Real-world patient characteristics, treatment patterns, and treatment outcomes of patients with diffuse large B-cell lymphoma by line of therapy.

BACKGROUND: Although initial treatment of diffuse large B-cell lymphoma (DLBCL) with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) can be effective, up to 50% of patients will develop refractory or relapsed (R/R) disease. This study aimed to provide contemporary data on characteristics, treatment patterns, and outcomes for R/R-DLBCL. METHODS: Patients with incident (January 2016 to March 2021) DLBCL age ≥18 years who initiated first-line (1L) therapy were identified from the COTA real-world database. Baseline characteristics, treatment patterns, and real-world outcomes, including time to next treatment (rwTTNT) and overall survival (rwOS), were assessed for the study population and by line of therapy (LOT). RESULTS: A total of 1347 eligible DLBCL patients were identified. Of these, 340 (25.2%) proceeded to receive 2L, of whom 141 (41.5%) proceeded to receive 3L, of whom 51 (36.2%) proceeded to receive 4L+. Most common treatments were R-CHOP in 1L (63.6%), stem cell transplant (SCT) in 2L (17.9%), polatuzumab vedotin, bendamustine, and rituximab (Pola-BR) in 3L (9.9%), and chimeric antigen receptor T-cell therapy (CAR-T) in 4L (11.8%). Treatment patterns were more variable in later LOTs. One- and 3-year rwOS from 1L initiation were 88.5% and 78.4%, respectively. Patients who received later LOTs experienced numerically lower 1- and 3-year rwOS (from 2L initiation: 62.4% and 46.4%, respectively). CONCLUSIONS: In this real-world analysis, 25.2% of patients experienced R/R-DLBCL after 1L with poor outcomes. Given the findings of this study, there is a high unmet need for novel, safe, and effective treatment options for patients with R/R DLBCL.

Real-world study of disease-free survival & patient characteristics associated with disease-free survival in early-stage non-small cell lung cancer: A retrospective observational study.

INTRODUCTION/ BACKGROUND: Surgical resection remains standard of care for patients with early-stage non-small cell lung cancer (NSCLC), but research shows that adjuvant therapy can reduce the risk of disease recurrence. Our objective was to characterize disease-free survival (DFS) using real-world data. MATERIALS AND METHODS: This was a retrospective study using the COTA real-world database derived from electronic health records in the United States (US). Adults diagnosed with stage IB-IIIA NSCLC from 2013 to 2018 who underwent complete surgical resection (index date) for NSCLC were included. DFS was analyzed using the Kaplan-Meier method. A multivariable Cox-Proportional Hazard (PH) model stratified by year of diagnosis was developed to evaluate covariates associated with DFS. RESULTS: 703 patients met the study criteria (mean age 66.2 years, female (56%), White (82%), and median follow-up time was 37.4 months from index date. Approximately 48% of patients experienced recurrence or death with a median DFS of 42.9 months (95% CI: 37.4-52.2). Patients who received adjuvant therapy, neoadjuvant and adjuvant therapy, neoadjuvant therapy, and surgery only experienced a median DFS of 43.7, 32.3, 33.7, and 49.4 months, respectively. After adjustment, stage at diagnosis and adjuvant therapy status were significantly associated with DFS events. CONCLUSIONS: Higher stage at diagnosis and lack of adjuvant therapy were associated with greater risk of recurrence. Future research should focus on the adoption and effect of adjuvant/ neoadjuvant therapies on disease recurrence, including in patients with oncogenic driver mutations.

Real-world outcomes of frontline venetoclax-based therapy in older adults with acute myeloid leukemia: an analysis utilizing EHR data.

Venetoclax (VEN) in combination with hypomethylating agents (HMA) or low-dose cytarabine has become the standard of care for patients with acute myeloid leukemia (AML) who are ineligible to receive intensive induction chemotherapy. Clinical trials are performed in a controlled setting that can be difficult to emulate in the real world. We sought to investigate outcomes of patients treated with VEN-based therapy in the real world. Patients with an age of ≥65 years who received frontline VEN-based therapy were identified using the COTA database (n = 112). The majority of patients (91%) were treated in the community setting and had adverse-risk AML (63%). The real-world overall response rate (rwORR) was 55% with a median real-world overall survival (rwOS) of 13 months after VEN/HMA. The rwORR was lower and median rwOS was shorter than those reported in the VIALE-A trial, underscoring the importance of studying novel therapies using real-world data.

Reality check: Real-world evidence to support therapeutic development in hematologic malignancies.

The landscape for evidence generation in hematologic malignancies is rapidly evolving. While randomized controlled trials (RCTs) remain the gold standard in support of drug efficacy, approval and use, the supplemental use of real-world data (RWD), generated as part of routine healthcare delivery, and real-world evidence (RWE), the insights derived from RWD, in this setting has become increasingly common. There is a wide variety of sources of RWD, each with its own strengths and weaknesses that need to be considered when determining its appropriate use in RWE generation. RWD and RWE have historically been utilized in the post-approval setting to assess real-world application, efficacy, and safety of approved therapies. However, due to increasing awareness of the advantages of additional sources of information, RWE sourced from clinical data are being increasingly used to provide context for regulatory decision-making across several diseases including hematologic malignancies. Today, many commercial vendors offer fully aggregated, de-identified and standardized real-world clinical data. To maximize the potential of RWD and RWE, important considerations are needed to ensure patient privacy and to reduce the potential for biases and residual confounding. Continued collaboration among researchers, regulators and industry partners are needed to optimize evidence generation to ensure that new therapies reach patients as quickly and safely as possible.

Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists.

INTRODUCTION: Cognitive computing point-of-care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown. METHODS: Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform. RESULTS: The cognitive computing "recommended" option was concordant with selection by breast cancer experts in 78.5% and "for consideration" option was selected in 9.4%, yielding agreements in 87.9%. Fifty-nine percent of non-concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with "recommended," 11.4% "for consideration", and 19.3% "not recommended." Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non-recommended option (P < .01) without WfO/Cota RWE and changed decisions in 39% cases. CONCLUSIONS: Watson for Oncology with Cota RWE options were largely concordant with disease expert judged best oncology practices, and was able to improve treatment decisions among breast cancer novices. The observation that nearly a fifth of patients with similar disease characteristics received non-recommended options in a real world database highlights a need for decision support.