bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between a national insurer’s pay-for-performance program for oncology and changes in prescribing of evidence-based cancer drugs and spending.
Authors: Bekelman Justin E. , Gupta Atul , Fishman Ezra , Debono David Joseph , Fisch Michael Jordan , Liu Ying , Sylwestrzak Gosia , Barron John , Navathe Amol S. 
Published: May 2020

2016 Background: Efforts to standardize quality and control cost growth for cancer care have focused heavily on promoting evidence-based cancer drug prescribing. We evaluated the association between a national commercial insurer’s ongoing pay-for-performance (P4P) program for oncology and changes in prescribing of evidence-based cancer drugs and spending. Methods: Retrospective difference-in-differences quasi-experimental study utilizing administrative claims data from the insurer’s commercial health plans in 14 states covering 6.7% of US adults. We included patients 18 years of age or older with breast, colon, or lung cancer who were prescribed cancer drug regimens by 1,867 participating oncology physicians between 2013 and 2017. We leveraged the geographically staggered, time-varying rollout of the P4P program to simulate a stepped-wedge study design. Specifically, we estimated a patient-level model clustered by physician and used physician fixed-effects to examine pre- to post-intervention changes in evidence-based prescribing and spending for patients of participating physicians eligible earlier versus later in the period of P4P program rollout. We evaluated four categories of spending over a 6-month episode period: cancer drug spending; other (non-cancer drug) health care spending; total episode spending; and patient out-of-pocket spending. Results: The P4P program was associated with an increase in evidence-based regimen prescribing from 57.1% of patients in the pre-intervention periods to 62.2% in the post-intervention periods for a difference of +5.1 percentage points (pp) (95% CI 3.0 pp to 7.2 pp, P< 0.001). The P4P program was also associated with a differential $3,235 (95% CI $1,004 to $5,466, P= 0.005) increase in cancer drug spending, a differential $253 (95% CI $101 to $406, P= 0.001) increase in patient out-of-pocket spending, but no significant changes in other health care spending or total health care spending over the 6-month episode period. Conclusions: A national insurer’s oncology P4P program was associated with a 5.1 percentage point increase in prescribing of evidence-based cancer drug regimens. Our findings suggest that P4P programs may be effective in increasing evidence-based cancer drug prescribing at national scale -- enhancing cancer care quality. However, they may also increase out-of-pocket expenses and may not lead to savings in total health care spending during the 6-month episode.
Pilot program of remote monitoring for high-risk patients on antineoplastic treatment.
Authors: Daly Robert Michael , Kuperman Gilad , Zervoudakis Alice , Ro Alice , Roy Ankita , Baldwin Abigail , Salvaggio Rori , Holland Jessie C. , Chow Kimberly , Lauria Tara , Rozenshteyn Margarita , Zablocki Melissa , Chiu Yeneat Ophelia , Silva Nicholas , Perry Claire , Sokolowski Stefania , Wagner Isaac , Simon Brett A , Reidy Diane Lauren , Perchick Wendy 
Published: May 2020

2027 Background: Early detection and management of symptoms in patients with cancer improves outcomes, however, the optimal approach to symptom monitoring and management is unknown. This pilot program uses a mobile health intervention to capture and make accessible symptom data for high-risk patients to mitigate symptom escalation. Methods: Patients initiating antineoplastic treatment at a Memorial Sloan Kettering regional location were eligible. A dedicated staff of RNs and nurse practitioners managed the patients remotely. The technology supporting the program included: 1) a predictive model that identified patients at high risk for a potentially preventable acute care visit; 2) a patient portal enabling daily ecological momentary assessments (EMA); 3) alerts for concerning symptoms; 4) an application that allowed staff to review and trend symptom data; and 5) a secure messaging platform to support communications and televisits between staff and patients. Feasibility and acceptability were evaluated through enrollment (goal ≥25% of new treatment starts) and response rates (completion of > 50% of daily symptom assessments); symptom alerts; perceived value based on qualitative interviews with patients and providers; and acute care usage. Results: Between October 15, 2018 and July 10, 2019, the pilot enrolled 100 high-risk patients with solid tumors and lymphoma initiating antineoplastic treatment (median age: 66 years, 45% female). This represented 29% of patients starting antineoplastics. Over six months of follow-up, the response rate to the daily assessments was 56% and 93% of patients generated a severe symptom alert (Table). Both patients and providers perceived value in the program and 5,010 symptom-related secure messages were shared between staff and enrolled patients during the follow-up period. There was a preliminary signal in acute care usage with a 17% decrease in ED visits compared to a cohort of high-risk unenrolled patients. Conclusions: This pilot program of intensive monitoring of high-risk patients is feasible and holds significant potential to improve patient care and decrease hospital resources. Future work should focus on the optimal cadence of EMAs, the workforce to support remote symptom management, and how best to return symptom data to patients and clinical teams. [Table: see text]
Nickel and dimed: Parking fees at NCI-designated cancer centers.
Authors: Lee Anna , Shah Kanan , Byun John , Chino Fumiko 
Published: May 2020

