bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing a team approach for successful accrual to and conduct of clinical trials in a community oncology practice.
Authors: Braun-Inglis Christa M , Agonoy Janela , Fukui Jami Aya 
Published: June 2022

e13524 Background: Oncologists and advanced practice providers (APPs) routinely work as part of a team to provide quality cancer care in today’s healthcare environment. However, this does not always translate into the realm of clinical research. It is estimated that only 2-8% of the adult oncology population enrolls in a clinical trial, with more than 20% of current trials failing to meet accrual goals. These numbers are projected to be even worse due to the impact of the COVID pandemic. Our Oncologist/APP team in conjunction with a clinical research coordinator (CRC) set out to develop a workflow to approach and offer clinical trials to patients in our practice with the goal of increasing clinical trial accrual and improving clinical trial conduct. Methods: Between 1/1/19-12/31/21, a community breast oncology focused practice in the Hawaii M/U NCORP implemented a plan with the goal to increase accrual to clinical trials. Building on the prior experience of both clinicians, the practice developed a comprehensive team approach between the Oncologist, APP, and CRC. This approach included four components: 1) Clinic workflow for clinical trial screening, introduction, and follow-up of potential participants; 2) Shared clinical trial visits between MD & APP; 3) Protocol reviews by MD who reviewed treatment protocols & APP who reviewed symptom management and cancer care delivery research (CCDR) protocols. All clinical trial reviews were discussed with CRC for coordination input; 4) Protocol leadership between MD serving as local primary investigator (PI) on treatment trials, APP serving as local PI and/or site champion on symptom management and CCDR protocols. Results: From 2019-2021, our practice was able to accrue over 23% of our patients to clinical trials in a community practice setting, despite the COVID-19 Pandemic. Of the 149 unique accruals during this period, 59% of accruals were interventional trials and 41% were observational trials. Clinical trial accrual was racially diverse and mirrored the patient population. During this period, there were minimal protocol deviations observed based on review of shadow charts on this group of clinical trial patients. Conclusions: Through this team approach, our practice has been successful in having a high rate of clinical trial accrual while maintaining excellent clinical trial conduct even during the pandemic. We believe our team approach that utilizes the APP to the full scope of practice along with CRC empowerment in addition to the engaged oncologist is key to our success. Oncology practices and healthcare systems must value and engage all potential members of the team to maximize their clinical trial ability and to continue to offer/enroll patients in clinical trials as the standard of care.
Thromboprophylaxis in lung cancer patients with tinzaparin: Results from the investigation on cancer & lung thromboprophylaxis—The iCaLTstudy.
Authors: Kouvela Marousa , Grammoustianou Maria , Sarropoulou Foteini , Mani Maria , Gkiozos Ioannis , Syrigos Kostas N. 
Published: June 2022

e13531 Background: It is known that malignancies exaggerate the risk of thrombosis up to 20%, while for Lung Cancer (LC) is articulated up to 14%. Likewise, thrombosis amplifies the LC progression, thrombosis-associated lung cancer (TALC). The pathophysiology promoting LCAT & TALC is multifactorial depending on the characteristics of LC, specific anti-cancer modalities, patient features, and biomarkers such as tissue factor, neutrophil extracellular traps released in response to cancer, cancer procoagulant, and cytokines. Low-molecular-weight heparins are used in prophylaxis and treatment for high-burden for thrombosis (HTB) patients, allowing specialists to focus on the cancer disease. Methods: iCaLT is a prospective observational study conducted in Thoracic Diseases General Hospital Sotiria (Athens, Greece) assessing the role of thromboprophylaxis with tinzaparin 10.000 Anti-Xa IU, OD with the systemic anti-cancer therapy (SACT). LC patients with active disease that are currently under treatment are enrolled after signing informed consent and monitored up to six months or until anticoagulation terminates. Results: Results from 106 patients reported, 29 (27%) are still ongoing: 79% were males. Histological results involve: adenocarcinomas 54%, squamous 25%, small cell 15%and others. LC patients received SACT: 55%, 23%, 10%, 6% in 1st, 2nd, 3rdand 4th line. HTB patients with active LC related to 4 factors: disease, treatment, biomarker and patient features are presented in table. On average patients accumulated 3.7±1.1risk factors, 91% of patients accumulated ≥3 risk factors. Median thromboprophylaxis duration was: 4.8 months (1st-3rd quartile: 2.8-6.0 months). One patient experienced thrombotic event in lower extremes and consequently pulmonary embolism (efficacy 99.1%, 95%CI: 94.6-99.8%), five patients experienced minor bleeding adverse events (haemoptysis) (4.7%, 95%CI: 2.0-10.6%), and two patients allergic reactions. Patients with haemoptysis had lower age (60 vs. 67, p=0.1357). Conclusions: Thromboprophylaxis with tinzaparin intermediate dose in high thrombotic burden patients with active lung cancer is effective and safe. Further research is needed.[Table: see text]
Impact of immunotherapy best practice alert on physician awareness of immune-related adverse events and patient outcomes in the urgent and emergent care setting.
