bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing adult child and spousal caregiver burden and potential causes.
Authors: Fenton Anny , Keating Nancy Lynn , Ornstein Katherine A. , Kent Erin E. , Litzelman Kristin , Rowland Julia H. , Wright Alexi A. 
Published: June 2021

12127 Background: Adult children caring for a parent with cancer comprise a significant segment of caregivers. Demographic trends indicate this caregiving population will grow as the baby boomer generation ages. Yet little is known about adult child caregivers’ needs and experiences and how they differ from the well-studied spousal caregiver. This knowledge gap may hinder efforts to ameliorate adult children’s caregiver burden and its impact on patients. Methods: We analyzed adult child and spousal/partner caregivers’ surveys from the Cancer Care Outcomes Research and Surveillance consortium, a multi-regional population-based study of approximately 10,000 persons with newly diagnosed colorectal and lung cancer. We used t-tests and a series of multivariate regression models to assess whether adult child and spousal caregivers’ caregiving responsibilities, social/emotional burden, and financial burden (scaled 0-10) differed and examined patient and caregiver characteristics’ mediation of variation in burden. Results: Compared to spouses/partners (N=1029), adult children (N=230) completed similar levels of caregiving tasks but spent less time (14 vs. 24 hours/week; p<0.001). However, adult children experienced higher social/emotional burden (2.9 vs. 2.4; p<0.01). In baseline models controlling for patient clinical factors, caregiving characteristics, and caregiver demographics, adult children’s average social/emotional and financial burdens were statistically higher than spouses/partners. Additional adjustment for caregivers’ childcare responsibilities and employment eliminated social/emotional and financial burden disparities. Additional adjustment to the baseline model for caregiver-patient gender concordance eliminated the social/emotional burden gap. Communication quality was a large and statistically significant predictor of both burdens (p<0.001). Conclusions: Adult children spend less time caregiving than spouses/partners but experience higher caregiving burden. Adult children’s childcare and career responsibilities help explain this increased burden. Gender concordance between caregiver and patient may also contribute to social/emotional burden, adding important context to prior research indicating female caregivers experience the greatest burden. Interventions to improve communication between caregivers and patients have the potential to reduce both adult child and spouses/partners caregiver burden. Adjusted average caregiver burden for adult child and spousal caregivers. [Table: see text]
Suicide risk following a new cancer diagnosis among veterans in Veterans Health Administration care.
Authors: Dent Kallisse R. , Szymanski Benjamin R. , Kelley Michael J. , Katz Ira , McCarthy John F. 
Published: June 2021

12130 Background: Patients diagnosed with cancer are at an increased risk of adverse mental health outcomes including suicidal behavior. Suicide rates among Veterans are 50 percent greater than for non-Veteran US adults. To inform Veterans Affairs (VA) suicide prevention initiatives, it is important to understand associations between cancer and suicide risk among Veterans receiving VA healthcare from the Veterans Health Administration (VHA). Study aims were to assess associations between new cancer diagnoses and suicide among Veterans in VHA care to identify high risk diagnostic subgroups and risk-periods. Methods: We used a cohort study design, identifying 4,926,373 Veterans with VHA use in 2011 and either 2012 or 2013 and without a VHA cancer diagnosis in 2011. Incident cancer diagnoses, assessed between first VHA use in 2012-2013 and 12/31/2018, were characterized by subtype and stage using the VHA Oncology Raw Data. Data from the VA/Department of Defense Mortality Data Repository identified date and cause of death. Cox proportional hazards regression, accounting for time-varying cancer diagnosis, was used to evaluate associations between a new cancer diagnosis and suicide risk. An initial model adjusted for VHA regional network and patient age and sex. Cancer subtypes with significant associations with suicide were further assessed using a model that also adjusted for suicide attempts and mental health, tobacco use disorder, and other substance use disorder diagnoses in the prior year. Crude suicide rates following a new cancer diagnoses were calculated among Veterans with new diagnoses, 2012-2018 (N = 240,410). Rates were assessed up to 84 months following diagnosis. Results: On average, Veteran VHA users were followed for 6.0 years after their first VHA use in 2012-2013 and for 2.7 years following a new cancer diagnosis. Receipt of a new cancer diagnosis corresponded to a 43% (Adjusted Hazard Ratio [AHR] = 1.43, 95% CI: 1.29, 1.58) higher suicide risk, adjusting for covariates. The cancer subtype associated with the highest suicide risk was esophageal cancer (AHR = 5.93, 95% CI: 4.05, 10.51) and other significant subtypes included head and neck (AHR = 3.44, 95% CI: 2.65, 4.46) and lung cancer (AHR = 2.28, 95% CI: 1.79, 2.90). Cancer stages 3 (AHR = 2.29, 95% CI: 1.75, 3.01) and 4 (AHR = 3.45, 95% CI: 2.75, 4.34) at diagnosis were also positively associated with suicide risk. Suicide rates were highest in the first three months following a diagnosis (Rate = 128.3 per 100,000 person-years, 95% CI: 100.4, 161.6) and remained elevated through the first 12 months. Conclusions: Among Veteran VHA users, suicide risk was elevated following a new cancer diagnosis and was especially high in the initial 3 months. Additional screening and suicide prevention efforts may be warranted for VHA Veterans newly diagnosed with cancer, particularly among those diagnosed with esophageal, head and neck, or lung cancer or at stages 3 or 4.
Impact of machine learning-directed on-treatment evaluations on cost of acute care visits: Economic analysis of SHIELD-RT.
Authors: Natesan Divya , Thomas Samantha M. , Eisenstein Eric , Eclov Neville , Dalal Nicole , Stephens Sarah J. , Malicki Mary , Shields Stacey , Cobb Alyssa , Mowery Yvonne Marie , Niedzwiecki Donna , Tenenbaum Jessica , Palta Manisha , Hong Julian C. 
Published: June 2021

