bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of use of remote patient monitoring (RPM) with reduced hospitalizations in cancer patients with COVID-19.
Authors: Pritchett Joshua , Desai Aakash , Borah Bijan J , Xie Zhuoer , Saliba Antoine N , Leventakos Konstantinos , Coffey Jordan , Pearson Kristin , Speicher Leigh , Orenstein Robert , Virk Abinash , Ganesh Ravindra , Paludo Jonas , Haddad Tufia C. 
Published: June 2021

1503 Background: Patients with cancer and COVID-19 are at risk for poor clinical outcomes. An established multi-site remote patient monitoring (RPM) service was rapidly adapted to support a novel, interdisciplinary COVID-19 program for outpatient management of patients at high-risk for severe illness. The goal of this study was to assess the impact of the RPM program on clinical outcomes and acute care utilization in cancer patients diagnosed with COVID-19. Methods: This is a cross-sectional analysis following a multi-site prospective observational study performed at Mayo Clinic Cancer Center (MCCC). All adult patients with active cancer – defined as currently receiving cancer-directed therapy or in recent remission on active surveillance – and PCR-confirmed SARS-CoV-2 infection between March 18 and July 31, 2020 were included. RPM was comprised of in-home technology to assess symptoms and physiologic data with centralized nurse and physician oversight. Results: During the study timeframe 224 cancer patients were diagnosed with COVID-19 at MCCC. Initial management included urgent hospitalization (within 48 hours of diagnosis) in 34 patients (15%). Of the remaining 190 patients (85%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM (OR 3.6, 95% CI 1.036 to 12.01, P = 0.044). Following balancing of patient characteristics by inverse propensity weighting, rates of hospital admission for RPM and non-RPM patients were 3.1% and 11% respectively, implying that RPM was associated with an 8% reduction in hospital admission rate (-0.077; 95% CI: -0.315 to -0.019, P = 0.009). Use of RPM was also associated with lower rates of prolonged hospitalization, ICU admission, and mortality, though these trends did not reach statistical significance. Conclusions: In the midst of a global pandemic associated with inpatient bed, ventilator, and PPE shortages, the RPM program provided an effective strategy for outpatient clinical management and was associated with decreased rates of hospitalization, ICU admission, and mortality in cancer patients with COVID-19. This care model enabled simultaneous opportunity to mitigate the increased risks of exposure, transmission, and resource utilization associated with conventional care.
Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology.
Authors: Parikh Ravi Bharat , Schnall Jill , Liu Manqing , Gabriel Peter Edward , Chivers Corey , Draugelis Michael , Ferrell Will , Lerman Caryn , Bekelman Justin E. , Chen Jinbo 
Published: June 2021

1510 Background: Machine learning (ML) algorithms based on electronic health record (EHR) data have been shown to accurately predict mortality risk among patients with cancer, with areas under the curve (AUC) generally greater than 0.80. While patient-reported outcomes (PROs) may also predict mortality among patients with cancer, it is unclear whether routinely-collected PROs improve the predictive performance of EHR-based ML algorithms. Methods: This cohort study included 8600 patients with cancer who had an outpatient encounter at one of 18 medical oncology practices in a large academic health system between July 1st, 2019 and January 1st, 2020. 4692 (54.9%) patients completed assessments of symptoms, performance status, and quality of life from the PRO version of the Common Terminology Criteria for Adverse Events and the Patient-Reported Outcomes Measurement Information System Global v.1.2 scales. We hypothesized that ML models predicting 180-day all-cause mortality based on EHR + PRO data would improve AUC compared to ML models based on EHR data alone. We assessed univariate and adjusted associations between each PRO and 180-day mortality. To train the EHR-only model, we fit a Least Absolute Shrinkage and Selection Operator (LASSO) regression using 192 EHR demographic, comorbidity, and laboratory variables. To train the EHR + PRO model, we used a two-phase approach to fit a model using EHR data for all patients and PRO data for those who completed assessments. To test our hypothesis, we compared the bootstrapped AUC, area under the precision-recall curve (AUPRC), and sensitivity at a 20% risk threshold for both models. Results: 464 (5.4%) patients died within 180 days of the encounter. Decreased quality of life, functional status, and appetite were associated with greater 180-day mortality (Table). Compared to the EHR-only model, the EHR + PRO model significantly improved AUC (0.86 [95% CI 0.85-0.86] vs. 0.80 [95% CI 0.80-0.81]), AUPRC (0.40 [95% CI 0.37-0.42] vs. 0.30 [95% CI 0.28-0.32]), and sensitivity (0.45 [95% CI 0.42-0.48] vs. 0.33 [95% CI 0.30-0.35]). Conclusions: Routinely collected PROs augment EHR-based ML mortality risk algorithms. ML algorithms based on EHR and PRO data may facilitate earlier supportive care for patients with cancer. Association of PROs with 180-day mortality.[Table: see text]
Daily step counts to predict hospitalizations during chemoradiotherapy for head and neck cancer.
Authors: Izmailova Elena S. , Cohen Andrew , Kabarriti Rafi , Eichler Jeremy , Huang Chengrui , Ohri Nitin 
Published: June 2021