Authors: Sassic Jessica , Ebrahimi Aren , Bollin Kathryn Blount 
Published: June 2022

e13540 Background: Immune checkpoint inhibitors (ICI) are used to treat a wide range of malignancies and can provoke inflammatory side effects in any organ system. These immune-related adverse events (irAEs) are graded in severity according to the Common Terminology Criteria for Adverse Events (CTCAE) which serves as the basis for strategic toxicity management. Cancer patients treated with ICIs often present to urgent care (UC) and emergency room (ER) settings for CTCAE grade 2-4 irAEs. Providers in these settings need to recognize these irAEs as the treatment differs from chemotherapy toxicity management. Best practice alerts (BPAs) have been integrated in the electronic health record (EHR) to raise provider awareness to patients’ unique risk factors and improve patient outcomes. For example, a BPA for patients prescribed second generation antipsychotics led to more in-hospital providers ordering the appropriate laboratory studies to screen for metabolic derangements. Here, we describe our retrospective institutional protocol for the assessment of provider awareness and patient outcomes prior to and after the implementation of a BPA for patients receiving ICIs and presenting to an UC or ER with symptoms attributable to irAEs. Methods: Our institution will implement a BPA that identifies patients treated with ICIs within 6 months of presentation to an institutional UC or ER. The BPA will require providers to check a yes/no box asking if they considered an irAE in the differential diagnosis for the patient’s presenting symptom prior to advancing in the EHR. The BPA will include a hyperlink to the current American Society of Clinical Oncology guidelines for management of irAEs. After BPA implementation we will retrospectively analyze the EHR of patients treated with ICIs who presented to an institutional UC or ER for the time periods of 6-months prior to and after the implementation of the BPA. Our analysis will include review of the UC/ER providers’ differential diagnoses, imaging and laboratory studies, subspecialty consultations, and treatments provided. Our primary endpoint will be the quantification of patients diagnosed with an irAE at presentation both prior to and after implementation of the BPA. The secondary endpoints will include quantification of patients who received guideline-based management of irAEs in the UC/ER setting, time to resolution of symptoms to CTCAE grade 1 or less, and analysis of subspecialist involvement in the management of an identified irAE. We plan to use the Chi-squared method to observe for statistical significance of endpoints between the pre- and post- periods of BPA implementation. Our aim is to demonstrate the utility of a BPA in promoting the appropriate assessment and management of irAEs in cancer patients receiving checkpoint immunotherapies.
The adoption of relugolix for treatment of prostate cancer (PCa) in a large community medical oncology practice.
