bluuebunny/crossref_metadata_2025 huggingface dataset:
First-line maintenance use among patients with advanced ovarian cancer in a community oncology setting: A retrospective observational study in the United States (US).
Authors: Chase Dana Meredith , Iadeluca Laura , Lim Jonathan , Tseng Wan-Yu , Bulsara Purva , Todoroff Karen , Patton Gregory
Published: October 2023

544

Background: Ovarian cancer (OC) accounts for 1.1% of all cancers and is the fifth leading cause of cancer-related death in women in the US. Poly(ADP-ribose) polymerase inhibitors (PARPi) are targeted therapies that induce synthetic lethality in tumor cells with homologous recombination deficiencies, including BReast CAncer gene (BRCA) mutations. Bevacizumab is a monoclonal antibody targeting vascular endothelial growth factor (VEGF) ligand to inhibit angiogenesis. Active surveillance (AS) is close monitoring of patients with OC without providing treatment. Both maintenance therapy and AS require frequent surveillance to achieve optimal outcomes. Previous studies have shown that maintenance therapy with PARPi and/or bevacizumab are effective treatment strategies for patients with advanced stage OC, however, they are often underutilized by physicians (Gogineni V et al. J Cancer 2021;12(1):38-53). Methods: This retrospective study accessed the Ontada iKnowMed database to review the charts of patients who initiated platinum-based chemotherapy (PBC) in the first-line (1L) setting between January 1, 2018–December 31, 2020. Follow-up data for patients were collected through September 30, 2021. Kaplan-Meier estimates were used to compare utilization of maintenance therapy versus AS following 1L PBC for real-world progression-free survival (rwPFS) and overall survival (OS). Results: Following 1L PBC, 239 (39.8%) patients received maintenance therapy (56.5% received PARPis, 34.3% received bevacizumab and 9.2% received PARPi and bevacizumab) and 315 (52.5%) received AS. Median (min, max) follow-up time from initial diagnosis was 20.1 (2.2, 43.3) months for patients who received AS and 20.0 (4.4, 44.2) months for patients who received 1L maintenance therapy. Mean (standard deviation [SD]) age was 62.4 (12.0) and 65.8 (12.9) years for the maintenance and AS groups, respectively. Biomarker testing was not available in >40% of patients. Observed median rwPFS was 26.9 months (95% confidence interval [CI]: 21.3, not reached) for patients who received 1L maintenance therapy and 11.3 months (95% CI: 9.5, 13.0) for patients who received AS (log rank p<0.0001). Whilst observed median OS was not reached for either group, OS probabilities were 95.7%, 88.3%, and 82.4% at 1 year, 2 years, and 3 years respectively for patients who received 1L maintenance therapy and 85.9%, 71.5%, and 58.0% for patients who received AS. Conclusions: Despite previous studies demonstrating the benefits of maintenance therapy, <50% of patients in this study received 1L maintenance therapy following 1L PBC. These results show that maintenance therapies can improve rwPFS and OS in patients with advanced OC compared to AS. Therefore, there is a need to better understand maintenance therapy provider preferences and/or challenges. Funding: GSK 217225.
Perceptions of emerging PARP inhibitor (PARPi) data among practicing oncologists in prostate cancer.
Authors: Moffett Nicholas , Bone Robert , Dulka Brooke , Ramnaraign Brian Hemendra , Gajra Ajeet , Jeune-Smith Yolaine
Published: October 2023

546

Background: Studies suggest that PARPi agents work best in cancers, including metastatic castration-resistant prostate cancer (mCRPC), that have mutations in DNA repair pathways regulated by homologous recombination repair ( HRR) genes . The first PARPis for prostate cancer (olaparib for HRR-mutated and rucaparib for BRCA-mutated CRPC) were approved in 2020. Two recent trials, TALAPRO-2 and PROpel, evaluate PARPis plus an androgen receptor pathway inhibitor for patients both with and without genetic mutations were presented at ASCO GU 2023. This study aimed to determine oncologists’ perceptions of these new data. Methods: In April 2023, US-based oncologists were invited to attend one of two meetings to discuss data from ASCO GU 2023. Demographics were collected in an online survey, and perceptions of the TALAPRO-2 and PROpel trials were captured via audience response system technology at the meetings. Results: Across the two meetings, 115 oncologists were surveyed; 99 oncologists (86%) indicated that they manage patients with prostate cancer. Of these, 74% reported that they either very often or always test for HRR mutations, and the majority are testing at initial diagnosis of metastatic disease. Prior to reviewing trial updates, 84% of oncologists reported olaparib as their preferred PARPi, and the top factors driving their preferences are efficacy (83%) and safety/tolerability (63%). Perceptions of the trial updates are reported in Table 1. A subset of oncologists were also asked their treatment preferences for patients with mCRPC before and after the trial updates. Altogether, there was a 41% increase in physicians who would use talazoparib plus enzalutamide in an ATM-mutated patient after progression on docetaxel. They were also more likely to report (35%) a preference for this combination for a BRCA1-mutated patient after progression on abiraterone acetate. Additionally, there was a 27% increase in physicians who would use olaparib plus abiraterone acetate in an HRR-wild type patient after enzalutamide. Conclusions: Overall, these data show that clinical updates regarding PARPi agents have an impact on practicing oncologists. However, further exploration of genetic testing practices among oncologists and the impact of emerging data on real-world practice is needed.[Table: see text]
Optimizing GCSF prophylaxis: Artificial intelligence (AI) models to predict chemotherapy-induced FN in patients with cancer.
Authors: Flanigan Jeanine , Aslam Saad , Woldman Robert L. , Zhang Yang , Johnson Mary , Blodgett Moira , Kilgore Emily , Anderson Amy J. , Arani Ramin , Li Edward C.
Published: October 2023