2029 Background: Nonmedical costs from cancer treatment can be a significant out-of-pocket expense. As treatment may span over months, parking costs can become a significant burden on patients and caregivers. This cross-sectional study aims to report parking fees at National Cancer Institute (NCI)-designated cancer centers and to project parking costs for the treatment duration of certain cancers. Methods: Parking fees from NCI treatment centers were obtained via online search or phone call in Fall of 2019. City cost of living, median city household income, and discount availability were documented. Pearson correlation was used between parking costs and city variables. Parking costs were estimated for treatment of node positive breast cancer (12 daily rates plus 20 1-hr rates), definitive head and neck cancer (35 1-hr rates) and acute myeloid leukemia (AML) (42 daily rates). RStudio Version 1.2.5033 was used for analyses. Results: Parking costs were obtained for 100% of the 63 NCI centers included. Median city cost of living relative to New York City was 75.0 (out of 100); median city household income was $55,295 (range $28,974-$120,573). Twenty-five (40%) of NCI centers did not have detailed parking cost information online. Average parking costs were $3.55/hr (median $2, range 0-$15) and $7.79/day (median $5, range 0-$40). Twenty centers (32%) offered completely free parking for patients. Free parking was available at 43 (68%) centers for radiation appointments and 34 (54%) centers for chemotherapy appointments. Averaged estimated parking costs including discounts for a course of treatment for breast cancer was $122.03 (range 0-$800); head and neck cancer, $85.56 (range 0-$665); and AML hospitalization, $327.33 (range 0-$1470). City cost of living was positively correlated with daily parking costs (R = 0.7, p < 0.01) and negatively correlated with both free daily parking (R = -0.33, p = 0.02) and free parking during radiation (R = -0.34, p = 0.02) or chemotherapy (R = -0.37, p < 0.01). The median city household income was correlated with the daily parking costs (R = 0.30 p = 0.02) but not with free daily parking (R = -0.19, p = 0.16), free parking for patients on radiation (R = -0.23, p = 0.09) or on chemotherapy (R = -0.21, p = 0.14). Conclusions: Patients may face significant nonmedical costs through parking fees, even at centers that reflect the highest standard of care. There was high variability in costs with the potential for patients to pay hundreds of dollars in parking in order to receive their care. Efforts to minimize financial toxicity should focus on this potentially under-reported patient concern.
Identifying and overcoming clinical trial enrollment barriers: Can an integrated clinical pathways tool help bridge the gap?
Authors: McKinney Mishellene , Samborski Cynthia , Stefaniak Nessa , Murphy Monica L. , Edge Stephen B. , Attwood Kristopher , Liu Lu , Lash Stephen , Yu Wei , Eakle Katherine 
Published: May 2020

