tbuckley/cabot-search huggingface dataset:
Impact of lung biomarker testing on out-of-pocket costs for metastatic non-small cell lung cancer.
Authors: Laila A. Gharzai, Sarah Bell, Divya Myadam Gupta, Ruth C. Carlos
Journal: Journal of Clinical Oncology (2024)

11087 Background: Biomarker testing in metastatic non-small lung cancer (NSCLC) is critical for appropriate selection of therapy options. Claims-based datasets offer real-world information on the use and cost of biomarker testing across the United States. Methods: We used de-identified administrative claims data from Optum’s Clinformatics Data Mart Database (CDM) from 2013 to 2021 to assess for trends in biomarker testing. Eligible patients were adults with two or more lung cancer diagnosis codes and with two or more claims of a secondary malignant neoplasm. Patients were excluded if they had another primary cancer or did not have continuous insurance coverage twelve months prior and six months after diagnosis. We assessed claims-based out-of-pocket (OOP) costs associated with testing and treatment. Descriptive statistics were used to assess biomarker testing rates, and multivariable analyses (MVA) were performed to assess factors associated with testing. Results: A total of 4377 patients with metastatic NSCLC were eligible (mean age 60 years (SD: 8.33 years), 49.6% female, 76.7% former smokers). Testing rates within two months of diagnosis increased from 58.15% in 2013 to 69.96% in 2021. On MVA, biomarker testing was associated with younger age, being non-smokers, living in Mountain geographic region, or having point-of-service insurance plans. Biomarker testing was associated with a median OOP cost of $98 (IQR: $43.87-$306.58). Patients who underwent biomarker testing had a median total OOP cost of all services within 6 months of diagnosis of $3560.20 (IQR: $1538.37-$6199.44) compared to $1979.58 (IQR: $725.75-$4003.06) for those who did not undergo biomarker testing. Conclusions: Using claims data, we find that 70% of patients with metastatic NSCLC undergo biomarker testing within two months of diagnosis with metastatic disease. Most patients undergo biomarker testing early in their treatment course (0-60 days), suggesting that testing is appropriately being obtained early on in their treatment course, but this testing is associated with substantially higher overall OOP costs to patients.
Cost-utility analysis of a supervised exercise program for patients with metastatic breast cancer in the PREFERABLE-EFFECT randomized controlled trial (RCT).
Authors: Anouk E. Hiensch, Aniek Schouten, Evelyn M. Monninkhof, Martina Schmidt, Dorothea Clauss, Mark Trevaskis, Helene Rundqvist, Joachim Wiskemann, Jana Mueller, Elsken Van Der Wall, Neil K Aaronson, Elzbieta Senkus-Konefka, Ander Urruticoechea, Wilhelm Bloch, Eva Zopf, Martijn M. Stuiver, Yvonne Wengstrom, Karen Steindorf, Miriam van der Meulen, Anne Maria May
Journal: Journal of Clinical Oncology (2024)

11121 Background: Exercise for patients with metastatic breast cancer (mBC) significantly reduced fatigue and improved quality of life (QoL) in the multinational PREFERABLE-EFFECT RCT (NCT04120298). Evidence on the cost-effectiveness of exercise for patients with mBC is lacking, albeit essential for implementation in clinical practice. In this study, we evaluated the cost-utility of an exercise program for patients with mBC in the EFFECT RCT. Methods: We conducted a cost-utility analysis using data from the 357 EFFECT trial participants (mean age 55 ± 11 yrs, 75% on 1 st /2 nd line treatment). These participants, from centers in NL, DE, SE, PL, ES, AU, were randomized to either a 9-month supervised exercise intervention (n=178) or a usual care control group (n=179). We used a societal perspective with a time-horizon of 9 months. Two different scenario analyses (SA) were used to determine intervention costs using a bottom-up micro-costing method: SA-1) 1-on-1 supervision; SA-2) 4-on-1 supervision. Data on healthcare resource use, productivity loss and QoL (converted to QALYs) were collected with country-adapted, self-report questionnaires, including the iMCQ, iPCQ and EQ-5D-5L, at 3-, 6- and 9-months post-baseline. Multiple imputation was performed, and bootstrapping was used to study uncertainty. Results: Compared to usual care, supervised exercise led to a QALY gain of 0.015 (95% CI: -0.02; 0.05) over a 9-month period, corresponding to an increase of 5.3 days in perfect health. The mean intervention costs were €1,696 in SA-1 and €609 in SA-2. The mean total cost differences (adjusted for center and line of treatment) were -€27 (SA-1), and -€1,112 (SA-2), in favor of the exercise group ( Table). The greatest cost-savings occurred in hospital costs, meaning that the exercise group had lower hospital costs compared to the control group (€4,430 vs €5,211). The probability of supervised exercise being cost effective at a willingness-to-pay threshold of €20.000 or €80.000 per QALY gained was 62% or 76% in SA-1, and 91% or 92% in SA-2, respectively. Conclusions: Exercise for patients with mBC is likely to be cost-effective when individually supervised and even dominant (greater cost-savings and more effective) when group-based. Based on our positive findings for both effectiveness and cost-effectiveness, we recommend supervised exercise to be reimbursed as supportive care during treatment for mBC. Clinical trial information: NCT04120298 . [Table: see text]
Sociodemographic differences in suicide/intentional self-harm among cancer survivors versus the general United States population.
