bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristics and survival of secondary male breast cancer: SEER database analysis.
Authors: Khoury John , Yadav Siddhartha , Rangarajan Tara , Zakalik Dana
Published: May 2020

e13690

Background: Male breast cancer (MBC) is rare accounting for less than 0.5% of all cancer diagnoses in men. We used the term secondary male breast cancer (sMBC) to refer to ipsilateral and contralateral recurrences in addition to new primary MBC. Given its low incidence, data regarding the risk of developing sMBC and its characteristics are scarce. Methods: Multiple Primary Standardized Incidence Ratios (MP-SIR) session was conducted from the SEER*Stat software. We included all patients diagnosed with stage I,II and III MBC between 1990 to 2015 from the Surveillance Epidemiology and End Results (SEER) 18 registry. The standardized incidence ratio (SIR) was calculated as an estimate of the risk of a second primary malignancy based on the incidence in the general population. Descriptive statistics and Kaplan-Meier analysis were performed using SPSS software. Results: Among all 2321 men diagnosed with a first primary MBC during the study period, 28 patients had a subsequent diagnosis of MBC. The risk of sMBC was significantly elevated with SIR of 33.12 (95% CI, 22.18 – 47.56). The median latency period between the initial and subsequent diagnoses was 5.9 years. 82.1% of the patients were White, 14.3% Black and 3.6% Asian/Pacific Islander. Majority of the cases constituting 85.7% of sMBC were diagnosed in the contralateral breast. 67.8% of the sMBC remained hormone receptors status positive similar to the initial status of the primary diagnosis. 42.9% of the sMBC patients were diagnosed with stage I, 17.9% with stage II, 3.6% with stage III, 17.9% with stage IV and 17.9% of unknown stage. The median overall survival for sMBC was 96 months (95% CI, 11.3-180.6). We also found an increased risk of developing liver cancer (SIR: 2.16), prostate cancer (SIR: 1.29), thyroid cancer (SIR: 3.08) and acute myeloid leukemia (SIR: 2.4) in individuals after a diagnosis of MBC. Conclusions: Men diagnosed with breast cancer are at increased risk of sMBC in addition to other malignancies which require careful monitoring after completing initial treatment. Contralateral mammogram screening or prophylactic contralateral mastectomy can be considered based on patient’s preferences and values.
The optimal living and wellness program: Piloting a novel survivorship care model.
Authors: Corcoran Stacie , Russell John , Smith Andrea , Kelly Bridget , Clayton Jill , Stein Bennet , Keenan Kathleen , Robson Mark E. , Iyengar Neil M.
Published: May 2020

e14003

Background: Multiple national agencies define cancer survivorship as beginning at the time of cancer diagnosis. However, traditional care models deliver “survivorship care” months or years after diagnosis, which can lead to disruption in care and “transition anxiety”. We hypothesized that an approach centered around wellbeing could be applied across the cancer continuum (starting at diagnosis) and serve as a novel survivorship care model. Methods: We developed the Optimal Living and Wellness Program, a multilevel intervention with 3 key components: 1) a centralized model that provides coordinated multidisciplinary care; 2) an individualized Wellness Plan (WP); and 3) enrollment at the time of cancer diagnosis in order to mitigate the experience of post-treatment transition. Participants complete a digital wellness questionnaire (WQ) prior to the first medical oncology consult, receive a validated algorithm-based WP reviewed by a Wellness Coordinator, and meet with a Wellness Advanced Practice Provider (WAPP) via tele-medicine. The WP consists of individualized education and referrals to indicated supportive services (e.g., nutrition, exercise physiology, financial counseling, integrative medicine). The WAPP will ultimately assume care of the patient in the post-treatment (“survivorship”) phase. We piloted this approach in patients diagnosed with breast cancer beginning in November 2019. The primary outcome is feasibility defined by completion of the WQ. Secondary outcomes include participation in the WAPP visits, attendance at referral appointments, and quality of life (QOL). Results: As of January 31, 2020, a total of 39 eligible patients with newly diagnosed breast cancer were approached. Of these, 36 (92%) have completed the WQ. All 36 participants received a WP with indicated supportive referrals and educational resources. Initial WAPP tele-visits will begin in May 2020. Qualitative data regarding patient experience and QOL will be obtained via focused interviews and feedback will be categorized in thematic domains. Conclusions: In this pilot, the majority of patients with newly diagnosed breast cancer completed a WQ in advance of their initial medical oncology visit, indicating preliminary feasibility. Patient enrollment is ongoing, and updated data including additional feasibility metrics and QOL will be presented. Earlier patient engagement and incorporation of wellness and supportive services from time of diagnosis may significantly improve the transition to post-treatment care.
Utilizing an advanced practice provider (APP) led acute care clinic to improve access to high quality, patient centered, cost effective cancer care.
Authors: Spradlin Megan , Bowden Kasey , Zabilla Sarena , Kessler Elizabeth R
Published: May 2020