1509 Background: SHIELD-RT was a randomized controlled quality improvement study (NCT03775265) that implemented electronic health record-based machine learning (ML) to direct supplemental visits for high risk (HR) patients undergoing radiotherapy (RT). Acute care visits (ER visits or hospitalizations) were reduced from 22% to 12%. We evaluated the costs associated with acute visits in this study. Methods: Patients who initiated RT between 1/7/19 and 6/30/19 at a single institution were evaluated by a ML algorithm to identify HR courses (>10% risk of acute visit during RT). HR patients were randomized to standard weekly (S) or intervention of twice weekly (TW) evaluation during RT. Cost data associated with acute visits were obtained and compared between patients who underwent S or TW evaluations. Missing cost data were imputed using disease related groups (DRGs). Mean costs (standard deviation) were compared between arms with non-parametric Wilcoxon Rank Sum tests. Results: 311 HR courses were identified and randomized to either S (n=157) or TW (n=154) evaluations during RT. 85 patients (S: 51; TW: 34) had 121 distinct acute care visits (S: 74; TW: 47). Patients in the TW evaluation arm had fewer hospitalizations (29 vs 41) and ER visits (18 vs 33) than those in the S arm. There were fewer acute visits per patient in the TW arm (0.34) compared to S arm (0.49). Actual cost data was available for 102 visits at our institution, and imputed for 19 outside hospital visits. Mean cost associated with acute visits was lower in the TW arm ($1939, SD $5912) compared with the S arm ($4002, SD $11568; p=0.03). Differences in mean cost between arms are presented in the table. Conclusions: ML-directed evaluations for HR patients undergoing RT resulted in decreased costs of ER visits and hospitalizations. Costs were decreased across revenue centers, with the largest difference related to inpatient room costs. Future analyses will incorporate intervention costs, which are currently bundled with RT reimbursement.[Table: see text]
Baseline and short-term financial burden (FB) in colorectal cancer (CRC) treated with curative intent: Early results of ECOG-ACRIN EAQ162CD.
Authors: Kircher Sheetal Mehta , Duan Fenghai , Gareen Ilana F. , Underwood Ellen , Sicks Jorean , Suga Jennifer Marie Marie , Kehn Heather , Mehan Paul Tracy , Acoba Jared David , Taylor Michael A , Wagner Lynne I. , Carlos Ruth 
Published: June 2021