Authors: Jason Semprini, Eric Adjei Boakye, Justin Michael Barnes, David B Goldston, Evan Michael Graboyes, Nosayaba Osazuwa-Peters
Journal: Journal of Clinical Oncology (2024)

11097 Background: Suicide is a national public health crisis, and one of the leading causes of death in the United States. Worse yet, the suicide mortality rate is at least double among cancer survivors compared to the general population. Suicide rates vary across the population, but it is unclear how sociodemographic factors explain differences in suicide rates among cancer survivors versus the general United States population. Methods: We analyzed two sets of population-based data. First, we retrieved suicide/self-harm mortality rates for the general population from the National Vital Statistics System. Next, we accessed cancer case data from the Surveillance, Epidemiology, End Results (SEER) program to estimate number of deaths due to suicide/self-harm among cancer patients, per 100,000 persons, with a 95% confidence interval. All analyses were restricted to contiguous states with full state representation in SEER (CA, CT, GA, ID, IA, KY, LA, NJ, NM, NY, TX, UT). We then compared subgroup differences by sex, race/ethnicity, age, and state. Results: In 2020, there were 15,813 deaths in the general population (12.1 suicide/100,000 persons; 95% CI: 11.9, 12.3), and 1,127 among cancer survivors (22.1 suicide/100,000 persons; 95% CI: 22.0, 22.1) due to suicide/intentional self-harm. Among racial/ethnic groups, the largest difference was found in the non-Hispanic White cancer survivors, with 10.4 more suicide/100,000 persons vs. White individuals in the general population. In contrast, two racial minority groups had higher suicide mortality rate in the general population vs. cancer survivors: Non-Hispanic Black individuals (general population = 7.4 suicide/100,000 persons vs cancer survivors = 6.9 suicide/100,000 persons), and Native American Indian (general population = 16.6 suicide/100,000 persons vs. cancer survivors = 11.3 suicide/100,000 persons). Among females, suicide mortality rate was slightly higher by 1.3 suicide/100,000 persons in cancer survivors vs. general population, and much higher among male cancer survivors vs. general population (19.9 suicide/100,000 persons more). For age groups, the lowest difference in suicide mortality between cancer survivors vs. general population was in the 65-84 age group (5.1 more suicide/100,000 persons). Across all other age groups, suicide rates were 11.9 to 14.4 higher in cancer survivors vs. the general population. Across the 12 states in our analysis, the smallest difference was in Iowa (1.9 more suicide/100,000 persons), and the largest difference was in Idaho (27.0 more suicide/100,000 persons). Conclusions: In most subgroups, people diagnosed with cancer appear to be at greater risk of death from suicide/intentional self-harm than the general population. As the number of people living with cancer continues to grow, policies increasing the quality of life for cancer patients are warranted.
Adopting combination machine learning models could reduce hospital length of stay for oncology patients.
