bluuebunny/crossref_metadata_2025_split huggingface dataset:
Commercial vs. Medicaid insurance and use of high-priced anticancer treatments.
Authors: Mitchell Aaron Philip , Kinlaw Alan C , Peacock Hinton Sharon , Dusetzina Stacie B , Sanoff Hanna Kelly , Lund Jennifer Leigh 
Published: June 2023

6599 Background: Financial incentives presented by the larger markups on cancer drugs among commercial payors may result in increased use of high-priced drugs among commercially insured vs. Medicaid insured patients. We evaluated the association between patient insurance type and the likelihood of receiving higher-priced treatments. Methods: We linked cancer registry, administrative claims, and demographic data for individuals diagnosed with cancer in North Carolina from 2004-2011, who had either commercial or Medicaid insurance. We selected cancer types with multiple FDA-approved, guideline-recommended chemotherapy options, and large differences in price between the higher-priced and lower-priced options during the study period: advanced colorectal, lung, and head-and-neck cancer (HNC). The primary outcome was receipt of the higher-priced treatment option, and the primary exposure was insurance type: commercial vs. Medicaid. We estimated risk ratios (RR) for the association between insurance type and higher-priced treatment using log binomial models with inverse probability of exposure weights to control confounding by age, county-level poverty prevalence, and selected comorbidities. We assessed for potential modification of the RR by setting (hospital outpatient vs. physician office) and academic designation (NCI-designated comprehensive cancer center vs. other); these were exploratory analyses due to sparse data and subsequently limited confounding control. Results: Of 812 patients: 209 (26%) had Medicaid insurance; 311 (38%) had NHC, 263 (32%) colon or rectal cancer, and 238 (29%) lung cancer. The crude risk of higher-priced treatment was 36% (215/603) for commercially insured and 27% (57/209) for Medicaid insured (RR:1.31, 95%CI 1.02-1.67). After weighting the association was attenuated (RR:1.15, 95%CI 0.81-1.65). While limited by imprecision and the potential for uncontrolled confounding, exploratory subgroup analysis suggested that, compared to Medicaid insurance, commercial insurance was associated with numerically higher risk of higher-priced treatment among patients treated by non-NCI designated providers (RR:1.53, 95%CI 1.14-2.04), and within both the physician office (RR:1.36, 95%CI 0.95-1.95) and hospital outpatient (RR:1.08, 95%CI 0.58-2.02) settings, but lower risk of high-priced treatment within NCI cancer centers (RR:0.39, 95%CI 0.22-0.71). Conclusions: Individuals with Medicaid vs. commercial insurance access high-priced treatments at a similar proportion after accounting for differences in case mix, morbidity, and SES. However, modification of the association by and academic setting, observed in exploratory subgroup analysis, suggests the possibility that insurance type may affect treatment for some patient groups. This study was limited by focus on a single state and the potential for residual confounding.
Integrating the 31-gene expression profile test into clinical decision-making to guide risk-aligned care decisions for patients with stage I-III cutaneous melanoma: NCI-SEER Analysis.
