bluuebunny/crossref_metadata_2025 huggingface dataset:
Patient-reported fatigue and risk of treatment-related adverse events (AEs) in patients receiving systemic therapy in cancer clinical trials.
Authors: Unger Joseph M. , Vaidya Riha , Fisch Michael Jordan , Jones Salene M. W. , Henry Norah Lynn , Hershman Dawn L.
Published: June 2024

11015

Background: Fatigue in patients with cancer is common and negatively impacts quality of life. Patient-reported outcomes (PROs) assess a patient’s direct report of their symptoms and well-being; research has highlighted their potential to predict cancer outcomes. We examined whether fatigue prior to treatment was associated with AEs. Methods: We analyzed 18 phase II and III cancer treatment clinical trials with baseline fatigue data conducted by SWOG between 1988-2018. The Common Terminology Criteria for Adverse Events was used. Symptomatic AEs were defined as those aligned with the NCI’s PRO-CTCAE; laboratory-based or measurable AEs were designated as objective (hematologic v nonhematologic). Thirteen symptomatic and 14 objective AE categories were examined. Fatigue was derived from multiple scales (e.g., FACT, EORTC QLQ-C30 and PROMIS-Fatigue 7a). Each measure was mapped to a 5-point Likert scale. Binary categories were compared (e.g., < some vs. > = some fatigue). Generalized estimating equations were used with a binomial distribution and logit link, adjusting for age, sex, race, and BMI, with clustering by study. Results: We examined N = 8,040 patients receiving systemic therapy. Among 18 trials (prostate, 5; lung, 3; colorectal, 2; lymphoma, 2; breast, 2; bladder, 1; melanoma, 1; ovarian, 1, pancreas, 1), n = 108,059 AEs were examined. Patient fatigue was present at levels of “a little” or greater in 75.8%, “some” or greater in 42.8%, and “quite a lot” or “very much” in 17.7%. Patients with some or greater fatigue were nearly twice as likely to have severe or worse toxicity (OR = 1.92, 95% CI, 1.53-2.40, p < .001) or life-threatening toxicity (OR = 1.93, 95% CI, 1.44-2.59, p < .001) and nearly threefold more likely to have fatal toxicity (OR = 2.76, 95% CI, 1.37-5.53, p = .004). Similar patterns were seen at a threshold of “quite a lot”. A dose response pattern was evident; patients reporting quite a lot/very much compared to no fatigue were > 5 times more likely to experience fatal toxicity (OR = 5.63, p = .002). These patterns were consistent in symptomatic, objective hematologic, and objective non-hematologic AEs. Conclusions: Baseline fatigue was highly predictive of subsequent risk of AEs. These findings suggest that in an era of precision medicine, patient-reported fatigue may be an important component of determining toxicity risk and could aid in treatment decision making.[Table: see text]
Post-market study requirements and associations with timely submission and regulatory action for oncology drugs receiving accelerated approval, 2011-2023.
Authors: Kurian Martin , Ferrell William J. , Perez Ernesto Ulloa , Hubbard Rebecca , Joffe Steven , Lynch Holly Fernandez , Mamtani Ronac , Parikh Ravi Bharat
Published: June 2024

11028

Background: For oncology drugs receiving accelerated approval (AA), the Food and Drug Administration (FDA) enforces Postmarketing Requirements (PMRs) to verify clinical benefit prior to regular approval. Although previous work has characterized the rigor and timeliness of confirmatory studies, the association between specificity and scope of PMR statements and the timeliness of PMR submission and ultimate approval status is not well understood. Methods: We identified 161 oncology indications granted AA between January 2011 and July 2023 using FDA’s Cancer AA database. We linked indications to 181 corresponding PMR statements using the Drugs@FDA database. We analyzed PMR statement scope and specificity based on the presence or requirement of eight characteristics: target population, comparator (active or placebo-controlled), randomization, multicenter trial, blinding (double-blind or open-label), enrollment target, follow-up duration, and endpoints. We used chi-square and t-tests to analyze associations between PMR characteristics and two primary outcomes: indication approval status (regular vs. withdrawn) and PMR timeliness (on time vs. late). We defined “late” submissions as PMRs submitted or withdrawals occurring after an expected PMR Final Report submission date. Ongoing PMRs were excluded from the timeliness analysis unless expected dates occurred before August 2023. Results: Among 181PMR statements, 98% percent specified target population, 81% endpoint (44% overall survival), 63% use of randomization, 54% comparator (40% active; 14% placebo-controlled), 30% follow-up duration 26% enrollment targets, 24% multicenter trial, and 13% double-blinding (and 8% open-label). Among AAs converted to regular approval, 82% of PMRs were submitted on time vs. 24% on-time for withdrawn AAs (p=0.001). Regular approval PMRs were submitted 10.2 months ahead of expected dates, whereas withdrawn AAs were submitted 14.5 months behind expected dates (p<0.001). Compared late PMR submissions, on-time PMR submissions had lower enrollment (400 vs. 665, p=0.01), less blinding (19% vs. 40%, p=0.03), more frequent use of a single trial for both AA and PMR (38% vs. 8%, p=0.001), and primary endpoints other than overall or progression-free survival (36% vs. 5%, p<0.001). Compared to late PMR submissions, on-time PMR submissions more often followed PMR statements that specified follow-up duration (39% vs. 13%, p=0.006) and non-survival primary endpoints (30% vs. 8%, p=0.01). Conclusions: There is marked variability in the contents of PMR statements for oncology AA drugs. Several characteristics of PMRs – including smaller enrollment size, surrogate endpoints, and single trials for AA and PMR – are associated with timely submission. These findings inform the specificity and scope of future FDA PMRs for oncology AA indications.
Association of characteristics of cancer, patient, and county with telemedicine services use: Impact of the waiver of reimbursement restrictions on telemedicine.
Authors: Tak Hyo Jung , Goldsweig Andrew M , Lee Min Sok , Verhoeven Dana , Horner Ronnie D
Published: June 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.
Inpatient burden and clinical outcomes of cytokine release syndrome in patients with cancer: A National Inpatient Sample study.
Authors: Makhani Sarah , Azhar Waqas , Shah Ejaz , Hussain Mohammad Junaid , Goel Ruchika
Published: June 2024

