Real-World Tumor Response of Palbociclib in Combination With an Aromatase Inhibitor as First-Line Therapy in Pre/Perimenopausal Women With Metastatic Breast Cancer.

BACKGROUND: Limited real-world data exist regarding the efficacy of palbociclib in combination with endocrine therapy in pre/perimenopausal women with metastatic breast cancer. OBJECTIVE: We aimed to compare real-world tumor responses among pre/perimenopausal women who initiated palbociclib plus an aromatase inhibitor (AI) or AI monotherapy as first-line treatment for hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer. METHODS: This retrospective observational cohort study (NCT05012644) used electronic health record data from The US Oncology Network. Tumor responses were determined based on treating clinicians' assessments of radiologic evidence for changes in disease burden. Normalized inverse probability treatment weighting was used to balance baseline characteristics between treatment cohorts. RESULTS: Of 196 pre/perimenopausal women, 116 and 80 were in the palbociclib plus AI cohort and AI cohort, respectively. Real-world response rates (complete or partial response) were 52.1% and 46.2%, respectively (odds ratio, 1.27 [95% confidence interval 0.72‒2.24]). Among patients with one or more tumor assessments on treatment, real-world response rates were 60.0% in the palbociclib plus AI cohort (n = 103) and 49.9% in the AI cohort (n = 71; odds ratio, 1.51 [95% confidence interval 0.82‒2.77]). CONCLUSIONS: This real-world analysis suggests that pre/perimenopausal patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer appear more likely to respond to palbociclib plus AI versus AI alone as first-line therapy, which may support the combination as a standard-of-care treatment for this patient population.

Palbociclib (Ibrance^®) is a medicine for patients with metastatic breast cancer (MBC). Metastatic means that the cancer has spread to other places in the body. Patients take palbociclib with hormone therapy, such as an aromatase inhibitor (AI). Palbociclib plus an AI is a treatment for a type of MBC called HR+/HER2‒ MBC. HR+/HER2‒ stands for hormone receptor positive, human epidermal growth factor receptor 2 negative. Researchers wanted to observe responses to treatment in routine clinical practice among women with HR+/HER2­ MBC who had not reached menopause. A response is if a tumor shrinks or disappears after treatment. This study used healthcare information reported in electronic medical records of patients seen by doctors in The US Oncology Network. This study included 196 women with HR+/HER2­ MBC who had not reached menopause and had not received prior treatment for MBC. A total of 116 women received palbociclib plus an AI, and 80 women received an AI alone. Researchers used standard statistical approaches to balance baseline characteristics between the two treatment groups. These adjustments made the groups more similar so that researchers could compare treatment responses. Sixty percent of patients who took palbociclib plus an AI responded, compared with 50% of patients who took an AI alone. These results suggest that palbociclib plus an AI may benefit women with HR+/HER2‒ MBC who have not reached menopause.

Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies.

Natural language processing (NLP) tools turn free-text notes (FTNs) from electronic health records (EHRs) into data features that can supplement confounding adjustment in pharmacoepidemiologic studies. However, current applications are difficult to scale. We used unsupervised NLP to generate high-dimensional feature spaces from FTNs to improve prediction of drug exposure and outcomes compared with claims-based analyses. We linked Medicare claims with EHR data to generate three cohort studies comparing different classes of medications on the risk of various clinical outcomes. We used "bag-of-words" to generate features for the top 20,000 most prevalent terms from FTNs. We compared machine learning (ML) prediction algorithms using different sets of candidate predictors: Set1 (39 researcher-specified variables), Set2 (Set1 + ML-selected claims codes), and Set3 (Set1 + ML-selected NLP-generated features), vs. Set4 (Set1 + 2 + 3). When modeling treatment choice, we observed a consistent pattern across the examples: ML models utilizing Set4 performed best followed by Set2, Set3, then Set1. When modeling the outcome risk, there was little to no improvement beyond models based on Set1. Supplementing claims data with NLP-generated features from free text notes improved prediction of prescribing choices but had little or no improvement on clinical risk prediction. These findings have implications for strategies to improve confounding using EHR data in pharmacoepidemiologic studies.