1571 Background: Wearable activity trackers could provide useful data for managing cancer patients with respect to treatment selection, toxicity monitoring, and implementation of supportive care measures. Here, we seek to evaluate the association between daily step counts and hospitalizations in a cohort of patients with head and neck cancer (HNC). Methods: This analysis consists of patients enrolled in one of three prospective trials involving activity monitoring (NCT02649569, NCT03115398, NCT03102229) during chemoradiation. Study subjects were asked to wear a commercial fitness tracker continuously during the therapy. ECOG performance status (PS) was assessed at baseline, and quality of life (QoL) EORTC QLQ-C30 questionnaires were completed weekly. Multivariable Cox regression models with time-dependent covariates (average step count over the past 3 days, most recent QoL score) and time-fixed covariates (age, sex, baseline PS, study number, baseline tumor volume, and treatment setting [definitive versus postoperative]) were used to identify predictors of first hospital admission during the chemoradiotherapy course. In addition to the Cox regression models, linear mixed models were fitted with daily step count as the dependent variable to examine its relationship with certain independent variables including age, sex, weekend status, days after treatment initiation, and study number. Results: Sixty-six HNC patients who received chemoradiotherapy between 2015 and 2019 were included in the analysis. Median age was 60 (range: 27-88). 47% of patients had ECOG PS score 0, 47% ECOG score 1, and 6% ECOG score 2. 29% of patients had HPV-positive oropharyngeal tumors, and the most common other tumor subsites were larynx (27%), and nasopharynx (12%). The Cox regression survival model demonstrated a 26% reduction in the short-term hospitalization risk for every 1000 daily steps (averaged over the past 3 days, hazard ratio 0.74; 95% confidence interval (CI) 0.55-0.98, p = 0.0367). Hospitalizations were not significantly associated with most recent QoL or baseline ECOG PS. Additionally, according to the linear mixed model results, daily step count was not associated with age (p = 0.8048). Study subjects moved less on weekends (on average 245 fewer steps on weekends than weekdays, 95% CI 134-357, p < 0.0001). Also, an increase in most recently measured ECOG PS was associated with a decrease in daily step count (167 fewer steps for every increase in ECOG PS, 95% CI -289 to -45, p < 0.0072). Conclusions: Daily step counts are a dynamic predictor of hospitalizations in patients undergoing chemoradiotherapy for head and neck cancer. Interventional studies are needed to demonstrate feasibility of leveraging physical activity data to optimize supportive care during cancer therapy and enhance cancer care quality. Clinical trial information: NCT02649569, NCT03115398, NCT03102229.
Implementation of insurance marketplaces and changes in diagnosis stage in low-income cancer patients.
Authors: Kim Uriel , Koroukian Siran M. , Stange Kurt C , Spilsbury James , Rose Johnie 
Published: June 2021