Authors: Hyams David , Petkov Valentina I. , Byun Jung S. , Martin Brian , Bailey Christine N. , Gastman Brian 
Published: June 2023

6601 Background: Cutaneous melanoma (CM) management depends on an estimate of recurrence risk or death. Because of recognized disease risk and drug efficacy, pembrolizumab immunotherapy has been approved for stage IIB-C patients. However, it is a treatment with potential financial and biological toxicities with 16% of patients experiencing severe treatment related adverse events. Improved risk stratification could identify individuals for whom more selective management would be appropriate. In collaboration with the NCI Surveillance, Epidemiology, and End Results (SEER) program, which includes information on patients tested with a 31-gene expression profile (31-GEP) test in an unbiased and unselected manner, we confirm the 31-GEP adds independent prognostic value in patients with stage I-III CM, and investigated whether the 31-GEP could identify stage IIB-C patients who might reasonably avoid adjuvant therapy while also identifying patients with stage I disease who might benefit from more aggressive diagnostic and/or therapeutic intervention. Methods: SEER registries linked CM cases diagnosed 2013-2018 to 31-GEP test results provided by Castle Biosciences (9,207 matched) using a registry trusted third party (honest broker), including subsets of stage IIB-IIC (n=775) and stage I (n=5,651) melanoma. The primary endpoint was melanoma-specific survival (MSS). The 31-GEP risk-groups included Class 1A (low), Classes 1B/2A (intermediate), and Class 2B (high). Each was compared using Kaplan-Meier analysis and the log-rank test. Results: In the overall cohort, patients with a Class 1A result had a significantly higher 5-year MSS than a Class 1B/2A or Class 2B result (97.4% vs. 93.5% vs. 83.3%, p<0.001). In multivariable analysis, a Class 2B result (HR=3.26, p<0.001) and positive lymph node status (HR=3.41, p<0.001) were the strongest predictors of diminished MSS. In stage IIB-C, 18.8% (146/775) of patients received a Class 1A result with an associated 5-year MSS of 89.1% (vs. Class 2B: 76.6%, p=0.001), despite the absence of pembrolizumab approval for the evaluated SEER population. Alternatively, 4.6% (260/5,651) of patients with stage I disease had a Class 2B result, with an associated 5-year MSS of 92.3% (vs. Class 1A: 98.0%, p<0.001). Conclusions: The SEER database allowed a non-biased analysis of a large cohort of patients with stage I-III CM who were tested with the 31-GEP. The 31-GEP identifies patients, including those with stage IIB-C disease, with favorable outcomes and could help physicians and patients decide whether to pursue adjuvant therapy. Moreover, the 31-GEP identities high-risk patients, including those with stage I disease, for whom more aggressive follow-up and early intervention should be considered.
Demographic disparities in lung cancer mortality and trends in the United States between 1999 and 2020: A population-based CDC database analysis.
Authors: Didier Alexander , Roof Logan , Stevenson James 
Published: June 2023

6603 Background: Lung cancer is the leading cause of cancer-related mortality in the US and is projected to account for 127,070 deaths in 2023. While the lung cancer mortality rate has been decreasing over the last decade, demographic disparities in mortality still exist. We sought to determine the impact of demographic factors on lung cancer mortality and trends in the US. Methods: We queried the Centers for Disease Control database for mortality statistics with an underlying cause of death of lung and bronchus cancer (ICD-10 code C34.0.x) between the years 1999 – 2020. Age-adjusted mortality rates (AAMR) were calculated per 100,000 deaths. We assessed the AAMR by demographic variables, including race (Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian or Pacific Islander, Non-Hispanic American Indian/Native American), geographic density (Urban, Suburban, Rural), sex, age (25-44, 45-64, 65+ years), and US Census Region. Temporal trends were evaluated using Joinpoint regression software. Average annual percent change (AAPC) was considered statistically significant if p < 0.05. Results: Between 1999 and 2020, lung cancer led to 3,380,830 deaths. The AAMR decreased by 42% from 55.1 to 31.8 with an associated AAPC of -2.6% (p < 0.001). In 1999, men had an AAMR of 76.8, almost twice as high as women, who had an AAMR of 40.2. These differences became less pronounced over time, and in 2020 men had an AAMR of 38.1, while women had an AAMR of 26.9. Between 1999 and 2020, rural populations experienced the highest AAMR at 52.3, and the slowest rate of decrease at -1.7% annually (p < 0.001), compared with urban populations who experienced the lowest AAMR of 40.6 and fastest AAPC decrease of -3.1% (p < 0.001). Non-Hispanic Black individuals experienced the highest AAMR at 49.8, with an annual decrease of -3.0% (p < 0.001), followed by Non-Hispanic White individuals who had an AAMR of 48.5 with an annual decrease of -2.5% (p < 0.001). Subgroup analysis demonstrated that Non-Hispanic Black men who resided in rural counties experienced the highest mortality at 60.2, with an associated decrease of -2.9% annually (p < 0.001). States in the 90th percentile of mortality included Arkansas and Kentucky; states in the 10th percentile of mortality included Hawaii and Utah. The West experienced the fastest decrease at -3.1% annually (p < 0.001), while the Midwest experienced the slowest decrease at -2.0% annually (p < 0.001). Conclusions: While lung cancer mortality has been decreasing since 1999, not all demographic groups have experienced the same rates of decline, and disparities in outcomes remain prevalent. Vulnerable subgroups may benefit from targeted interventions, such as tailored smoking cessation education and efforts to increase participation in clinical trials. Expansion of screening CT scans and telehealth platforms may also improve access to care for these populations.