High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.

Real-world evidence used for regulatory, payer, and clinical decision-making requires principled epidemiology in design and analysis, applying methods to minimize confounding given the lack of randomization. One technique to deal with potential confounding is propensity score (PS) analysis, which allows for the adjustment for measured preexposure covariates. Since its first publication in 2009, the high-dimensional propensity score (hdPS) method has emerged as an approach that extends traditional PS covariate selection to include large numbers of covariates that may reduce confounding bias in the analysis of healthcare databases. hdPS is an automated, data-driven analytic approach for covariate selection that empirically identifies preexposure variables and proxies to include in the PS model. This article provides an overview of the hdPS approach and recommendations on the planning, implementation, and reporting of hdPS used for causal treatment-effect estimations in longitudinal healthcare databases. We supply a checklist with key considerations as a supportive decision tool to aid investigators in the implementation and transparent reporting of hdPS techniques, and to aid decision-makers unfamiliar with hdPS in the understanding and interpretation of studies employing this approach. This article is endorsed by the International Society for Pharmacoepidemiology.

Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting.

Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study-from initial study design to final report-to facilitate rationalized and transparent decision-making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.

Categorization of COVID-19 severity to determine mortality risk.

PURPOSE: Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data. METHODS: Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID-19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non-invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28-day maximum to report mortality risks and rates overall and by stratified by severity. Trends for heterogeneity in mortality risk and rate across severity classifications were evaluated using Cochran-Armitage and Logrank trend tests, respectively. RESULTS: Among 118 117 patients, the algorithm categorized patients in increasing severity as NEITHER (36.7%), O2/NIV (54.3%), and IMV (9.0%). Associated mortality risk (and 95% CI) was 11.8% (11.6-12.0%) overall and increased with severity [3.4% (3.2-3.5%), 11.5% (11.3-11.8%), 47.3% (46.3-48.2%); p < 0.001]. Mortality rate per 1000 person-days (and 95% CI) was 15.1 (14.9-15.4) overall and increased with severity [5.7 (5.4-6.0), 14.5 (14.2-14.9), 32.7 (31.8-33.6); p < 0.001]. CONCLUSION: As expected, we observed a positive association between the algorithm-defined severity on admission and 28-day mortality risk and rate. Although performance remains to be validated, this provides some assurance that this algorithm may be used for confounding control or stratification in treatment effect studies.

Durability of the Single-Dose Ad26.COV2.S Vaccine in the Prevention of COVID-19 Infections and Hospitalizations in the US Before and During the Delta Variant Surge.

Importance: Vaccination against the SARS-CoV-2 virus is critical to control the pandemic. Randomized clinical trials demonstrated efficacy of the single-dose Ad26.COV2.S COVID-19 vaccine, but data on longer-term protection in clinical practice and effectiveness against variants are needed. Objective: To assess the association between receiving the Ad26.COV2.S vaccine and COVID-19-related infections and hospitalizations before and during the Delta variant surge. Design, Setting, and Participants: This cohort study included adults aged 18 years and older who were newly Ad26.COV2.S-vaccinated matched to as many as 10 unvaccinated individuals by date, location, age, sex, and comorbidity index. This was followed by 1:4 propensity score matching on COVID-19 risk factors. Data were collected from US insurance claims data from March 1, 2020, through August 31, 2021. Exposures: Vaccination with Ad26.COV2.S vs no vaccination. Main Outcomes and Measures: Vaccine effectiveness (VE) was estimated for recorded COVID-19 infection and COVID-19-related hospitalization, nationwide and in subgroups by age, high-risk factors, calendar time, and states with high incidences of the Delta variant. VE estimates were corrected for underrecording of vaccinations in insurance data. Results: Among 422 034 vaccinated individuals (mean [SD] age, 54.7 [17.4] years; 236 437 [56.0%] women) and 1 645 397 matched unvaccinated individuals (mean [SD] age, 54.5 [17.5] years; 922 937 [56.1%] women), VE was 76% (95% CI, 75%-77%) for COVID-19 infections and 81% (95% CI, 78%-82%) for COVID-19-related hospitalizations. VE was stable for at least 180 days after vaccination and over calendar time. Among states with high Delta variant incidence, VE during June to August 2021 was 74% (95% CI, 71%-77%) for infections and 81% (95% CI, 75%-86%) for hospitalizations. VE for COVID-19 was higher in individuals younger than 65 years (78%; 95% CI, 77%-79%) and lower in immunocompromised patients (64%; 95% CI, 59%-68%). All estimates were corrected for vaccination underrecording; uncorrected VE, which served as a lower bound, was 66% (95% CI, 64%-67%) for any recorded COVID-19 infection and 72% (95% CI, 69%-74%) for COVID-19-related hospitalization. Conclusions and Relevance: This cohort study in US clinical practice showed stable VE of Ad26.COV2.S for at least 6 months before as well as during the time the Delta variant emerged and became dominant.