547

Background: NCCN guidelines recommend primary prophylaxis with granulocyte-colony stimulating factors (GCSF) for select risk classifications; however, the recommendations for a patient at <20% risk of FN are not as explicit. This study builds on previously developed FN risk models and aims to use AI modeling techniques to predict individual risk of neutropenic complications in patients receiving cancer treatment. Methods: An AI-based FN risk modeling study was conducted using retrospective administrative claims of adult commercial and Medicare Advantage patients treated for non-myeloid malignancies from Jan 01 2009 to March 31 2020. Exclusions were myeloid malignancy, pregnancy, clinical trial participation, HIV positive status, hematopoietic cell transplantation, baseline GCSF, surgery or radiation in the first six days of the first chemotherapy cycle. Model features included baseline patient clinical and demographic characteristics, baseline HCRU, chemotherapy treatment patterns, FN risk factors, and use of primary GCSF prophylaxis. The models were developed with an 80/20 train/test split and compared using standard measures of goodness of fit. Various traditional and AI techniques, such as logistic and lasso regressions, TensorFlow Keras, and XGBoost, were trained to predict FN events in this sample. Based on performance, XGBoost was selected for analysis (ROC of 0.86). Results: The sample comprised of (mean age 68 years, 50% female). Among 17,233 patients (20% test set), 2,773 patients (all risk categories) received primary GCSF prophylaxis; 14,460 patients did not receive primary GCSF prophylaxis. The top 20% risk score threshold was deemed optimal to identify high-risk patients. Model outcomes were bifurcated and analyzed based on NCCN guided risk levels; for the high risk chemo regimen, intermediate risk regimen, and remaining patients without primary GCSF prophylaxis, model sensitivity was 74%, 88%, and 56% respectively. Model specifications for the overall top 20% of the test cohort without primary GCSF prophylaxis are described in the table as overall, high risk, intermediate risk, and other risk subsets. Conclusions: While the XGBoost model provided the best fit, the logistic regression model is a strong candidate to identify characteristics associated with higher odds of an FN event. Through identification of these characteristics, the rate of FN in this population can be better addressed and optimize utilization of GCSFs for primary prophylaxis.[Table: see text]
ACS CARES: A digital approach to supporting health information and social needs among people with cancer and caregivers.
Authors: Morris Bonny , Sivendran Shanthi , Rocque Gabrielle Betty , Kamal Arif
Published: October 2023

548

Background: ACS CARES (The American Cancer Society Community Access to Resources, Education, and Support) is the first nationwide non-clinical navigation program for people with cancer and caregivers. ACS CARES is a multi-channel approach providing direct, individualized, non-clinical assistance through a native app, telephonic or in-person support. Here we examine preliminary feasibility and acceptability of digital navigation through the ACS CARES app. Methods: The ACS CARES app became available for download on June 1, 2023. It includes sociodemographic and cancer-related questions, and a health information and social needs assessment adapted from the NCCN distress thermometer and problem list. Based on patient or caregiver responses, the app curates information and resources tailored to each user’s cancer journey and endorsed concerns. The aim is to deliver timely information to reduce distress, resources to mitigate social determinants of health, and emotional support to improve the cancer experience. Results: In the first three weeks, 349 people downloaded the app and 231 individuals (45% people with cancer, 55% caregivers) created profiles. Users represented 39 different cancer types across all stages, with 54% currently receiving treatment. Users are 75% white, 11% Hispanic, 63% female, with an average age of 55 (SD: 15). 11% of users are military/veteran, 15% rural, and from 43 states and US territories. The average initial distress screening was 4.0 on a scale of 0-10. Practical concerns were most frequently endorsed (n = 380, 36%). Cancer or health-related information was the most frequently endorsed practical concern with information on ‘Treatments, medications, procedures, and other therapies’ being the most common health information need. Fatigue, and worry, anxiety or fear were the most frequently endorsed physical and emotional concerns, respectively. Virtual support from someone with a similar cancer experience was the most commonly endorsed social concern. Users have accessed 357 health information or social needs resources in the app. Conclusions: Preliminary data suggest feasibility and acceptability of using a native app to access information and resources for cancer support. ACS has targeted efforts planned to support diverse user engagement. Peer support within the app and in-person support at four pilot cancer centers will begin in September.
Biomarker testing rates among metastatic cancer patients with receipt of anti-cancer systemic therapy: A real-world data analysis.
Authors: DaCosta Byfield Stacey , Bapat Bela , Becker Laura , Schroeder Brock , Chatzitheofilou Ismini , Hostin Damon , Reyes Carolina , Fox John Leonard
Published: October 2023