Authors: Schuster Victoria , Patel Dipen , Barnes Martin Christopher , Cyriac George , Staszewski Harry 
Published: June 2022

e13541 Background: Relugolix was approved by the FDA in late 2020 as an oral gonadotropin-releasing hormone (GnRH) receptor antagonist for the treatment of adults with advanced PCa. The approval was based on a large randomized trial (Shore et al NEJM) of patients with evidence of biochemical or clinical relapse after local primary intervention with curative intent (50%), newly diagnosed mCSPCa (23%), and advanced localized disease not suitable for primary surgical intervention with curative intent (28%). Our study aims to observe the adoption of relugolix in a large community-based medical oncology practice with affiliated urologists. Methods: A retrospective study using our EMR identified 39 males with PCa for whom relugolix was initiated between January 2021 and December 2021. The charts were analyzed for demographics, indications for treatment, barriers encountered to initiation of treatment, duration of therapy, previous therapies, specialty, primary insurance, and cardiovascular history. Results: Thirty-nine (39) patients with either localized PCa (40%) or with metastases to lymph nodes or distant sites (51%) were recommended initiation of relugolix by either urology (26%) or oncology (74%). Of these, 77% actually started the medication. 9 patients did not start therapy, of which 3 were due to insurance denial, 1 due to death, 2 changed their mind, and 3 did not start for unclear reasons. Medicare covered 56% of the patients. Cardiovascular disease was notable in 87%. 28% switched from another ADT due to intolerance, 10% started relugolix due to progressive disease, and 33% started as initial ADT. 13% discontinued within 6 months. Of these, 1 completed planned therapy, 3 were intolerant, 1 was switched to alternate therapy due to progression, and 1 had complications due to COVID. In comparison, 689 patients initiated ADT with leuprolide over the same time. Conclusions: ADT using GnRH agonists or antagonists form the backbone of PCa treatment, typically given as injections. The HERO trial showed that relugolix, a newer oral therapy, is associated with lower rates of cardiovascular events compared to leuprolide. While having at least one cardiovascular risk factor is a consideration for patients to either start with or switch to relugolix, the large majority of our patients still received first-line therapy with leuprolide. This study demonstrates limitations to initiating relugolix including financial constraints (e.g., insurance coverage), patient preference, and medication intolerance.[Table: see text]
Automatic identification of oncology patients with unsupervised phenotyping and AutoML.
Authors: Zhang Jingqing , Sharma Atri Vivek , Bolanos Trujillo Luis , Gupta Vibhor , Guo Yike 
Published: June 2022

e13547 Background: Recent studies suggest ICD codes often cannot characterise patients with specific diseases like oncology accurately in real-world clinical practice due to a high probability of miscoding, while manual inspection of millions of patients is not feasible as it is highly costly and slow. Therefore, we present an AI driven workflow, which leverages structured discrete values and unstructured textual data from Electronic Health Records (EHRs), to automatically identify cancer patients especially those who might be miscoded or undiagnosed. Methods: The proposed AI driven workflow is trained on 29637 and tested on 29339 patients from MIMIC-III database. First, the workflow builds patient profiles by leveraging the state-of-the-art unsupervised Natural Language Processing (NLP) method to extract phenotypic features from textual discharge summaries. Second, the workflow classifies if a patient has the risk of ovarian cancer (ICD-9: 183.0) or lung cancer (ICD-9: all subcodes of 162) based on their profiles using an Automated Machine Learning (AutoML) framework. Third, we improve the workflow through 100 gold-labelled patients out of the train set who are labelled as positive and negative ovarian (lung) cancer cases by three clinicians with consensus and we subsequently measure its performance against another 100 gold-labelled patients out of the test set and the entire test set with 29339 patients. Results: For ovarian cancer, on the 100 gold-labelled patients (52 ovarian cancer cases) out of the test set, the workflow achieves 0.877 AUCROC with precision 84.7% and sensitivity 96.2% whereas ICD codes have precision 94.6% and sensitivity 74.1%. On the entire test set with 29339 patients, the workflow identifies 111 ovarian cancer patients while ICD codes find only 38 patients which lead to 192% lifting. Similarly, for lung cancer, on the 100 gold-labelled patients (66 lung cancer cases) out of the test set, the workflow achieves 0.814 AUCROC with precision 88.7% and sensitivity 83.3% while ICD codes have precision 96.0% and sensitivity 72.7%. On the entire test set with 29399 patients, the workflow identifies 926 lung cancer patients while ICD codes find 585 patients which lead to 58% lifting. Conclusions: This study shows the AI driven workflow on textual clinical notes can help find more cancer patients with high accuracy especially those who are miscoded and undiagnosed by ICD codes. Since ICD codes are primarily used for billing purposes, this workflow also shows the opportunity to reconcile patient characteristics with ICD codes through the application of such novel AI at scale. Moreover, the results of this study have shown this approach can be scaled across other disease areas within oncology and beyond to help find new intelligence to characterise hard to diagnose conditions and improve patient outcomes through a significant improvement in speed and accuracy of screening and early diagnosis of patients.
