bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance of a trained large language model to provide clinical trial recommendation in a head and neck cancer population.
Authors: Hung Tony , Kuperman Gilad , Sherman Eric Jeffrey , Ho Alan Loh , Wong Winston , Kriplani Anuja , Dunn Lara , Fetten James Vincent , Michel Loren S. , Baxi Shrujal S. , Weng Chunhua , Pfister David G. , Mao Jun J. 
Published: June 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 & 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 <diagnosis>, <cancer stage>, <treatment setting>, 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.
Association between financial fragility and treatment patterns in multiple myeloma.
Authors: Su Christopher , Banerjee Rahul , Li Li , Fedorenko Catherine R. , Cowan Andrew , Ramsey Scott David , Shankaran Veena 
Published: June 2024

11085 Background: Multiple myeloma (MM) drugs are associated with significant out-of-pocket costs. We hypothesize that patients with financial fragility (FF) may receive suboptimal MM treatment, as they might be less likely to be able to access and afford these medications compared to patients in good financial standing. We examined the association between FF and MM treatment by using a novel database that links patient-level credit records to cancer and claims data in Washington (WA) state. Methods: We conducted a retrospective analysis of newly diagnosed patients with MM (2012-2020) with Medicare and commercial health insurance, via a database linking WA cancer registry data (including Western WA SEER), health insurance claims, and depersonalized credit reports (TransUnion). All eligible patients received combination therapy with an oral immunomodulatory drug and at least one injectable medication within 6 months of diagnosis. FF was defined as evidence of at least one of the following in credit reporting around diagnosis (50 days before or after): charge-offs, collections, liens, foreclosures, repossessions, and bankruptcy in the last 12 months. Patients were categorized as receiving suboptimal treatment (the composite outcome) if they experienced at least one of the following: delay in time to treatment initiation (>40 days from diagnosis), treatment interruption (gap of >30 days without treatment in the first 6 months), or lack of autologous stem cell transplantation (ASCT, within the first 12 months). We performed chi-squared tests followed by multivariable logistic regressions, adjusting for age, sex, race (white/nonwhite), insurance type, and Area Deprivation Index (dichotomized; 1-5 and 6-10). Results: A total of 204 eligible patients (median age 69 years, 60% male, 91% White, and 60% Medicare-insured) met eligibility criteria. Of these, 39 patients (19%) had evidence of FF and 120 patients (59%) met the composite outcome. FF was significantly associated with the composite outcome of suboptimal treatment in bivariate analysis (74% vs. 55%, p=0.03) and adjusted multivariable analysis (OR 2.41 [95% CI: 1.05-5.51], p=0.04). As 182 (89%) patients received ASCT within 12 months in this cohort, we performed a sensitivity analysis by removing ASCT as a component outcome; in this analysis, 108 patients (53%) met the composite outcome. FF was associated with the composite outcome of suboptimal treatment in bivariate analysis (69% vs. 49%, p=0.02) and adjusted multivariable analysis (OR 2.31 [95% CI: 1.05-5.10], p=0.04). Conclusions: FF is significantly associated with suboptimal MM treatment patterns, including delay to treatment initiation, treatment interruption, and decreased receipt of ASCT. Larger prospective studies with more diverse demographic representation and incorporation of clinical data are in development to elucidate the mechanisms through which FF leads to suboptimal MM treatment.
Impact of lung biomarker testing on out-of-pocket costs for metastatic non-small cell lung cancer.
Authors: Gharzai Laila A. , Bell Sarah , Gupta Divya Myadam , Carlos Ruth C. 
Published: June 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.
Post-acute care facility utilization and outcomes among Medicare beneficiaries undergoing inpatient cancer surgery.
Authors: Lage Daniel E , Landrum Mary Beth , Melamed Alexander , Wright Alexi A. , Luster Spencer H , Wolf Robert E , Keating Nancy Lynn 
Published: June 2024

