bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between primary care and acute care services utilization in the year following diagnosis of breast cancer.
Authors: Tran Vincent , Hamal Ruchi , Hu Xiao , Byrne Margaret , Cao Yu 
Published: June 2021

e13522 Background: Primary care physicians (PCPs) play a pivotal role during cancer diagnosis. Lack of access to primary care has been linked to worse outcomes including increased mortality in patients with cancer. Higher PCP continuity of care has been associated with lower likelihood of acute care services use among breast cancer survivors, however the impact of PCP utilization is not well studied in those newly diagnosed. This study examines the association between PCP visits and emergency department (ED) use and hospitalization in the year following diagnosis of breast cancer. Methods: Electronic medical records of women newly diagnosed with primary breast cancer from 2013 to 2019 at a single institution (Tufts Medical Center, Boston, Massachusetts) with an established PCP in the same system were retrospectively reviewed. Demographic (age at diagnosis, menopausal status, race/ethnicity), clinical (Charlson Comorbidity Index (CCI), Eastern Cooperative Oncology Group (ECOG) performance status, body mass index, mental health disorder, chronic pain syndrome), and tumor (histology, stage, grade, hormone receptor (HR) positivity, HER2 status) characteristics were extracted in addition to information on treatment modalities (if patient received surgery, radiation (RT), chemotherapy (CT), endocrine therapy (ET), ovarian suppression (OFS)). Univariate and multivariate logistic regression models were used to examine associations between frequency of PCP visits and ED use and hospitalizations within a year after diagnosis of breast cancer. Results: Of 182 women ages 33-93 (mean 60) years old, 131 (72%) were postmenopausal, 116 (64%) Caucasian; 176 (97%) had stages 1-3, and 157 (86%) had HR positivity. In the year following diagnosis, 178 (98%) received surgery, 127 (67%) RT, 142 (78%) ET, 19 (10%) OFS, 70 (39%) CT; 68 (37%) had ≤1 PCP visit, and 45 (25%) had 4-10 PCP visits. Increased PCP visits correlated with advancing age, increased BMI, greater mental health disorders and chronic pain syndromes, and increasing CCI and ECOG score. After adjusting for these confounders and tumor and treatment characteristics, logistic regressions showed that while frequency of PCP visits was not associated with hospitalizations (univariate: OR = 1.02, 95% CI [0.53, 1.62], p = 0.93; multivariate: OR = 0.35, 95% CI [0.05, 1.50], p = 0.184), increased PCP visits tended toward association with increased ED use (univariate: OR = 1.12, 95% CI [0.97,1.29], p = 0.124; multivariate: OR = 1.15, 95% CI [0.94, 1.41], p = 0.188), however did not demonstrate statistical significance. Conclusions: Primary care utilization was not associated with acute care services utilization in women newly diagnosed with breast cancer. Higher PCP utilization tended toward correlation with increased ED use. These findings may help in evaluating multidisciplinary care support in patients newly diagnosed with breast cancer.
Predicting symptom complexity: Using autoregressive integrated moving average (ARIMA) models to create responsive clinic scheduling.