11067

Background: Cytokine release syndrome (CRS) is a potentially life-threatening complication commonly seen in patients receiving chimeric antigen receptor (CAR)-T cell therapy, allogeneic stem cell transplant, or immunotherapies. This study aims to evaluate the inpatient burden outcomes of CRS leading to hospitalization in patients diagnosed with cancer. Methods: Hospitalizations for cytokine release syndrome (CRS) were identified using ICD-10 coding from the 2020 National Inpatient Sample (NIS), the largest all-payer inpatient database in the US. Only CRS patients with a co-diagnosis of cancer (identified using the Clinical Classification Software (CCS) coding) were included in this study. The underlying severity of illness index was used to verify CRS grading. Sampling weights were applied to generate nationally representative estimates. Results: In 2020, there were 4,765 hospitalizations coded with CRS. 27.2% of these admissions had a co-diagnosis of cancer (n=1,295) and were included in the final analysis. The top co-diagnoses in the patients with CRS without cancer were COVID-19 infection and sepsis. Hematologic malignancies had the highest incidence amongst cancer types (75.7%), the distribution was as follows: 29.6% with diffuse large B-cell lymphoma, 19.9% with acute lymphoblastic leukemia, 12.8% with multiple myeloma, 12.2% with acute myeloid leukemia, and 7.7% with mantle cell lymphoma. The most common procedures in oncology patients hospitalized with CRS were CAR-T cell therapy (29.3%), chemotherapy (28.6%), stem cell transplant (8.5%), or Blinatumomab bispecific antibody (5.4%). 11.4% of patients with CRS received tocilizumab. Of those that received tocilizumab, 10% were CRS grade 2, 56.7% were grade 3, 33.3% were grade 4. 51.7% of tocilizumab was given in the first 48 hours of admission. A minority of CRS patients required critical care interventions: 12.0% requiring intubation, 5.8% requiring vasopressor support, and 2.7% received dialysis. Overall, 10.4% of all oncology admissions with CRS resulted in death during hospitalization (n=135). Of these deaths, 92.6% were in the extreme severity of illness and 63.0% were CRS grade 4. Those with CRS who died during hospitalization had a significantly longer length of stay (24.6 vs 13.6 days, p<0.001). The overall median hospital charges of a CRS-related admission were $531,692. Conclusions: This study represents the inpatient burden of CRS-related admissions in patients diagnosed with cancer. CRS admissions are commonly seen in patients with hematologic malignancies, those undergoing CAR-T cell or immunotherapies (bispecific antibodies). CRS poses a significant healthcare burden, with average hospital charges being greater than 0.5 million US dollars per admission. About 1 in 10 patients with CRS died during hospitalization with higher mortality likely with severe CRS grades and longer length of stay.
Health-related quality of life (HRQoL) and symptoms in LIBRETTO-431 patients with RET fusion-positive advanced non-small-cell lung cancer (NSCLC).
Authors: Zhou Caicun , Novello Silvia , Garrido Pilar , Dooms Christophe Alfons , Alatorre Alexander Jorge Arturo , Reinmuth Niels , Gilligan Adrienne M. , Payakachat Nalin , Cocks Kim , Worthy Gill , Goto Koichi
Published: June 2024

