tbuckley/cabot-search huggingface dataset:
Quantifying clinical actionability of updated tumor profiling reports through electronic health record (EHR)-enabled, on-demand ordering and resulting.
Authors: Karen M. Huelsman, James F. Maher, Andrew J. Parchman, Ryan D Jones, Leo Posteraro, James Lin Chen
Journal: Journal of Clinical Oncology (2024)

e13621 Background: Next-generation sequencing reports provide matched therapies and clinical trials based on genomic alterations to drive therapeutic decisions in clinical practice. Biomarker-matched oncology drug approvals and trials have grown tremendously over the past decade. From January 2000 to October 2022 alone, a total of 573 agents were approved for various oncology indications, with nearly half being targeted therapies. While matched therapies and clinical trials are current at time of issue, updated variant classification, changes in FDA approvals, and trials available are not reflected in static reports. It is not always practical nor feasible to provide a new biopsy specimen. To address this gap in clinical care, we piloted Tempus Refresh, an on-demand informatics service that provides clinicians with refreshed therapy and trial matches from prior molecular reports directly through the EHR. Here we quantify the clinical actionability of Refresh reports. Methods: TriHealth piloted the Tempus Refresh orderable through the Epic EHR at the treating physician’s discretion. The clinical team specified prior DNA molecular reports (tissue or blood) to update. Orders were transmitted to Tempus through an HL7 interface. All alterations provided on the prior report were updated for therapeutic and trial matches through existing workflows. An updated structured report was then delivered to the TriHealth Epic EHR linked in the lab tab. Results: From 2/2023 to 11/2023, 10 TriHealth providers participated. A total of 35 unique Refresh orders were placed via custom HL7 Epic order, of which 3 were canceled due to orders outside of pilot parameters. Of the 32 Refresh reports, 34% (11/32) contained new FDA-approved therapies (OncoKB Level 1 or 2), and 78% (25/32) were annotated with new clinical trials. The average elapsed time from original date of report issuance to Refresh order delivery was 14.5 months (range 1 month to 45 months). The mean turnaround time for issuance of a Refresh report was 29 hours (range 5 hours to 53 hours). Conclusions: These results demonstrate that on-demand report updates increase the availability of tumor biomarker-matched therapies and trials. Furthermore, the study exhibits the feasibility of integrating updates directly into the standard EHR clinical workflow. This service provides visibility across teams and can support specialty pharmacists in therapy authorization. Considering the success of this pilot, future studies will assess the expansion of Tempus Refresh to additional sites.
Impact of staffing an inpatient oncology consult service with dedicated hospital-based oncologists.
Authors: Sam Brondfield, Daniel H Kwon, Andrew H. Ko, Robert M. Wachter, Eric J. Small
Journal: Journal of Clinical Oncology (2024)

e13538 Background: No standard care model for hospitalized patients with cancer currently exists. The majority of oncology care is provided in the outpatient setting. Consequently, for many oncologists, inpatient care is a secondary responsibility. To address this issue, several U.S. institutions have changed their inpatient oncology models to include dedicated inpatient oncology-trained attendings (hospital-based oncologists). In this study, we investigate outcomes following such a change on an inpatient solid oncology consult service. Methods: This is a single-institution retrospective study of the University of California, San Francisco (UCSF)’s inpatient solid oncology consult service, which switched from a rotating outpatient oncologist attending model to a dedicated inpatient oncologist model in 2018. Since data prior to the switch were not available, we compared all inpatients who received an oncology consult (n=3974) with a comparison group (CG) of all inpatients with a solid tumor diagnosis who did not receive an oncology consult (n=3200) between fiscal years 2018 and 2023. We analyzed trends in case mix index (case complexity), in-hospital mortality index (observed/expected), 30-day unplanned readmission rate, length of stay index, and direct cost index using the Pearson correlation coefficient. In 2022, we conducted an anonymous retrospective pre/post-switch survey of hospitalists who had worked on the hospital medicine service both before and after the switch and compared mean satisfaction with oncology consultation (5-point scale) using Student’s t test. Results: Following the switch, the number of weeks per year covered by dedicated inpatient oncologists increased from seven (2018) to 39 (2023) out of 52. From 2018 to 2023, we found a significant increase in annual consults (495 to 919, r=0.95, p&lt;0.01) and case mix index (2.30 to 3.30, r=0.90, p=0.01; CG 1.60 to 1.70, r=0.60, p=0.20), and a significant decrease in mortality index (1.59 to 0.70, r=-0.98, p&lt;0.01; CG 0.90 to 0.48, r=-0.66, p=0.15). There were non-significant decreases in 30-day unplanned readmission rate (35.8% to 30.5%, r=-0.77, p=0.07), length of stay index (1.45 to 1.32, r=-0.75, p=0.08), and direct cost index (2.2 to 1.8, r=-0.61, p=0.20), similar to the CG. Mean (standard deviation) satisfaction increased from 3.27 (1.22) pre-switch to 4.53 (0.92) post-switch ( p=0.02, response rate 15/51, 29%). Conclusions: Switching to a dedicated inpatient oncologist model on a consult service was associated with decreased in-hospital mortality and increased satisfaction with oncology consultation despite growing case volume and complexity, while readmission rate, length of stay, and cost remained unchanged. This study adds to growing literature supporting dedicated inpatient oncologist attending models of care and is in line with recent literature from other specialties supporting similar inpatient care models.
Effect of alternative dosing strategies on sustainable healthcare: A carbon footprint analysis of nivolumab and pembrolizumab treatment in the Netherlands.
Authors: Ruben Malmberg, Jurrien Loosveld, Hans-Peter Schilte, Roelof W.F. van Leeuwen, Alex Burdorf
Journal: Journal of Clinical Oncology (2024)

e13564 Background: Hospitals contribute significantly to greenhouse gas (GHG) emissions and face a moral obligation to prioritize reduction of GHG emissions. Drugs constitute an important component of the GHG emissions of hospitals. Alternative dosing strategies (ADS) have been implemented to improve the cost-effectiveness of pembrolizumab and nivolumab (Table). However, the impact of these ADS on GHG emissions remains unknown. Therefore, this study aimed to analyse the effect of ADS implementation on the carbon footprint of treatment with pembrolizumab and nivolumab. Methods: Life Cycle Assessment (LCA) methodology was used to quantify the environmental impact. The impact category climate change was used to quantify the carbon emissions (CO 2 e). The GHG Protocol was used to categorize the emissions into scopes 1, 2 and 3. Life Cycle Inventory and Impact data from an academic hospital in Rotterdam were used in the LCA to calculate the CO 2 e for pembrolizumab and nivolumab, their different dosing intervals, and the impact of ADS. Results: In 2022, the carbon emissions related to nivolumab and pembrolizumab treatment were 445 tons CO 2 e, with an average of 94 kg CO 2 e per dosage. Pharmaceutical production was the main driver of treatment-related GHG emissions (93% of total emissions on average). Applying ADS, resulted in carbon emission reductions of 21.4-26.2% and 9.3-11.2% for pembrolizumab and nivolumab respectively (table 1). Conclusions: This study shows the substantial environmental impact of cancer treatments with pembrolizumab and nivolumab and calls for further implementation of ADS for pembrolizumab and nivolumab and other anti-PD-(L)1 monoclonal antibodies and more sustainable pharmaceutical production processes. These findings help to create environmental awareness and contribute to the promotion and understanding of sustainable low-carbon healthcare practices. Overview of registered doses of pembrolizumab and nivolumab, alternative dosing strategies and potential reductions in GHG emissions, based on real world data in The Erasmus University Medical Center in Rotterdam, the Netherlands. [Table: see text]
Improving sleep outcomes in patients with cancer with chronic insomnia using cognitive behavioral therapy: A retrospective study.