Assessment of patient reported outcomes (PROs) completion patterns in patients with cancer: Examining real-world data.
Authors: Al-Antary Nada , Adjei Boakye Eric , Tam Samantha , Wilson Carl , Poisson Laila , Zatirka Theresa , Ryan Michael , Hirko Kelly , Chang Steven S. , Movsas Benjamin 
Published: June 2023

6609 Background: Use of Patient Reported Outcomes (PROs) in clinical practice plays a major role in improving care, quality of life, hospitalization, emergency room visits and consequently improving overall survival. However, robust information on real-time assessment of PROs in cancer patients is insufficient, as most available data are limited to specific populations enrolled in clinical trials. This thereby increases disparities among minorities of race, age, and socioeconomic status, creating a barrier between the benefits of PROs and these underserved populations. This study examines the response rates, patterns and characteristics of patients completing PROs in a tertiary cancer center. Methods: Patients with a cancer diagnosis and an oncologic provider visit at a tertiary cancer center were offered an opportunity to complete Patient Reported Outcome Measures (PROMs) between August 2020 and July 2022.We used the National Institute of Health’s computer adaptive tests Patient-Reported Outcomes Measurement Information System (PROMIS) instruments in pain interference, physical function, fatigue, and depression. Seven days prior to their clinical appointment, patients were assigned the PROMIS instruments in MyChart, then offered in-clinic completion with a tablet at check-in, if not completed online. A decision tree model was employed to assess the factors that may influence patients' PROMs completion (age, gender, race, marital status, insurance, stage, comorbidity score, and provider specialty and location). Results: A total of 8,535 patients were offered the PROMIS CAT version 2.0 instrument during the study period. The two most important factors that determine whether a patient completed PROMs in order of importance were provider specialty and patient race. Patients were more likely to complete PROMs if they had a visit with a provider in Radiation Oncology (RC) or Surgery specialty compared with Medicine or Supportive Oncology specialty (40.86% versus 29.68%). Among patients who had a visit with a provider in RC or Surgery specialty, there was a better chance of PROMs completion with White race compared to Black or Other races (45.83% versus 33.69%). Of those who had a visit with a provider in Medicine or Supportive Oncology specialty, there was a better chance of PROMs completion with Other or White races compared to Black race (32.40% versus 22.19%). Conclusions: We found that provider specialty and patient race were the most important factors influencing patients’ PROMs completion. In order to realize the full benefit of PROs in patient care, multilevel interventions can be employed to increase patient-provider utilization of PROs. Moreover, efforts should focus on a patient-centered design to address patient and provider barriers impeding PROs accessibility and completion.
Molecular diagnostic classification for cancers of unknown primary (CUP): Post-testing diagnosis and treatment impact analysis from real-world claims data.
