Use of a real-world synthetic control arm for direct comparison of lisocabtagene maraleucel and conventional therapy in relapsed/refractory large B-cell lymphoma.

This study used a real-world population as a synthetic comparator for the single-arm TRANSCEND NHL 001 study (TRANSCEND; NCT02631044) to evaluate the efficacy of lisocabtagene maraleucel (liso-cel) compared with conventional (noncellular) therapies in patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL). Inclusion and exclusion criteria for the real-world study closely matched the enrollment criteria in TRANSCEND. The analytic comparator cohort was created by matching and balancing observed baseline characteristics of real-world patients with those in TRANSCEND using propensity score methodology. Efficacy outcomes comparing liso-cel- (n = 257) and conventional therapy-treated (n = 257) patients, respectively, significantly favored liso-cel: overall response rate (74% vs 39%; p < 0.0001), complete response rate (50% vs 24%; p < 0.0001), median overall survival (23.5 vs 6.8 months; p < 0.0001), and median progression-free survival (3.5 vs 2.2 months; p < 0.0001). These results demonstrated a statistically significant and clinically meaningful benefit of liso-cel in patients with third- or later-line R/R LBCL relative to conventional therapies.Clinical trial registration: ClinicalTrials.gov identifier: NCT02631044.

Hospital Admission Patterns in Adult Patients with Community-Acquired Pneumonia Who Received Ceftriaxone and a Macrolide by Disease Severity across United States Hospitals.

(1) Objective: There are limited data regarding community-acquired pneumonia (CAP) admissions patterns in US hospitals. Current expert CAP guidelines advocate for outpatient treatment or an abbreviated hospital stay for CAP patients in pneumonia severity index (PSI) risk classes I-III (low risk); however, the extent of compliance with this recommendation is unclear. This study sought to estimate the proportion of admissions among CAP patients who received ceftriaxone and macrolide therapy, one of the most commonly prescribed guideline-concordant CAP regimens, by PSI risk class and Charlson comorbidity index (CCI) score. (2) Methods: A retrospective cross-sectional study of patients in the Vizient® (MedAssets, Irving, Texas) database between 2012 and 2015 was performed. Patients were included if they were aged ≥ 18 years, had a primary diagnosis for CAP, and received ceftriaxone and a macrolide on hospital day 1 or 2. Baseline demographics and admitting diagnoses were used to calculate the PSI score. Patients in the final study population were grouped into categories by their PSI risk class and CCI score. Hospital length of stay, 30-day mortality rates, and 30-day CAP-related readmissions were calculated across resulting PSI-CCI strata. (3) Results: Overall, 32,917 patients met the study criteria. Approximately 70% patients were in PSI risk classes I-III and length of stay ranged between 4.9 and 6.2 days, based on CCI score. The 30-day mortality rate was <0.5% and <1.4% in patients with PSI risk classes I and II, respectively. (4) Conclusions: Over two-thirds of hospitalized patients with CAP who received ceftriaxone and a macrolide were in PSI risk classes I-III. Although the findings should be interpreted with caution, they suggest that there is a potential opportunity to improve the efficiency of healthcare delivery for CAP patients by shifting inpatient care to the outpatient setting in appropriate patients.

Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data.

BACKGROUND: Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. OBJECTIVES: To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. METHODS: US Integrated Delivery Network data (2010-2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes-based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes-based and claims-based algorithms. RESULTS: From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes-based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%-100%) for the EHR clinical notes-based algorithms and 94.6% (95% CI, 89.1%-97.8%) to 94.9% (95% CI, 89.8%-97.9%) for the claims-based algorithms. CONCLUSIONS: The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence.

Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts.

INTRODUCTION: The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data. OBJECTIVE: Our objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS). METHODS: A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS. FINDINGS: A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS. CONCLUSIONS: An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

Performance of a semi-automated approach for risk estimation using a common data model for longitudinal healthcare databases.

Different structures and coding schemes may limit rapid evaluation of a large pool of potential drug safety signals using multiple longitudinal healthcare databases. To overcome this restriction, a semi-automated approach utilising common data model (CDM) and robust pharmacoepidemiologic methods was developed; however, its performance needed to be evaluated. Twenty-three established drug-safety associations from publications were reproduced in a healthcare claims database and four of these were also repeated in electronic health records. Concordance and discrepancy of pairwise estimates were assessed between the results derived from the publication and results from this approach. For all 27 pairs, an observed agreement between the published results and the results from the semi-automated approach was greater than 85% and Kappa coefficient was 0.61, 95% CI: 0.19-1.00. Ln(IRR) differed by less than 50% for 13/27 pairs, and the IRR varied less than 2-fold for 19/27 pairs. Reproducibility based on the intra-class correlation coefficient was 0.54. Most covariates (>90%) in the publications were available for inclusion in the models. Once the study populations and inclusion/exclusion criteria were obtained from the literature, the analysis was able to be completed in 2-8 h. The semi-automated methodology using a CDM produced consistent risk estimates compared to the published findings for most selected drug-outcome associations, regardless of original study designs, databases, medications and outcomes. Further assessment of this approach is useful to understand its roles, strengths and limitations in rapidly evaluating safety signals.

Association between cervical dysplasia and human papillomavirus in HIV seropositive women from Johannesburg South Africa.

OBJECTIVE: To examine the association between CD4 counts, HPV infection and the risk of cervical neoplasia among HIV-seropositive women. METHODS: A cross-sectional observational study was conducted among 1,010 HIV-seropositive women using cytology-based Pap smears. HPV DNA testing using Linear Array genotyping assay (Roche) was carried out in a subset of 191 patients. Multivariable-adjusted prevalence ratios (mPR) and 95% confidence intervals (CIs) were estimated with log-binomial regression. RESULTS: Among 1,010 HIV-seropositive women, the prevalence of AGC/ASCUS, LSIL and HSIL or greater was 8.3, 23.5 and 18.0%, respectively. The risk of cervical lesions was higher with CD4 < 200 cells/mm(3) vs. CD4 levels > 500/mm(3). HPV types 16 (41.7%) and HPV 56 (22.2%) were the most common types in HSIL cases. Women with CD4 levels < 200/mm(3) had a higher prevalence of HPV types 16 (p < 0.01) and 66 (p = 0.04). No statistical relationship between cervical lesions and HAART use was found. CONCLUSION: The burden of HPV infection and HSIL was high and correlated with HIV-induced immunosuppression. HPV 16 was the most common type in HSIL and increased in prevalence with greater immune suppression. Prophylactic HPV 16 vaccination could prevent approximately 40% of HSIL cases. Strengthening screening programs is imperative in this population.