e14007

Background: The medical oncology patient population is at high risk for adverse effects and complications related to anti-cancer treatments. The cost of oncology care is projected to reach almost $160 billion in 2020. The ongoing cost trajectory has placed a significant financial burden on patients, families, and society as a whole. The Centers for Medicare and Medicaid Service (CMS) has developed a new payment and delivery model that identifies potentially preventable conditions for ED visits/rehospitalizations among patients that are receiving anti-cancer therapies. Proposed CMS changes have created increased urgency around avoiding preventable ED visits/rehospitalizations. In response to the need for high quality, patient centered, cost effective care, we have established an APP-led acute care clinic that specializes in oncology care. Methods: The Clinical Assessment and Rapid Evaluation (CARE) Clinic offers acute care and symptom management catered to the oncology population. Patients are able to receive lab and imaging work up in addition to intervention with IV fluids, anti-emetics, opioids, electrolytes or antibiotics, as well as blood/platelets as warranted. To assess patient satisfaction, we created an anonymous four question survey delivered to CARE clinic patients over a two month period. Results: Seventy five surveys were collected. Pain and nausea were the leading reasons for encounters. 91% of patients rated their care as excellent, while 9% of patients rated their care as good. 33% of patients reported they would have been seen in the ED if the CARE Clinic was not available. Conclusions: The CARE Clinic represents a unique, one of the first of its kind, acute care and symptom management entity that utilizes an APP model to provide high quality care at a low cost. More research is needed to identify if this model can create statistically significant reduction in ED visits and rehospitalizations, however current data suggests that patients are highly satisfied with this unique method of care delivery. Sources Handley NR, Schuchter LM, Bekelman JE. Best Practices for Reducing Unplanned Acute Care for Patients With Cancer. Journal of Oncology Practice. 2018;14(5):306-313. doi:10.1200/jop.17.00081.
An optimal care coordination model for Medicaid patients with lung cancer: Lessons learned from the beta testing phase of a multisite initiative in the United States.
Authors: Smeltzer Matthew , Boehmer Leigh , Kramar Amanda , Asfeldt Thomas , Faris Nicholas Ryan , Ray Meredith , Lathan Christopher S. , Osarogiagbon Raymond U.
Published: May 2020

e14010

Background: Medicaid patients with lung cancer often have less favorable outcomes than non-Medicaid patients, which, given provision of care is typically comparable, may be due to socioeconomic disparities between these populations. In 2016, the Association of Community Cancer Centers (ACCC) embarked on a 3-year initiative to develop and test the Optimal Care Coordination Model (OCCM) to improve care coordination for Medicaid patients with lung cancer. A framework of 13 independent care delivery areas, spanning access to care to survivorship and supportive care, aids cancer programs in identifying barriers to access and use of their care, with a focus on Medicaid patients, and therefore enables optimal care coordination. Methods: Seven community-based healthcare systems in 6 U.S. states were selected as OCCM beta testing sites and then supported by the ACCC via site visits and biweekly calls. Sites self-assessed the quality of their care in selected OCCM areas to identify relevant quality improvement projects and improve understanding of needs specific to Medicaid patients. Results: Total patient enrollment across all sites was 926 (257 Medicaid; 669 non-Medicaid). Each site selected 1–2 priority OCCM areas, including patient access to care, prospective multidisciplinary case planning, or tobacco cessation, for projects. Enhanced collaboration, improved programming (e.g., patient navigation and formation of lung health leadership team), and organic programmatic changes due to the OCCM work were identified as successes. Site-specific challenges included inadequate staffing at project start and lack of centralized data collection and coordination. The importance of lung cancer–dedicated navigation, multidisciplinary conference use for treatment planning, and understanding needs specific to Medicaid patients were key transferable lessons. Examples of institutional support received by sites during the project included opportunities for staff training and leadership commitment from other hospital departments to assist with care delivery improvements. Use of the existing OCCM framework; increased staffing, particularly for lung cancer navigation; and expanded community outreach were identified in the sustainability plans. Conclusions: OCCM beta testing helped sites self-assess care delivery and identify areas for improvement. Ultimately, it was apparent that Medicaid patients need to be treated differently to obtain equity of outcomes with non-Medicaid patients.
Provider and patient insights into the cancer care journey.
Authors: McLarty Sarah , Johnson Kjel , Francis Ann , Johlie Melissa , Sawicki Christine , Johnson Kjel Andrew
Published: May 2020