Authors: Watson Linda , Qi Siwei , Deiure Andrea , Link Claire , Hildebrand April , Chmielewski Lindsi , Smith Louise , Ruether Dean , Rawson Krista 
Published: June 2021

e13529 Background: Increasing cancer incidence, coupled with a trend in treating patients for longer periods of time, presents challenges in addressing all patients’ symptoms/concerns within the allotted time for ambulatory clinic appointments. Consequently, the ability to forecast and monitor the percentage of cancer patients with different symptom complexity levels is extremely valuable. Symptom complexity is a summary score that weighs the severity of all patient reported symptom scores at one time point. If a clinic could predict how many patients may need more time due to complex symptom management needs, clinic-scheduling templates could be adjusted to include a set number of longer appointments. Methods: Auto Regressive Integrated Moving Average (ARIMA) models were utilized to forecast the percentage of patients with a high symptom complexity level within one cancer clinic in Alberta, Canada. Goodness-of-fit measures such as Bayesian information criterion (BIC) and Ljung-Box test were used to determine optimal form for the ARIMA model. Following model selection, the autocorrelation function (ACF) was performed. These tests together verified that chosen AR, MA and differencing (I) were appropriate. Model performance on the historical data for model fit was summarized by Mean Absolute Error (MAE) and Root Squared Mean Error (RSME). Forecasting accuracy was assessed using mean absolute prediction error by comparing the forecasts with actual clinic data. Results: Of the multiple model structures tested, ARIMA (0, 0, 1) was selected, with the lowest BIC and non-significant Ljung-Box test. We obtained forecasts of the percentage of patients with high symptom complexity levels, with an MAE at 4.0%. To assess forecast accuracy, we calculated the absolute prediction error by comparing the forecasted percentages of patients with high symptom complexity levels to actual clinic visit data and the mean absolute prediction error was 5.9%. Conclusions: This forecasting model has important implications, allowing clinics to adjust scheduling templates to provide a select number of longer timeslots and therefore, be better prepared to meet the symptom management needs of cancer patients who are considered highly complex. This model could be applied to other clinical populations to allow for a tailored scheduling approach based on each clinic’s symptom complexity forecasting.
An assessment of the NAVIFY clinical trial match application using clinical simulation-based research methods.
Authors: Prime Matthew Stewart , Guo Chaohui , Fontana Gianluca , Ghafur Saira , Halligan Jack , Gardner Clarissa 
Published: June 2021

e13552 Background: Cancer clinical trials require matching patients to strict, and sometimes lengthy, eligibility criteria. NAVIFY Clinical Trial Match (CTM) is a digital solution that uses data on an individual patient’s condition, genomic alterations and institution’s postal code to automatically find suitable clinical trials. We assessed the efficiency, accuracy and cognitive burden of NAVIFY CTM on matching patients to cancer clinical trials in the UK. Methods: 10 clinicians (8 medical doctors, 2 clinical trial practitioners) participated. Synthetic patient cases were developed with input from two oncologists, a histopathologist and a radiologist. Participants were instructed to identify appropriate trials for five patients using the NAVIFY CTM and five patients with the legacy approach (i.e. online trial databases) within one hour. For each method, participants were advised to approach the exercise with the level of scrutiny employed in a normal clinical setting. The efficiency, quality, and cognitive burden of trial matching was measured for each participant. The quality of trial matches was independently scored by an oncologist who did not participate in the simulations. The cognitive burden was measured subjectively, via a single-item questionnaire used to measure mental effort (Paas scale), and objectively, via Stroop test before and after each method. A survey was also conducted. Results: Efficiency: Participants completed the trial matching more efficiently using NAVIFY CTM, with trial matches completed on average one minute and 42 seconds faster using the digital solution (n = 73 matches) compared with the most commonly used online trial database, ClinicalTrials.gov (n = 52). Participants were more likely to ‘completely agree’ that they had enough time to complete the task using the NAVIFY CTM (70%) compared to online trial databases (40%). Quality of the matches: On the survey, more participants reported that trial suggestions were ‘mostly relevant’ for NAVIFY CTM (70%) compared with ClinicalTrials.gov (30%). The quality of trial matches was the same for both methods (as scored independently by an oncologist). Cognitive burden: On the subjective measure of cognitive burden, more participants required ‘low’ or ‘rather low’ mental effort to use NAVIFY CTM (50%) compared to online trial databases (7%). On the objective measure, there is a seemingly less pronounced Stroop effect (often interpreted as higher mental agility) using NAVIFY CTM vs. legacy approach. Conclusions: Preliminary findings suggest that, compared to online trial databases, the NAVIFY CTM can reduce the amount of time to match patients to clinical trials, provide more relevant trial suggestions, and decrease self-reported mental effort for the decision-maker.
Predicting 180-day mortality for women with ovarian cancer using machine learning and patient-reported outcome data.