11068

Background: LIBRETTO-431 (NCT04194944), a randomized phase 3 trial, compared first-line selpercatinib to platinum-based chemotherapy +/- pembrolizumab (pembro). Selpercatinib, a highly selective and potent CNS penetrant RET inhibitor, is approved for the treatment of advanced RET fusion+ NSCLC. This analysis reported NSCLC symptoms and HRQoL from LIBRETTO-431. Methods: This analysis used data (cut-off date: May 01, 2023) from the intent-to-treat (ITT) pembro subpopulation [selpercatinib (n=129); chemotherapy + pembro (control, n=83)] to assess time to confirmed deterioration (TTCD) of NSCLC symptoms (cough, dyspnea, pain, fatigue, poor appetite using NSCLC-Symptom Assessment Questionnaire [SAQ]). TTCD of NSCLC symptoms were defined as time from randomization to the first score that met the pre-specified meaningful within-patient change thresholds, confirmed at next assessment. TTCD was compared between treatment arms using log-rank test and Cox proportional hazards model. Changes of NSCLC-SAQ total score (meaningful important difference [MID] ≥2 points) and HRQoL (using EORTC QLQ-C30 Physical Function (MID ≥6 points) and Global Health Status (GHS)/QoL (MID ≥5 points)) up to 1 year were evaluated and compared between the arms using a growth curve model and mixed model for repeated measures. Results: Selpercatinib significantly (p <.05) delayed TTCD of all individual symptoms with hazard ratio ranging from 0.41 (cough and pain) to 0.57 (dyspnea), compared to control (Table). Selpercatinib also showed a significant and clinically meaningful difference in the mean NSCLC-SAQ total score (difference = -2.0, p <.001) and physical function (difference = 8.1, p =.003) at 1 year, compared to control. GHS/QoL was improved in both arms with no difference in the mean scores between the arms at 1 year. Conclusions: Selpercatinib significantly delayed TTCD of NSCLC symptoms and improved physical function compared to control in this patient population after 1 year of treatment. The findings were consistent with the favorable efficacy of selpercatinib compared with platinum-based chemotherapy + pembro and further support 1L use of selpercatinib in this population. [Table: see text]
Access barriers and cost of varenicline for smoking cessation: A national comparative analysis of Medicare standalone part D and Medicare Advantage plan.
Authors: Jiang Changchuan , Wang Qian , Hong Arthur S , Mullins Megan A , Xing Jiazhang , Wang Tianci , Hu Xin , Nipp Ryan David , Yabroff Robin , Liao Joshua , Han Xuesong , Shih Ya-Chen Tina
Published: June 2024

11080

Background: Smoking cessation is a critical part of cancer prevention and control. Oral varenicline is the most cost-effective medication for smoking cessation, yet seniors enrolled in Medicare Part D Prescription Drug Plans (PDPs) can potentially face costs and access barriers to varenicline in the forms of prior authorization and quantity limits. Although over half of Medicare beneficiaries are now insured and receiving drug benefits through Medicare Advantage, it remains unclear how costs and access barriers compare between PDPs and MA drug plans (MADPs). This study compared plan-level costs and prevalence of prior authorization and quantity limit policies between PDPs and MADPs for oral varenicline. Methods: We used 2023 Q3 MA and PDP data from the Center for Medicare and Medicaid Services, excluding employer-sponsored and Supplemental Need Plans (including dual-eligible plans) and plans with <10 enrollees. We focused on two generic varenicline forms: 0.5mg and 1mg tablets. To compare medication quantities, we calculated the plan-level median (IQR) cost for 30-day supply, based on the average unit cost. Price benchmarks were defined based on average wholesale acquisition cost (WAC) from Micromedex Red Book. T-test and Chi-squared tests were used to compare costs and prevalence of prior authorization and quantity limits. Results: Across 3512 MADPs and 813 PDPs, median costs for a 30-day supply of varenicline were similar (0.5mg tab: $375.32 vs. $376.87, p=0.64; 1mg: $371.88 vs. $376.87, p=0.49, respectively). Median costs for both PDPs and MAPDs were higher than benchmarks; p<0.01 for all). For both varenicline dosages, MAPDs had higher rates of prior authorization (27.1% vs 16.2%, p<0.01) and quantity limits (48.0% vs 30.4%, p<0.01) than PDPs. Conclusions: Varenicline prices in both PDPs and MAPDs were much higher than benchmarks. Compared to PDPs, MAPDs used more prior authorization and quantity limit to control cost. This can create access barriers to this cost-effective medication, burdening patients, physicians, health systems, and pharmacies. [Table: see text]
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 , , , 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.
Time-to-diagnosis and peri-diagnostic healthcare utilization between screen- and non-screen detected cancers: Evidence from SEER-Medicare.
Authors: Cao Xiting , Brouwer Elizabeth DeYoung , Chen Yilin , Ramsey Scott David , Tyson Chris , Li Kevin , Rego Seema P , Choudhry Omair A , Veenstra David L , Ebbert Jon , Beer Tomasz M.
Published: June 2024