e14019

Background: Research regarding primary care physicians’ (PCPs) involvement in the cancer treatment journey is limited. When a patient is first diagnosed with cancer, navigating healthcare can be confusing without the proper guidance. We conducted this study to understand a PCP’s involvement in the cancer care journey and how to optimize a patient’s journey through healthcare once diagnosed with cancer. Methods: We conducted a cross-sectional survey of 53 PCPs who have practiced over two years and have had at least one patient referred to an oncologist in the past year. We asked PCPs their method of oncologist referral, utilization of an oncologist selection tool that directs patients to high-quality and low-cost oncologists, and when an advanced directive should be established. We conducted a second survey with 112 current and previous cancer patients over the age of 25 who had received chemotherapy. We asked participants to reflect upon their oncologist’s referral, utilization of an oncologist selection tool that identifies high-quality and low-cost providers, participation in clinical trials, and guidance in creating an advanced directive during their cancer care journey. We conducted both surveys through online survey panels in June 2019. Results: PCPs tend to refer patients to oncologists that are part of their health system (67.9%) and who they know through networking (39.6%). While the majority of PCPs (79.2%) were not currently utilizing an oncology selection tool, the majority (77.3%) expressed a willingness to use such a tool for their newly diagnosed cancer patients. The majority of PCPs (69.8%) believe that cancer diagnosis is the best time to talk about establishing an advance directive, which should be initiated by the patient’s PCP (64.2%) or oncologist (56.6%). Patients ranked a PCP referral (52.7%) as “very important” when selecting an oncologist. One-third of patients used an oncology selection tool; 65.2% responded that a tool to understand true quality and cost of an oncologist would be “somewhat important” or “very important.” Most (71.4%) patients reported that it would be “somewhat important” or “very important” to participate in a clinical trial even though none participated in one. Most patients (54.5%) had an advance directive, although most (54.1%) had completed one before their cancer diagnosis. Conclusions: These results present a compelling rationale for developing a data-driven oncologist selection tool, optimize clinical trial involvement and expand the portion of patients who have an advanced directive at the start of their cancer care.
Patterns in detection of recurrence among patients treated for breast cancer.
Authors: Saltbæk Lena , Horsbøll Trine Allerslev , Offersen Birgitte Vrou , Andersson Michael , Friberg Anne Sofie , Skriver Signe Korsgaard , Bidstrup Pernille , Overgaard Jens , Johansen Christoffer , Dalton Susanne Oksbjerg
Published: May 2020