Authors: Constanza Martinez, Martha M Kato, Maria Currier
Journal: Journal of Clinical Oncology (2024)

e13555 Background: Cancer patients experience a threefold higher rate of insomnia (23-54%) than the general population. Cognitive behavioral therapy for Insomnia (CBT- i) is the gold standard treatment of chronic insomnia. Few studies, however, have demonstrated CBT- i efficacy for chronic insomnia in cancer patients. To address the needs of cancer patients with insomnia, a psycho-oncology physician trained to provide CBT- i therapy established an insomnia clinic in an ambulatory oncology center tailored for oncology patients. The objective of this study is to assess the efficacy of CBT- i for chronic insomnia among cancer patients. Methods: A retrospective review of electronic medical records was performed on consecutive referrals to an insomnia clinic in an ambulatory oncology center. All patients with ICD-10 confirmed chronic insomnia diagnosis who received at least 2 sessions up to a maximum of 7 sessions of CBT- i were included in the analysis. Demographic variables, cancer-related disease and treatment variables, and sleep parameters at initial session (T1) and last session (T2) were collected. Sleep parameters included sleep efficiency, total sleep time, sleep onset latency, number of nightly awakenings, and sleep quality. Semi-structured psychiatric clinical interviews, past medical and sleep history, and patient-rated Pittsburgh Sleep Quality Index (PSQI) scores at T1 were collected. Data analysis utilized SPSS descriptive statistics and T-test to compare differences between the baseline (T1) and post-CBT- i treatment (T2) sleep variable outcome measures. Results: The sample included 18 cancer patients (mean age 59 years; 12 Hispanics, 6 non-Hispanics; 16 females, 2 males) with ICD-10-confirmed chronic insomnia treated with CBT- i. Cancer-related variables among the sample included a mean duration of 18 months since cancer diagnosis, disease staging ranging from 0-IV, and 16 patients completed curative treatment. The sample included 13 breast cancer patients. Mean duration of insomnia was 6 years. PSQI score was 14 (PSQI score ranges from 0 to 21, scores above 5 are indicative of poor sleep quality). The mean number of sessions delivered was 5. Sleep parameters at T2 showed a significant improvement compared to those at T1 (Table). The number of nightly awakenings decreased from 3 at T1 to 2 at T2 (p=0.01), and 78% (14/18) of patients reported an improvement in their sleep quality at T2. Conclusions: Cognitive Behavioral Therapy for Insomnia demonstrates efficacy in the treatment of chronic insomnia in cancer patients and is easily administered within an ambulatory oncology center. [Table: see text]
Changes in non-small cell lung cancer (NSCLC) next-generation sequencing (NGS) rates after electronic health record (EHR) integration using large-scale, multi-institutional, real-world data.
Authors: Marc Ryan Matrana, Patrick Mergler, Leo Posteraro, James Lin Chen
Journal: Journal of Clinical Oncology (2024)

e13619 Background: NSCLC treatment guidelines recommend comprehensive NGS testing to detect genomic alterations and gene expression in key biomarkers that are relevant in diagnosis, therapy selection, prognosis, and clinical trials. While 70% of patients with NSCLC will have biomarker alterations related to therapeutic options, a large minority of patients still do not receive biomarker testing. Clinical workflow challenges outside the EHR ecosystem, like paper requisitions and faxed results, have been cited as barriers to adoption. To reduce sequencing barriers, Tempus deploys NGS through direct result EHR connection. To evaluate whether integrating comprehensive NGS ordering and resulting into the EHR would improve testing rates, we retrospectively analyzed NSCLC NGS orders from 29 clinical networks which represent over 1500 medical oncologists, before and after EHR integration. Methods: Clinical networks that partnered with Tempus to perform an NGS EHR integration were identified. Only networks with at least 6 months of pre- and post-integration unique patient NGS orders were included. De-identified patient information from these sites were obtained from the Tempus laboratory information management system. NSCLC diagnosis was determined through ICD9/10 codes. NGS tests ordered were limited to xT/xR (solid tumor DNA and RNA), xE (solid tumor whole exome), or xF (circulating tumor DNA). Aggregate unique patients with orders were summated on a per site basis. Descriptive statistics were used to characterize the findings. Results: From May 2019 to July 2023, 29 clinical cancer networks (48% academic medical center, 45% regional health system) that underwent a Tempus NGS EHR integration were identified. Of the 29 networks considered, 3 were excluded due to insufficient data. Of the evaluable 26 networks, Epic EHR was used in the majority (24 of 26). In aggregate, a total of 1825 NSCLC patients were sequenced pre-integration as compared to 2796 patients sequenced post-integration, representing a 53% increase. Academic medical centers experienced a higher increase versus regional health systems (65% v 48%). When examined on a per network basis, the median and mean increase in NSCLC patients sequenced were 49% and 118%, respectively. Conclusions: In this retrospective analysis, Tempus EHR integrations increased ordering of comprehensive NSCLC NGS by 53%. Despite study limitations such as the incomplete resolution into internal alternate sequencing workflows, this large-scale analysis provides further evidence that EHR-based workflow improvements reduce the barrier to appropriate sequencing.