The Role of Real-World Evidence in FDA-Approved New Drug and Biologics License Applications.

The US Food and Drug Administration (FDA) is open to accepting real-world evidence (RWE) to support its assessment of medical products. However, RWE stakeholders lack a shared understanding of FDA's evidentiary expectations for the use of RWE in applications for new drugs and biologics. We conducted a systematic review of publicly available FDA approval documents from January 2019 to June 2021. We sought to quantify, by year, how many approvals incorporated RWE in any form, and the intended use of RWE in those applications. Among approvals with RWE intended to support safety and/or effectiveness, we classified whether and how those studies impacted FDA's benefit-risk considerations, whether those studies were incorporated into the product label, and the therapeutic area of the medical product. Finally, we qualified FDA's documented feedback where available. We found that 116 approvals incorporated RWE in any form, with the proportion of approvals incorporating RWE increasing each year. Of these approvals, 88 included an RWE study intended to provide evidence of safety or effectiveness. Among these 88 approvals, 65 of the studies influenced FDA's final decision and 38 were included in product labels. The 88 approvals spanned 18 therapeutic areas. FDA's feedback on RWE study quality included methodological issues, sample size concerns, omission of patient level data, and other limitations. Based on these findings, we would anticipate that future guidance on FDA's evidentiary expectations of RWE use will incorporate fit-for-purpose real-world data selection and careful attention to study design and analysis.

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients.

BACKGROUND: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. METHODS: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. RESULTS: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. CONCLUSION: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

Analyse d'ensemble des complications de la COVID-19 : étude d'auto-appariement pré- et post-exposition.

CONTEXTE: De nombreuses études sur les complications de la maladie à coronavirus 2019 (COVID-19) ont reposé sur des séries de cas et de petites cohortes qui ne permettaient pas d'établir un lien causal avec la COVID-19 ni d'estimer les risques dans les différents milieux de soins. Nous avons voulu étudier toutes les complications possibles de la COVID-19 afin de confirmer les complications précédemment déclarées et d'identifier de potentielles complications encore inconnues. MÉTHODES: À partir des données sur les demandes de remboursement de frais médicaux aux États-Unis, nous avons comparé la fréquence de tous les codes de diagnostic de la Classification internationale des maladies, 10 e révision, modification clinique (CIM-10-MC) enregistrés avant et après le déclenchement de la pandémie de COVID-19 dans un modèle d'auto-appariement pré- et post-exposition. Nous avons inclus les patients ayant reçu un diagnostic de COVID-19 entre le 1er mars 2020 et le 30 avril 2020, et calculé les estimations de risque et les rapports de cotes (RC) pour le lien avec la COVID-19 de chaque code de diagnostic de la CIM-10-MC. RÉSULTATS: Sur les 1724 codes de diagnostic de la CIM-10-MC attribués à 70 288 patients atteints de COVID-19, 69 étaient significativement liés à la COVID-19. Les diagnostics étroitement liés à la COVID-19 et comportant un risque absolu élevé comprenaient la pneumonie virale (RC 177,63; intervalle de confiance [IC] à 95 % 147,19­214,37; risque absolu 27,6 %), l'insuffisance respiratoire (RC 11,36; IC à 95 % 10,74­12,02; risque absolu 22,6 %), l'insuffisance rénale aiguë (RC 3,50; IC à 95 % 3,34­3,68; risque absolu 11,8 %) et la sepsie (RC 4,23; IC à 95 % 4,01­4,46; risque absolu 10,4 %). Les diagnostics étroitement liés à la COVID-19, mais comportant un risque absolu faible comprenaient la myocardite (RC 8,17; IC à 95 % 3,58­18,62; risque absolu 0,1 %), la coagulation intravasculaire disséminée (RC 11,83; IC à 95 % 5,26­26,62; risque absolu 0,1 %) et le pneumothorax (RC 3,38; IC à 95 % 2,68­4,26; risque absolu 0,4 %). INTERPRÉTATION: Nous avons confirmé et établi les estimations du risque de plusieurs complications de la COVID-19. Ces résultats pourraient orienter le pronostic, les décisions thérapeutiques et les conseils aux patients.

