Monitoring Pregnancies Exposed to Galcanezumab for Migraine in a United States Administrative Claims Database.

PURPOSE: Galcanezumab is a calcitonin gene-related peptide monoclonal antibody indicated for migraine prevention in adults. Due to the long half-life of galcanezumab and the prevalence of migraine in women of childbearing age, galcanezumab exposure may occur during pregnancy. However, real-world use and safety of galcanezumab during pregnancy has not been fully described. To help fill this gap, galcanezumab has two ongoing pregnancy safety studies, one of which is an insurance claims database study. METHODS: This database study is actively identifying and following pregnancies exposed to galcanezumab using commercial claims from the Healthcare Integrated Research Database (HIRD). Patient accrual is planned from September 2018 to June 2026, with a final study report planned for December 2027. This study requires 430 galcanezumab-exposed pregnancies with linked infants to reach power for comparative analysis of major congenital malformations. RESULTS: Recent monitoring of patient accrual, including data from 28 September 2018 to 31 January 2023, identified 207 galcanezumab-exposed pregnancies in women with migraine in the HIRD, of which 110 were live births and 73 of which were linked to an infant. This represents an annual accrual rate of approximately 17 pregnancies linked to infants, which is substantially lower than the 55 required annually to reach target size within current regulatory-committed study timelines. CONCLUSIONS: The accrual of a sufficient number of galcanezumab-exposed pregnancies represents a substantial, but not uncommon, barrier to conducting comparative analyses in pregnancy studies. Potential solutions that would allow for timely dissemination of important safety information to patients and providers may be available.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review.

PURPOSE: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. METHODS: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. RESULTS: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. CONCLUSIONS: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

Real-world safety of palbociclib in breast cancer patients in the United States: a new user cohort study.

BACKGROUND: There is limited real-world safety information on palbociclib for treatment of advanced stage HR+/HER2- breast cancer. METHODS: We conducted a cohort study of breast cancer patients initiating palbociclib and fulvestrant from February 2015 to September 2017 using the HealthCore Integrated Research Database (HIRD), a longitudinal claims database of commercial health plan members in the United States. The historical comparator cohort comprised patients initiating fulvestrant monotherapy from January 2011 to January 2015. Propensity score matching and Cox regression were used to estimate hazard ratios for various safety events. For acute liver injury (ALI), additional analyses and medical record validation were conducted. RESULTS: There were 2445 patients who initiated palbociclib including 566 new users of palbociclib-fulvestrant, and 2316 historical new users of fulvestrant monotherapy. Compared to these historical new users of fulvestrant monotherapy, new users of palbociclib-fulvestrant had a greater than 2-fold elevated risk for neutropenia, leukopenia, thrombocytopenia, stomatitis and mucositis, and ALI. Incidence of anemia and QT prolongation were more weakly associated, and incidences of serious infections and pulmonary embolism were similar between groups after propensity score matching. After adjustment for additional ALI risk factors, the elevated risk of ALI in new users of palbociclib-fulvestrant persisted (e.g. primary ALI algorithm hazard ratio (HR) = 3.0, 95% confidence interval (CI) = 1.1-8.4). CONCLUSIONS: This real-world study found increased risks of several adverse events identified in clinical trials, including neutropenia, leukopenia, and thrombocytopenia, but no increased risk of serious infections or pulmonary embolism when comparing new users of palbociclib-fulvestrant to fulvestrant monotherapy. We observed an increased risk of ALI, extending clinical trial findings of significant imbalances in grade 3/4 elevations of alanine aminotransferase (ALT).

Development and validation of a predictive model algorithm to identify anaphylaxis in adults with type 2 diabetes in U.S. administrative claims data.

