ADE.

Results: We identified over 500 potential adverse three-drug combinations from approximately two million case-control pairs (all odds ratios ≥ 1.3 and FDR < 0.05). For the signals, compared with a high-dose level of all three drugs, 74% of three-drug combinations had a lower risk by decreasing the dose of one drug without any drug discontinuation (p value < 0.05).

Conclusions: Certain three-drug combinations are associated with an increased risk of ADE. Dose of exposure might be used to evaluate the risk of ADE for a majority of adverse three-drug combinations.

Plain language summary: Use of three-drug combinations is increasingly prevalent and associated with an increased risk of adverse drug events (ADEs). We identified potential adverse three-drug combinations from real-world data, and revealed the corresponding relationships between doses and risks of ADEs. We find doses might be used to evaluate the risk of ADE for many adverse three-drug combinations. Additionally, we find risk of many adverse three-drug combinations might be decreased by reducing the dose of one drug without any drug discontinuation.

Purpose: Investigating pediatric neurodevelopmental outcomes (NDO) in studies using secondary data is often challenging due to heterogeneous clinical definitions and medical coding systems. This study aims to identify the algorithms used to define NDO in studies using electronic healthcare data through a systematic literature review.

Methods: A search strategy was developed to identify studies on NDO that describe phenotype algorithms from January 1, 2010, to March 10, 2025. The search strategy included terms to identify studies containing algorithms for NDO as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants/children. Two independent reviewers assessed eligibility criteria and performed data extraction, with inconsistencies reviewed by a third reviewer. Descriptive statistics were used to summarize categorical and continuous variables appropriately.

Results: The review included 156 publications that implemented algorithms for NDO, with 18 of these studies validating the outcomes. Most publications studied autism spectrum disorder (ASD) (n = 103, 65.6%) and attention deficit hyperactivity disorder (ADHD) (n = 72, 45.9%) either as a single outcome or as a composite.

Conclusions: Instead of presenting NDO as a composite outcome, it is recommended to present multiple single outcomes. Validated outcomes in data from Nordic countries demonstrate a high positive predictive value when using one code for diagnoses, while more complex algorithms are required for US data. Clearly detailing and establishing the time of assessment for each NDO is critical to inform valid epidemiological estimates.

Purpose: Given the increased likelihood for individuals with severe asthma to experience comorbidities, disease complications, emergency room visits, and hospitalizations, the ability to stratify asthma populations on severity is often important. Although pharmacoepidemiologic studies using administrative healthcare databases sometimes categorize asthma severity, there is no consensus on an approach.

Methods: Individuals with asthma (≥ 2 ICD-10-CM diagnosis codes J45) aged ≥ 6 years were identified in Optum's de-identified Clinformatics Data Mart Database between January 2017 and November 2023. Severe asthma was inferred, consistent with the Global Initiative for Asthma (GINA), from prescription claims for high-dose inhaled corticosteroids (ICS) in combination with long-acting beta-agonists (LABA) (Step 5 treatment). Two algorithm versions were employed to isolate the impact of dose estimation methods: (1) the "code-based method" considered high-dose ICS-LABA to be an inhaler property and defined severe asthma based on claims for ICS-LABA from our pre-determined list; (2) the "calculation-based method" considered high-dose ICS-LABA to be a regimen property and defined severe asthma based on derived patient-level average daily dose.

Results: A total of 1 221 732 individuals with asthma were identified, 3.1% of which were severe by the code-based method and 4.2% by the calculation-based method. Both methods appeared to be consistent with the benchmark cited by GINA (3.7%). No meaningful differences were observed in the characteristics of the cohorts. 27% of calculation-based individuals with severe asthma were not captured by the code-based method.

Conclusions: Estimating patient-level average daily ICS dose based on prescription claims using either a code-based or a calculation-based algorithm appears to be a reasonable method to identify individuals with severe asthma. The discrepancy between methods suggests that physician instructions sometimes vary from recommended administration instructions. Future work will validate these algorithms using electronic medical records.

Background: Multi-national/database pharmacoepidemiological studies are increasingly used to address questions that require pooled evidence across populations but introduce challenges in design, harmonization, and analysis.

Objective: To share 10 practical considerations and common pitfalls in planning, executing, and reporting multi-national/database studies, with strategies to mitigate them.

Approach: Practical guidance article synthesizing experience from multi-national/database projects and literature-based exemplars; no original data collection.

Results: We summarize ten considerations spanning: protocol development; follow-up time and eligibility assessment; data harmonization (including thorough metadata and master mapping tables); feasibility checks within each source; statistical model diagnostics; and transparent reporting. We illustrate how local clinical practices, coding systems, and reimbursement policies can shape outcomes and interpretation, and we emphasize the need for proactive, consistent communication among collaborators to ensure aligned implementation.