Authors: Sidey-Gibbons Chris , Sun Charlotte C. , Xu Cai , Schneider Amy , Lu Sheng-Chieh , Subbiah Ishwaria Mohan , Wright Alexi A. , Meyer Larissa 
Published: June 2021

e13555 Background: Contra to national guidelines, women with ovarian cancer often receive aggressive treatment until the end-of-life. We trained machine learning algorithms to predict mortality within 180 days for women with ovarian cancer. Methods: Data were collected data from a single academic cancer institution in the United States. Women with recurrent ovarian cancer completed biopsychosocial patient-reported outcome measures (PROMs) every 90 days. We randomly partitioned our dataset into training and testing samples with a 2:1 ratio. We used synthetic minority oversampling to reduce class imbalance in the training dataset. We fitted training data to six machine learning algorithms and combined their classifications on the testing dataset into a voting ensemble. We assessed the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) for each algorithm. Results: We recruited 245 patients who completed 1319 assessments. The final voting ensemble performed well across all performance metrics (Accuracy = .79, Sensitivity = .71, Specificity = .80, AUROC = .76). The algorithm correctly identified 25 of the 35 women in the testing dataset who died within 180 days of assessment Conclusions: Machine learning algorithms trained using PROM data offer state-of-the-art performance in predicting whether a woman with ovarian cancer will reach the end-of-life within 180 days. We highlight the importance of PROM data in ML models of mortality. Our model exhibits substantial improvements in prediction sensitivity compared to other similar models trained using electronic health record data alone. This model could inform clinical decision making and improve the uptake of appropriate end-of-life care. Further research is warranted to expand on these findings in a larger, more diverse sample.
Comparing phase 3 “go” decisions (Ph3-GO) between single arm trials with real-world external control (SAT+rwEC) versus randomized phase 2 trials (rPh2).
Authors: Hobbs Brian , Ton Thanh , Li Xiao , Hong David S. , Hubbard Rebecca A. , Mangat Pam K. , Capra William , Kong Shengchun , Pang Herbert , Samant Meghna , Segal Brian D. , Garrett-Mayer Liz 
Published: June 2021

e13564 Background: Traditional rPh2 trials have limitations that may yield suboptimal Ph3-GO. Compared to a rPh2 of equivalent sample size, SAT+rwEC allows more patients to receive experimental therapies while preserving the ability to compare experimental and control groups. Bias arising from measurement error and confounding in the rwEC, however, poses challenges to statistical inference. Preliminary studies suggest higher response rates are observed in rwEC than randomized controls. We compared Ph3-GO decisions between SAT+rwEC and rPh2. Methods: Ph3-GO probability was compared using simulation studies that resembled the oncology setting with objective response rate (ORR) endpoint. rPh2 simulated parameters were: sample size (60-120) with 1:1 randomization, ORR in rPh2 control (15%-50%), true treatment effect (ΔORR: 0-50). For each rPh2 of a given sample size, we evaluated an SAT+rwEC that re-allocated all rPh2 control patients to the experimental arm (i.e., doubling the sample size of the experimental arm) and added an rwEC. SAT+rwEC were simulated with assumptions for size (rwEC to SAT ratio: 0.5 to 2) and net bias (-10 to +10), which was simulated as a composite representing ORR measurement error plus residual confounding after multivariable adjustment. Positive direction of net bias corresponds to higher ORR in the rwEC. Ph3-GO thresholds varied from 10-30%. Ph3-GO was considered “False-GO” when true treatment effect < threshold, and “True-GO” when true treatment effect ≥ threshold. Results: With positive net bias of +10, SAT+rwEC had lower False-GO and True-GO decisions compared to rPh2. With negative net bias of -10, both False-GO and True-GO probabilities were higher for the SAT+rwEC. When net bias=0, the increased size of SAT+rwEC resulted in observable Ph3-GO improvements with lower False-GO and higher True-GO than corresponding rPh2. Conclusions: An interactive dashboard was developed for users. The magnitude and direction of net bias relative to the decision threshold affect the performance of SAT+rwEC. The relative sample size of rwEC to rPh2 may also impact performance. The dashboard can provide quantitative guidance for Ph3-GO if net bias can be estimated by independent studies. Further work to quantify net bias and refine Ph3-GO criteria can help reduce the currently high False-GO rates while increasing opportunities for patients to receive experimental therapies through the SAT+rwEC design. Ph3-GO probability for rPh2 vs. SAT+rwEC with threshold=15%, baseline ORR=20% (select scenarios).[Table: see text]
Results from a global survey of clinical cancer researchers on clinical trial design and conduct during the COVID-19 pandemic and beyond.