PURPOSE: To use medical record adjudication and predictive modeling methods to develop and validate an algorithm to identify anaphylaxis among adults with type 2 diabetes (T2D) in administrative claims. METHODS: A conventional screening algorithm that prioritized sensitivity to identify potential anaphylaxis cases was developed and consisted of diagnosis codes for anaphylaxis or relevant signs and symptoms. This algorithm was applied to adults with T2D in the HealthCore Integrated Research Database (HIRD) from 2016 to 2018. Clinical experts adjudicated anaphylaxis case status from redacted medical records. We used confirmed case status as an outcome for predictive models developed using lasso regression with 10-fold cross-validation to identify predictors and estimate the probability of confirmed anaphylaxis. RESULTS: Clinical adjudicators reviewed medical records with sufficient information from 272 adults identified by the anaphylaxis screening algorithm, which had an estimated Positive Predictive Value (PPV) of 65% (95% confidence interval [CI]: 60%-71%). The predictive model algorithm had a c-statistic of 0.95. The model's probability threshold of 0.60 excluded 89% (84/94) of false positives identified by the screening algorithm, with a PPV of 94% (95% CI: 91%-98%). The model excluded very few true positives (15 of 178), and identified 92% (95% CI: 87%-96%) of the cases selected by the screening algorithm. CONCLUSIONS: Predictive modeling techniques yielded an accurate algorithm with high PPV and sensitivity for identifying anaphylaxis in administrative claims. This algorithm could be considered in future safety studies using similar claims data to reduce potential outcome misclassification.

Outcome misclassification: Impact, usual practice in pharmacoepidemiology database studies and an online aid to correct biased estimates of risk ratio or cumulative incidence.

PURPOSE: It is well documented that outcome misclassification can bias a point estimate. We aimed to understand current practice in addressing this bias in pharmacoepidemiology database studies and to develop an open source application (app) from existing methodology to demonstrate the impact and mechanism of this bias on results. METHODS: Studies of an exposure and a clinical outcome were selected from all Pharmacoepidemiology and Drug Safety publications during 2017 and any reference to outcome misclassification described. An app to correct risk ratio (RR) and cumulative incidence for outcome misclassification was developed from a published methodology and used to demonstrate the impact of correction on point estimates. RESULTS: Eight (19%) of 43 papers selected reported estimates of outcome ascertainment accuracy with positive predictive value (PPV) the most commonly reported measure (7 of 8 studies). Three studies (7%) corrected for the bias, 1 by exposure strata, and 5 (12%) restricted analyses to confirmed cases. The app (app http://apps.p-95.com/ISPE/) uses values of PPV and sensitivity (or a range of possible values) in each exposure strata and returns corrected point estimates and confidence intervals. The app demonstrates that small differences between comparison groups in PPV or sensitivity can introduce bias even when accuracy estimates are high. CONCLUSIONS: Outcome misclassification is not usually corrected in pharmacoepidemiology database studies although correction methods using routinely measured indices are available. Error indices are needed for each comparison group to correct RR estimates for these errors. The app should encourage understanding of this bias and increase adjustment.

Validation of an Algorithm for Claims-based Incidence of Prostate Cancer.

BACKGROUND: Prostate cancer is a commonly studied outcome in administrative claims studies, but there is a dearth of validated case identifying algorithms. The long-term development of the disease increases the difficulty in separating prevalent from incident prostate cancer. The purpose of this validation study was to assess the accuracy of a claims algorithm to identify incident prostate cancer among men in commercial and Medicare Advantage US health plans. METHODS: We identified prostate cancer in claims as a prostate cancer diagnosis within 28 days after a prostate biopsy and compared case ascertainment in the claims with the gold standard results from the Georgia Comprehensive Cancer Registry (GCCR). RESULTS: We identified 74,008 men from a large health plan claims database for possible linkage with GCCR. Among the 382 prostate cancer cases identified in claims, 312 were also identified in the GCCR (positive predictive value [PPV] = 82%). Of the registry cases, 91% (95% confidence interval = 88, 94) were correctly identified in claims. Claims and registry diagnosis dates of prostate cancer matched exactly in 254/312 (81%) cases. Nearly half of the false-positive cases also had claims for prostate cancer treatment. Thirteen (43%) false-negative cases were classified as noncases by virtue of having a biopsy and diagnosis >28 days apart as required by the algorithm. Compared to matches, false-negative cases were older men with less aggressive prostate cancer. CONCLUSIONS: Our algorithm demonstrated a PPV of 82% with 92% sensitivity in ascertaining incident PC. Administrative health plan claims can be a valuable and accurate source to identify incident prostate cancer cases.

Predictive model algorithms identifying early and advanced stage ER+/HER2- breast cancer in claims data.