1576 Background: Millions of low-income Americans gained insurance coverage through Medicaid expansion and the “Marketplaces” of the Affordable Care Act (ACA). How Marketplaces have specifically improved cancer outcomes among these individuals is unclear. Thus, we examined changes in insurance status and diagnosis stage following the ACA among low-income (139-250% of the Federal Poverty Level [FPL]), non-elderly patients (ages 30-64). Methods: In Ohio’s cancer registry, we identified patients diagnosed with one of the top 16 cancers before (2011-2013, “Pre-ACA”) and after (Q3 of 2014-2016, “Post-ACA”) the implementation of the ACA’s insurance Marketplaces and either had private insurance or no insurance. Low-income patients were isolated using a novel, geographically-driven approach called probability weighting. Results: The uninsured percentage dropped from 12.9% to 4.9% between the Pre- and Post-ACA periods in the study sample (N = 10,747). An estimated 11.1% of individuals had Marketplace insurance Post-ACA. A significant but modest Post-ACA (versus Pre-ACA) shift toward non-metastatic disease was identified (Adjusted Odds Ratio [AOR]: 0.95, 95%CI: 0.90-0.99). The largest site-specific shifts were observed for thyroid (AOR: 0.50, 95%CI: 0.30-0.83) and ovarian (AOR: 0.74, 95%CI: 0.58-0.93) cancers. In a control analysis of wealthier (400%+ FPL), privately insured individuals, no significant shifts were identified (AOR: 0.97, 95%CI: 0.92-1.02). Conclusions: This is the first study to show an effect in cancer stage at diagnosis from the Affordable Care Act’s Insurance Marketplaces. We found that the Marketplaces greatly reduced the number of low-income, uninsured cancer patients, translating to significant improvements in cancer stage at diagnosis. As policy makers contemplate modifications to the ACA, they should carefully consider the impact of those changes on the highly vulnerable population of low-income cancer patients.
Patterns of telehealth utilization during the COVID-19 pandemic and preferences for post-pandemic telehealth use: A national survey of oncology clinicians.
Authors: Manz Christopher , Baxter Nancy N. , duPont Nefertiti C. , Markham Merry Jennifer , Drumheller Caitlin , Durakovic Lela , Kennedy Angela ,   ASCO Telehealth Disparities Taskforce
Published: June 2021

1580 Background: Rarely used in routine practice pre-pandemic, telehealth utilization for cancer care rose significantly during the COVID-19 pandemic. Increased familiarity with telehealth has led to calls to continue its use after the pandemic ends. Yet national patterns of oncology telehealth utilization by visit type, preferences for telehealth use post-pandemic and barriers to telehealth for patients with cancer have not been described. Methods: 9,336 survey invitations were emailed to US-based ASCO members who have agreed to receive communications. Survey distribution was equally divided over five US regions, and practice type (e.g., academic, community) was reflective of ASCO membership proportions. The survey was open and data collected from January 4-28, 2021. Non-respondents received two reminder emails at week intervals. Analysis is descriptive. Results: 200 respondents completed the survey (2%). Respondents were 72% medical oncologists, 66% urban, 64% academic-affiliated, and from 42 states. 99% currently offered telehealth. 63% used telehealth for <=30% of all patient visits in the last 30 days; 18% used telehealth for more than half of visits. Telehealth utilization varied by visit type (table). 64% reported that the care delivered in telehealth visits was similar quality to in-person visits (29% worse). Assuming no regulatory or financial barriers to telehealth use after the pandemic, 92% would like to use telehealth for at least some visit types; only 8% prefer not to use telehealth. 20% would like to use telehealth for all visits types, and 64%, 54%, 33% and 17% would like to use telehealth for survivorship, symptom management, evaluation of patients receiving treatment and new patient visits, respectively (multiple selections allowed). Major barriers to telehealth were lack of patient access to technology (reported by 81%), limited patient technological proficiency (80%), language barriers (45%), uncertainty about future reimbursement (41%) and lack of administrative resources to support clinicians (33%). 68% agreed that the barriers increase cancer care disparities. Conclusions: Telehealth utilization was widespread during the COVID pandemic and varied by visit type. Most respondents plan to use telehealth in the future, but report barriers to continued use that worsen disparities.[Table: see text]
Improving cancer patient care with a digital telemonitoring platform: The ConnectPatienToDoctor study.
Authors: Helissey Carole , Duverger Claire , Vanquaethem Hélène , Meghiref Yasmine , Parnot Charles 
Published: June 2021