Authors: De Paula Bruno Henrique Rala , Doherty Gary 
Published: June 2021

e13576 Background: The COVID-19 pandemic has had profound detrimental consequences on cancer patient outcomes and clinical cancer research, with considerable adaptations made to both standard care and clinical research to optimise patient safety. Detailed perspectives of clinical researchers on such risk mitigation, and granular insight into which adaptions should persist long term, are required to inform future clinical trial design. Methods: We designed and performed an in depth, global survey (open between 04/22/20 and 06/10/2020) of cancer research professionals to assess the impact of the pandemic on clinical care and research, and how researchers prioritise and view clinical research methodologies. Results: 162 valid responses were received (58.7% oncologists, 14.8% research nurses; 69.1% working in academic centers) from six continents. There was significant heterogeneity in how the pandemic has affected healthcare workers, patients on standard of care or investigational trial treatments, and clinical trial recruitment. Our results detail how trial and non-trial patient care has altered in respondents’ centres and how respondents would prioritise specific trial designs while the pandemic continues. We show marked variance in the perceived value of specific trial protocol features and assessments (regardless of the pandemic). More respondents agreed than disagreed that trial eligibility criteria are too restrictive (48% agreed versus 28% disagreed), that screening assessments generally have unnecessary tests (51% versus 23%), that physical examination should only be performed when clinically indicated (43% versus 30%), that electronic patient outcomes/questionnaires are better at detecting adverse events that a consultation with a study doctor (33% versus 29%), that wearable technology/validated tools for patients to perform self-examination would provide more useful information than vital sign assessment in the study center (48% versus 13%), and that replacing routine in person visits with virtual visits would not significantly compromise patient safety (36% versus 28%). However, 65% of respondents indicated that fully virtual clinical trials (in the absence of reported clinically significant toxicities) would not be sufficiently safe (versus 15% who indicated that they would be). Conclusions: These detailed data provide further evidence of a significant adverse impact of the pandemic on cancer patient care and drug development, may help inform risk mitigation for studies in times of heightened infection risk, and aid with future clinical trial design.
Futility and toxicity monitoring without halting for interim analyses.
Authors: Urbauer Diana L , Westin Shannon Neville , Yuan Ying 
Published: June 2021

e13579 Background: Many trial designs with futility or toxicity monitoring require that accrual halt to wait for currently enrolled patients to complete their assessment window. However, logistics often prevent this. In these cases, the performance of those designs might be compromised. This study examines the performance of 2 popular designs when enrollment is not halted at interim. Methods: Simulations were run to examine the effect of continuous enrollment on the operating characteristics (OCs) of a Simon’s 2-stage design for futility monitoring and a design using Bayesian posterior probabilities for toxicity monitoring. Both sets of 10,000 simulations examined the OCs when accrual rate was 0.5, 1.5, 3 and 5 patients/month with an assessment window of 30, 60 and 180 days. Results: The first scenario examined the OCs of a Simon design with 12 patients in the first stage and 21 at the end of the second stage. Regardless of accrual rate, the expected number of patients (EN0) increased and probability of early termination (PET0) decreased under the null hypothesis. Rate of change increased as assessment window increased. EN0 was 16 and PET0 was 54% when halting enrollment between stages. With continuous enrollment, EN0 ranged from 16-19, 17-21, and 18-21 patients for the 30-, 90- and 180-day assessment windows. PET0 ranged from 54%-50% with a 30-day assessment window. It halved to 24% with 3 patients/month enrolled and a 90-day window. PET0 was essentially 0 with a 180-day