e14020

Background: Follow-up programs for breast cancer should according to ASCO and ESMO consist of regular visits including anamnestic screening for symptoms of recurrence and physical examination for ten years in addition to regular mammography. Recently, the Danish guideline for follow-up after early breast cancer was revised. Today most patients are offered open access to an outpatient clinic, but no scheduled visits following the first year after surgery. This strategy has been studied in regards to psychological and health related quality of life outcomes, but not in regards to detection of recurrence and survival. The aim of this study was to quantify the recurrences detected at scheduled outpatient visits in Denmark before the implementation of revised follow-up guidelines. Furthermore, we aimed to assess whether the duration of symptoms prior to recurrence detection differed depending on type of visit when recurrence was detected. Methods: We conducted a cross-sectional study among 310 patients with recurrent breast cancer. Through medical records, we retrieved information on type of visit when recurrence was detected, localization of recurrence, symptoms reported, and duration of symptoms. Tumor characteristics were obtained from the Danish Breast Cancer Group database. Results: Among the 310 breast cancer recurrences, more than half were distant (59%), 26% were loco-regional, and 15% loco-regional and distant. Among the 199 patients still in outpatient follow-up at time of recurrence, recurrence was detected at a scheduled outpatient visit (21%), at a patient-requested extra outpatient visit (15%), by the general practitioner or other specialist (47%), or on a scheduled mammography (11%). Among patients with recurrence detected at a scheduled outpatient visit, the majority (88%) reported symptoms related to the recurrence. Patients who waited for the scheduled outpatient visit despite symptoms had a markedly longer duration of symptoms (median 21 weeks) prior to detection of recurrence than patients who requested an extra visit (median 8 weeks). Conclusions: One-fifth of recurrences among patients attending outpatient follow-up were detected at scheduled outpatient visits. The majority was symptomatic, but the patients had not altered their scheduled appointment. Whether recurrences like these will be detected in follow-up without scheduled visits is unknown. Some recurrences may be detected earlier, if patients can be educated to react on relevant symptoms, but some recurrences may be detected with further delay. Thus, more research in this area is needed.
A dedicated lung cancer immunotherapy outpatient clinic: The first experience in China.
Authors: Xu Chongrui , Tu Hai-Yan , Sun Yueli , Li Yang-Si , Xu Bing-Fei , Wang Bin-Chao , Chen Hua-Jun , Wang Zhen , Zhou Qing , Yang Jin-Ji , Li Chanchan , Wu Yi-Long
Published: May 2020

e14022

Background: Immunotherapy (IO) has become the fourth pillar of clinical cancer care. There is an increasing need to the treatment of more patients for a longer period with acceptable cost and risk. We here described the development and operationalization of the first dedicated lung cancer IO outpatient clinic in China using an iterative process. Methods: The clinic is staffed by one attending physician, one medical oncologist, several nurses, and medical assistants along with a dedicated care coordinator. Patients (pts) were scheduled to visit every Thursday morning for the treatment and then visit every 2-4 weeks as per IO regimen. Written informed consent were obtained after well-informing about the IO and possible side-effects, and administration of self-purchased drugs. The minimum workup including routine general physical examinations or tests along with tests specific for IO, and toxicities was undertaken during the clinic visits. Treatments response, adverse events (AE), quality of life (QOL), and patient reported outcomes were measured/reviewed by the oncologist accordingly (CTCAE 5.0, RECIST 1.1, and iRECIST criteria). Pts can report their symptoms and be noticed/alerted during the clinic visits or between visits via electronic interfaces. All these data were integrated into the EMR database (LinkDoc) and valued as continuous documentation to support clinical decisions making, so as to improve clinical symptom management and overall QOL. Also, the pts were coached about possible symptoms and corresponding self-monitoring and management strategies. A standardized workflow was established, as well as staff education and training programs. Results: During May to December 2019, 99 pts presented at our IO clinic, and 79 received IO therapy (46 pembrolizumab, 32 nivolumab, and 1 camrelizumab). Pts on average received 3 cycles (Total: 280; range 1-11 cycles). AE were noted in 57 (72.2%) pts. All pts were well. Conclusions: This study demonstrates that our dedicated lung cancer IO clinic worked well. Therapeutic alliance of IO clinics has already been established with primarily 15 hospitals participated in. The design process and standardized workflow components will be further expanded to other cancers or conditions.
Single-institutional analysis of clinicopathologic features and overall survival (OS) in melanoma patients (pts).
Authors: Wang Ding , Wang Jonathan , Krajenta Richard , Zhu Liping
Published: May 2020