550

Background: Biomarker testing to direct individualized therapy options can optimize cancer patient outcomes, particularly for patients with advanced disease. Guideline-recommended molecular testing, comprehensive genomic profiling [CGP] and non-CGP testing, is covered by both Medicare and commercial private insurers (at least non-CGP).. This study examined real-world utilization of biomarker testing among cancer patients with metastatic disease who received anti-cancer systemic therapy. Methods: A retrospective analysis was conducted using de-identified administrative claims data from the Optum Labs Data Warehouse. Adults identified with 1 of 6 advanced cancer types from 1/2018 to 8/2021 were identified; index date was the first claim date for advanced disease. Patients with diagnosis codes indicating only lymph node involvement around the primary cancer were excluded. Continuous enrollment in a commercial (COM) or Medicare Advantage (MA) health plan with both medical and pharmacy benefits was required for 12 months prior to the index date (baseline), and ≥6 months after the index date (follow-up); patients with <6 months follow-up due to death were included. Receipt of anti-cancer systemic therapy during the follow-up was required. Biomarker testing was captured using Current Procedural Terminology (CPT®) codes indicating CGP (> 50 gene panels) or non-CGP (≤50 gene panels or single gene tests) during the study period. Rates of biomarker testing by cancer and insurance type, and receipt of targeted therapy and/ were assessed. Results: Result: There were 27,434 metastatic cancer patients meeting study criteria: 10,320 non-small cell lung (NSCLC), 5,525 breast (BC), 5,429 colorectal (CRC), 2,314 ovarian (OC), 872 gastric (GC) and 2,974 pancreatic (PC) cancer patients. 66% of patients were MA enrollees. The median age was 70 years and ranged from 67 years for BC patients to 71 years for NSCLC and PC patients. The median follow-up was 383 days, ranging from 246 days (for PC patients) to 564 days (for BC patients). Overall biomarker testing rates in the follow-up period were: 47% for NSCLC, 34% BC, 58% CRC, 61% OC, 48% GC, and 47% PC. Testing rates were lower among MA patients compared to COM patients (46% vs 53%). Receipt of targeted/monoclonal antibody therapy was higher (47% vs 33%, p<0.01) among patients with biomarker testing compared to those without. Conclusions: Rates of biomarker testing across metastatic tumor types are far from optimal despite guideline recommendations and insurance coverage for testing, and may affect quality of care. To improve adherence to biomarker testing guidelines, interventions to help overcome obstacles to biomarker testing are needed. Future analysis with this cohort will examine patient management and outcomes by receipt of testing, timing of testing and type of therapy received.
Smart phone application for early detection of adverse clinical outcomes in solid malignancy.
Authors: Sainamthip Panot , Kudtiyakarn Sirintip , Sakulma Palita , Supamontri Kittipoom , Sarojpaponpat Thithanan
Published: October 2023

562

Background: The widespread application of mobile technology in healthcare, particularly in the realm of telemedicine and Patient-Reported Outcomes (PRO), is revolutionizing care. We have developed a mobile application that leverages artificial intelligence (AI) and The Internet of Medical Things (IoMT) using machine learning (ML) assisted dashboard to enable real-time documentation and communication between patients and healthcare providers. The aim of our research is to explore the potential of this innovative combination in boosting patient quality of life (QoL) and enabling prompt, effective patient management. Methods: We conducted a pilot randomized controlled trial. The trial focused on comparing the quality of life at baseline, 1 month, and 3 months of cancer patients receiving standard conventional follow-up care versus those using our intervention. Patients receive a smartwatch and download our application, while a monitoring care team was established. Our application incorporates guidelines from MacMillan Cancer providing ML-guided symptom assessment. If adverse drug reactions or treatment effects occurred, the application provided prompt self-care suggestions. The secondary outcomes evaluated included cumulative emergency visits, cancer care knowledge assessment results, and the feasibility. Results: Between December 1, 2022, and June 1, 2023, a total of 30 patients were recruited for the trial. The average age of the patients was 51 years, with a majority being women (76.6%). The cohort comprised five types of solid malignancies, with breast cancer accounting for 56.6%. Most patients were in stage 4, and the aim of treatment for them was palliative (60%). The mean FACT-G scores were similar between the two groups at all time points. However, the control group showed a significant decrease in Functional Well-Being at 3 months compared to baseline (P = .025). Additionally, while the Intervention group had zero emergency visits, the Control group experienced a total of five visits (P = .016). The literacy test scores showed no significant difference. On average, individuals spent 60 minutes per day using the application, indicating high user engagement and overall feasibility. Conclusions: Mobile application, powered by AI and IoMT, has the potential to significantly impact patient care and further research is warranted to explore its broader implementation in healthcare settings. Clinical trial information: TCTR20230331002 .[Table: see text]
Automating the identification of patients admitted to tertiary care hospital with immunotherapy related adverse events (irAEs).
Authors: Manela Alon , Chaudhuri Anamika , Pimkina Julia S. , Camacho Svetlana , Ali Mohamed , Grosse Danielle , Day Angela , Jacobson Joseph
Published: October 2023