Association of SARS-CoV-2 Seropositive Antibody Test With Risk of Future Infection.

Importance: Understanding the effect of serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on susceptibility to infection is important for identifying at-risk populations and could have implications for vaccine deployment. Objective: The study purpose was to evaluate evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among patients with positive vs negative test results for antibodies in an observational descriptive cohort study of clinical laboratory and linked claims data. Design, Setting, and Participants: The study created cohorts from a deidentified data set composed of commercial laboratory tests, medical and pharmacy claims, electronic health records, and hospital chargemaster data. Patients were categorized as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test in the database. Main Outcomes and Measures: Primary end points were post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, including recorded signs and symptoms or prior evidence of coronavirus 2019 (COVID) diagnoses or positive NAAT results and recorded comorbidities. Results: The cohort included 3 257 478 unique patients with an index antibody test; 56% were female with a median (SD) age of 48 (20) years. Of these, 2 876 773 (88.3%) had a negative index antibody result, and 378 606 (11.6%) had a positive index antibody result. Patients with a negative antibody test result were older than those with a positive result (mean age 48 vs 44 years). Of index-positive patients, 18.4% converted to seronegative over the follow-up period. During the follow-up periods, the ratio (95% CI) of positive NAAT results among individuals who had a positive antibody test at index vs those with a negative antibody test at index was 2.85 (95% CI, 2.73-2.97) at 0 to 30 days, 0.67 (95% CI, 0.6-0.74) at 31 to 60 days, 0.29 (95% CI, 0.24-0.35) at 61 to 90 days, and 0.10 (95% CI, 0.05-0.19) at more than 90 days. Conclusions and Relevance: In this cohort study, patients with positive antibody test results were initially more likely to have positive NAAT results, consistent with prolonged RNA shedding, but became markedly less likely to have positive NAAT results over time, suggesting that seropositivity is associated with protection from infection. The duration of protection is unknown, and protection may wane over time.

Real-World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID-19.

The emergence and global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an urgent need for evidence on medical interventions and outcomes of the resulting disease, coronavirus disease 2019 (COVID-19). Although many randomized controlled trials (RCTs) evaluating treatments and vaccines for COVID-19 are already in progress, the number of clinical questions of interest greatly outpaces the available resources to conduct RCTs. Therefore, there is growing interest in whether nonrandomized real-world evidence (RWE) can be used to supplement RCT evidence and aid in clinical decision making, but concerns about nonrandomized RWE have been highlighted by a proliferation of RWE studies on medications and COVID-19 outcomes with widely varying conclusions. The objective of this paper is to review some clinical questions of interest, potential data types, challenges, and merits of RWE in COVID-19, resulting in recommendations for nonrandomized RWE designs and analyses based on established RWE principles.

Real-world evidence of bariatric surgery and cardiovascular benefits using electronic health records data: A lesson in bias.