Authors: Srisairam Achuthan, Shaikh Salamatullah, Jeffrey A. Scott, Karna Sheth, Logan Lantrip, Drew Lilienthal, Joseph Ricca, Scott C. Pepper, Rishov Chatterjee
Journal: Journal of Clinical Oncology (2024)

11084 Background: Integra Connect previously created in-patient (IP) admission prediction model based on OCM data. One of the challenges for practices in a value-based care (VBC) program is to provide continuous care-coordination during and after an IP admission. Our objective is to show how adopting combination machine learning (ML) models can predict IP length of stay (LOS). Potential benefits include an overall reduction in LOS which could minimize the risk of hospital acquired conditions. Methods: ML models were trained on 5 major cancer types (Lung Cancer, Multiple Myeloma, Lymphoma, Small Intestine / Colorectal Cancer, High-risk Breast Cancer') from 7 OCM practices of PP4-PP9 data, excluding surgery IP admission. The top 5 cancer types accounted for ~50% of total in-patient admissions and ~50% of total LOS in days. The IP admissions were divided into 4 major cohorts in terms of LOS in days (1-3: class 1, 4-8: class 2, 9-15: class 3, and more than 15: class 4). To reduce the overall LOS, we adopted two ML models: 1) To classify LOS, a multi-classification model (Model1, an eXtreme Gradient Boosted Trees Classifier) and 2) To predict the LOS for classes 1-3, a regression-based model (Model2, an eXtreme Gradient Boosted Trees Regressor with Early Stopping). Class 4 cohort had a wide range (16- 90+ days) for LOS and therefore was excluded from Model2. The other three cohorts belonged to Q1, IQR, and Q4, respectively. Model1 was trained on 4,280 randomly selected sample IP admissions to balance each class and Model2 was trained on 19,636 IP admissions, both with 102 features. Results: The models were tested on PP10-PP11 claims excluding 0 days LOS. Model1 predicted 296 (3,945) IP admissions in class 4 of which 88 were true positives. Assuming at least a 10% reduction in associated total LOS translated to lower LOS of 1-3% for 5 (7) practices. The remaining 3,649 IP admissions were used to predict LOS by Model2. The difference between actual LOS and Predicted LOS was found as 18%-24%. To address model prediction errors, we defined the target LOS to be predicted LOS with an upper bound of at least 10%. This led to reductions of 6% -16% across practices. Finally, combining the results of both ML models we determined that the potential to lower LOS was at least 6% and at most 19%. Conclusions: Our ML models identified opportunities to reduce LOS across multiple OCM cancer practices for 5 major cancer types. We also identified a cohort of patients with critical condition (class 4) that is vital for practice transformation initiatives. These identified reduction in LOS for oncology patients provides cost reduction and quality improvement opportunities in VBC programs. In the future, more opportunities to lower LOS will be explored with a re-admission prediction model.
Association of characteristics of cancer, patient, and county with telemedicine services use: Impact of the waiver of reimbursement restrictions on telemedicine.
Authors: Hyo Jung Tak, Andrew M Goldsweig, Min Sok Lee, Dana Verhoeven, Ronnie D Horner
Journal: Journal of Clinical Oncology (2024)

11058 Background: Telemedicine services have great potential to improve access to care of cancer patients who are vulnerable in health status and may experience financial hardship due to the cost of their care. However, very little is known about how the Centers for Medicare and Medicaid Services’ waiver of reimbursement restrictions on telemedicine and characteristics of cancer affected their use of telemedicine services. We examined the changes in frequency of telemedicine use before and after the waiver and, after the waiver, the determinants of telemedicine use across medical provider types. Methods: We used the 5% Surveillance, Epidemiology and End Results registry file linked to Medicare data among patients who had a newly diagnosed cancer in 2019 and were enrolled in Medicare Parts A and B during 2019-2020, and Area Health Resources File. Our outcomes were binary variables of telemedicine services use in outpatient (OP), office-based clinics (OB), and emergency department (ED), respectively, and for detailed provider types in OB. Primary independent variables included the ten most frequently observed cancer sites (reference: all other sites), sequence of cancer, stage of diagnosis, and quarter of diagnosis in 2019. Analysis was by a multilevel mixed effects logistic regression model, adjusting characteristics of cancer, patient and county. Results: Among eligible patients (n = 14,226), telemedicine services use for all provider types increased from 4.3% in pre-waiver period to 40.3% (5.2% in OP, 39.3% in OB, and 0.2% in ED) in post-waiver period. Telemedicine services use varied by primary cancer site, but was higher for those with a cancer recurrence (odds ratio [OR] 1.36, 95% confidence interval [CI] 1.16-1.60) and advanced cancer stage (OR 1.24, 95% CI 1.03-1.48). For visits to oncologists, cancer characteristics such as recurrence (OR 1.67, 95% CI 1.34-2.08), advanced stage (OR 5.52, 95% CI 4.12-7.40) and diagnosis in the last quarter in 2019 (OR 1.39, 95% CI 1.20-1.61) were associated with greater use of telemedicine. Patients of older age and minority race/ethnicity and in non-Northeast regions were less likely to use telemedicine, while women, patients with dual eligibility for Medicaid, and those living in a county with high median household income and high availability of hospital beds were more likely to use telemedicine. Conclusions: Cancer patients used telemedicine services more frequently after the waiver and their cancer characteristics were most strongly associated with that use, implying their greater need for, and benefit from, telemedicine. Disparities in telemedicine use related to individual and county-level socio-demographics and medical supply suggest health policy changes to improve equitable access to telemedicine and efficient cancer care planning.