11088 Background: Patients with cancer using post-acute care facilities have poor outcomes, including delayed return home and increased health care utilization. However, little is known about post-acute care use and outcomes among patients with cancer undergoing surgery. Methods: We examined Medicare claims of 100% of fee-for-service Medicare beneficiaries from 2010-2022, to identify patients who underwent inpatient cancer-directed surgery and were thus eligible for a post-acute facility stay. We used billing codes within 3 days of hospital discharge to identify post-acute facility stays, defined as skilled nursing facility (SNF), long-term acute care hospital (LTACH), or inpatient rehabilitation facility (IRF) stays. We used logistic regression to identify patient sociodemographic and clinical factors associated with post-acute care facility use. We also compared hospital readmissions within 30 days and days at home (defined as days not in an acute or post-acute facility) in the 90 days after discharge by setting, using Chi-square and Wilcoxon rank sum tests. Results: We studied 1,637,792 Medicare beneficiaries who underwent inpatient cancer surgery from 2010 to 2022. About half (48.9%) were women; median age was 73.0 years. The most common cancer diagnoses were colorectal (28.4%), lung (14.4%), and prostate (12.1%), and 22.7% had a Charlson comorbidity index (CCI) of ≥3. Overall, 16.0% of patients were discharged to a post-acute care facility (11.4% SNF, 4.5% LTACH/IRF). Discharge to post-acute care was greater among patients who were aged ≥80 vs 65-69 (Adjusted Odds Ratio[AOR] 3.85, 95% Confidence Interval[CI] 3.79,3.90), had CCI ≥3 versus 0 (AOR 2.92, 95%CI 2.88,2.95), were dual-eligible (AOR 2.01, 95%CI 1.99,2.04), or had metastatic cancer (AOR 1.26, 95%CI 1.24,1.28). Patients undergoing brain or spinal surgeries for primary or metastatic cancers had highest odds of PAC facility utilization. Compared to those discharged home, patients discharged to post-acute care facilities had a higher 30-day hospital readmission rates (18.6% vs. 9.5%, p<0.0001), and fewer days at home in the 90 days after discharge from their index surgical admission (median 68 vs. 90 days, p<0.0001). Conclusions: Patients with cancer undergoing inpatient surgery who are older, have comorbidities, or have advanced disease have higher rates of post-acute care facility use, and such post-acute care is associated with higher hospital readmissions and fewer post-operative days at home. Further work is needed to improve pre-operative decision-making and optimization as well as to develop supportive care and rehabilitative interventions that can improve post-operative outcomes for patients who need post post-acute care.
Sociodemographic differences in suicide/intentional self-harm among cancer survivors versus the general United States population.
Authors: Semprini Jason , Adjei Boakye Eric , Barnes Justin Michael , Goldston David B , Graboyes Evan Michael , Osazuwa-Peters Nosayaba 
Published: June 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.
Patient-reported outcomes corresponding to most common symptomatic adverse events in lung cancer clinical trials.
Authors: Horodniceanu Erica , Datla Tejaswi , Larkins Erin A. , Singh Harpreet , Kluetz Paul Gustav , Bhatnagar Vishal 
Published: June 2024

11103 Background: FDA recommends assessing the most relevant expected symptomatic adverse events (AEs) with patient-reported outcomes (PROs) in cancer clinical trials (CTs) to complement traditional safety data. To inform selection of symptomatic AEs, we previously identified the most common (i.e., all-grade, clinician-reported in ≥20% of patients) symptomatic AEs from FDA-approved lung cancer product approvals from 2015 through 2021 based on CT monotherapy experimental arm safety data from US Prescribing Information (USPI). The present research aims to assess whether these CTs included PROs which adequately captured common symptomatic AEs. Methods: We assessed 30 CTs that supported lung cancer approvals from 2015 through 2021. We reviewed the study protocol, statistical analysis plan, and/or clinical study report to determine the PRO measures included. We matched symptoms included within PRO measures and items to the most common clinician-reported symptomatic AEs per the USPI to determine the extent to which the PROs and symptomatic AEs corresponded. Results: Twenty-three out of 30 (77%) CTs supporting non-small cell lung cancer (NSCLC) approvals included PRO(s). Nineteen included more than one PRO measure. The most frequently used PRO measures were the European Organisation for Research and Treatment of Cancer-Core Questionnaire (EORTC QLQ-C30; n=19), EORTC-Lung Cancer (EORTC QLQ-L13; n=17), EuroQoL 5-Dimension (EQ-5D; n=12), and selected items from the Patient-reported Outcomes version of the Common Terminology Criteria for Adverse Events item library (PRO-CTCAE; n=4). Nearly all (20 of 23) CTs included PRO items corresponding to more than half of the common symptomatic AEs reported. Symptomatic AEs for types of pain corresponded to general pain PRO items (e.g., “musculoskeletal pain” matched to EORTC item for “pain”). However, PRO items for specific types of pain were not often used. While important symptomatic AEs such as fatigue, diarrhea, nausea, and decreased appetite were often assessed by PROs, others including rash and edema were not usually captured. Ultimately, only 4 of 23 (17%) CTs with PROs included items that assessed all symptomatic AEs that occurred in ≥20% of patients. Of the 4 CTs which included an item library, 3 (75%) comprehensively assessed patient-reported symptomatic AEs, compared to 3 of 19 (16%) CTs that did not employ an item library for patient-reported symptom collection. Conclusions: Based on USPI safety data and PRO measures from CT monotherapy experimental arms of recently approved NSCLC cancer drugs, we determined that while the majority of CTs include PRO items for some relevant symptomatic AEs, other important expected symptomatic AEs (e.g., edema, rash, specific types of pain) were not measured. Future NSCLC CTs should include PRO items for common expected symptomatic AEs which is feasible using well-established PRO item libraries.
SPPADE symptom prevalence and severity in a diverse sample of patients living with metastatic cancers.
Authors: Pachman Deirdre R. , Ruddy Kathryn Jean , Grzegorczyk Veronica , Kroenke Kurt , Griffin Joan M. , Ridgeway Jennifer , Chlan Linda L. , Tofthagen Cindy , Leventakos Konstantinos , Strand Jacob J. , Austin Jessica , Smith Ashley Wilder , Mitchell Sandra A. , Herrin Jeph , Cheville Andrea L. 
Published: June 2024