Effect of a comprehensive cancer care pathway and a navigation cancer program on breast cancer care in a middle-income country.
Authors: Marilen Escobar, Maria A Bravo, Sergio Cervera, Ana M Osorio, Jose Joaquin Caicedo, Sandra Ximena Franco, William Armando Mantilla
Journal: Journal of Clinical Oncology (2024)

e13536 Background: Breast cancer (BC) is the most common cancer in women globally and in Colombia, with around 16.000 new cases estimated in 2022. Mortality associated with BC has decreased for 2 main reasons: screening that leads to early diagnosis and improved treatment of BC. However, BC related mortality remains stable in Colombia, with a 5-year overall survival of 74%, lower than other countries on the continent. This difference could be explained, among other things, by delays, barriers, and fragmentation of cancer care. Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (CTIC) is an institution designed for comprehensive cancer care, with specialized patient care through cancer clinics dedicated to a single disease (breast cancer, lung cancer, etc.). Every cancer clinic is associated with a cancer navigation program and has clinical care pathways with metrics that seek to monitor and reduce the time to diagnosis and treatment, among others. This study aims to describe the results of the comprehensive breast cancer care program and cancer navigation support in time to breast cancer diagnosis and treatment access. Methods: This descriptive study was carried out by retrospective reviewing of the navigation database from the breast cancer unit of the CTIC, sociodemographic information, clinical characteristics, and time to diagnosis (from first appointment to pathology report) and treatment were collected and described. A comparison between patients with comprehensive and fragmented care was done. Results: Since August 2022, 600 patients with confirmed breast cancer have been evaluated by the BC clinic, 401 (66.8%) patients were admitted through the cancer navigation program, 436 (72.6%) cases presented with a previously established diagnosis of breast cancer, and 164 (27.3%) were the novo cases. Of the novo cases, 37 (23.3%) cases received comprehensive care from diagnosis, with a time to diagnosis of 14 days, and time to treatment of 30.5 days, patients who did not receive comprehensive care presented a time to treatment of 68.1 days. Conclusions: A comprehensive cancer care and support from a navigation program significantly reduce the time to treatment in breast cancer patients. A comprehensive care model in Colombia has the opportunity to generate a better opportunity for access to diagnosis (14 days) and treatment (30.5 days) compared to the time to treatment data reported in the high-cost account (CAC) (27 and 52 days respectively). [Table: see text]
Automating the detection of treatment progression in patients with lung cancer using large language models.