Variation in liquid biopsy cfDNA yield predicted by somatic mutation and clinical phenotypes across primary cancers.
Authors: Bubie Adrian , Jiang Tingting , Gaile Daniel , Hutchins Jamie , Drusbosky Leylah , Kiedrowski Lesli A. , Chuang Han-Yu 
Published: June 2022

e13553 Background: Liquid biopsy provides a non-invasive alternative to tissue biopsy by profiling the genetic mutations and biomarker characteristics of cell-free DNA (cfDNA) derived from primary and metastatic tumors found in patients’ peripheral blood. Previous studies have demonstrated significant variation in cfDNA and circulating tumor DNA (ctDNA) yield based on patient age, sex, primary tumor type, disease stage, and treatment regimen. Somatic mutations are a primary determinant of tumor phenotype, characterizing therapeutic sensitivity and resistance, as well as growth and metastasis rates. However, there has not been extensive investigation as to how presence of somatic mutations affect downstream cfDNA yield and ctDNA fraction. Methods: We screened 140,000 samples analyzed using Guardant360 liquid biopsy test for patient age, diagnosis, treatment, mutations, and associated cfDNA yield (from assay input of 3-10ml peripheral blood, 1 streck tube). We selected and grouped somatic mutations based on prevalence across the cohort. Estimated ctDNA fractions of cfDNA yields were made using the maximum mutant allele frequency (MAF) from among somatic mutations. Samples were organized by phenotype and cfDNA yield and ctDNA fraction was assessed across groups using the Wilcoxon and Kruskal-Wallis for bi-variate and multivariate comparisons, respectively. To understand impact of cfDNA yield variation on assay performance, group comparisons were further evaluated on samples binned by input cfDNA (< 5ng, 5-15ng, 15-30ng, > 30ng) representing cfDNA input requirement ranges current commercial liquid biopsy tests advertise. Results: We found significant yield variation in sample cfDNA and estimated ctDNA fraction between primary disease types ( Myield diff = 16.35ng, p < 0.0001), therapy histories ( Myield diff = 1.49ng, p < 0.001), and patient age groups ( Myield diff = 2.24ng, p < 0.0001), although this finding did not extend to comparisons within input yield bins. We found significant differences in cfDNA and ctDNA fraction across several common somatic mutations, adjusting for test multiplicity, with the greatest difference between TERT splice + and - patients ( Myield diff = 8.57ng, p < 0.0001). Further, we found significant and strong correlations (𝝆 > 0.8, p < 0.001) between EGFR exon 19 deletions and EGFR L858R MAF and estimated ctDNA fraction, and moderate correlation between MAF and cfDNA (𝝆 > 0.25, p < 0.001). Conclusions: Our results indicate that input cfDNA and ctDNA fraction varies by patient age, diagnosis and treatment, as well as with the presence of several common somatic mutations. The demonstrated correlation between somatic mutation presence and cfDNA yield offers a potential explanation for the broad variation of cfDNA yields within tumor and patient phenotypes, and may help inform blood collection strategies for patients with known somatic variants or diagnoses.
Preventing clinical deterioration in cancer outpatients: Human centered design of a predictive model and response system.