11108 Background: Advances in cancer treatment have led to a growing population of patients living with metastatic cancer. Symptom burden for these patients is understudied. Our objective is to report the prevalence and severity of SPPADE symptoms (sleep disturbance, pain, physical function impairment, anxiety, depression, and low energy /fatigue) in a large cohort of patients with metastatic cancer. Methods: These data are drawn from the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) stepped wedge pragmatic trial evaluating a symptom management intervention for cancer patients (NCT03892967). This analysis was limited to those identified with metastatic disease using ICD- codes and a natural language processing algorithm prior to or within 30 days of their baseline symptom survey. Symptom severity was measured on a 0-10 scale with scores of 1-3 categorized as “mild”, 4-6 as “moderate”, and ≥7 as “severe.” A SPPADE composite score was calculated by summing six individual symptom scores and is an indicator of total symptom burden. We report descriptive statistics for each symptom by severity category. We also test for differences in SPPADE composite score by race, disability status, employment status, education and cancer type using Kruskal-Wallis tests. Results: 12,824 patients with metastatic disease completed a survey between March 2019 and January 2023. Discretized score distributions for the SPPADE symptoms are reported in the Table. The SPPADE composite mean score for the cohort was 17.6 (SD +/- 12.0). Significant differences were found in total symptom burden by sociodemographic and clinical characteristics ( p<0.01): Higher total symptom burden (Mean [SD])was experienced by individuals who were American Indian/Alaska Native (AI/AN) (23.8[13.5]) or African American (AA) (20.5[14.7]), disabled (24.0[12.8]) or unemployed (21.1[13.0]), and had a high school education (20.0[13.3]) or less than high school education (18.5[12.4]). There were also statistically significant differences ( p<0.01) in symptom burden across cancer types, with patients with metastatic lung cancer reporting the highest symptom burden (20.0[12.2]) and those with metastatic melanoma reporting the lowest (14.7[11.4]). Conclusions: SPPADE symptoms are common in patients with metastatic cancer, especially fatigue, sleep disturbance, and physical function impairment. Symptom burden is especially high in AI/AN, AA, disabled/unemployed, and less educated patients. Further research is needed to tailor symptom interventions among those living with metastatic disease. [Table: see text]
Association between comorbidity clusters and mortality in patients with cancer: A machine learning analysis of data from the US National Health and Nutrition Examination Survey 1999–2018.
Authors: Lam Chun Sing , Hua Rong , Loong Herbert H. , Ngan Chun-Kit , Cheung Yin Ting 
Published: June 2024

11132 Background: Multimorbidity is common among patients with cancer; however, research on cancer prognosis has predominantly focused on cancers in isolation. This study identified comorbidity clusters among patients with cancer using machine learning and examine their associations with survival outcomes in a nationally representative sample of the US. Methods: This study used 10 survey cycles of the National Health and Nutrition Examination Survey from 1999 to 2018. Participants aged ≥20 years with a self-reported history of cancer were included. Comorbidities were ascertained through self-reports and quantitative measurements. Machine learning techniques, including Bernoulli mixture model and partition-based methods, were used to identify comorbidity clusters. Cox proportional hazards models were used to analyze the associations between comorbidity clusters and mortality outcomes, including all-cause mortality and cause-specific mortality (cancer, cardiovascular disease [CVD], and respiratory diseases), adjusting for relevant covariates. Results: The study included 4,390 participants. Four comorbidity clusters were identified: Low Comorbidity, Metabolic, CVD, and Respiratory. After adjusting for confounders, participants in the Respiratory Cluster had the highest risk of all-cause mortality (adjusted hazard ratio[aHR]=1.62, 95% confidence interval [CI]=1.26-2.08, p<0.001), followed by the CVD Cluster (aHR=1.50, 95%CI = 1.26-1.80, p<0.001) and the Metabolic Cluster (aHR=1.15, 95%CI=1.02-1.29, p=0.03) compared to the Low Comorbidity Cluster. The Metabolic, CVD, and Respiratory clusters were associated with higher risks of cardiovascular disease-related mortality (aHR=1.48-3.05, p<0.003), but no significant differences in cancer mortality were observed among the clusters. The effects of comorbidity clusters on all-cause mortality were modified by income-to-poverty ratio ( p for interaction=0.04), diet quality ( p for interaction=0.02), time since cancer diagnosis ( p for interaction=0.009), and cancer prognosis ( p for interaction=0.005). Conclusions: High comorbidity burden clusters showed increased all-cause and CVD-related mortality. Moreover, diet quality and socioeconomic status modified these associations. Machine learning approaches can provide valuable insights into complex multimorbidity profiles in patients with cancer. Further research is needed to deepen understanding of the relationships between multimorbidity, mortality, and cancer-specific outcomes.