566

Background: Immune checkpoint inhibitors (ICIs) have revolutionized oncology by offering prolonged survival in advanced cancers that were previously treatment-refractory. However, the effectiveness of these agents is accompanied by the potential for autoimmune toxic effects known as immune-related adverse events (irAEs). These adverse effects are difficult to predict, can be long-lasting, and occasionally fatal. Early identification and treatment of irAEs can reduce morbidity and enable prompt resumption of anti-cancer therapy, ultimately improving patient outcomes. Methods: We manually reviewed 658 patient charts of individuals who received ICIs and were admitted to a tertiary care hospital over a three-month period. This process identified 750 phrases indicative of irAEs. Subsequently, an in-house Epic reporting team developed a program to search for these specific phrases within the charts of patients admitted to the hospital who had received ICIs in the 24 months prior to their admission. During the program's development, its performance was compared to the previous manual chart review process. Results: A comparative analysis was conducted between the new program and the current manual chart review over a three-month period. Implementing the new program resulted in a 68% reduction in the average number of charts requiring manual review (69 vs. 219 per month). Furthermore, the program identified an additional 17% of cases (21 vs. 18 per month). The positive predictive value increased by 400% (32% vs. 8% per month), and the time required to complete the task decreased by 70% (4.6 vs. 15 hours per month). Conclusions: Utilizing an EMR-based program that automatically searches for patients admitted with irAEs can significantly shorten the time required for identification and subsequent management of these complex patients.
Natural language processing identification of adverse events on immunotherapy clinical trials: Accuracy compared to trial reporting and physician review.
Authors: Friesner Isabel D , Kwon Daniel H , Ni Lisa , Bridge Mark , Fong Lawrence , Hong Julian C.
Published: October 2023

571

Background: Adverse event (AE) assessment is an important component of clinical trials and routine clinical care. Natural language processing (NLP) approaches may facilitate automated methods to identify AEs, potentially allowing both early intervention and streamlined trial AE documentation. We previously demonstrated that an NLP pipeline had high accuracy for identifying toxicities associated with radiotherapy from clinical notes compared to physician reviewers. This study aims to evaluate this pipeline in comparison with clinical trial reporting and physician reviewers. Methods: We identified a total of 53 encounters across 20 patients enrolled over four immunotherapy trials with both corresponding notes and associated AEs. Two independent physician reviewers (DK & LN) identified symptoms documented in notes using a National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 reporting tool. Likewise, a previously described NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was used on the notes to identify CTCAE symptoms. NLP-extracted symptoms and physician reviewer identification were assessed for sensitivity and specificity compared to trial-reported AEs. Cohen kappa score was used to assess inter-rater reliability between physician reviewers. Results: The five most reported trial AEs were diarrhea (17%), fever (17%), fatigue (15%), constipation (13.2%), and nausea (11.3%) (Table).Except for anorexia, NLP had greater or equal sensitivity for reported AEs compared to both reviewers (R1, R2). However, both reviewers had greater specificity than NLP. Between the reviewers, vomiting and anorexia had the highest Cohen kappa scores (1, 0.88), while fever and constipation had the lowest (0.31, 0.47, Table). Conclusions: NLP can detect toxicity symptoms from clinical notes with higher or equal sensitivity than physician reviewers, while physician reviewers had greater specificity. These results indicate that our NLP approach over-identifies symptoms while manual reviewers are more conservative in their approach. Therefore, NLP may best be served in the screening setting. NLP may also help reduce reviewer inter-rater variability, particularly in fever, constipation, and fatigue symptoms.[Table: see text]
Streamlining chemotherapy prior authorizations: Leveraging resources to cut delays and boost revenue.
Authors: Bhardwaj Aarti Sonia , Liu Mark , Pintova Sofya , Williams Chanda , Noble-Kirk Alana , Sheng Tianxiang , Liggins Jennifer , Favre Daniel , Smith Cardinale B.
Published: October 2023