AIM: To reproduce and correct studies on bariatric surgery and the reduction in major adverse cardiovascular events (MACE) among patients with obesity and type 2 diabetes (T2D). METHODS: We used electronic healthcare records (EHR) from in and outpatient facilities around the United States to identify a cohort of patients with T2D, aged 18 to 80 years and with a body mass index (BMI) of 30 kg/m2 or higher undergoing bariatric surgery. We compared against hip/knee arthroplasty to establish an active comparison group that reduced bias from differential information and confounding. The main outcome was six-point MACE. Pre-exposure characteristics were adjusted in propensity score (PS) models with 1:2 matching plus high-dimensional PS matching. RESULTS: After a range of exclusions, the final cohort included 344 bariatric surgery patients (65% female; mean age 58 years) and 551 PS-matched patients undergoing arthroplasty (65% female; 59 years). Median follow-up was 2.5 years in both groups. Bariatric surgery patients showed a sustained 20% weight reduction and an HbA1c reduction by 1% point. We found no benefits of bariatric surgery for six-point MACE (HR = 0.99; 95% CI 0.76-1.30). We observed known increases in risks for vitamin B12 deficiency anaemia (HR = 3.06; 1.10-8.49) and cholelithiasis (HR = 1.72; 0.94-3.13). CONCLUSIONS: This real-world evidence study found reductions in HbA1c and BMI following bariatric surgery similar to trials, and no meaningful cardiovascular benefit compatible with the underpowered trials but in contrast to earlier EHR studies. We showed how information bias typical in EHR analyses and confounding may cause substantial bias.

Empirical assessment of case-based methods for identification of drugs associated with acute liver injury in the French National Healthcare System database (SNDS).

PURPOSES: Drug induced acute liver injury (ALI) is a frequent cause of liver failure. Case-based designs were empirically assessed and calibrated in the French National claims database (SNDS), aiming to identify the optimum design for drug safety alert generation associated with ALI. METHODS: All cases of ALI were extracted from SNDS (2009-2014) using specific and sensitive definitions. Positive and negative drug controls were used to compare 196 self-controlled case series (SCCS), case-control (CC), and case-population (CP) design variants, using area under the receiver operating curve (AUC), mean square error (MSE) and coverage probability. Parameters that had major impacts on results were identified through logistic regression. RESULTS: Using a specific ALI definition, AUCs ranged from 0.78 to 0.94, 0.64 to 0.92 and 0.48 to 0.85, for SCCS, CC and CP, respectively. MSE ranged from 0.12 to 0.40, 0.22 to 0.39 and 1.03 to 5.29, respectively. Variants adjusting for multiple drug use had higher coverage probabilities. Univariate regressions showed that high AUCs were achieved with SCCS using exposed time as the risk window. The top SCCS variant yielded an AUC = 0.93 and MSE = 0.22 and coverage = 86%, with 1/7 negative and 13/18 positive controls presenting significant estimates. CONCLUSIONS: SCCS adjusting for multiple drugs and using exposed time as the risk window performed best in generating ALI-related drug safety alert and providing estimates of the magnitude of the risk. This approach may be useful for ad-hoc pharmacoepidemiology studies to support regulatory actions.

Diagnosis-wide analysis of COVID-19 complications: an exposure-crossover study.