Authors: Rivers Zachary , Huey Ryan W , Ben-Shachar Rotem , Avadhani Anjali Narayan , Taxter Timothy J. , Beauchamp Kyle A. , Hockenberry Adam J , Nimeiri Halla , Allam Joelle , Kalyan Aparna , Raghav Kanwal Pratap Singh , Lee Michael Sangmin 
Published: June 2023

6610 Background: Next-generation sequencing-based molecular diagnostic classifiers can help identify the tissue of origin for CUP, and enable the selection of site-specific therapies (as opposed to empiric chemotherapy) for this vulnerable population with high unmet need. NCCN guidelines do not endorse the use of tissue of origin classifiers as standard of care for CUP patients due to limited clinical evidence. Here, we linked results of a commercial molecular diagnostic classifier with claims data to understand how this test impacted patient care. Methods: We assessed de-identified claims data from the Komodo Healthcare Map (a database including provider visits, laboratory tests, procedures, imaging, and prescriptions) linked to the Tempus Tumor Origin (TO) test—a machine learning classifier that uses RNA-seq data to classify tumors into one of 68 histological subtypes. Eligible patients had pathologist-confirmed CUP and were classified as one of 9 subtypes (each having n>10). Impact was determined by identifying either one or more new diagnostic codes or new subtype-related medication claims following TO testing. Results: We analyzed data from 490 patients: 483 for the diagnosis analysis and 213 for the medication analysis (206 patients appear in both, due to differences in timing of diagnosis vs. medication claims). We found that post-TO testing, 49.9% (n=241) of patients had a diagnostic code change, 63.8% (n=136) had a treatment change, and 41% (n=85) had both. In total, 59.6% (n=292) of patients were impacted by the use of this classifier, with variation according to predicted subtype (Table). Cancer types with specific treatment options were more likely to have changes in diagnostic code or treatment. For CUP predicted as lung adenocarcinomas, 85% of the cases had a new subtype-aligned medication, and 78% were specifically placed on immunotherapy (IO) compared with 24% of overall patients. Conclusions: Using real-world claims data, we show that molecular diagnostic testing utilizing Tempus TO impacted care for a majority of CUP patients. This cohort will be followed to identify how TO testing decisions impact outcomes. [Table: see text]
Patient reported outcomes (PROs) collection modalities among patients diagnosed with cancer: Online vs in-person.
Authors: Adjei Boakye Eric , Wilson Carl , Zatirka Theresa , Tam Samantha , Al-Antary Nada , Nair Mrudula , Poisson Laila , Hirko Kelly , Ryan Michael , Chang Steven S. , Movsas Benjamin 
Published: June 2023

6612 Background: Patient reported outcomes (PRO) can be valuable clinical tools to embed the voice of patients into the clinical assessment. PROs provide important metrics to guide treatment decision making, improve quality of life, reduce acute care, and extend survival in cancer patients. Different modalities for collecting patient reported outcome measures (PROMs) exist (e.g., electronic, paper, telephone); yet little is known about factors associated with PROMs completion modality. More information on PROMs completion modality may determine addressable barriers. We sought to determine whether patients’ sociodemographic and clinical factors differed by completion modality. Methods: Beginning in 2021 all patients diagnosed with cancer who had a visit with an oncologic provider at a tertiary cancer center were assigned the National Institute of Health’s computer adaptive tests Patient-Reported Outcomes Measurement Information System (PROMIS) instruments in pain interference, physical function, fatigue, and depression through the MyChart patient portal 7 days prior to the visit. If this was not completed at the time of the visit, it was available for completion on a tablet during check-in. The outcome variable was completion modality defined as the method a patient used to complete their PROMs (MyChart vs. In-Person). Multivariable logistic regression model was used to estimate the association between patients’ sociodemographic and clinical factors (age, sex, race/ethnicity, marital status, insurance type, stage, provider specialty) and completion modality. Results: A total of 2915 patients completed PROMs, of which 54% completed using MyChart and 46% completed in-person. The average age of patients was 59.6 (SD=12.4) years, most were females (63.1%), White (69.4%) and married (59.2%). Compared to male patients, females were less likely to complete PROMs in-person (aOR=0.80, 0.67–0.95). However, patients were more likely to complete PROMs in-person if they were of Black race (aOR=1.85, 1.52–2.24) or Other race (aOR=1.48, 1.12–1.96) vs. White; single (aOR=1.30, 1.05–1.62) vs. married; or have Medicaid/other insurance (aOR=1.52, 1.15–2.01) vs. private insurance. Patients who had visits with a radiation oncology provider (aOR=1.50, 1.20–1.86) or surgical oncology provider (aOR=1.32, 1.07–1.62) were more likely to complete PROMs in-person compared to those who had visits with a medical oncology provider. Conclusions: Almost half of the patients completed PROMs in-person during check-in, which was unexpected in the context of trends toward mobile-based patient engagement. Patients in underserved populations were the most likely to complete PROMs in-person. Although offering PROMs remotely may be more efficient and allow monitoring between visits, offering an in-person option helps to capture PROs from underserved populations.