PURPOSE: Claims databases offer large populations for research, but lack clinical details. We aimed to develop predictive models to identify estrogen receptor positive (ER+) and human epidermal growth factor negative (HER2-) early breast cancer (ESBC) and advanced stage breast cancer (ASBC) in a claims database. METHODS: Female breast cancer cases in Anthem's Cancer Care Quality Program served as the gold standard validation sample. Predictive models were developed from clinical knowledge and empirically from claims data using logistic and lasso regression. Model performance was assessed by classification rates and c-statistics. Models were applied to the HealthCore Integrated Research Database (claims) to identify cohorts of women with ER+/HER2- ESBC and ASBC. RESULTS: The validation sample included 3184 women with ER+/HER2- ESBC and 1436 with ER+/HER2- ASBC. Predictive models for ER+/HER2- ESBC and ASBC included 25 and 20 factors, respectively. Models had robust discrimination in identifying cases (c-stat = 0.92 for ESBC and 0.95 for ASBC). Compared with a traditional a priori algorithm developed with clinical insight alone, the ER+/HER2- ASBC-predictive model had better positive predictive value (PPV) (0.91, 95% CI, 0.90-0.93, vs 0.69, 95% CI, 0.66-0.73) and sensitivity (0.54 vs 0.35). Models were applied to the claims database to identify cohorts of 33 001 and 3198 women with ER+/HER2- ESBC and ASBC. CONCLUSION: We conducted a validation study and developed predictive models to identify in a claims database cohorts of women with ER+/HER2- ESBC and ASBC. The models identified large cohorts in the claims data that can be used to characterize indications in the evaluation of targeted therapies.

Development and Validation of an Algorithm for Identifying Patients with Hemophilia A in an Administrative Claims Database.

BACKGROUND: The accuracy with which hemophilia A can be identified in claims databases is unknown. OBJECTIVE: Develop and validate an algorithm using predictive modeling supported by machine learning to identify patients with hemophilia A in an administrative claims database. METHODS: We first created a screening algorithm using medical and pharmacy claims to identify potential hemophilia A patients in the US HealthCore Integrated Research Database between January 1, 2006 and April 30, 2015. Medical records for a random sample of patients were reviewed to confirm case status. In this validation sample, we used lasso logistic regression with cross-validation to select covariates in claims data and develop a predictive model to estimate the probability of being a confirmed hemophilia A case. RESULTS: The screening algorithm identified 2,252 patients and we reviewed medical records for 400 of these patients. The screening algorithm had a positive predictive value (PPV) of 65%. The predictive model identified 18 predictors of being a hemophilia A case or noncase. The strongest predictors of case status included male sex, factor VIII therapy, office visits for hemophilia A, and hospitalizations for hemophilia A. The strongest predictors of noncase status included hospitalizations for reasons other than hemophilia A and factor VIIa therapy. A probability threshold of ≥0.6 resulted in a PPV of 94.7% (95% CI: 92.0-97.5) and sensitivity of 94.4% (95% CI: 91.5-97.2). CONCLUSIONS: We developed and validated an algorithm to identify hemophilia A cases in an administrative claims database with high sensitivity and high PPV.

Risk of severe acute liver injury among patients with brain cancer treated with temozolomide: a nested case-control study using the healthcore integrated research database.

Temozolomide (TMZ) is used to treat adult patients with glioblastoma multiforme (GBM). Cases of hepatotoxicity have been reported among patients using TMZ. The objective of the study was to assess the relation, if any, between exposure to TMZ and serious acute liver injury (SALI). We used the HealthCore Integrated Research Database to perform a case-control study nested within a retrospective cohort of adult patients aged 18-100 years with at least two diagnoses of brain cancer anytime between 2006 and 2014. Patients without continuous eligibility or with a SALI diagnosis within 6 months prior to the date of incident brain cancer diagnosis were excluded. Medical records were sought for potential SALI cases and reviewed by two hepatologists. Five controls were selected for each case using incidence density sampling, matched on age and calendar year of index date. The analysis included 61 confirmed SALI cases and 305 selected controls. Exposure to TMZ was classified according to dispensing date and days supply of medication dispensed. We estimated odds ratios using conditional logistic regression models. The odds ratio for any exposure to TMZ was 0.91 (95% CI 0.44-1.91), for recent exposure to TMZ was 0.62 (95% CI 0.21-1.85). There was no increased risk of SALI with increasing duration of exposure to TMZ. When patients with unconfirmed SALI were included in the analysis, results were similar (OR 1.04; 95% CI 0.70-1.54). In conclusion, this study did not find an association between TMZ and SALI risk among patients with brain cancer.