Conclusion: Multi-national/database studies are complex but feasible with structured planning, clear communication, and proactive problem-solving. Adopting the outlined practices can reduce avoidable heterogeneity and improve the robustness and interpretability of findings.

Purpose: To assess the incidence of substance use disorders (SUDs) after long-term prescription opioid use (LTOU) and to identify socioeconomic and clinical risk factors associated with SUD among individuals with LTOU.

Methods: Cohort study using linked nationwide registers (2011-2019). We identified 114 916 individuals who used opioids for more than 3 months (LTOU) without previous LTOU or SUD diagnosis. Outcomes were any incident SUD diagnosed in primary or secondary care (ICPC-2: P15-P16, P18-P19; ICD-10: F10-F16, F18-F19) and opioid use-related disorders (OUD) diagnosed in secondary care (ICD-10: F11). We calculated age- and sex-stratified incidence rates (IR), incidence rate ratios (IRR) and age-standardized incidence rates (ASIR). Adjusted hazard ratios (aHR) were calculated using Cox proportional hazards regression.

Results: In total, 5.3% (6069/114916) were diagnosed with SUD (ASIR = 28.7 per 1000 person-years), and males had higher IRs compared to females (IRR). Males had higher risk of SUD in both the younger (aHR = 1.59, 95% CI 1.47-1.72) and older (1.66, 1.54-1.78) age group. Low education (1.87, 1.66-2.11) and unemployment (1.26, 1.15-1.38) had the strongest association with SUD in the younger age group versus low income (1.37, 1.21-1.57) and living alone (1.53, 1.41-1.65) in the older age group. Previously diagnosed mental disorders and use of benzodiazepines- or benzodiazepine-related drugs (BZDRs) were associated with SUD in both age groups (1.85, 1.71-2.01; 2.37, 2.18-2.57). Being male and having used BZDRs were the covariates strongest associated with OUD.

Conclusions: Being male, young, having low socioeconomic status, previous mental disorders or BZDR use were associated with SUD diagnosis among individuals with LTOU.

Background: The conflicting findings on the association between proton pump inhibitors (PPIs) and ischaemic stroke could be due to residual confounding. Self-controlled case series (SCCS) can be used to avoid time-invariant confounding. Additionally, different baseline risks of stroke should be considered, as some individuals may be prescribed PPIs for gastroprotection from bleeding with antithrombotic drugs.

Methods: We identified adult patients with incident ischaemic stroke from 2003 to 2014 in Hong Kong and applied the modified SCCS. The exposure window was pre-defined as Days 1-30, 31-60, 61-90, and 91 to the prescription end, since the PPI prescription. All other periods were referent windows. We estimated incidence rate ratios (IRR) and stratified them further using antithrombotic drugs.

Results: A total of 18 170 patients were included. The IRRs for ischaemic stroke were 1.55 (95% CI: 1.00-2.42) during days 61 to 90, 1.51 (95% CI: 1.14-2.00) during days 91 to end, versus the referent window. There was no evidence of an increased risk in other risk windows versus the referent windows. In the stratified analysis, we observed an increased risk in people co-prescribed PPIs with antithrombotic drugs in all risk periods, but no increased risks among those with PPI monotherapy versus the referent window.

Conclusion: No evidence of a higher ischaemic stroke after monotherapy of PPI use. The increased risk of ischaemic stroke associated with PPIs could be due to their high baseline risk prescribed with antithrombotic drugs for primary prevention. Clinical monitoring of ischaemic stroke is recommended in these people.

Background: Assessing medication safety in the pediatric population can take many forms, but given the shortcomings of traditional methods, there has been a shift toward leveraging real-world data to bolster these efforts.

Objectives: To characterize demographics, enrollment, and health characteristics among pediatric members in the Sentinel Distributed Database (SDD).

Methods: Using administrative healthcare data from the SDD between January 1, 2000, and May 8, 2023, we used descriptive statistics to characterize the demographics, enrollment, and select health characteristics of pediatric members in the following age groups: 29 days-< 24 months (infants), 2-< 6 years (young children), 6 -< 12 years (older children), 12-< 18 years (early adolescents), and 18-21 years (late adolescents).

Results: Older children (6-< 12 years of age) represented the largest pediatric age group in the SDD, with over 46 million members, though there were between 27.5 and 45.4 million members in each of the other age groups as well. Estimates of common health conditions and medication use were in line with current national estimates.

Conclusions: The FDA's Sentinel Distributed Database accurately captures key aspects of pediatric health and can be used as an adjunct to current methods to assess and monitor the safety of approved medical products in the pediatric United States population.

Purpose: Anticoagulation therapy is required to prevent thromboembolic complications in patients with heart valve surgery (HVS). However, caution must be taken due to the risk of bleeding. This study aimed to identify bleeding risk factors in patients with stable warfarin therapy and develop a predictive tool for high-risk patients.