1581 Background: During COVID19 pandemic, clinicians have had to face two challenges in an unprecedented context: ensuring continuity of patient care for a disease that involves a life-threatening prognosis while reducing patients' vulnerability to this virus. The ConnectPatientToDoctor study aimed to evaluate a connected telemonitoring platform, Cureety, in cancer patient care. Patients and Methods: This prospective study was conducted at the Military Hospital Bégin. Each cancer patient was allowed to respond to a personalized symptomatology questionnaire based on CTCAE v.5.0, and personalized to their pathology and treatment. An algorithm evaluated the health status of the patient based on the reported adverse events and provided adapted recommendations. The calculated score allows to classify the patient into four different levels: (1) Correct State (green) (2) Compromised State (yellow) (3) State to be monitored (orange) (4) Critical State (red) In the case of orange or red classification, the patient was invited to call the hospital. In case of green or yellow classification, the patient received therapeutic advice set by the doctor, for each of the adverse events reported. The primary endpoint was to assess the feasibility of monitoring cancer patient with the connected platform. The secondary endpoint was to assess patient and caregivers’ satisfaction. Results: There were 108 patients included in the program between July 1st, 2020 and January 31st, 2021. The median age was 70 and 77% presented a metastatic stage. The most represented cancer was prostate (50%), followed by lung (24%) and breast (15%). Overall, 1864 questionnaires were completed by the patients, resulting in 543 yellow alerts and 133 orange or red alerts. More than 60% of the alerts was managed through outpatient care or with a rapid intervention to resolve the adverse events. On a satisfaction scale from 1 to 10, where 10 was the highest, 72% of the patients responded with a 9 or 10. And all caregivers were satisfied because they could offer a better care, prioritize care, and save time. Conclusions: Participants in ConnectPatientToDoctor study reported that they had a clear benefit from using a connected platform to monitor their treatment. Patients felt less isolated and felt that they benefited from personalized care. This solution makes it possible to streamline the patient care process.[Table: see text]
Changes in the blood-brain barrier properties in vitro after treatment with sera from breast cancer patients with primary cancer and visceral, bone or cerebral metastases.
Authors: Curtaz Carolin Julia , Constanze Schmitt , Meybohm Patrick , Wöckel Achim , Burek Malgorzata 
Published: June 2021

2026 Background: The progression of brain metastases during breast cancer correlates with poor overall survival, but also with a reduced quality of life. During the metastatic progression of breast cancer, the key event for entry into the brain is the migration of cancer cells across the blood-brain barrier (BBB). To date, it is still controversial which serum factors play a role in cerebral expansion and what effects they have on the BBB. We hypothesize that sera from breast cancer patients with cerebral metastases contain unique factors that can affect BBB integrity. Methods: We used the CD34+ cells-derived human in vitro BBB model (brain-like endothelial cells, BLECs) in co-culture with brain pericytes, which was validated in our previous studies, to analyse the BBB properties after patient sera treatment. We used paracellular permeability measurements for fluorescein (400 Da), immunofluorescence staining, Western blot and mRNA analysis to examine the effects of patient sera on the properties of BBB in vitro. We collected serum samples from five patient cohorts (30 patient per group planned/150 in total/ current recruited patients 130): 1) healthy donors (current recruited patients- 23), 2) breast cancer patients with primary cancer (recruited patients- 30), 3) breast cancer patients with bone metastases (recruited patients- 29), 4) visceral metastases (recruited patients- 30), or 5) cerebral metastases (current recruited patients- 18). We then used 2% patient sera in cell culture medium to treat the cells for 24 hours. Results: Sera from breast cancer patients with cerebral and bone metastases led to a significant increase in the paracellular permeability for fluorescein. This could also be visualized by immunostaining cells with anti-claudin-5 antibody. Tight junction protein claudin-5 and occludin mRNA was reduced in BLECs, while mRNA of efflux pumps Breast cancer resistance protein (BCRP) and P-glycoprotein (P-GP) was induced in BLECs treated with serum from breast cancer patients with primary cancer, cerebral and visceral metastases. At the protein level, efflux pumps BCRP and P-GP were induced in cells treated with sera from breast cancer patients with cerebral metastases, while they were reduced in cells treated with sera from breast cancer patients with bone metastases. Conclusions: Sera from breast cancer patients with primary cancer and breast cancer metastases have an effect on the integrity of BBB in vitro. Reduced barrier properties of brain endothelial cells can contribute to the formation of cerebral metastases in advanced stages of breast cancer. Keywords: metastatic breast cancer, blood-brain barrier, in vitro models
PD-L1-R: A MR based surrogate for PD-L1 expression in Glioblastoma multiforme.
Authors: Sforazzini Francesco , Salome Patrick , Kudak Andreas , Ulrich Matthias , König Laila , Warta Rolf , Bougatf Nina , Debus Juergen , Herold-Mende Christel , Knoll Maximilian , Abdollahi Amir 
Published: June 2021