An application of extrapolating clinical data from single-arm trials to assess comparative effectiveness.
Authors: Suh Kangho , Ramsey Scott David , Kang Ashley , Ko Gilbert , Williamson Todd E. , Liao Nick , Sullivan Sean D. 
Published: June 2023

6614 Background: Single arm studies pose some challenges to determining comparative effectiveness of treatments. Such is the case with the tumor agnostic tropomyosin receptor kinase (TRK) inhibitors, larotrectinib and entrectinib. Larotrectinib and entrectinib are indicated for treatment of advanced cancer in patients (pts) with NTRK gene fusions. Lacking head-to-head trials, modeling methods are useful to estimate relative benefit. A direct naïve comparison, where single arms from separate trials are compared, can be used. Here, we estimate and compare long term benefit for pts receiving larotrectinib or entrectinib for NTRK gene fusion-positive non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) by extrapolating survival curves to generate expected life-years (LYs) and quality-adjusted life-years (QALYs). Methods: We used partitioned survival methods to forecast comparative effectiveness. Larotrectinib survival data were derived from an updated July 2021 analysis of 21 adults (≥18 years of age) with NSCLC and 15 pts with CRC (NCT02122913, NCT02637687, and NCT02576431). Survival data for entrectinib were derived from published literature. Extrapolation of results were necessary to estimate long-term outcomes since the mean follow-up was less than three years. Progression free survival and overall survival for both treatments were estimated using several parametric survival distributions (Exponential, Weibull, Log-logistic, and Log-normal), then selecting based on AIC and clinical plausibility. QALYs were estimated by adjusting the time spent in the pre progression and post progression health states by utility values from the literature. A discount rate of 3% was applied to LYs and QALYs. Model uncertainty was evaluated using one-way and probabilistic sensitivity analyses. Results: In the larotrectinib trials, response rates were 86% and 33% in NSCLC and CRC, respectively. Exponential curves were fit for both treatments. In the entrectinib trials, response rates were 70% in NSCLC and 29% in CRC. The larotrectinib model estimated 7.3 LYs (95% Credible Interval [CrI]: 3.3, 11.4) and 4.3 QALYs (95% CrI: 2.2, 6.5) in NSCLC and 1.7 LYs (95% CrI: 0.8, 3.2) and 1.3 QALYs (95% CrI: 0.7, 2.3) in CRC. The entrectinib model estimated 2.7 LYs (95% CrI: 1.7, 4.2) and 1.6 QALYs (95% CrI: 1.1, 2.3) in NSCLC and 0.5 LYs (95% CrI: 0.2, 1.6) and 0.4 QALYs (95% CrI: 0.1, 1.1). Compared to entrectinib, larotrectinib produced additional gains of 4.7 LYs and 2.7 QALYs in NSCLC and 1.1 LYs and 0.9 QALYs in CRC. Limitations include non-matched baseline differences in the trial populations. Conclusions: In the absence of head-to-head trials, comparative effectiveness can be assessed by extrapolating from single armed studies indicated for the same patient population. Larotrectinib improved survival and quality adjusted survival vs entrectinib. Additional studies are needed to confirm these findings.