1518 Background: Cancer therapy costs continue to rise, resulting in FB. FB in CRC treated with curative intent remains unexplored, particularly in the community oncology setting. We assess baseline FB and 3-month change, including predictors. Methods: Patients with newly diagnosed CRC treated with curative intent were enrolled through NCI Community Oncology Research Program (NCORP) Community Sites in a longitudinal study and completed the validated FACIT Comprehensive Score for Financial Toxicity (COST) instrument at baseline and 3 months. Higher COST score (range 0-44) indicates greater financial well-being. Pearson correlation compared baseline and 3-month measurements. Effects of patient demographics, clinical, self-efficacy variables (Table) and practice safety net affiliation were assessed using linear regression for baseline COST and COST difference at 3 months. F-tests identified covariates significantly predicting FB. Results: 450 and 296 participants completed the baseline and 3-month survey with a mean COST score 23.5 ± 11.9 and 24.6 ± 12.4, respectively (r=0.80, p<0.001), considered grade 1 or mild. Financial resource indicators such as income, insurance type, high-deductible insurance and savings, along with self-efficacy strongly predicted baseline FB (Table). No assessed covariates predicted COST difference. Safety-net affiliation (54/450, 12%) did not predict COST outcomes. Conclusions: Among those with CRC treated with curative intent in community settings, FB at treatment initiation and 3-month follow-up are highly correlated. Financial resources predict FB at treatment initiation. Self-efficacy to manage finances predicted FB, suggesting interventions such as financial counseling and navigation delivered early in the treatment course may minimize downstream FB. Clinical trial information: NCT03516942. [Table: see text]
Comparing FOLFOX delivery in trial and real-world populations using longitudinal cumulative dose.
Authors: Webster-Clark Michael , Sanoff Hanna Kelly , Keil Alexander P. , Sturmer Til , Westreich Daniel , Lund Jennifer Leigh 
Published: June 2021

1521 Background: Patterns of chemotherapy delivery are likely to differ between trial and real-world populations. Typical measures used to compare these patterns are calculated at treatment completion, potentially missing key differences in the timing and trajectory of delays and dose reductions. We used a new measure, longitudinal cumulative dose (LCD), to compare treatment delivery over time in trial and real-world populations. Methods: We compared chemotherapy delivery in patients with stage II-III colon cancer enrolled in the MOSAIC trial of 5-fluorouracil (5FU) vs oxaliplatin + 5FU (FOLFOX4) to patients treated from 2008-2019 in the US Oncology Network with FOLFOX4, FOLFOX6, or mFOLFOX6. For each patient, we computed oxaliplatin LCD as the cumulative oxaliplatin dose received at a given timepoint (t) divided by the final standard oxaliplatin dose. We then estimated the median and 25th and 75th percentiles for oxaliplatin LCD within each regimen at day 68 (before the standard timing of the 7th dose), 168 (two weeks after the standard end of treatment), and 250. Results: The table shows the number of patients receiving each treatment regimen and the median and interquartile range for oxaliplatin LCD at each time. Higher LCDs in the trial show delivery closer to standard treatment, meaning fewer delays, dose reductions, and discontinuations. Differences between the medians, 25th percentiles, and 75th percentiles of LCD in each regimen were small at day 68 but grew considerably by days 168 and 250. Conclusions: Divergence from the standard dosing schedule was larger in real-world versus trial settings and varied by oxaliplatin regimen. LCD, as a longitudinal measure, showed that differences in delivery between trial and real-world populations grew substantially over time (even after 168 days and the standard end of treatment) possibly as real-world patients experienced more side effects and barriers to treatment than trial participants. These discrepancies in LCD may cause poorer outcomes in real-world settings than expected based on randomized trials.[Table: see text]
Impact of a regimen-level prior authorization tool on provider adherence to clinical guidelines’ and cost savings in a Medicare advantage population.
Authors: Yeon Heeseon , Brito Rogelio Alberto , Kong Wenjing , Cavers William , Mcgovern-Siembab Allendre , Claussen Anne , Johnson Kjel Andrew , Avalos-Reyes Elisea 
Published: June 2021