Performance of a trained large language model to provide clinical trial recommendation in a head and neck cancer population.
Authors: Tony Hung, Gilad Kuperman, Eric Jeffrey Sherman, Alan Loh Ho, Winston Wong, Anuja Kriplani, Lara Dunn, James Vincent Fetten, Loren S. Michel, Shrujal S. Baxi, Chunhua Weng, David G. Pfister, Jun J. Mao
Journal: Journal of Clinical Oncology (2024)

11081 Background: Chatbots based on large language model (LLM) have demonstrated ability to answer oncology exam questions; however, leveraging LLM in medical-decision support have not yet demonstrated suitable performance in oncology practice. We evaluated the performance of a trained a LLM, GPT-4, to recommend appropriate clinical trials for a head &amp; neck (HN) cancer population. Methods: In 2022, we developed an artificial intelligence powered clinical trial management mobile app, LookUpTrials, and demonstrated promising user engagement among oncologists. Using LookUpTrials database, we applied direct preference optimization to train GPT-4 as an in-app assistant to LookUpTrials. From Nov 7 to Dec 19, 2023, we collected consecutive, new patient cases and their respective clinical trial recommendations from oncologists in the HN medical oncology service at Memorial Sloan Kettering Cancer Center. Cases were categorized by diagnosis, cancer stage, treatment setting, and physician recommendation on clinical trials. Trained GPT-4 is prompted using a semi-structured template: “Given patient with a &lt;diagnosis&gt;, &lt;cancer stage&gt;, &lt;treatment setting&gt;, what are possible clinical trials?” Physician recommendations were compared with trained GPT-4 responses. We analyzed the performance of GPT-4 based on its response precision (positive predictive value), recall (sensitivity), and F1 score (harmonic mean of precision and recall). Results: We analyzed 178 patient cases, mean age 65.6 (SD 13.9), primarily male (75%) with local/locally advanced (68%) HN (61%), thyroid (16%), skin (9%), or salivary (8%) cancers. Majority were treated in the definitive setting with combined modality therapy (42%) and modest proportion were treated under clinical trials (10%). Overall, trained GPT-4 achieved a moderate performance matching physician clinical trial recommendations with 63% precision and 100% recall (F1 score 0.77), narrowing a total list of 56 HN clinical trials to a range of 0-4 relevant trials per patient case (mean 1, SD 1.2). Comparatively, performance of our trained GPT-4 exceeded historic performance of untrained LLMs to provide oncology treatment recommendation by 4-20 folds (F1 score 0.04 - 0.19). Conclusions: This proof-of-concept study demonstrated that trained LLM can achieve moderate performance in matching physician clinical trial recommendation in HN oncology. Our results suggest the potential of embedding trained LLM into oncology workflow to aid clinical trial search and accelerate clinical trial accrual. Future research is needed to optimize precision of trained LLM and to assess whether trained LLM may be a scalable solution to enhance the diversity and equity of clinical trial participation.
In search of strategies to mitigate financial toxicity in survivorship care: A survey of 347 head and neck cancer survivors.
