Maintenance rituximab in Veterans with follicular lymphoma

Halwani, Ahmad; Rasmussen, Kelli M; Patil, Vikas; Morreall, Deborah; Li, Catherine; Yong, Christina; Burningham, Zachary; Dawson, Keith; Masaquel, Anthony; Henderson, Kevin; Delong-Sieg, Elisha; Sauer, Brian C.

doi:10.1002/cam4.3420

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Using data from the Veterans Affairs (VA) Cancer Registry System and pharmacy dispensation records from the VA Corporate Data Warehouse, we identified a nationwide cohort of patients with grade 1–3a, stage II–IV FL diagnosed from 1 January 2006 to 31 December 2014 in the VHA, who received any of three widely used L1 therapies—rituximab combined with cyclophosphamide, doxorubicin, vincristine, and prednisone; rituximab combined with cyclophosphamide, vincristine, and prednisone; or bendamustine combined with rituximab [ 26 ]. Table 1 shows patients’ characteristics by treatment.…”

Section: Methodsmentioning

confidence: 99%

Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning

Patil

Rasmussen

et al. 2021

IJERPH

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The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years’ observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006–2014 in the Veterans Health Administration into traditionally used prognostic variables (“curated”), commonly measured labs (“labs”), and International Classification of Diseases diagnostic codes (“ICD”) sets. We compared performance of random survival forests (RSF) vs. traditional Cox model using four datasets: curated, curated + labs, curated + ICD, and curated + ICD + labs, also using Cox on curated + POD24. We evaluated variable importance and partial dependence plots with area under the receiver operating characteristic curve (AUC). RSF with curated + labs performed best, with mean AUC 0.73 (95% CI: 0.71–0.75). It approximated, but did not surpass, Cox with POD24 (mean AUC 0.74 [95% CI: 0.71–0.77]). RSF using EHR data achieved better performance than traditional prognostic variables, setting the foundation for the incorporation of our algorithm into the EHR. It also provides for possible future scenarios in which clinicians could be provided an EHR-based tool which approximates the predictive ability of the most accurate known indicator, using information available 24 months earlier.

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Section: Methodsmentioning

confidence: 99%

Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning

Patil

Rasmussen

et al. 2021

IJERPH

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Study on the additional financial burden of breast cancer disease on cancer patients and their families. Financial toxicity in cancer

Fernandez-Rodriguez,

Taboada-Taboada,

Garcia-Martin

et al. 2024

Front. Public Health

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IntroductionBreast cancer is among the most frequently diagnosed cancers worldwide, with 2.3 million new cases reported annually. The condition causes a social and economic impact known as financial toxicity of cancer. The study aims to explore the extra expenses borne by patients and their families on being diagnosed with breast cancer.MethodologyAn observational, descriptive, cross-sectional study was conducted. The data was collected between November 2021 and March 2022 at the Medical Oncology Service in Complejo Asistencial Universitario de Salamanca, Spain. The variables under investigation were additional economic costs, physical disability (as measured by the Barthel Index), instrumental activities of daily living (as measured by the Lawton-Brody Scale), and caregiver burden (measured using the ZARIT scale).ResultsThe final sample size was N = 107. The study yielded the following outcomes: the median age was 55 years old and the majority of participants were female, with a proportion of 99.1%. The incidence rates for stage I and II were 31.8 and 35.5%, respectively. The median Barthel score was 100 points, while the Lawton and Brody score were 8 points and the ECOG score was 2 points. The analysis of primary caregiver burden resulted in a median ZARIT score of 15 points. The expenses related solely to the cancer diagnosis totaled 1511.22 euros per year (316.82 euros for pharmaceuticals; 487.85 euros for orthopedic equipment; 140.19 euros for home help; and 566.36 euros for housing adaptation or transfer to a hospital). The average annual income before diagnosis was 19962.62 euros. However, after being diagnosed with breast cancer, there is a significant income decrease of 15.91%, resulting in a reduced average annual income of 16785.98 euros. Additionally, a significant correlation was found between total expenditure and the level of dependency (p = 0.032) and functional status (p = 0.045).ConclusionThese findings indicate that breast cancer patients experience a considerable economic burden, which worsens as their functional status deteriorates. Therefore, we believe policies should be implemented to help control this economic deterioration resulting from a serious health condition.

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Maintenance rituximab in Veterans with follicular lymphoma

Cited by 2 publications

References 55 publications

Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning

Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning

Study on the additional financial burden of breast cancer disease on cancer patients and their families. Financial toxicity in cancer

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