The feasibility of using multiple databases to study rare outcomes: the potential effect of long-acting beta agonists with inhaled corticosteroid therapy on asthma mortality.

PURPOSE: Long-acting beta agonists (LABAs) when used without concomitant inhaled corticosteroids (ICS) increase the risk of asthma-related deaths, but the effect on asthma-related death of LABA used in combination with ICS therapy is unknown. To address this question, we explored the feasibility of conducting an observational study using multiple US health care data sources. METHODS: Retrospective cohort study to evaluate the likelihood of getting an upper 95% confidence limit ≤1.4 for the asthma mortality rate ratio and ≤0.40 per 10 000 person-years for the mortality rate difference, assuming no effect of new use of combined LABA + ICS (versus non-LABA maintenance therapy) on asthma mortality. Ten research institutions executed centrally distributed analytic code based on a standard protocol using an extracted (2000-2010) persistent asthma cohort (asthma diagnosis and ≥4 asthma medications in 12 months). Pooled results were analyzed by the coordinating center. Asthma deaths were ascertained by linkage with the National Death Index. RESULTS: In a cohort of 994 627 persistent asthma patients (2.4 million person-years; 278 asthma deaths), probabilities of the upper 95% confidence limit for effect estimates being less than targeted values, assuming a null relation, were about 0.05. Modifications in cohort and exposure definitions increased exposed person-time and outcome events, but study size remained insufficient to attain study goals. CONCLUSIONS: Even with 10 data sources and a 10-year study period, the rarity of asthma deaths among patients using certain medications made it infeasible to study the association between combined LABA + ICS and asthma mortality with our targeted level of study precision. Copyright © 2016 John Wiley & Sons, Ltd.

Idiopathic Pulmonary Fibrosis in United States Automated Claims. Incidence, Prevalence, and Algorithm Validation.

RATIONALE: Estimates of idiopathic pulmonary fibrosis (IPF) incidence and prevalence from electronic databases without case validation may be inaccurate. OBJECTIVES: Develop claims algorithms to identify IPF and assess their positive predictive value (PPV) to estimate incidence and prevalence in the United States. METHODS: We developed three algorithms to identify IPF cases in the HealthCore Integrated Research Database. Sensitive and specific algorithms were developed based on literature review and consultation with clinical experts. PPVs were assessed using medical records. A third algorithm used logistic regression modeling to generate an IPF score and was validated using a separate set of medical records. We estimated incidence and prevalence of IPF using the sensitive algorithm corrected for the PPV. MEASUREMENTS AND MAIN RESULTS: We identified 4,598 patients using the sensitive algorithm and 2,052 patients using the specific algorithm. After medical record review, the PPVs of these algorithms using the treating clinician's diagnosis were 44.4 and 61.7%, respectively. For the IPF score, the PPV was 76.2%. Using the clinical adjudicator's diagnosis, the PPVs were 54 and 57.6%, respectively, and for the IPF score, the PPV was 83.3%. The incidence and period prevalences of IPF, corrected for the PPV, were 14.6 per 100,000 person-years and 58.7 per 100,000 persons, respectively. CONCLUSIONS: Sensitive algorithms without correction for false positive errors overestimated incidence and prevalence of IPF. An IPF score offered the greatest PPV, but it requires further validation.

Assessing bias in administrative database studies of RotaTeq vaccine completion due to exclusion of subjects with incomplete follow-up.

