Comparison of Real-World On-Label Treatment Persistence in Patients with Psoriatic Arthritis Receiving Guselkumab Versus Subcutaneous Tumor Necrosis Factor Inhibitors.

BACKGROUND: Treatment persistence among patients with psoriatic arthritis (PsA) is essential for achieving optimal treatment outcomes. Guselkumab, a fully human interleukin-23p19-subunit inhibitor, was approved by the United States (US) Food and Drug Administration for the treatment of active PsA in July 2020, with a dosing regimen of 100 mg at week 0, week 4, then every 8 weeks. In the Phase 3 DISCOVER-1 and DISCOVER-2 studies of patients with active PsA, 94% of guselkumab-randomized patients completed treatment through 1 year and 90% did so through 2 years (DISCOVER-2). Real-world evidence is needed to compare treatment persistence while following US prescribing guidelines (i.e., on-label persistence) for guselkumab versus subcutaneous (SC) tumor necrosis factor inhibitors (TNFis). METHODS: Adults with PsA receiving guselkumab or their first SC TNFi (i.e., adalimumab, certolizumab pegol, etanercept, or golimumab) between 14 July 2020 and 31 March 2022 were identified in the IQVIA PharMetrics® Plus database (first claim defined the treatment start date [index date]). Baseline characteristics and biologic use (biologic-naïve/biologic-experienced) were assessed during the 12-month period preceding the index date. Baseline characteristics were balanced between cohorts using propensity-score weighting based on the standardized mortality ratio approach. The follow-up period spanned from the index date until the earlier of the end of continuous insurance eligibility or end of data availability. On-label persistence, defined as the absence of treatment discontinuation (based on a gap of 112 days for guselkumab or 56 days for SC TNFi) or any dose escalation/reduction during follow-up, was assessed in the weighted treatment cohorts using Kaplan-Meier (KM) curves. A Cox proportional hazards model, further adjusted for baseline biologic use, was used to compare on-label persistence between the weighted cohorts. RESULTS: The guselkumab cohort included 526 patients (mean age 49.8 years; 61.2% female) and the SC TNFi cohort included 1953 patients (mean age: 48.5 years; 60.2% female). After weighting, baseline characteristics were well balanced with a mean follow-up of 12.3-12.4 months across cohorts; 51.5% of patients in the guselkumab cohort and 16.7% in the SC TNFi cohort received biologics in the 12-month baseline period. Respective rates of treatment persistence at 3, 6, 9, and 12 months were 91.2%, 84.1%, 75.9%, and 71.5% for the guselkumab cohort versus 77.3%, 61.6%, 50.0%, and 43.7% for the SC TNFi cohort (all log-rank p < 0.001). At 12 months, patients in the guselkumab cohort were 3.0 times more likely than patients in the SC TNFi cohort to remain persistent on treatment (p < 0.001). Median time to discontinuation was not reached for the guselkumab cohort and was 8.9 months for the SC TNFi cohort. CONCLUSION: This real-world study employing US commercial health-plan claims data to assess on-label treatment persistence in PsA demonstrated that, at 12 months, guselkumab was associated with a 3 times greater likelihood of persistence compared with SC TNFi.

Enhancement and external validation of algorithms using diagnosis codes to identify invasive Escherichia coli disease.

OBJECTIVE: To assess the predictive accuracy of code-based algorithms for identifying invasive Escherichia coli (E. coli) disease (IED) among inpatient encounters in US hospitals. METHODS: The PINC AI Healthcare Database (10/01/2015-03/31/2020) was used to assess the performance of six published code-based algorithms to identify IED cases among inpatient encounters. Case-confirmed IEDs were identified based on microbiological confirmation of E. coli in a normally sterile body site (Group 1) or in urine with signs of sepsis (Group 2). Code-based algorithm performance was assessed overall, and separately for Group 1 and Group 2 based on sensitivity, specificity, positive and negative predictive value (PPV and NPV) and F1 score. The improvement in performance of refinements to the best-performing algorithm was also assessed. RESULTS: Among 2,595,983 encounters, 97,453 (3.8%) were case-confirmed IED (Group 1: 60.9%; Group 2: 39.1%). Across algorithms, specificity and NPV were excellent (>97%) for all but one algorithm, but there was a trade-off between sensitivity and PPV. The algorithm with the most balanced performance characteristics included diagnosis codes for: (1) infectious disease due to E. coli OR (2) sepsis/bacteremia/organ dysfunction combined with unspecified E. coli infection and no other concomitant non-E. coli invasive disease (sensitivity: 56.9%; PPV: 56.4%). Across subgroups, the algorithms achieved lower algorithm performance for Group 2 (sensitivity: 9.9%-61.1%; PPV: 3.8%-16.0%). CONCLUSIONS: This study assessed code-based algorithms to identify IED during inpatient encounters in a large US hospital database. Such algorithms could be useful to identify IED in healthcare databases that lack information on microbiology data.

A 5-Year Retrospective, Observational Study Assessing Rheumatoid Arthritis Disease Outcome Measures to Characterize Systemic Lupus Erythematosus Burden in the USA.