BACKGROUND: Many studies reporting coronavirus disease 2019 (COVID-19) complications have involved case series or small cohorts that could not establish a causal association with COVID-19 or provide risk estimates in different care settings. We sought to study all possible complications of COVID-19 to confirm previously reported complications and to identify potential complications not yet known. METHODS: Using United States health claims data, we compared the frequency of all International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes occurring before and after the onset of the COVID-19 pandemic in an exposure-crossover design. We included patients who received a diagnosis of COVID-19 between Mar. 1, 2020, and Apr. 30, 2020, and computed risk estimates and odds ratios (ORs) of association with COVID-19 for every ICD-10-CM diagnosis code. RESULTS: Among 70 288 patients with COVID-19, 69 of 1724 analyzed ICD-10-CM diagnosis codes were significantly associated with COVID-19. Disorders showing both strong association with COVID-19 and high absolute risk included viral pneumonia (OR 177.63, 95% confidence interval [CI] 147.19-214.37, absolute risk 27.6%), respiratory failure (OR 11.36, 95% CI 10.74-12.02, absolute risk 22.6%), acute kidney failure (OR 3.50, 95% CI 3.34-3.68, absolute risk 11.8%) and sepsis (OR 4.23, 95% CI 4.01-4.46, absolute risk 10.4%). Disorders showing strong associations with COVID-19 but low absolute risk included myocarditis (OR 8.17, 95% CI 3.58-18.62, absolute risk 0.1%), disseminated intravascular coagulation (OR 11.83, 95% CI 5.26-26.62, absolute risk 0.1%) and pneumothorax (OR 3.38, 95% CI 2.68-4.26, absolute risk 0.4%). INTERPRETATION: We confirmed and provided risk estimates for numerous complications of COVID-19. These results may guide prognosis, treatment decisions and patient counselling.

Real-world data suggest antibody positivity to SARS-CoV-2 is associated with a decreased risk of future infection.

Importance There is limited evidence regarding whether the presence of serum antibodies to SARS-CoV-2 is associated with a decreased risk of future infection. Understanding susceptibility to infection and the role of immune memory is important for identifying at-risk populations and could have implications for vaccine deployment. Objective The purpose of this study was to evaluate subsequent evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among individuals who are antibody-positive compared with those who are antibody-negative, using real-world data. Design This was an observational descriptive cohort study. Participants The study utilized a national sample to create cohorts from a de-identified dataset composed of commercial laboratory test results, open and closed medical and pharmacy claims, electronic health records, hospital billing (chargemaster) data, and payer enrollment files from the United States. Patients were indexed as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test recorded in the database. Patients with more than 1 antibody test on the index date where results were discordant were excluded. Main Outcomes/Measures Primary endpoints were index antibody test results and post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, as measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, such as recorded signs and symptoms or prior evidence of COVID-19 (diagnoses or NAAT+) and recorded comorbidities. Results We included 3,257,478 unique patients with an index antibody test. Of these, 2,876,773 (88.3%) had a negative index antibody result, 378,606 (11.6%) had a positive index antibody result, and 2,099 (0.1%) had an inconclusive index antibody result. Patients with a negative antibody test were somewhat older at index than those with a positive result (mean of 48 versus 44 years). A fraction (18.4%) of individuals who were initially seropositive converted to seronegative over the follow up period. During the follow-up periods, the ratio (CI) of positive NAAT results among individuals who had a positive antibody test at index versus those with a negative antibody test at index was 2.85 (2.73 - 2.97) at 0-30 days, 0.67 (0.6 - 0.74) at 31-60 days, 0.29 (0.24 - 0.35) at 61-90 days), and 0.10 (0.05 - 0.19) at >90 days. Conclusions Patients who display positive antibody tests are initially more likely to have a positive NAAT, consistent with prolonged RNA shedding, but over time become markedly less likely to have a positive NAAT. This result suggests seropositivity using commercially available assays is associated with protection from infection. The duration of protection is unknown and may wane over time; this parameter will need to be addressed in a study with extended duration of follow up.

Empirical assessment of case-based methods for identification of drugs associated with upper gastrointestinal bleeding in the French National Healthcare System database (SNDS).