2046 Background: Barriers to clinical trial (CT) enrollment continue to be a national challenge, with only about 3% of adult cancer patients treated within a CT. A key recommendation from the 2013 NCI-ASCO Cancer Trial Accrual Symposium was to use information technology to enhance identification of potentially eligible patients for CTs. We assessed if the implementation of a Clinical Oncology Pathways System (COP) with integrated CT information increased enrollment of CT’s, and categorized physician-identified reasons for non-enrollment. Methods: In 2018, Roswell Park Comprehensive Cancer Center (RP) implemented ClinicalPath pathways (formerly Via Oncology) for medical oncology. The COP software embeds interventional CTs that are open to accrual at RP in the pathway specific to a patient’s disease type, stage and biomarkers. The provider is presented with relevant CTs and must select screening for the trial or provide a reason for bypassing the CT from a drop-down list prior to being presented standard care options. CT screening requests from the COP system from 6/1/18-5/31/19 were reviewed. Screening requests and actual enrollment data were matched. The accrual-to-study ratio (ASR), defined as the number of consented accruals divided by the number of CTs open to accrual at RP at any time during the period, was calculated for the study period and the baseline from 6/1/14-6/1/18. The reasons physicians did not elect to screen for CTs were summarized. Results: There can be multiple trials presented for each pathway decision.There were 1,606 decision points with at least one embedded trial. Of these, 1,289 decision points matched 2,242 CTs that were not selected for screening. 317 trials were selected for screening. The most common reasons for not screening were patient ineligibility (41%), provider bypassing the CT by selecting treatment “off pathway” (28%), patient not interested (12%), patient already on CT (8%) and “other” (9%). Audits confirmed that the majority of ineligible patients had co-morbidities such as organ dysfunction or brain metastasis that precluded them from the CT. Among the 317 trials selected for CT screening, 108 (34%) patients enrolled in CTs. The ASR increased from the four-year historical average baseline of 4.08 to 4.33 one year post-implementation. Conclusions: The use of COP with embedded CT was associated with a modest increase in ASR. Stringent eligibility criteria was the primary barrier to enrollment. Adopting a broader set of clinical trial eligibility criteria could increase enrollment to CT.
Machine learning algorithms to predict financial toxicity associated with breast cancer treatment.
Authors: Sidey-Gibbons Chris , Asaad Malke , Pfob André , Boukovalas Stefanos , Lin Yu-Li , Offodile Anaeze 
Published: May 2020

2047 Background: Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but objective identification of individuals in need is difficult. Accurate predictions of an individual’s risk of financial toxicity prior to initiation of breast cancer treatment may facilitate informed clinical decision making, reduce financial burden, and improve patient outcomes. Methods: We retrospectively surveyed 611 patients who had undergone breast cancer therapy at MD Anderson Cancer Center to assess the financial impact of their care. All patients were over 18 and received either a lumpectomy or a mastectomy. We collected data using the FACT-COST patient-reported outcome measures alongside other financial indicators including income and insurance status. We extracted clinical and perioperative data from the electronic health record. Missing data were imputed using multiple imputation. We used this data to train and validate a neural network, LASSO-regularized linear model, and support vector machines. Data were randomly partitioned into training and validation samples (3:1 ratio). Analyses were informed by international PROBAST recommendations for developing multivariate predictors. We combined algorithms into a voting ensemble and assessed predictive performance using area under the receiver operating characteristics curve (AUROC), accuracy, sensitivity, and specificity. Results: In our validation sample, 48 of 203 (23.6%) women reported FACT-COST scores commensurate with significant financial burden. The algorithm predicted significant financial burden relating to cancer treatment with high accuracy (Accuracy = .83, AUROC = .82, sensitivity = .81, specificity = .82). Key clinical predictors of financial burden from linear models were neo-adjuvant therapy (βregularized 0.12) and autologous, rather than implant-based, reconstruction (βregularized 0.10). Conclusions: Machine learning models were able to accurately predict the occurrence of financial toxicity related to breast cancer treatment. These predictions may be used to inform decision making and care planning to avoid financial distress during cancer treatment or to enable targeted financial support for individuals. Further research is warranted to further improve this tool and assess applicability for other types of cancer.
Ranking of therapeutic regimens for hormone receptor-positive, HER2-negative, metastatic breast cancer (MBC) using information theoretic network meta-analysis.
Authors: Li Xuanyi , Tavana Hossein , Bhavnani Suresh , Warner Jeremy Lyle 
Published: May 2020