BACKGROUND: RotaTeq® pentavalent human rotavirus vaccine (RV5) is effective against rotavirus illness and rotavirus-related hospitalizations and death. Effectiveness depends on adherence to the dosing schedule, which includes 3 doses at ages 2, 4 and 6 months. Two studies have used automated claims databases to estimate the proportion of vaccinated infants who complete the dosing schedule, but excluded from analysis vaccinated infants who were not enrolled in the database for a sufficient period to observe all 3 doses. Restricting study populations based on duration of follow-up can introduce bias if a large number of subjects are excluded due to insufficient follow-up, and if their outcomes differ from subjects who are included. To address the possibility that exclusions may have been extensive and led to biased estimates of completion rates, we conducted a claims database analysis in the HealthCore Integrated Research Database(SM) to evaluate the proportion of rotavirus vaccinated infants who completed the 3 dose series of RV5. We evaluated potential error introduced by restricting analyses to infants with complete follow-up by estimating completion rates among infants with complete follow-up, and using Kaplan-Meier analyses to estimate completion rates including infants with incomplete follow-up. RESULTS: The inclusion criterion requiring continuous enrollment for the first year of life resulted in only 108,533 (40%) of 233,143 vaccinated infants from 2006-2012 being included in the analysis. After relaxing inclusion criteria, we were able to include 86% of vaccinated infants. The estimated completion rate among infants with continuous enrollment from birth through the first year of life was 78.1% (95% confidence limits [CLs] 77.8%, 78.3%), and among the expanded population the estimated completion rate was 77.4% (95% CLs 77.2%, 77.6%). CONCLUSIONS: These results indicate that most infants were not followed in the database through the first year of life, but the impact of excluding infants with incomplete follow-up was negligible when assessing RV5 completion rates for this commercially insured population. Nonetheless, to increase the size of study populations and reduce the potential for bias, it is preferable to include subjects with incomplete follow-up in automated database analyses, and adopt more robust approaches to defining and analyzing study populations that account for missing data.

Identifying health outcomes in healthcare databases.

PURPOSE: The purpose of this review is to assist researchers in developing, using, and interpreting case-identifying algorithms in electronic healthcare databases. METHODS: We review clinical characteristics of health outcomes, data settings and informatics, and epidemiologic and statistical methods aspects as they pertain to the development and use of case-identifying algorithms. RESULTS: We offer a framework for thinking critically about the use of electronic health insurance data and electronic health records to identify the occurrence of health outcomes. Accuracy of case ascertainment in database research depends on many factors, including clinical and behavioral aspects of the health outcome, and details of database construction as it pertains to completeness and reliability of database content. Existing methods for diagnostic and screening tests, misclassification, validation studies, and predictive modelling can be usefully applied to improve case ascertainment in database research. CONCLUSIONS: Good case-identifying algorithms are based on a sound understanding of care-seeking behavior and patterns of clinical diagnosis and treatment in the study population and details about the construction and characteristics of the database. Researchers should use quantitative bias analyses to take into account the performance characteristics of case-identifying algorithms and their impact on study results.

Role of pharmacoepidemiology studies in addressing pharmacovigilance questions: a case example of pancreatitis risk among ulcerative colitis patients using mesalazine.

PURPOSE: Well-designed pharmacoepidemiology studies address several limitations of postmarketing spontaneous reports in regard to signal evaluation. This study evaluated a signal of disproportionate reporting of acute pancreatitis cases observed in patients with ulcerative colitis (UC) treated with MMX Multi Matrix System® (MMX®) mesalazine and demonstrated how inherent limitations of postmarketing reports were overcome. METHODS: Adults with UC who were new users of MMX mesalazine or another branded mesalazine (controlled-release, delayed-release, or extended-release mesalazine; balsalazide disodium; olsalazine sodium; sulfasalazine; or sulfasalazine delayed-release) were identified from a large US administrative healthcare claims database. Acute pancreatitis incidence rates were compared between patients on MMX mesalazine versus comparator therapies. Propensity scores were used to match patients on MMX mesalazine with patients on comparator drugs to achieve a balance of baseline patient factors. RESULTS: Crude incidence rates [95 % confidence interval (CI)] of acute pancreatitis among patients on MMX mesalazine were similar to those of patients on comparator therapies [8.55 (5.54-13.21) vs 10.05 (7.54-13.41) per 1000 person-years]; the resulting incidence rate ratio (IRR) was [0.85 (0.48-1.47)]. Propensity score-matching had little influence on the IRR [0.84 (0.46-1.55)]; nor did further adjustment by demographic characteristics, daily dose, and causes of acute pancreatitis [0.76 (0.41-1.43)]. CONCLUSION: Findings of no increase in pancreatitis risk with MMX mesalazine demonstrate the value of pharmacoepidemiology studies for evaluating a drug's postmarket safety profile when confronted with spontaneous reporting data suggestive of a safety issue.