11135

Background: Cancer screening programs can improve outcomes and may reduce treatment cost and utilization by shifting cancers to earlier, more curable stages at diagnosis (Dx). Few studies have evaluated the impact of screening on outcomes prior to diagnosis. Such information is needed to better understand the potential impact of newer blood-based multicancer early detection technologies. Methods: We conducted a retrospective cohort study using SEER cancer registry data linked with Medicare claims. Patients with a breast cancer (BC) or colorectal cancer (CRC) Dx between 2010-2019 were included if they were 65-74 years old at Dx, continuously enrolled in Medicare Parts A&B, had a Charlson Comorbidity Index of ≤2, and no cancer for ≥12 months prior to Dx. Those enrolled in an HMO or with high genetic risk for BC (in BC cohort) were excluded. Patients were assigned to screening groups prior to Dx; screening was defined as ≥1 screening procedure in the 6 months prior to Dx using CPT/HCPCS codes. Screening included FIT/FOBT, sigmoidoscopy, colonoscopy, CT colonography, mt-sDNA (CRC), and mammography (BC). Screened and not screened groups were compared for statistical significance using chi-squared for categorical variables, Wilcoxon rank-sum test for continuous variables. Results: Screening procedures were more prevalent in the BC cohort (68%) than the CRC cohort (44%) (table). Both screened subgroups (vs non-screened subgroups) had significantly lower proportions of patients diagnosed at stage 4 (2% vs 10% BC, 16% vs 27% CRC) and shorter times from first screening or imaging procedure to confirmed diagnosis (BC: 33 vs 227 days, CRC: 14 vs 29 days). Screened patients in both cancer cohorts had a similar number of average procedures prior to treatment initiation as non-screened (BC: 4.8 vs 4.2, CRC: 3.3 vs 3.3 respectively, including screening procedures). Conclusions: Individuals diagnosed with BC and CRC through screening exhibit earlier stage diagnoses and shorter intervals to diagnosis. Novel screening technologies such as MCED tests can potentially improve peri-diagnostic outcomes in cancers lacking existing or widely adopted screening methods. [Table: see text]
Impact of carboplatin and cisplatin shortages on treatment patterns in patients with metastatic solid tumors.
Authors: Castellanos Emily , Yuan Qianyu , Wadé Niquelle , Patel Khilna , Rinaldi Catherine , Reiss Samantha , Hankinson Eunice , Cohen Aaron B. , Estevez Melissa
Published: June 2024

11149

Background: The United States experienced cisplatin (CP) and carboplatin (CB) shortages in 2023, leading to potential rationing or switching to alternative therapies in multiple cancers. Using a US nationwide, real-world oncology dataset, we assessed changes in the use of CP and CB in 7 cancers during the shortage period. Methods: All patients (pts) from the Flatiron Health electronic health record-derived de-identified database (~280 cancer clinics, ~800 sites of care) with a machine learning-extracted metastatic (met) diagnosis of a cancer of interest between March 2022 and June 2023 and evidence of first-line (1L) therapy, were selected. Pts with multiple primaries were excluded. Platinum of interest was identified based on frequency of pre-period 1L use for a given cancer. The pre-period was June 2022 through first month of reported shortage (February 2023 for CP; April 2023 for CB); data cutoff was June 2023. Clinical characteristics of platinum-treated pts stratified by pre- vs post-period by disease were assessed. 1L platinum utilization rates (UR), calculated as the proportion of 1L-treated pts initiating CB/CP within 30 days of met diagnosis, were plotted by month. Cohort-level impact was evaluated with an interrupted time series analysis—overall and stratified by practice type, practice size, and socioeconomic status (SES). Multiple testing was controlled using the Benjamini-Hochberg procedure. Results: 10,983 pts received 1L therapy in the study period. Monthly platinum UR were stable across diseases in the pre-period. However, there was a significant post-period decrease in CB usage for non-small cell lung (NSCLC) and endometrial cancers (EC), and a decreasing trend in UR for CB in bladder (BC), ovarian (OC), and small cell lung cancers (SCLC). We also observed a decreasing trend in CP usage for cholangiocarcinoma (CC). Post-period monthly UR were stable for CB and CP in head and neck cancer (HNC), and for CP in BC. Odds ratios (OR) representing the month-over-month change in the odds of receiving platinum therapy during both pre- and post-periods are shown (table). OR trends varied by practice type and disease; no notable trends were seen by practice size or SES. UR with additional follow-up times will be presented. Conclusions: We observed decreased platinum use across multiple cancers during the shortage period, most notably for met NSCLC and EC, suggesting shortages led oncologists to seek alternatives to standard 1L platinum-based regimens. Further study of how treatment changes due to platinum shortages impacted pt outcomes is warranted. [Table: see text]