1522 Background: In 2019, a large national health system developed a comprehensive approach to improve the quality and cost of cancer care; this solution included a Web-based clinical decision support prior authorization (PA) tool, Novologix (NLX), which approves at the regimen rather than drug level to reduce administrative burden and elevate the quality of care. Evidence-based guidelines in NLX are updated in real-time via a partnership with the National Comprehensive Cancer Network (NCCN). The purpose of this study is to evaluate the NCCNconcordance of PAs submitted via NLX and total cost of care for Medicare Advantage (MA) patients with non-small cell lung cancer (NSCLC) who received NCCNconcordant versus non-concordant therapies. Methods: Eligible patients included MA patients diagnosed with NSCLC; our initial analysis included only the NSCLC subset of patients. NCCN regimen concordance was identified from pharmacy and medical claims and defined as concordant if the entire prescribed treatment regimen matched an NCCNregimen; patients not receiving an NCCN recommended regimen were deemed to be non-concordant. Total cost of care for MA patients with NSCLC were calculated. Results: From April-December 2020, 279 PAs were submitted via NLX and 83% were automatically approved in real-time. PAs not automatically approved were deemed concordant after peer-to-peer consultations that led to NCCN concordance; no PA denials were made. In the first half of 2020, 2,690 MA patients with NSCLC were identified; 2,166 (81%) patients were defined as NCCN concordant. Beginning with and including the first treatment and for 30-days thereafter, total cost of care for concordant patients averaged $19,321 while non-concordant patients averaged $26,405, a statistically significant savings of $7,084 (p < 0.001). Conclusions: Preliminary findings with a MA NSCLC population suggest engaging oncology practices through an enhanced payer-provider collaboration and implementing an automated regimen-level precertification process with real-time NCCNupdates can facilitate lower cost, and more efficient oncology care. Financial savings by encouraging providers to follow NCCN guidelines may lead to a 27% reduction in total cost of care with further proper adjustment on population bias; this may lead to similar findings in other cancer types. Future studies are needed to measure the long-term impact of this program on total cost of care for other care models and cancer types.
An automated EHR-based tool to facilitate patient identification for biomarker-driven trials.
Authors: Lakhanpal Shailendra , Hawkins Kailee , Dunder Steven G. , Donahue Karri , Richey Madeline , Liu Edward , Brachfeld Alexandra , Sutton Lauren , Shelley Addison , Green Dominic , Siu Evelyn , Hamrick James 
Published: June 2021

1539 Background: Clinical trial eligibility increasingly requires information found in NGS tests; lack of structured NGS results hinders the automation of trial matching for this criterion, which may be a deterrent to open biomarker-driven trials in certain sites. We developed a machine learning tool that infers the presence of NGS results in the EHR, facilitating clinical trial matching. Methods: The Flatiron Health EHR-derived database contains patient-level pathology and genetic counseling reports from community oncology practices. An internal team of clinical experts reviewed a random sample of patients across this network to generate labels of whether each patient had been NGS tested. A supervised ML model was trained by scanning documents in the EHR and extracting n-gram features from text snippets surrounding relevant keywords (i.e. 'Lung biomarker', 'Biomarker negative'). Through k-fold cross-validation and l2-regularization, we found that a logistic regression was able to classify patients' NGS testing status. The model's offline performance on a 20% hold-out test set was measured with standard classification metrics: sensitivity, specificity, positive predictive value (PPV) and NPV. In an online setting, we integrated the tool into Flatiron's clinical trial matching software OncoTrials by including in each patient's profile an indicator of "likely NGS tested" or "unlikely NGS tested" based on the classifier's prediction. For patients inferred as tested, the model linked users to a test report view in the EHR. In this online setting, we measured sensitivity and specificity of the model after user review in two community oncology practices. Results: This NGS testing status inference model was characterized using a test sample of 15,175 patients. The model sensitivity and specificity (95%CI) were 91.3% (90.2, 92.3) and 96.2% (95.8, 96.5), respectively; PPV was 84.5% (83.2, 85.8) and NPV was 98.0% (97.7, 98.2). In the validation sample (N = 200 originated from 2 distinct care sites), users identified NGS testing status with a sensitivity of 95.2% (88.3%, 98.7%). Conclusions: This machine learning model facilitates the screening for potential patient enrollment in biomarker-driven trials by automatically surfacing patients with NGS test results at high sensitivity and specificity into a trial matching application to identify candidates. This tool could mitigate a key barrier for participation in biomarker-driven trials for community clinics.
Trends of cancer associated with modifiable behavior in the U.S.: Is there a difference based on age, gender, or race?
Authors: Liao Cheng-I , Caesar Michelle Ann P. , Chan Chloe , Richardson Michael , Kapp Daniel Stuart , Francoeur Alex Andrea , Chan John 
Published: June 2021