Variation in incidence rates of outcomes relevant to vaccine safety monitoring in a US commercially insured population by case identification algorithm.

BACKGROUND: Large health insurance claims databases can be used to estimate rates of rare safety outcomes. We measured incidence rates of rare outcomes that could be used to contextualize adverse events among people receiving pneumococcal vaccines in clinical trials or clinical practice. However, algorithms used to identify outcomes in administrative databases are subject to error. Using two algorithms for each outcome, we assessed the influence of algorithm choice on the rates of the outcomes. METHODS: We used closed administrative medical and pharmacy claims in the Healthcare Integrated Research DatabaseSM (HIRD) to construct a broad cohort of individuals less than 100 years old (i.e., the target cohort) and a trial-similar cohort of individuals resembling those potentially eligible for a vaccine clinical trial (e.g., for a pneumococcal vaccine). We stratified by age and sex and used specific and sensitive algorithms to estimate rates of 39 outcomes including cardiac/cerebrovascular, metabolic, allergic/autoimmune, neurological, and hematologic outcomes. Specific algorithms intended to reduce false positive errors, while sensitive algorithms intended to reduce false negative errors, thereby providing lower and upper bounds for the "true" rates. RESULTS: We followed approximately 40 million individuals in the target cohort for an average of 3 years. Of 39 outcomes, 14 (36 %) had a rate from the specific algorithm that was less than half the rate from the sensitive algorithm. Rates of cardiac/cerebrovascular outcomes were most consistent (mean ratio of rates from specific algorithms compared to rates from sensitive algorithms = 0.76), while the rates of neurological and hematologic outcomes were the least consistent (mean ratio of rates = 0.33 and 0.36, respectively). CONCLUSIONS: For many cardiac/cerebrovascular outcomes, rates were similar regardless of the algorithm. For other outcomes, rates varied substantially by algorithm. Using multiple algorithms to ascertain outcomes in claims data can be informative about the extent of uncertainty due to outcome misclassification.

Risk of Death and Cardiovascular Events Following an Exacerbation of COPD: The EXACOS-CV US Study.

Purpose: This study estimated the magnitude and duration of risk of cardiovascular events and mortality following acute exacerbations of chronic obstructive pulmonary disease (AECOPD), and whether risks varied by number and severity of exacerbation in a commercially insured population in the United States. Methods: This was a retrospective cohort study of newly diagnosed COPD patients ≥40 years old in the Healthcare Integrated Research Database from 2012 to 2019. Patients experiencing exacerbations comprised the "exacerbation cohort". Moderate exacerbations were outpatient visits with contemporaneous antibiotic or glucocorticoid administration; severe exacerbations were emergency department visits or hospitalizations for AECOPD. Follow-up started on the exacerbation date. Distribution of time between diagnosis and first exacerbation was used to assign index dates to the "unexposed" cohort. Cox proportional hazards models estimated risks of a cardiovascular event or death following an exacerbation adjusted for medical and prescription history and stratified by follow-up time, type of cardiovascular event, exacerbation severity, and rank of exacerbation (first, second, or third). Results: Among 435,925 patients, 170,236 experienced ≥1 exacerbation. Risk of death was increased for 2 years following an exacerbation and was highest during the first 30 days (any exacerbation hazard ratio (HR)=1.79, 95% CI=1.58-2.04; moderate HR=1.22, 95% CI=1.04-1.43; severe HR=5.09, 95% CI=4.30-6.03). Risks of cardiovascular events were increased for 1 year following an AECOPD and highest in the first 30-days (any exacerbation HR=1.34, 95% CI=1.23-1.46; moderate HR=1.23 (95% CI 1.12-1.35); severe HR=1.93 (95% CI=1.67-2.22)). Each subsequent AECOPD was associated with incrementally higher rates of both death and cardiovascular events. Conclusion: Risk of death and cardiovascular events was greatest in the first 30 days and rose with subsequent exacerbations. Risks were elevated for 1-2 years following moderate and severe exacerbations, highlighting a sustained increased cardiopulmonary risk associated with exacerbations.