Authors: Salwei Megan Elizabeth , Anders Shilo , Novak Laurie , Reale Carrie , Slagle Jason , Harris Joyce , Unertl Kim , Nath Paromita , Mahadevan Sankaran , Agarwal Rajiv , Elliott Natalie , Lee Rachel , Weinger Matthew B. , France Daniel 
Published: June 2022

e13567 Background: Patients (pts) with cancer in the outpatient setting are at a high-risk for adverse events, such as unplanned hospitalizations and ER visits. A recent study found that up to 30% of hospital admissions were preventable. Identifying pts at risk of avoidable clinical deterioration remains a challenge, as clinicians may not be aware of pts’ experiences at home. The growing use of health IT presents an opportunity to identify and respond to clinical deterioration in patients before an adverse event occurs. In this study, we describe a human-centered design (HCD) process used to develop a clinical deterioration risk prediction system to improve the detection of and response to deterioration in cancer outpatients. Methods: Predictive model: We enrolled eligible cancer pts and collected data from each one including: FitBit, geolocation, EHR, and weekly patient-reported outcome measures (PROMs). Pts and their family caregivers could also report non-routine events (NREs), defined as any deviation from expected optimal care. We also captured unplanned treatment events (UTEs), a clinically meaningful change in the pt’s treatment course or care pathway. We developed a predictive model that generates a pt’s 7-day risk of clinical deterioration. Response system: We are developing a risk communication system (RCS) to communicate predicted risk scores to clinical teams. Using a HCD process, we first conducted 36 observations across 100 patient encounters to understand the environment of use. Next, we conducted 18 clinician interviews to define user needs. We have conducted 7 multi-disciplinary design sessions to iteratively develop prototypes of the RCS. We are currently conducting formative usability testing to assess the prototype and gather clinician feedback. Results: Predictive model: We have enrolled 36 cancer outpatients (24 head & neck, 9 gastrointestinal, and 3 lung). Pts completed a total of 219 weekly PROM surveys, reported 107 NREs and experienced 18 UTEs (e.g., infection). So far, models using EHR and PROM data are the most sensitive and precise (AUC: 0.983; 0.999). More patient data are required to develop higher quality stable models. Response system: We identified key design elements to include in the RCS, such as the caregiver’s phone number and the pt’s weight over time. Preliminary findings demonstrate high usability of the prototype RCS. Oncologists identified opportunities for the system to better support team communication and coordination, and to improve the identification and response to clinical deterioration in cancer outpatients. Conclusions: We have developed and tested a clinical deterioration risk prediction system for cancer outpatients. Future studies will implement the response system and evaluate its impact on clinical care.
Prostate cancer prediction using classification algorithms.
Authors: Huo Xingyue , Finkelstein Joseph 
Published: June 2022

e13590 Background: The object of this study was to evaluate the efficacy of machine learning classification algorithms for prostate cancer prediction using patients' age, CCI score, and medical procedures. Methods: We analyzed a dataset of 112,605 individuals from the IBM MarketScan database between 2016 and 2019, deviding them into two groups based on whether they diagnosis with prostate cancer in 2019. Then we retrospectively reviewed these subjects' medical records including the CCI, medical procedure records and ages. Machine learning classifiers consisting of support vector machine (SVM), decision tree (DT), random forest (RF), Extreme Gradient Boosting (XGBoost), and adaptive boosting (Adaboost) were used to analyze the prediction accuracy of prostate cancer. For all machine learning prediction models, 70% of the randomly chosen data samples were used for training, and 30% were used as a test set. The classifier with the highest accuracy were selected for prediction and identify feature importance. Results: The study included 112,605 subjects, of which 19,564 (17.4%) men who diagnosed with prostate cancer in 2019. Overall, The best prediction model for prostate cancer prediction was XGBoost, with the cancer prediction accuracy in the test set of 84%. The “Minor male genital procedures” was the highest scored feature importance, while age is not an important feature in prostate cancer prediction among elderly males. Conclusions: The more urethral/male genital examination procedures an individual has received in the past two years, the more attention should be paid to the risk of having prostate cancer. These results suggest that the analysis of individual medical records using machine learning classification algorithms can increase the prediction rate of prostate cancer.