window and an enrollment of 3 patients/month. OCs for toxicity monitoring were examined for the early stopping rule Pr(toxicity rate > 0.3 | data) > 0.85 with toxicity rate ̃ beta(1, 1) with a maximum sample size of 20 and cohort size of 5. Expected number of patients (EN) increased and probability of early termination (PET) decreased as accrual rate increased, with rate of change increasing as assessment window increased. When the true probability of toxicity was 50% and enrollment halted between cohorts, EN was 10 patients and PET was 78%. EN was 17 and PET 54% with an assessment window of 30 days and 5 patients were enrolled per month. With a 90-day assessment window and 3 patients/month enrolled, EN was 16 and PET 59%. EN was 20 and PET was 12% with a 180-day assessment window and 3 patients were enrolled per month. Similar results were noted for cohorts of size 10 and a maximum number of 40 patients. Conclusions: The performance of designs that require halting enrollment while waiting for results of an interim analysis can be compromised by continuous accrual when assessment windows are lengthy and the accrual is fast. In these circumstances, consideration should be given to designs, such as Bayesian multiple imputation for delayed outcomes (Cai et al Stat Med 2014) and TOP2 (Lin et al JNCI 2019), that do not require accrual halt to make real-time interim analysis in the presence of pending patients, which protects patients from excessive toxicity or a futile intervention.
Breast cancer and molecular testing in Brazil.
Authors: Aleixo Sabina B. , Pulido Jose Zago , Parmanhane Narelle 
Published: June 2021

e13602 Background: Breast cancer is a common malignancy among women worldwide. Regardless of the economic status of a country, breast cancer poses a burden in prevention, diagnosis and treatment.Accurate pathologic and molecular diagnosis of high quality in breast cancer is a challenge in low- and middle-income countries (LMIC). The two most important aspects needed are the hormonal and epidermal growth factor receptor 2 (HER2) status as they are used as prognostic factor and as predictive biomarker for treatment. The most commonly used tests for assessing HER2 status are immunohistochemistry (IHC) assays for HER2 protein expression and fluorescence in situ hybridization (FISH) for HER2 gene amplification.In this study, we evaluated the hormonal and HER2 status of women with breast cancer and the need for FISH in HER2+++. Methods: We retrospectively reviewed the database of the Hospital Evangelico de Cachoeiro de Itapemirim, a regional cancer center from the south of Espirito Santo State – Brazil.The period covered in the study was from 01/08/2019 to 01/08/2020. We analyzed all new cases of invasive breast cancer diagnosed by IHC for the estrogen (ER), progesterone (PR) and HER2 receptors. All the HER 2++ and HER2 +++ were tested by FISH. We had during this period 152 newly diagnosed cases, 11 patients were excluded due to missing data. Results: The 141 cases were separated into four categories by hormonal status: 109 ER+PR+ (77,3%), 9 ER+PR− (6,4%), 2 ER−PR+ (1,4%) and 21 ER−PR− (14,9%). When these results were associated to HER 2 status we found 11 (7,8 %) triple negative breast cancer (TNBC) , 26 (18,43%) HER2 ++ and 23 (16,31%) HER 2+++. The HER2++ were tested by FISH and 8 (30,7%,) were found positive, 10 (38,6%) negative and 8(30,7%)indeterminate. Among the HER 2 +++ tested by FISH we found 15 (65,3 %) positive and 8 (34,7 %) indeterminate. Conclusions: Our study demonstrated a lower incidence of TNBC and higher incidence of hormone-receptor positive cases as compared with other institutions. Different from other countries, all HER 2++ and HER2+++ in Brazil need to go to molecular testing. Altough trastuzumab is available in the public Brazilian Health System (SUS) for HER 2 positive women, diagnosis with IHC alone is not permitted and there is a need for molecular testing confirmation. Therefore, the most troublesome data in this study was that 34,7% of HER 2+++ had indeterminate FISH results, in these cases SUS does not offer traztuzumab. Newer guidelines allow the use of IHC only in HER2 +++ . We consider important for SUS to review the recommendation for need of molecular testing for all HER2 +++ so this will avoid subtreating women in Brazil