2041 Background: Efficacy of PD-L1 immune checkpoint inhibitor therapy in Glioblastoma multiforme (GBM) is limited and the prognostic value of PD-L1 in GBM is an active field of research. We therefore sought to identify a MR based surrogate for PD-L1 expression in GBM. Methods: T1 post contrast images (T1ce) acquired immediately before surgery of 121 subjects with primary GBM (RTK I, II and mesenchymal subtype, as determined from Illumina Human Methylation array data) were analyzed. Following standard pre-processing (bias filed correction, brain extraction), 1150 radiomics features were calculated from gross tumor volumes (GTV). The cohort was then divided into training/validation (70%/30%). Cross-validation and model-selection were applied to identify features associated with PD-L1-M expression (estimated from methylation data and highly correlated with PD-L1 RNA-sequencing based measure, as recently reported). Features were used to identify two groups of tumors differing in PD-L1-M expression (PD-L1-R high and low), for which a logistic regression model was trained. Overall survival was assessed between PD-L1-R high/low. Results: PD-L1-R high and low groups showed significant differences in PD-L1-M values (training: p=0.002, validation: p=0.04, full cohort: p<0.001). The same model was used to split tumors into 2 groups, using features from non-T1ce sequences. All of the tested MR modalities showed at least a trend in PD-L1-M values in the two groups (T2w: p=0.037, T1w: p=0.089, FLAIR: p=0.091). Further investigations on the whole cohort (121 subjects) showed that PD-L1-R low group was enriched for RTK II sub-type (48%), while PD-L1-R high for mesenchymal sub-type (48%). Refer to Table 1 for more information. In addition, evaluation of survival data showed a difference in overall survival (likelihood ratio test p value 0.048, OR 0.52, 95% CI [0.27; 0.98]), with the PD-L1-R high group having a better prognosis. Conclusions: We presented a radiomics model PD-L1-R which allowed to identify GBM with low and high PD-L1-M expression from T1 post contrast agent images. Future work should validate these findings in independent cohorts.[Table: see text]
Improved risk stratification via integration of radiomics and dosiomics features in patients with recurrent high-grade glioma undergoing carbon ion radiotherapy (CIRT).
Authors: Salome Patrick , Sforazzini Francesco , Kudak Andreas , König Laila , Kickingereder Philipp , Bougatf Nina , Wick Wolfgang , Jäkel Oliver , Debus Juergen , Knoll Maximilian , Abdollahi Amir 
Published: June 2021

2043 Background: Unique radiobiological and physical properties of carbon ion radiotherapy (CIRT) may be favorably utilized to improve outcome in recurrent High-Grade Glioma (rHGG). There are currently no standardized criteria for stratification of rHGG patients for re-irradiation (re-RT). This study evaluated the impact of morphological data (radiomics) and physical information (dosiomics) in stratifying rHGG patients for CIRT. Methods: Quantitative radiomics and dosiomics features were extracted from CIRT planning CTs with dose distribution (DD) and multiparametric MRIs (mpMRI, pre re-RT) of 141 patients (recurrent grade III: n=56 40%, grade IV: n=85 60%) treated with a median dose of 42 Gy (RBE) and a median fraction of 13. The MR sequences considered are T1 weighted pre-and post-contrast agent, fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC). Benefit of a re-RT risk score (RRRS), comprising the initial tumour grade, age and the Karnofsky Performance Score was shown to correlate with superior outcome in CIRT and conventional re-RT and was also studied here in parallel. Feature sets - a) RRRS, b) radiomics, c) dosiomics features - were evaluated both separately and combined. Multiple feature selection methods were used independently on the CT, DD and the MR sequences, followed by a stepwise Cox's Proportional Hazard model selection per modality or combination thereof. Multivariable models were ranked by 10-fold cross-validated concordance index (C-I). Results: Compared to the RRRS model (OS/PFS, C-I: 0.68/0.61), the multimodality model considering radiomics and dosiomics features (RD) allowed improved prognostic separation (OS/PFS, C-I: 0.77/0.70). The RD signature consisted of 12 and 10 textural features for the OS and PFS models. Combining the RD model with RRRS yielded the best performance (OS/PFS, C-I: 0.78/0.73). No significant correlation between the textural features and the prescribed dose, tumor grade and volume was found, with the Spearman's correlation coefficient ranging between -0.06 to 0.17. Conclusions: Integrating multimodal information outperforms unimodal prognostic separation of rHGG following CIRT, highlighting the importance to consider biological, physical and morphological data for patient stratification. Prospective validation studies of this multimodal stratifier is warranted.[Table: see text]