Authors: Ya-Chen Tina Shih, Eden Brauer, Patricia A. Ganz
Journal: Journal of Clinical Oncology (2024)

11060 Background: High costs of cancer treatment exert substantial financial toxicity for patients undergoing treatment. Growing literature suggests lasting financial toxicity (FT) after treatment completion. This calls for the development and integration of mitigating strategies in survivorship care to reduce FT; thereby improving the overall well-being of survivors. Methods: Cross-sectional survey data from 347 head and neck cancer survivors were analyzed to explore the relationship between FT and potential targets for intervention. We measured FT using the COmprehensive Score for financial Toxicity (COST, v1), and defined severe, moderate, and mild-to-no FT as having COST score ≤13, 14 to 25, and ≥26, respectively. The survey also collected information on demographics, quality of life, work and activity impairment, and cancer care coordination (CCC) communication and navigation subscales. We conducted logistic regression to examine which COST item is most predictive of severe FT, and mediation analysis using structural equation model to explore potential targets for interventions to reduce FT. Results: The average age of respondents was 65.5 yrs (SD=11.3). 334 completed COST; 6.3%, 22.2% reported severe and moderate FT, respectively. Study cohort included 20% diagnosed within 2 yrs, 45% 3-4 yrs and 35% ≥5 yrs; the proportion of patients reporting severe FT did not decrease by year since diagnosis (6%, 5.3%, and 7.7%, respectively, P=0.73). Of the 11 items in COST, financial stress was most predictive of severe FT. Survivors who rated financial stress as “very much” or “quite a bit” were 92.8 (P=0.02) times more likely to have severe FT. Higher financial stress was significantly associated with being &lt; age 65, having higher comorbidity score, lower CCC-navigation score, higher activity impairment; navigation and activity impairment are potentially actionable. None of these four variables were statistically significantly associated with severe FT. Mediation analysis showed financial stress has strong mediation effect on severe FT, and survivors with low CCC-navigation scores and high activity impairment were 3.2 times (95% CI: 1.3 – 7.8; P=0.01) and 2.5 (95% CI: 1.1 – 5.2; P=0.02) more likely, respectively, to report high financial stress. Survivors with high financial stress were significantly more likely to be interested or very interested in receiving resources covering financial issues compared to those without (63.4% vs 17.8%, P&lt;0.01). Conclusions: Survivorship care that incorporates interventions designed to improve coordination and navigation and decrease activity impairment can potentially mitigate FT for cancer survivors through reducing their financial stress. The Centers for Medicare and Medicaid Services began paying for navigation services in 2024, opening the opportunity to integrate these interventions into covered navigation services.
Enhancing the patient journey to clinical trial enrollment with navigation to optimize accrual: A pilot study for a pragmatic multicentre, stepped wedge, cluster randomized controlled trial (The CTN Pilot Trial).
Authors: Emmanuel Akingbade, Rija Fatima, Megan Delisle, Rhonda Abdel-Nabi, Mahmoud Hossami, Kayla Touma, Renee Nassar, Depen Sharma, Anthony Luginaah, Caroline M. Hamm
Journal: Journal of Clinical Oncology (2024)

11093 Background: Clinical trials are essential to the advancement of clinical therapies, yet accrual rates remain disappointing. Multiple challenges lead to less than 5% of cancer patients enrolled onto clinical trials. The Clinical Trials Navigator (CTN) program was established to assist patients and health care professionals identify appropriate clinical trials for patients. Methods: Between March 2019 to January 2024, a novel navigator-assisted clinical trials search program was offered to Canadian patients. Three non-medical navigators were trained to receive referrals, review medical information, and search five different clinical trial search engines. Eligibility criteria was scrutinized. A second review of the clinical trial list was conducted by two physicians. The final curated list of clinical trials was provided to patients and their oncologist. Results: A total of 373 patients were referred to the CTN program during the study period. A unique clinical trial search was performed for each patient yielding a median of only one potentially eligible trial per patient. Clinical trial enrolment occurred in 3.2% of patients in our database which translates to a 19% rate of successful enrolment of those referred to a trial by the CTN. Most patients (78%) were referred to clinical trial sites that conducted more than 100 clinical trials at any time. Compared to the Canadian cancer statistics, lung, lymphoma, pancreatic and brain cancers were overrepresented in referrals to the CTN program while prostate cancer was underrepresented. Type of cancer played a significant role in the likelihood of a successful referral (p &lt; 0.01). Lung cancer was the most frequently reported cancer that resulted in referrals and breast cancer showed a lower frequency of referrals. The cancer type, stage and number of lines of prior therapy were not significantly associated with the patient enrollment onto a clinical trial. An increase in survival of referred patients from last analysis from 3.0 months to 5.3 months. Conclusions: The CTN program is a successful tool to identify clinical trials for cancer patients and can improve clinical trial accrual, as almost one fifth of patients (19%) who were referred to a clinical trial were enrolled. Ongoing iterative changes to the program to improve these metrics are underway and efforts to improve implementation of the CTN program across Canada are ongoing.