Authors: Sonish Sivarajkumar, Subhash Edupuganti, Manisha Bhattacharya, David Lazris, Michael Davis, Yufei Huang, Yanshan Wang
Journal: Journal of Clinical Oncology (2024)

e13620 Background: Accurate assessment of treatment response is fundamental to advancing cancer patient care, particularly in lung cancer, where treatment modalities are diverse and complex. The extraction of treatment response, particularly disease progression information, from Electronic Health Records (EHRs) is essential for several reasons: It facilitates generation of real-world evidence from real-world data, enables personalized treatment planning, and contributes to a broader understanding of cancer therapeutics. However, much of treatment response information is documented in free-text EHR notes and traditional methods of extracting such information from notes are labor-intensive, error-prone, and inefficient, presenting significant barriers to timely and accurate real-world evidence studies. The advancement of Natural Language Processing (NLP) technologies, especially Large Language Models (LLMs), presents a transformative opportunity to automate the extraction of treatment responses. Methods: We used a cohort of 1,953 primary lung cancer patients identified from UPMC Hillman Cancer Center cancer registry and retrieved ~113,000 clinical notes from UPMC clinical data warehouse. We focused on extracting disease progression information, following the RECIST guidelines which define progression as an increase in tumor size or cancer markers after therapy. We selected ~50 notes to perform manual annotations by an experienced oncologist and created a gold standard dataset to validate the NLP models. We fine-tuned a state-of-the-art open-source LLM named LLAMA-2 and compared against a traditional rule-based NLP system for the automated extraction of disease progression from notes. The process of fine-tuning involved adjusting the model's parameters specifically to improve its ability to recognize and classify instances of disease progression. Results: Our analysis demonstrated a significant enhancement in performance with the LLM compared to the traditional rule-based NLP system. LLM exhibited a remarkable increase in sensitivity by ~37%, indicating its superior ability to accurately identify disease progression. Additionally, the model maintained high specificity and PPV, achieving scores nearly comparable to the rule-based system but with a notable improvement in the F1-score by ~14%. Conclusions: Our research highlights the transformative potential of LLM-based NLP algorithms in automating the extraction of treatment responses from free-text EHRs. This methodology not only provides a scalable and efficient mechanism for processing large volumes of clinical text but also significantly enhances the accuracy of lung cancer treatment response assessments. [Table: see text]
Opportunity cost of oncology subspecialist opinion.
Authors: Afsaneh Barzi, Crystal Liang, Carol Wright, Nitya Nathwani, Debra A. Wong, Todd M. Sachs
Journal: Journal of Clinical Oncology (2024)

e13578 Background: The adoption of precision oncology has resulted in a drastic rise in the cost of cancer care in the United States. Payers, providers, and professional organizations are implementing strategies to curb this rising cost. These efforts are mainly focused on the providers patterns of care, such as prescribing behavior and risk assessment for needs such as ER visits and hospitalizations. Most efforts are cross-cutting and intended to work across different tumor types. Yet, with the precision nature of cancer care these cross-cutting efforts have not succeeded. AccessHope (AH) provides an independent subspecialist review of the management cancer as a covered benefit through the employer (Accountable Precision Oncology: APO). The aim of this service is optimizing care delivery to the current and evolving paradigms for each cancer type and sub-type. Here we describe overutilization of treatments and assessments of APO. Methods: AH database was accessed for de-identified cases (with contractual de-identification rights) for this analysis. Cases were completed 1/1/2022 – 12/31/2023. Descriptive statistics was used to describe the observed patterns identified. Results: Among 3644 APOs, 428 (11.7%) had evidence of overutilization. Gastrointestinal, hematological, and thoracic cancers represented 75% of the over utilization cases (Table). Overutilization was categorized by systemic therapy, radiation therapy, invasive procedures, imaging, and testing. Systemic therapy was the most common category (67.8%) and included non-indication use and extended duration of use. This was followed by imaging (22.2%). Systemic agents overutilized varied by cancer site. Bevacizumab and oxaliplatin were commonly overutilized in gastrointestinal cancers; rituximab, imatinib, and dasatinib in hematological malignancies; and check point inhibitors (pembrolizumab, atezolizumab, and durvalumab) in thoracic malignancies. In the imaging category, PET scan overutilization was observed across different tumor types (14.7% of the total volume). We also observed overutilization of molecular testing with ctDNA (Signatera) being at the top of the frequently overutilized tests in cases with no data to support a role for it. Conclusions: This patient centered approach identified areas of overutilization in evaluation and treatment of patients through the expert review process. The reviewer’s depth of knowledge about reported data and limitation of treatments and evaluations is the key to this process. Further analyses of patterns of care and categorization of the overutilization patterns is underway. [Table: see text]