573

Background: Prior authorizations (PAs) for systemic cancer treatments are a barrier to timely quality cancer care delivery. The high administrative burden to complete peer to peer’s (P2P) and appeals leads to care delays and revenue losses. At our tertiary academic cancer center, in partnership with our cancer registry and Epic teams, we shifted the clerical work required for PAs from clinicians to certified tumor registrars (CTRs) to increase authorization efficiency and decrease financial losses from denials. Methods: Clinicians place the treatment plan order which goes to the CTR work queue. We leveraged the Epic staging smart form to consolidate and auto-populate common elements needed for PA. The CTRs then complete and validate the form prior to the authorization specialists obtaining the PA. We compared the pre (1/2022-9/2022) and post-implementation (11/2022-5/2023) periods on our primary outcomes: average monthly number of PAs pending review (including P2P, appeals and authorizations requiring clarifying clinical documentation) and average time spent on PAs by the authorization specialists. We compared the same post implementation period to the same time the year before on the financial loss from denied PAs. Analyses were conducted using Welch's t-test. Results: There was an 11% reduction in the average monthly number of PAs pending review (350 vs 313; p=0.27) from pre to post implementation. There was an average improvement of 6 hours spent obtaining a final approval/per authorization (100 vs 106 hours; p=0.64) from pre to post implementation. In addition, there was a trend toward improvement in financial losses from PA denials from Dec 2022 to March 2023 and a cost savings of ~ 7 million dollars from the same time period pre-intervention; p=0.01 (Table). Conclusions: By leveraging the EHR and optimizing existing non-clinical staff workflows, we demonstrated a sustained decrease in the number of ambulatory PAs pending review, time spent on the PAs and significantly decreased financial losses from denials. Future work will include treatment and disease specific optimizations and evaluating the impact on clinician well-being.[Table: see text]
The use of mobile electronic prescribing to drive quality of oncology care delivery.
Authors: Speicher Stephen , Wynn Franklin , Porter Jason
Published: October 2023

586

Background: This pilot technology enhancement project set out to improve the quality of care for oncology patients by developing a mobile application for writing prescriptions (non-chemotherapy) and narcotics. We describe usage of this new mobile application and associated timeliness of care delivery, provider workflow efficiencies and patient safety metrics. Methods: Agile methodology was used to develop the workflow and software for the mobile application. Internal Patient Safety Usability Testing (PSUT) was used to assess high-risk tasks amongst potential users (n = 7). High-risk tasks were defined as those which, based on human error, could lead to potential patient harm or unsafe conditions. High-risk tasks included the ability for users to understand/acknowledge allergy and drug interactions, edit a selection, select and send to the appropriate pharmacy, and locate the record of the completed prescription within the patient’s chart through the mobile application. A pilot study was also conducted among oncologists (n = 14). A quantitative metric was measured to understand the adoption and usability of the mobile application. Qualitative feedback was also obtained. Results: 1. PSUT identified a need for improvement with recognition of interaction severity and differentiating an allergy versus a drug interaction. A design modification was also made to introduce a three-second hold to acknowledge an allergy. This was done to encourage users to take appropriate time to visualize and comprehend allergy and interaction information before advancing the workflow. 2. During the pilot study, 400 mobile prescriptions were written. After-hours writing of prescriptions in the mobile application increased (11.8%) compared to the EMR (3.5%). The average amount of time from initiation of writing a prescription to completion was 62 seconds in the mobile application. There were no high-risk patient safety incidents observed during the pilot study. Qualitative data from participants included themes around convenience, feasibility, improved access to care, and safety. Conclusions: Usage of the pilot mobile application demonstrated a safe and efficient approach to increase the quality of care delivery for oncology patients. Usage also suggests an increase in patient access to care after hours.
Objective physical activity measures to predict hospitalizations among patients with cancer receiving chemoradiotherapy.
Authors: Scotina Anthony D. , Lavine Jennie S. , Izmailova Elena S. , Kabarriti Rafi , Mehta Keyur J. , Garg Madhur , Kalnicki Shalom , Ohri Nitin
Published: October 2023

592

Background: There is an unmet need for measures that predict adverse events during cancer therapy. Previous studies have demonstrated feasibility and value of collecting physical activity (PA) data from cancer patients undergoing curative-intent chemoradiotherapy using a commercial fitness tracker. Here, we compare the predictive value of objective PA measures versus standard clinical information and assess whether additional PA measures beyond daily step count help predict hospitalization events during treatment. Methods: Cancer patients enrolled in NCT03115398 (2017-2020) wore a Garmin VivoFit continuously during chemoradiotherapy for a variety of solid tumors. ECOG performance status (PS) was assessed at baseline. PA measures tabulated for each day included total step count, peak 30-minute cadence (average step rate over the 30 most active minutes [not necessarily consecutive]), and sedentary time. Baseline PA measures were defined as mean values from up to 14 days prior to start of chemoradiotherapy. Cox regression models with time-dependent (3-day rolling averages of PA measures and recent EORTC QLQ-C30 quality of life [QoL] score) and time-fixed (baseline PA variables, age, sex, cancer diagnosis) covariates were used to identify predictors of hospitalization. The incremental predictive value of PA measures was assessed using logistic regression models and five-fold cross validation. Results: One hundred thirty-eight patients were included in the analysis. The most common cancer diagnoses were gastrointestinal cancer (29%) and head and neck cancer (28%). Twenty-four patients (17%) were hospitalized during chemoradiotherapy. Univariate Cox regression models identified baseline step count (hazard ratio [HR] = 0.85 per 1000 steps, 95% confidence interval [CI]: 0.74-0.98, p = 0.03) and baseline peak 30-minute cadence (HR = 0.86 per 10 steps/min, 95% CI: 0.74-0.99, p = 0.03) as the most significant prognostic predictors of hospitalization. Neither baseline PS nor baseline sedentary time were significant predictors of hospitalization risk. Including both baseline step count and peak 30-minute cadence in multivariable models did not improve predictive power. During treatment, peak 30-minute cadence was identified as the most powerful dynamic predictor of short-term hospitalization risk (HR = 0.83 per 10 steps/min, 95% CI: 0.75-0.92, p < 0.001). Conclusions: This study examined the relationship between a suite of PA measures and risk of hospitalization during concurrent chemoradiotherapy. Our results suggest that before treatment, daily step count average can serve as a simple and effective metric for identifying high-risk patients. During treatment, measures of peak activity may have additional utility for identifying patients in need of enhanced supportive care.
Impact of medically integrated pharmacies on oral anticancer medication prescription abandonment.
Authors: Doshi Gury K. , Wilfong Lalan S. , Dave Neal , Hemmen Wendy
Published: October 2023