INTRODUCTION: We evaluated the use of rheumatoid arthritis (RA) disease measures in patients with systemic lupus erythematosus (SLE) in a US community-based rheumatology physician network over 5 years. METHODS: This retrospective, observational cohort study (GSK Study 213818) of patients with SLE utilized electronic medical records (01 January 2010-31 December 2019) from the United Rheumatology Normalized Integrated Community Evidence database. The index was the date of first SLE diagnosis recorded in the database; the observation period was 5 years post-index. RA disease measures evaluated were: Pain Index, Multi-Dimensional Health Assessment Questionnaire (MD-HAQ), Patient Global Assessment (PtGA), Physician Global Assessment (PGA), Swollen Joint Count (SJC), Tender Joint Count (TJC), Routine Assessment of Patient Index Data 3 (RAPID3), Clinical Disease Activity Index (CDAI), Simplified Disease Activity Index (SDAI), and Disease Activity Score 28 (DAS-28). The number of patients with measures utilized, the score on each measure, and proportion of patients per disease activity category were assessed. RESULTS: Overall, 5990 patients with SLE were included. The most frequently used measures were Pain Index, SJC, TJC, MD-HAQ, PtGA, RAPID3, and PGA (cumulative use over Years 1-5: 23.9-71.3%). For all measures, frequency of use was lowest in Year 1, followed by a general increase from Year 1 to Year 5. Scores remained relatively stable for most measures, and the proportion of patients in remission or with low/moderate disease activity per RAPID3 increased. CONCLUSION: RA disease measure utilization in SLE was generally infrequent but increased over time. Pain Index and MD-HAQ were the most commonly applied cumulatively across 5 years of follow-up. The rationale for the increased use of these measures in SLE over time requires further exploration. In the absence of a clinically applicable SLE-specific measure, the use of RA measures, for example in conjunction with SLE measures, may provide an alternative approach for measuring disease activity, representing an opportunity to improve patient outcomes.

Real-World Healthcare Resource Utilization (HRU) and Costs of Patients with Paroxysmal Nocturnal Hemoglobinuria (PNH) Receiving Eculizumab in a US Population.

INTRODUCTION: To evaluate the economic burden and treatment patterns of patients with paroxysmal nocturnal hemoglobinuria (PNH) treated with eculizumab, a C5 inhibitor, who were defined as blood transfusion-dependent (TD) versus blood transfusion-free (TF) in the US population. METHODS: Patients aged at least 12 years with at least two claims for eculizumab infusion (first claim was the index date) were identified from the IBM® MarketScan® Research Databases (April 1, 2014-September 30, 2019). The overall PNH eculizumab user cohort was stratified into the TD cohort (i.e., at least one claim for blood transfusion within 6 months following any eculizumab infusion, including on the infusion date) or the TF cohort (i.e., all non-TD patients). Treatment patterns, healthcare resource utilization (HRU), and costs were evaluated and compared during follow-up (i.e., index date to end of enrollment or data availability). RESULTS: Of 151 patients in the overall cohort (mean age 36.7 years; 55.6% female), 55 were TD (mean age 35.1 years; 67.3% female) and 96 were TF (mean age 37.6 years; 49.0% female). A total of 61% of patients (TD, 66%; TF, 58%) discontinued eculizumab, with TD patients having a shorter median time to discontinuation (TD, 0.5 years; TF, 0.9 years). TD patients had more all-cause hospitalizations than TF patients (p < 0.05). TD patients incurred higher all-cause direct medical costs (adjusted cost difference = $247,848) and medical-related absenteeism costs (adjusted cost difference = $4186) than TF patients (all p < 0.05), largely driven by hospitalizations. Similar trends were observed for PNH-related HRU and costs. CONCLUSIONS: The economic burden of patients with PNH treated with eculizumab is greater among those dependent on blood transfusions.

Real-World Treatment Patterns and Overall Survival of Patients with Metastatic Castration-Resistant Prostate Cancer in the US Prior to PARP Inhibitors.

INTRODUCTION: Therapeutic options for metastatic castration-resistant prostate cancer (mCRPC) patients are continuously advancing. We described mCRPC treatment patterns in the US from 2013 to 2019. METHODS: Patients with a confirmed mCRPC diagnosis and adenocarcinoma histology were included in the US Flatiron Health Electronic Health Record-derived de-identified database. Treatment patterns [including treatment per lines of therapies (LOTs), LOT sequences, and time on treatment] and overall survival (OS) have been described in mCRPC settings. RESULTS: Of 5213 patients (mean age: 72.6 years), 4374 (83.9%) were treated with ≥ 1 LOT post-mCRPC diagnosis (among those with ≥ 1 LOT, 55.3%, 29.5%, 14.7%, and 6.7% had ≥ 2, 3, 4, and 5 LOTs, respectively). In first line (1L), the main treatment class was next-generation hormonal agents (NHA; 62.5% of patients with ≥ 1 LOT), while the shortest and longest time on 1L were observed for chemotherapy (median 2.8 months) and NHA (median 5.1 months), respectively. The most common LOT sequences were NHA â†’ NHA (29.4% of patients with ≥ 2 LOTs) and NHA â†’ NHA â†’ chemotherapy (16.7% of patients with ≥ 3 LOTs). In Kaplan-Meier analyses, the median OS was 19.4, 14.6, and 11.1 months post-1L, 2L, and 3L start, respectively. Patients who moved rapidly through LOTs had an increased risk of death. CONCLUSIONS: NHA were widely used as 1L therapy in mCRPC patients from 2013 to 2019, but time on 1L NHA treatment was on average < 6 months. While NHA â†’ NHA was the most observed 1L â†’ 2L LOT sequence, a plethora of other LOT sequences were observed. OS was poor, highlighting an unmet need for life-prolonging treatments.