2050 Background: Hormone receptor-positive (HR+), HER2-negative (HER2-) metastatic breast cancer (MBC) is treated with targeted therapy, hormone therapy, chemotherapy, or combinations of these modalities. Evaluating the increasing number of treatment options is challenging, especially since few regimens have been compared head-to-head in randomized clinical trials (RCTs). Potential solutions include expert-driven guidelines (e.g. NCCN guidelines), algorithmic scales (e.g. the ASCO and ESMO Value Frameworks), traditional Bayesian network meta-analysis (NMA), and information theoretic (IT) NMA, a graph theory-based approach that also enables dynamic ranking of regimens over time. Methods: We used IT-NMA to rank regimens for HR+/HER2- MBC. The analysis includes RCTs of regimens identified from HemOnc.org and a recent large traditional NMA (Giuliano et al. 2019). Variables used in ranking include primary endpoints, no. of patients enrolled, p-value, hazard ratio for time-based outcomes (e.g. overall survival) or odds ratio for fixed endpoints (e.g. response rate), and year of publication. Results: The analysis included 238 RCTs enrolling 92,971 patients published between 1974-2019. There were 277 unique regimens, taking into account variations in dosage, frequency, and no. of cycles. As of 2019, out of 85 ranks, combinations of targeted therapy and hormone therapy (e.g. letrozole & palbociclib) are ranked the highest (Table). Over time, we observe that novel treatments tested in escalation trials tend to rise to the top of the rankings (e.g. paclitaxel & bevacizumab in 2007, driven by ECOG E2100), and monotherapy approaches tend to fall to the bottom. Conclusions: In 2019, the combinations of hormone or chemotherapy and targeted therapy are ranked higher than hormone therapy or chemotherapy alone. Our ranking result is similar to previous studies with a notably larger number of comparisons (Giuliano et al. is the largest published study, with 131 regimens/50,029 pts analyzed). Informatic theoretic NMA is a promising method of indirect rankings of treatment that also enables dynamic regimen ranking over time. [Table: see text]
Actionable policy barriers for receiving standard of care treatment among unresected stage III non-small cell lung cancer (NSCLC) patients in the United States.
Authors: Zheng Zhiyuan , Simone Charles B. , Chun Stephen G. , Han Xuesong , Sineshaw Helmneh M. , Zhao Jingxuan , Seal Brian S. , Yong Candice , Makari Doris , Rengan Ramesh 
Published: May 2020

2069 Background: Recent data suggests that a significant number of good performance, unresectable stage III non-small cell lung cancer (NSCLC) patients do not receive standard-of-care treatment, i.e. concurrent chemoradiotherapy (cCRT) followed by durvalumab, despite being eligible. However, little is known about actionable policy barriers to delivery of cCRT to this patient population. Methods: The National Cancer Database (2004-2016) was used to identify unresected stage III NSCLC patients aged 18-79 years with Charlson comorbidity score ≤ 1. cCRT was defined as the initiations of chemotherapy (CT) and radiation therapy (RT) that were ≤14 days (n = 53,444) apart. The remaining treatment groups included sequential CRT (sCRT; n = 16,666), CT only (n = 15,416), RT only (n = 11,579), and no first course treatment (n = 16,691). Multinomial logistic regressions were used to examine the likelihoods of receiving different treatment modalities, controlling for patient demographics, Charlson comorbidity score, health insurance, facility type, social deprivation index (SDI, a comprehensive socio-economic measure; higher SDI indicates lower socioeconomic status [SES]), driving time to facility, diagnosis year, and region. Results: Of the total 113,796 patients assessed (median age 66 years), most were male (55.7%), non-Hispanic white (81.7%), and with SDI score ≥50 (51.3%). 29.5% had Charlson comorbidity score = 1 while the rest had 0. In adjusted analyses (predicted margins), 47.0% patients received cCRT (sCRT: 14.6%; CT only: 13.5%; RT only: 10.2%; no treatment: 14.7%). Compared to the privately insured, Medicaid, Medicare, and uninsured patients were more likely to receive RT only (relative risk ratios [95%CI]: 1.93 [1.77-2.11]; 1.51 [1.41-1.61]; 1.80 [1.61-2.01], respectively) and no treatment (1.84 [1.71-1.99]; 1.54 [1.45-1.63]; 2.19 [2.01-2.40], respectively) rather than cCRT (all p < .001). Moreover, higher SDI was associated with higher likelihood of receiving RT only (highest vs lowest SDI scores: 1.42 [1.33-1.52]), or no treatment (1.46 [1.38-1.55]) rather than cCRT (all p < .001). Longer driving time was associated with higher likelihood of receiving CT only ( > 120 mins vs < 30 mins: 1.24 [1.10-1.39]), or no treatment (1.33 [1.18-1.50]) rather than cCRT (all p < .001). Conclusions: Health policies should focus on patients who are not privately insured and live in neighborhoods with low SES. Moreover, helping their transportation needs may also improve the likelihood of receiving cCRT.
Opioid prescribing patterns among generalists & oncologists for Medicare Part D beneficiaries from 2013-2017.
Authors: Royce Trevor Joseph , Roberts Andrew , Dusetzina Stacie , Agarwal Ankit 
Published: May 2020