66

Background: Due to the high cost of oral anticancer medications, financial toxicity may limit patient access to treatment. Abandonment occurs when a patient does not fill a prescription. Higher out of pocket costs have been associated with higher rates of prescription abandonment of treatment across cancers with national abandonment rates typically reported as 18%. An integrated patient assistance process within the care team as occurs in a medically integrated pharmacy is postulated to lower abandonment rates. We measured prescription abandonment rates in the Texas Oncology (TXO) medically integrated pharmacy (MIP). Methods: We reviewed prescription data from the electronic health record (EHR) and our pharmacy dispensing system, for all new oral anticancer medications prescribed from January 2022 to December 2022. Prescriptions that were sent electronically from the EHR but did not have a corresponding dispense in the pharmacy dispensing software were classified as having a barrier to fill. Among the prescriptions that had a barrier to fill, we randomly selected cases for further detailed review to identify cause of barrier. By reviewing the EHR, we also examined whether patients filled any alternate oral or injectable anticancer agent within 60 days due to a formulary block, patient choice or physician choice. Abandonment was defined when patients did not receive their treatment or appropriate alternative treatment within 60 days of the written prescription date. Results: 17,442 prescriptions for oral anticancer medications were prescribed by Texas Oncology physicians. Of those 8460 were filled by the TXO MIP without extensive barriers. 8982 of prescriptions were not filled at our TXO MIP and of those 2895 (32%) prescriptions were further reviewed to determine cause of barrier to fill. 1435 (49.5%) prescriptions had a pharmacy benefit manager (PBM) design that routed the prescription to an external pharmacy and out of the continuity of care of the MIP. 827 (29%) of patients experienced financial toxicity and received free drug assistance with the help of the MIP patient assistance team. 72 (1.3 %) of patients were started on an appropriate alternative therapy with input from the medically integrated care team. 15 patients did not start their oral anticancer treatment which resulted in an abandonment rate of less than 1%. Conclusions: National abandonment is reported as high as 18% largely due to high out of pocket costs. The Texas Oncology MIP has a dedicated patient assistance team. Within the MIP, although 29% of patients had a financial barrier to fill, the prescription abandonment rate was <1%. Oral anticancer medications filled in the MIP led to decreased prescription abandonment due to the integrated patient assistance, pharmacy, and clinical teams. A medically integrated pharmacy should be the preferred approach for specialty cancer medications.
ASCO Patient-Centered Cancer Care Certification (APC4) pilot: Assessing and improving care delivery gaps.
Authors: Cox John V. , Bowman Ronda Marie , Martin Eric , Woofter Kimberly , Gamble Bo , Gorman Veronica , Birch Walter , Grubbs Stephen S. , Polite Blase N.
Published: October 2023

67

Background: APC4 is a comprehensive oncology care delivery certification built on oncology medical home (OMH) standards developed by ASCO and COA. APC4 consists of 7 domains supported by 39 specific standards, including the chemotherapy safety domain, which incorporates the existing ASCO QOPI Certification Program. To test the standards and assess the feasibility of a vigorous certification process, ASCO embarked on a two-year pilot with twelve oncology practices. All twelve practices achieved certification, although each practice had to address gaps. We present the results of the gap assessment as determined by both onsite survey and practice self-assessment. Methods: The APC4 process involves an onsite survey, followed by cycles of monitored improvement. Certification is awarded after a practice has satisfactorily met all standards. We report here on the 17 care delivery standards not included in QCP. Onsite survey teams consisting of qualified oncology professionals evaluated practice performance against the standards. The final scoring was reviewed and agreed upon by a governance committee. In the final phase of the pilot, practices were surveyed to determine which standards required practices to adopt new procedures or to improve existing care processes to meet certification requirements. Results: At the initial site survey none of the twelve practices met 100% of the 17 standards. Combining the experience of all 12 practices – 68% of the care delivery standards were met (# total standards met by 12 practices / 17 standards X 12 practices) (range 47-82%). Following recalibration of care through implementing new or modified processes of care all 17 standards were met resulting in certification. For example, 85-95% of the practices had to implement new or improve existing processes to meet advanced care planning & goals of care standards; and 90% to deal with the health equity standard (notably 5 of the 12 practices developed completely new equity initiatives). Conclusions: The APC4 pilot was deemed to be feasible and is now being expanded. The presented pilot data suggest that even among oncology practices experienced in the implementation of therapy administration safety (10/12 were already QOPI certified) and practice transformation (11/12 participated in OCM) there is significant room for improvement to meet the standards of APC4 certification. The pilot also demonstrated that practices could identify and rectify gaps through a continued quality improvement focus. [Table: see text]
Outpatient versus inpatient delivery of intensive chemotherapy for patients with hematological neoplasms: A 5-year experience from a tertiary center.
Authors: Bin Dayil Luluh , Omer Mohamed H. , Salama Hind , Alaskar Ahmed , Alzahrani Mohsen , Alahmari Bader , Alharbi Mohammad , Alhejazi Ayman
Published: October 2023