1542 Background: To examine trends in modifiable behaviorally related cancers among men and women in the United States. Methods: Data were obtained from the United States Cancer Statistics (USCS) database for all cancers diagnosed between 2001 and 2017. Alcohol-associated cancers, HPV-associated, obesity-associated, physical inactivity-associated, and tobacco-associated were defined using ICD-O-3 site codes. SEER*Stat 8.3.8 and Joinpoint regression program 4.8.0.1 were used to calculate the trends of associated cancers expressed per 100,000. Results: In 2017 the incidence of cancers in women associated with alcohol, smoking and obesity were 168/100,000, 134/100,000 and 121/100,000 respectively. Based on analysis of trends of women from 2001 to 2017, alcohol, smoking and physical inactivity related cancers decreased with an annual percent change (APC -0.51%, -0.96%, -0.92% respectively, p < 0.001). However, there was no significant change in obesity, HPV, or post menopausal female breast cancer related cancers (APC: 0.07%, 0.09%, -0.08% respectively, p = 0.303, 0.181, 0.569). Based on age, in women less than 65, rates of obesity related cancers are increasing. Based on racial groups, all rates of cancer associated with modifiable factors are decreasing, however Hispanic women have an increasing rate of obesity related cancers (APC 0.46%, p < 0.001). When examining differences in region, all rates of cancer are decreasing or unchanged except the south has an increasing rate of obesity related cancer (APC 0.28%, p < 0.001). Using a projection model, obesity will become the highest incidence cancer in Hispanic women by 2035, surpassing alcohol and tobacco. In 2017 the incidence of cancers in men associated with tobacco, obesity, and alcohol were 209/100,000, 111/100,000 and 81/100,000 respectively. Based on analysis of trends in men alcohol, smoking, physical inactivity and obesity related cancers decreased (APC -1.42%, -1.59%, -3.15%, -0.41% respectively, p < 0.001). HPV related cancers have increased (APC 2.36%, p < 0.001). In men less than 60 years old, the rates of obesity related cancers are increasing. Using a prediction model, obesity is predicted to surpass tobacco as the most common social cause of cancer in 2020 for men 35-39, 2024 in men 40-44 and in 2030 in men 45-49. Conclusions: In women, most modifiable factors associated with cancer are decreasing except in obesity and HPV related cancers. In men, these rates of cancer are decreasing except HPV related cancers. However, rates of obesity related cancers are on the rise in Hispanic women and younger men. Obesity is set to become the major modifiable factor for many associated cancers.
Automated identification of immune related adverse events in oncology patients using machine learning.
Authors: Xu Wenxin , Gusev Alexander , Groha Stefan , Rahma Osama E. , Schrag Deborah , Choueiri Toni K. , Kehl Kenneth L. 
Published: June 2021

1551 Background: Immune related adverse events (irAEs) are a major cause of morbidity among cancer patients treated with immune checkpoint inhibitors (ICIs). irAEs are difficult to identify systematically, which represents a major barrier to the conduct and reproducibility of irAE research. Automated approaches would facilitate cohort identification and understanding of risk factors for irAEs following ICI therapy. Methods: Patients treated with one or more ICIs at a single tertiary cancer center were identified. Patients who received ICIs outside the clinical trial context were used as a development cohort. For each date containing clinical documentation, proxy outcomes expected to correlate with grade 2+ irAEs including irAE related diagnosis codes, key laboratory values, prescriptions for topical and systemic steroids, and irAE keywords were extracted. Intermediate machine learning models were trained to predict the presence of each proxy outcome using structured and unstructured patient data. We used clinical trial irAEs extracted from adverse event tables found in the electronic health record as the “gold standard” outcome for a final training and evaluation cohort. A logistic regression model was used to combine predictions from each intermediate model and generate an overall probability score for each irAE type on a given encounter date. Ten-fold cross-validation was used to evaluate the final machine learning model on a held-out sample of clinical trial patients. Encounter level models were evaluated for predicting the onset of a given irAE on a given date, and patient level models for predicting irAE onset within 6 months of ICI initiation. Results: We identified 3,765 patients treated with ICIs off-trial and 1106 patients treated on ICI clinical trials. Among trial patients, overall incidence of any grade 2+ irAE was 21%. The combined irAE models were able to predict prospective gold standard irAE labels with accurate discrimination at both the encounter and patient level (Table). Conclusions: Machine learning models can identify irAEs among cancer patients in an automated manner, which may facilitate research to mitigate toxicities and optimize clinical outcomes. Validation of these methods in an external institutional cohort is underway.[Table: see text]