PURPOSE: Upper gastrointestinal bleeding (UGIB) is a severe and frequent drug-related event. In order to enable efficient drug safety alert generation in the French National Healthcare System database (SNDS), we assessed and calibrated empirically case-based designs to identify drug associated with UGIB risk. METHODS: All cases of UGIB were extracted from SNDS (2009-2014) using two definitions. Positive and negative drug controls were used to compare 196 self-controlled case series (SCCS), case-control (CC) and case-population (CP) design variants. Each variant was evaluated in a 1/10th population sample using area under the receiver operating curve (AUC) and mean square error (MSE). Parameters that had major impacts on results were identified through logistic regression. Optimal designs were replicated in the unsampled population. RESULTS: Using a specific UGIB definition, AUCs ranged from 0.64 to 0.80, 0.44 to 0.61 and 0.50 to 0.67, for SCCS, CC and CP, respectively. MSE ranged from 0.07 to 0.39, 0.83 to 1.33 and 1.96 to 4.6, respectively. Univariate regressions showed that high AUCs were achieved with SCCS with multiple drug adjustment and a 30-day risk window starting at exposure. The top-performing SCCS variant in the unsampled population yielded an AUC = 0.84 and MSE = 0.14, with 10/36 negative controls presenting significant estimates. CONCLUSIONS: SCCS adjusting for multiple drugs and using a 30-day risk window has the potential to generate UGIB-related alerts in the SNDS and hypotheses on its potential population impact. Negative control implementation highlighted that low systematic error was generated but that protopathic bias and confounding by indication remained unaddressed issues.

Empirical assessment of case-based methods for drug safety alert identification in the French National Healthcare System database (SNDS): Methodology of the ALCAPONE project.

OBJECTIVES: To introduce the methodology of the ALCAPONE project. BACKGROUND: The French National Healthcare System Database (SNDS), covering 99% of the French population, provides a potentially valuable opportunity for drug safety alert generation. ALCAPONE aimed to assess empirically in the SNDS case-based designs for alert generation related to four health outcomes of interest. METHODS: ALCAPONE used a reference set adapted from observational medical outcomes partnership (OMOP) and Exploring and Understanding Adverse Drug Reactions (EU-ADR) project, with four outcomes-acute liver injury (ALI), myocardial infarction (MI), acute kidney injury (AKI), and upper gastrointestinal bleeding (UGIB)-and positive and negative drug controls. ALCAPONE consisted of four main phases: (1) data preparation to fit the OMOP Common Data Model and select the drug controls; (2) detection of the selected controls via three case-based designs: case-population, case-control, and self-controlled case series, including design variants (varying risk window, adjustment strategy, etc.); (3) comparison of design variant performance (area under the ROC curve, mean square error, etc.); and (4) selection of the optimal design variants and their calibration for each outcome. RESULTS: Over 2009-2014, 5225 cases of ALI, 354 109 MI, 12 633 AKI, and 156 057 UGIB were identified using specific definitions. The number of detectable drugs ranged from 61 for MI to 25 for ALI. Design variants generated more than 50 000 points estimates. Results by outcome will be published in forthcoming papers. CONCLUSIONS: ALCAPONE has shown the interest of the empirical assessment of pharmacoepidemiological approaches for drug safety alert generation and may encourage other researchers to do the same in other databases.