7

Background: There has been a shift towards the utilization of outpatient delivery of intensive chemotherapy due to potential improvements in quality of life measures and resource utilization. Herein, we aim to report the findings of a five-year quality improvement project comparing the outcomes and healthcare costs of outpatient and inpatient delivery of post-induction intensive chemotherapy amongst patients with hematological neoplasms. Methods: Using Plan-Do-Study-Act (PDSA) methodology, a multidisciplinary team was established to determine appropriate strategies for delivering outpatient intensive chemotherapy and improving healthcare resource utilization. Records of patients receiving inpatient chemotherapy from 2016-2017 were retrospectively reviewed. Process mapping was utilized to design interventions to facilitate the outpatient delivery of chemotherapy. The interventions included the initiation of an outpatient chemotherapy clinic, creating clinical pathways for chemotherapy protocols, developing educational materials for patients and healthcare staff, and facilitating access to emergency care services. During 2018-2022, patients were prospectively identified and enrolled to receive outpatient chemotherapy. Results: 288 patients were enrolled with 70 (24.3%) receiving inpatient chemotherapy and 218 (75.7%) receiving outpatient chemotherapy. The median age for the inpatient and outpatient chemotherapy groups was 34 and 38, respectively [P = 0.027]. Patients enrolled in this project had diagnoses of acute leukemia (53.2%), Hodgkin’s lymphoma 16.3%, and non-Hodgkin’s lymphoma (30.6%). Febrile neutropenia was observed in 64.3% of patients receiving inpatient chemotherapy and in 27.5% of patients receiving outpatient chemotherapy [P < 0.001]. Death during the course of the chemotherapy regimens was seen in 4% of patients receiving inpatient chemotherapy compared to 0.5% of outpatient chemotherapy patients [P = 0.003]. Outpatient chemotherapy resulted in a 53.4% reduction in total bed days occupied. The estimated total cost saving due to this intervention during the years of 2018-2022 was $2.88 million USD. Conclusions: Outpatient delivery of intensive chemotherapy for hematological malignancies is an effective approach which results in comparable safety outcomes and significant improvements in cost-effectiveness and resource utilization.
Accuracy of an AI prediction platform in predicting tumor origin: A real-world study.
Authors: Bakshi Bea , Payling Miles
Published: October 2023

74

Background: Only four cancers are covered by screening programs. The remaining cancers are diagnosed after a point-of-care presentation to a primavry care physician (PCP). Patients with these cancers typically present with a combination of vague and non-specific symptoms, lacking specificity to a particular tumor type. The difficulties in interpreting these symptoms by PCPs leads to increased diagnostic intervals and the use of invasive diagnostic modalities with unnecessary radiation exposure. Localizing the cancer origin is critical to reduce time to diagnosis in order to improve earlier diagnosis and survival in these cancer types. C the Signs is a clinical platform integrated with electronic health records (EHR) that uses artificial intelligence to help identify patients at risk of cancer. Used by PCPs during a point-of-care assessment, C the Signs analyses the symptoms the patient presents with, alongside clinical data in the EHR, to identify current cancer risk, predict the tumor site of origin, and recommend the most effective diagnostic or referral pathway. The aim of this study is to evaluate the accuracy of C the Signs in predicting cancer origin for patients in a real-world setting. Methods: An observational study was conducted between 1st January 2021 and 31st October 2022 in the National Health Service in England utilizing all patients who were risk-assessed with the C the Signs EHR integrated platform. There was no pre-selection criteria for the patient population assessed. All patients were followed up with for 6 months post-risk assessment to determine if they had a cancer diagnosis. Analysis was performed on the patients who were identified at risk of cancer by the C the Signs platform and subsequently diagnosed with cancer, to establish the cancer origin accuracy. Results: 111,421 patients were risk-assessed by C the Signs, of which 7,360 were diagnosed with cancer. Of the patients diagnosed with cancer, 7,257 patients were identified at risk of cancer by C the Signs (98.67%). The platform predicted cancer origin accurately in 93.2% of patients. The platform was most accurate in predicting cancer origin for brain and CNS, breast, lung, gynecological, head & neck, bowel, skin, and prostate cancers. Conclusions: This study demonstrates how a cancer prediction platform such as C the Signs can be used to accurately detect tumor origin in patients with vague symptoms and facilitate targeted investigations appropriate to the tumor type. Further research is required to identify which cancers can be most accurately detected taking a computational approach.
Payor-driven denials and delays for cancer-related supportive care medications.
Authors: Thom Bridgette , Chino Fumiko , Ghazal Lauren Victoria , Jinna Sankeerth , Persaud Sonia , Salz Talya
Published: October 2023