Risk of Anaphylaxis Among New Users of GLP-1 Receptor Agonists: A Cohort Study.

OBJECTIVE: To assess risk of anaphylaxis among patients with type 2 diabetes mellitus who are initiating therapy with a glucagon-like peptide 1 receptor agonist (GLP-1 RA), with a focus on those starting lixisenatide therapy. RESEARCH DESIGN AND METHODS: A cohort study was conducted in three large, U.S. claims databases (2017-2021). Adult (aged ≥18 years) new users of a GLP-1 RA who had type 2 diabetes mellitus and ≥6 months enrollment in the database before GLP-1 RA initiation (start of follow-up) were included. GLP-1 RAs evaluated were lixisenatide, an insulin glargine/lixisenatide fixed-ratio combination (FRC), exenatide, liraglutide or insulin degludec/liraglutide FRC, dulaglutide, and semaglutide (injectable and oral). The first anaphylaxis event during follow-up was identified using a validated algorithm. Incidence rates (IRs) and 95% CIs were calculated within each medication cohort. The unadjusted IR ratio (IRR) comparing anaphylaxis rates in the lixisenatide cohort with all other GLP-1 RAs combined was analyzed post hoc. RESULTS: There were 696,089 new users with 456,612 person-years of exposure to GLP-1 RAs. Baseline demographics, comorbidities, and use of other prescription medications in the 6 months before the index date were similar across medication cohorts. IRs (95% CIs) per 10,000 person-years were 1.0 (0.0-5.6) for lixisenatide, 6.0 (3.6-9.4) for exenatide, 5.1 (3.7-7.0) for liraglutide, 3.9 (3.1-4.8) for dulaglutide, and 3.6 (2.6-4.9) for semaglutide. The IRR (95% CI) for the anaphylaxis rate for the lixisenatide cohort compared with the pooled other GLP-1 RA cohort was 0.24 (0.01-1.35). CONCLUSIONS: Anaphylaxis is rare with GLP-1 RAs. Lixisenatide is unlikely to confer higher risk of anaphylaxis than other GLP-1 RAs.

Impact of health care payer type on HIV stage of illness at time of initiation of antiretroviral therapy in the USA.

There is evidence that earlier initiation of HIV antiretroviral therapy (ART) is associated with better outcomes, including lower morbidity and mortality. Based on recent studies indicating that Medicaid enrollees are more likely to have suboptimal access to medical care, we hypothesized that HIV severity at time of ART initiation is worse for Medicaid patients than patients with other health care coverage. We conducted a US retrospective analysis of GE Centricity Outpatient Electronic Medical Records spanning 1 January 1997 through 30 September 2009. Subjects included all adult HIV patients initiating first-line ART who had CD4+ results within 90 days pre-initiation. HIV stage was defined using CD4 ranges: >500 (n=520), 351-500 (n=379), 201-350 (n=580), or ≤200 (n=406) cells/mm(3), with lower CD4 count being indicative of increased disease severity. Payer type was defined as the patient's primary payer: Medicaid, Medicare, commercial insurance, self-pay or other/unknown. After controlling for demographic and clinical covariates, cumulative logit models assessed the effect of payer type on HIV stage at ART initiation. The study included 1885 subjects with the primary payer being Medicaid (n=218), Medicare (n=330), commercial insurance (n=538), self-pay (n=159) or other/unknown (n=640). Final logit models demonstrated that, compared to patients on Medicaid, the odds of initiating ART at a higher CD4 range were significantly greater for those commercially insured (odds ratio [OR]=1.53; P=0.005), self-paying (OR=1.56; P=0.023) and other/unknown (OR=1.79; P<0.001) and similar for patients enrolled in Medicare (OR=1.11; P=0.521). Medicaid patients initiated ART at a more advanced stage of HIV than patients who were commercially insured, self-paying, or had other/unknown coverage. With HIV treatment guidelines now supporting ART initiation in patients with higher CD4 counts, these findings underscore the need for mitigating barriers, particularly in the Medicaid population, that may delay treatment initiation.