Actionable Real-World Evidence to Improve Health Outcomes and Reduce Medical Spending Among Risk-Stratified Patients with Diabetes.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic condition with a high economic burden as well as drug treatments that have not all demonstrated effects on longevity. Managed care organizations want to improve health outcomes in these complex patients but lack actionable evidence to make informed decisions on which therapies are most effective among their members and may also control total health care spending. OBJECTIVE: To produce actionable evidence by identifying antidiabetic treatments that are effective and may reduce total cost of care in various risk groups of patients with T2DM, using insurance claims data that includes medical claims and pharmacy dispensing data among members of Horizon Blue Cross Blue Shield of New Jersey with T2DM. METHODS: We identified patients with T2DM in longitudinal claims data from Horizon between 2014 and 2017 with demographic and enrollment information, inpatient and outpatient diagnoses and procedures, and pharmacy dispensing. Outcomes included myocardial infarction, heart failure (HF), stroke, percutaneous revascularization, health care services utilization, and plan costs (i.e., medical, pharmacy, and total cost of care). After propensity score decile adjustment on over 20 covariates, we evaluated the effectiveness and safety of second-line antidiabetic treatment that included sodium-glucose co-transporter-2 (SGLT-2) inhibitors, sulfonylureas (SUs), dipeptidyl peptidase-4 (DPP-4) inhibitors, and glucagon-like peptide-1 (GLP-1) receptor agonists. RESULTS: Among 115,308 members with T2DM, the most common comorbidities were cardiovascular risk factors, including hyperlipidemia (56%), hypertension (50%), and existing cardiovascular disease (CVD; 55%). Among members receiving dual antidiabetic treatment (n = 20,204), the most prevalent treatments were metformin plus the following second-line medications: SUs (42%), DPP-4 inhibitors (29%), SGLT-2 inhibitors (10%), or GLP-1 receptor agonists (3%). Approximately 20% of members accounted for 79% of total cost of care, with an average of $9,605 per member per year (PMPY). Compared with SU initiation and after propensity score decile adjustment, new users of SGLT-2 inhibitors had a reduced risk for HF hospitalization (HR = 0.35, 95% CI = 0.13-0.89), hypoglycemia, albuminuria, microvascular disease, and metabolic failure. Among SGLT-2 inhibitor initiators with established CVD, the savings in total cost of care compared with SU initiators was $5,520 per member over an average treatment duration of 6 months and an approximate savings of $11,000 PMPY if patients persisted on treatment for 12 months. CONCLUSIONS: In the Horizon membership, we confirmed that SGLT-2 inhibitors reduce HF hospitalizations, resulting in reduced medical spending and savings in total cost of care. Regulatory-grade analytics of local data provided the confidence to encourage increased SGLT-2 inhibitor use to produce better outcomes and save total cost of care despite higher pharmacy spending. DISCLOSURES: This research did not receive outside funding; however, Aetion has since begun a contractual relationship with Horizon Blue Cross Blue Shield of New Jersey. Garry, Petruski-Ivleva, Cheever, and Rajan are employees of and have stock options in Aetion, a company that makes software for the analysis of real-world data. Eapen was an employee of Aetion during the implementation of this study. Rassen is an employee of and has ownership interest in Aetion. Murk is a consultant to Aetion of which he owns equity. Schneeweiss is a consultant to WHISCON and to Aetion, of which he also owns equity. He is the principal investigator of investigator-initiated grants to the Brigham and Women's Hospital from Bayer, Genentech, Boehringer Ingelheim, and Vertex. Gambino is an employee and officer at Horizon Blue Cross and Blue Shield of New Jersey. He was recently appointed to a board observer position at Aetion, as Horizon has small equity interest in Aetion. Jan is an employee of Rutgers State University and Horizon Blue Cross Blue Shield of New Jersey and has no conflict of interest or association with Aetion or any pharmaceutical company. Jang and Rubin are employees of Horizon Blue Cross and Blue Shield of New Jersey and have no conflict of interest or association with Aetion. This work was presented as a poster at AMCP Nexus 2018, October 22-25, 2018, in Orlando, FL; as part of a continuing education session at the AMCP Managed Care & Specialty Pharmacy 2019 Annual Meeting in San Diego, CA, March 25-28, 2019; as invited podium presenter at the Blue Cross Blue Shield 2019 National Summit conference in Grapevine, TX, April 29-May 2, 2019; and was accepted for a podium presentation at the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2019 annual conference in New Orleans, LA, May 18-22, 2019, where it won an award for Best Podium Presentation.

Graphical Depiction of Longitudinal Study Designs in Health Care Databases.

Pharmacoepidemiologic and pharmacoeconomic analysis of health care databases has become a vital source of evidence to support health care decision making and efficient management of health care organizations. However, decision makers often consider studies done in nonrandomized health care databases more difficult to review than randomized trials because many design choices need to be considered. This is perceived as an important barrier to decision making about the effectiveness and safety of medical products. Design flaws in longitudinal database studies are avoidable but can be unintentionally obscured in the convoluted prose of methods sections, which often lack specificity. We propose a simple framework of graphical representation that visualizes study design implementations in a comprehensive, unambiguous, and intuitive way; contains a level of detail that enables reproduction of key study design variables; and uses standardized structure and terminology to simplify review and communication to a broad audience of decision makers. Visualization of design details will make database studies more reproducible, quicker to review, and easier to communicate to a broad audience of decision makers.