Methods: This study is a nested case-control design using the Korean National Health Insurance Service-National Sample Cohort Data. We identified patients who underwent HVS and were prescribed warfarin within 1 week after the procedure. Of these, patients with the last two identical warfarin prescriptions within 6 months before the bleeding events were defined as the case group, while patients with no bleeding events within 6 months after HVS and two consecutive identical warfarin prescriptions were defined as the control group. Three machine learning models-logistic regression, support vector machine, and random forest-were trained and scored by fivefold validation to validate our feature selection processes. We developed a risk scoring system using adjusted odds ratios from multivariate logistic regression.

Results: Of 1 137 861 subjects, 1093 patients were eligible for the study cohort; 173 and 298 were selected as the case and control groups, respectively. After a series of machine learning processes, eight features were identified as significant risk factors for bleeding events.

Conclusion: Our finding suggests that furosemide, spironolactone, lacrimal system disorders, ursodeoxycholic acid, captopril, chronic kidney disease, zolpidem, and valsartan are the most important features for predicting bleeding events in patients taking a stable warfarin dose after HVS.

Purpose: The potential of vancomycin to cause acute kidney injury (AKI) in adult intensive care patients is subject to debate due to suboptimal designs of past studies. Therefore, we aimed to estimate the effect of initiating vancomycin versus one of several minimally nephrotoxic alternative antibiotics on the 14-day risk of AKI using the target trial emulation framework.

Methods: A hypothetical trial was emulated using routinely collected data from 15 Dutch intensive care units (ICUs) spanning 2010-2019. We used an active comparator control group with the following alternative antibiotics: clindamycin, linezolid, teicoplanin, meropenem, cefazolin, and daptomycin. AKI was diagnosed according to the KDIGO serum creatinine (SCr) criteria. Cumulative incidence curves were estimated using the Aalen-Johansen method and adjusted for confounding and selection bias through inverse probability of treatment and censoring weighting. Given the time lag of 24-48 h between changes in renal function and SCr, we summarized the estimates by calculating the absolute risks and risk differences at both 2 and 14 days after initiation.

Results: We included 1809 ICU admissions. After adjustment, vancomycin was associated with a higher risk of AKI at 14 days of follow-up compared to the alternative antibiotics (0.28 [95% confidence interval (CI) 0.21-0.34] vs. 0.17 [95% CI 0.14-0.20]; risk difference 0.11 [95% CI 0.04-0.19]), but not at 2 days of follow-up (0.10 [95% CI 0.06-0.12] vs. 0.10 [95% CI 0.08-0.11]; risk difference 0.00 [95% CI -0.03-0.03]).

Conclusions: Our findings indicate that vancomycin causes a higher risk of AKI compared to the alternative antibiotics. We recommend clinicians to be compliant with vancomycin-induced AKI prevention strategies, such as therapeutic drug monitoring or the consideration of an alternative antibiotic if possible.

Background: The quality of real-world data (RWD) directly impacts the value of real-world evidence (RWE) generated for regulatory decision-making. Data owners and investigators must be prepared to provide documentation on data quality assessments to regulators when submitting secondary data for regulatory purposes. While robust feasibility is required to justify the relevance of a data source for a specific research question, the reliability of the data, including the chain of custody and data journey prior to reaching the end user, is of equal importance for drawing valid, meaningful conclusions.

Aims: Recently, Castellanos et al. constructed a definition of RWD quality by synthesizing definitions across published guidelines to characterize quality attributes of Flatiron Health RWD. In this paper, the transparent reporting of how data quality attributes (as defined by Castellanos et al.) are met in a single RWD source is replicated for the Guardian Research Network (GRN), a database of aggregated electronic health records (EHRs) collected from a geographically representative consortium of regional community health systems with experienced cancer research programs.

Materials & methods: We first describe GRN, including the data elements collected, timeliness of data availability, representativeness, and data access considerations. We then provide descriptions of how data reliability (accuracy, traceability, timeliness, completeness) and relevance (availability, sufficiency, representativeness) are ensured and assessed in GRN, including illustrative examples of relevant data quality checks.

Results: Descriptions of GRN's data quality processes demonstrate structured approaches to ensuring both reliability and relevance, aligned with published guidelines. Illustrative examples highlight the application of specific quality checks and their outcomes for GRN data.

Discussion: These findings illustrate the importance of documenting and communicating data quality attributes for RWD sources intended for regulatory use. Structured, transparent reporting can support more informed feasibility assessments and facilitate regulator confidence in RWE generation.

Conclusion: Continued development of structured approaches to identifying data fit for regulatory use underscores the need for comprehensive information about putative data sources during feasibility to inform decision making, study design, and elicit transparent conversations with regulators.