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Background: Prior authorization (PA) requires providers to obtain payor pre-approval for a prescribed treatment in order for it to be covered financially. While PA can be used to ensure adherence to evidence-based guidelines and to contain payor costs, it often results in delayed or denied care, which is problematic for patients with cancer who are seeking urgent management of symptoms. This analysis sought to describe PA-related denials and delays for supportive care medications among patients with cancer. Methods: All outpatient prescriptions at an urban comprehensive cancer center requiring PA from 2021-2022 were reviewed to characterize PA for supportive care medications. Prescriptions included long-acting opioids (buprenorphine, fentanyl, hydromorphone, methadone, morphine, oxycodone, oxymorphone); non-opioid analgesics (duloxetine, pregabalin); and anti-emetics (aprepitant, dronabinol, granisetron, ondansetron, scopolamine). Prescription-level outcomes included decision status (approval vs. denial) and days to insurance decision. Bivariate analyses identified variations in PA denials by drug class and payor type. Among a subset of PA approvals for which data were available, we conducted bivariate analyses to evaluate associations of payor type and drug class with length of delay. Results: In the analysis period, 3304 supportive care medication prescriptions required PA: 2489 (75%) long-acting opioids; 131 (4%) non-opioid analgesics; 684 (21%) anti-emetics. Of these, 246 (8%) prescriptions were denied by the payor. The proportion of denied PAs for non-opioid analgesics (n=17; 13%) was greater than the proportion for opioids (n=182; 7%) and anti-emetics (n=17; 2%: χ2=6.5, df=2, p=.04). The proportion of denials among Medicare beneficiaries was greater than the proportion of denials among patients with private insurance, Medicaid, or Medicare Advantage plans (χ2=12.6, df=3, p=.006). Among denials, length from prescription entry to PA final denial ranged from 0-78 days (median=2.0), with no variation by payor type (F=0.85, p=.47). Among approved prescriptions with available data (n=851), 349 (41%) had same-day approval, with delays ranging from 1-54 days (median=2.0). Medicaid patients faced longer delays than patients from other payors (F=4.98, p=.002), with no difference in length of delay by drug class. Conclusions: The acute burden of cancer-related pain and nausea requires immediate mitigation, often with medication. PA can have a negative impact on the medication management of cancer-related pain and nausea: over one-half of supportive care medications had a ≥1 day delay, and 8% were denied altogether. Denials, or even brief delays, of provider-prescribed medication to ease symptom burden may lead to downstream increases in patient morbidity and payor costs, including symptom-related emergency room visits and hospitalization. Urgent efforts to reform PA are needed.
Geographic accessibility to palliative care clinicians for patients with newly diagnosed stage 4 cancer in the United States.
Authors: Jiang Changchuan , Fan Qinjin , Shi Kewei Sylvia , Hu Xin , Shao Yaxiong , Nipp Ryan David , Yabroff Robin , Han Xuesong
Published: October 2023

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Background: Early integration of palliative care is recommended for patients with newly diagnosed advanced cancer. However, many patients with cancer only receive this essential care during hospitalization or at the end of life, likely resulting in poor symptom management and increased morbidity, suffering, and financial burden for patients and families. This study aimed to examine disparities in geographical access to palliative care in the US. Methods: The study utilized data from the 2018-2019 National Cancer Database (NCDB) and CMS Medicare Physician and Other Practitioners dataset. Included patients were newly diagnosed with stage 4 cancer with geographic information for patients’ residential and their treating facilities’ zip codes. We geocoded the addresses of practices where physicians provided palliative care services 2018 -2019. The OpenStreetMap and Open Source Routing Machine (OSRM) model was employed to estimate the travel distance between patients' residences and the nearest palliative care clinician, as well as between their treating facilities and the nearest palliative care clinicians. Results: 335,191 stage 4 cancer patients and 1,063 clinicians who specialize in palliative care were included in the analysis. The most common cancer diagnoses were lung (28.8%), colorectal (10.2%), pancreatic (7.8%), and breast (4.9%) cancers. Distance to the nearest palliative care clinician was more than 12.5 miles for 63.4% of patients and 45.4% of treating facilities, and more than 50 miles for 24.1% of patients and 19.1% of treating facilities (Table). Patients who lived ≥50 miles away from the nearest palliative care clinic tended to be White, have a lower household income, and live in rural areas. Facilities located ≥50 miles away from the nearest palliative care clinics tended to be in rural areas and community or comprehensive community facilities. (all p<0.0001). Conclusions: 63.4% and 24.1% patients with newly diagnosed stage 4 cancers lived at least 12.5 miles and 50 miles from their nearest palliative care clinicians. Geographic disparities in palliative care availability underscore the need for policy and practice interventions aimed at improving equitable access to palliative care for patients with advanced cancer.[Table: see text]