2070 Background: In response to the opioid crisis, recent policies aiming to reduce opioid prescribing, misuse, & abuse have generated concern that patients with cancer pain may unintentionally experience reduced access to necessary opioid therapy. It is unknown how opioid prescribing patterns have changed between generalists and oncologists during this era. Methods: We conducted a longitudinal repeated cross-sectional study estimating adjusted annual national trends in opioid prescribing among generalists & oncologists using the Medicare Part D Prescriber Public Use Files 2013-2017. Poisson models estimated annual adjusted predicted mean rates of opioid prescribing-per-1,000 total prescriptions & long-acting opioid prescribing per-1,000 opioid prescriptions. Poisson models estimated adjusted incidence rate ratios (aIRRs) to quantify annual changes in prescribing rates. Results: From 2013-2017 the annual adjusted predicted mean rate of opioid prescriptions per 1,000 total prescriptions decreased from 53.4 to 41.3 among generalists (aIRR = 0.78; p < 0.01) and from 133.2 to 105.9 among oncologists (aIRR = 0.83; p < 0.01). The rate of long-acting opioid fills per 1,000 opioid prescriptions decreased from 96.0 to 87.0 (aIRR = 0.87; p < 0.01) and 235.1 to 222.5 (aIRR = 0.95; p < 0.01) for generalists & oncologists, respectively (Table). Conclusions: We found large declines in overall opioid prescribing rates among generalists (-22%) and oncologists (-17%) from 2013-2017. Long-acting opioid prescribing rates decreased over 2.5-times more among generalists than oncologists. Opioid policy & advocacy have been effective in reducing the extent of opioid prescribing in the Medicare population but how much of the decrease in prescribing by oncologists is ‘appropriate’ versus ‘inappropriate’ deserves further investigation. [Table: see text]
Mismatch between mortality burden and number of FDA registration trials in highly lethal cancers.
Authors: Gyawali Bishal , Yang Peter C. , Rubinstein Sam , Schoen Martin W. , Khaki Ali Raza , Warner Jeremy Lyle 
Published: May 2020

2072 Background: Treatment successes in cancer are achieved through new drugs tested in clinical trials. However, drug discovery has been disparate across cancer types for various reasons. We sought to investigate if the number of trials used to support United States Food and Drug Administration (FDA) drug approvals is proportional to the incidence and mortality burden of highly lethal cancers, i.e. those with an expected relative mortality of >5% per Cancer Statistics, 2020 (Siegel et al.). Methods: All FDA labels for 258 antineoplastic cancer drugs approved as of January 2020 were reviewed for citations of registration trials supporting initial approval and additional indications. Trials were identified by matching described characteristics (e.g., patients enrolled, clinical trial NCT codes) to publications indexed on HemOnc.org. Trials were labeled by cancer type studied and type of trial (randomized vs non-randomized). Results: We identified 559 registration trials in total. Results for the six highly lethal cancers are shown in the table. The percent of registration trials was roughly proportional to incidence, but not mortality burden. For example, despite the 22% expected mortality burden of lung cancer, it had a share of only 11% of registration trials whereas breast cancer has an expected 7% mortality burden, with a share of 14% of registration trials. Chronic myeloid leukemia is expected to cause 1,130 deaths in 2020 (0.2%) and has had 20 registration trials (3.6%). The highly lethal cancers had a higher rate of randomized trials supporting approval than other cancers (84% vs 56%, p<0.001 [Chi-square]). Conclusions: While the findings may in part be due to disease biology (e.g., pancreatic ductal adenocarcinoma has proven resistant to many novel therapies), our evaluation highlights a potential mismatch between resources and needs. Randomized trials were more often used to support new drug approvals in highly lethal cancers. These findings will be important in regulatory policy. [Table: see text]