e14023

Background: Melanoma is ranked 5th commonest malignancy in United States and SEER has projected 3,290 new skin melanoma cases in Michigan in 2020. Diagnosis of recurrent & metastatic melanoma has associated with poor prognosis and survival. Henry Ford Cancer Institute (HFCI) is a tertiary cancer care facility serves cancer patients from communities throughout metropolitan Detroit tri-county areas where a higher melanoma presentation (1.56%) in black pts than this group at national database (0.6%). A subgroup of white Michiganders travel to Southern sunshine states in winter months known as “snowbirds” have contributed to melanoma incidences in Michigan. We aim to review our institute tumor registry data to analyze the clinicopathological features, care pattern and correlated survival outcomes. Methods: We have identified adult melanoma pts through institute tumor registry between 2007 – 2017, who had demographics, initial diagnosis, recurrence, then death or last follow-up for overall survival (OS) outcome. Data were analyzed using IBM SPSS, R-statistical and log-rank test. Results: Of 1327 eligible pts, median age was 63, and 874 (65.86%) pts were invasive at onset diagnosis. 1298 (97.81%) pts were white, 23 (1.73%) pts were black. Slightly more females (55.54%) over males and overall 301 deaths (22.68%) were observed. 1277 pts with cutaneous melanoma had better 5- and 10-year (yr) OS at 78.14%, and 68.32% compared to 50 (3.77%) non-cutaneous pts at 13.31% and 8.87%, respectively. Black pts carried a poor 5-yr OS at 34.68% vs white at 76.4%. 116 (17.9%) pts presented with recurrences, had 5-yr OS at 34.58%. Of 121 (9.1%) pts who never had been disease-free, showed worse 5-yr OS at 15.87%, with trend of improvement since FDA-approval of immune checkpoint inhibitor therapies. Conclusions: We have analyzed our melanoma care pattern, experience and survival outcomes at 5-yr and/or 10-yr from a tertiary community cancer care facility. We have assessed our current practice benchmarks with plan to improve our care delivery by having built a multidisciplinary team, and launched Melanoma & Skin Cancer Clinical Care Pathway through Tumor Board to improve care access, to decrease care disparity, then to standardize quality of care including encouraging clinical trial enrollments which will enhance quality of melanoma care and survival outcomes of advanced and metastatic cases.
Optimizing infusion wait time: Redesigning medication order verification/approval at a comprehensive cancer center.
Authors: Hashemi-Sadraei Neda , Dayao Zoneddy R. , Sasankan Shenthol , Cox Andrea , Peacock Sandra , Abernathy Janet , Kittson Ronald , Tawfik Bernard , Crozier Nick , Lauer Richard C.
Published: May 2020

e14042

Background: Nationwide, many cancer centers experience challenges with infusion center efficiency while maintaining high safety standards. Many factors contribute to long wait times for patients on the day of their infusion appointments. At University of New Mexico Comprehensive Cancer Center (UNMCCC), a contributing factor is the delays in verification or approval of medications. We conducted a project to improve order verification/approval workflow within a Plan-Do-Study-Act (PDSA) framework with the objective to decrease the infusion wait time. Methods: A multidisciplinary working group was formed consisting of the infusion floor physician lead, nurse lead, pharmacy lead, and analytics and process improvement leads. Upon exploring the infusion workflow database, disruptions in verification or approval of orders had a large impact on wait times. Order verification workflow was broken down into 3 steps: 1) physician assessment of patient and approval of orders, 2) infusion nurse assessment of patient, 3) pharmacist verification of order. Beginning Feb 2019, the following interventions were implemented in each section: 1) once patient was assessed by physician and orders approved, the patient was marked as “ready-to-treat”. 2) Pharmacist verified the order once “ready-to-treat” was communicated and initiated preparation of medications prior to arrival of patient to the infusion suit. 3) Infusion nurse assessment occurred once patient was seated on infusion chair. 4) Physicians were encouraged to pre-approve selected injections by the morning of patient appointment. Results: Prospective wait time was gathered for May 2019 using the real-time data available in the electronic medical record. Wait times were analyzed for patients receiving chemotherapy or flat dose injections. By marking appropriate patients “ready-to-treat” and moving pharmacist verification prior to infusion nurse assessment, there was an immediate decrease in wait time from 79 to 60 min. Selected injections which did not require mixing were pre-approved by the physician and stored in the medication dispensing system (Pyxis). This resulted in decrease in the injection wait time by 8.5 minutes, without wasting of drugs. Conclusions: Redesigning the medication order verification/approval workflow resulted in reduced wait times for patients receiving infusions or injections. We aim to further refine our PDSA cycles and ensure sustainability of change.
Non-user physician perspectives about an oncology clinical decision-support system: A qualitative study.
Authors: Rocha Hermano Alexandre Lima , Emani Srinivas , Arruda Carlos Andre Moura , Rizvi Rubina , Garabedian Pamela , Machado de Aquino Camila , Felix Winnie , Hekmat Rezzan , Sands-Lincoln Megan , Meneleu Pedro , Jackson Gretchen Purcell , Bates David Westfall , Juaçaba Sergio Ferreira
Published: May 2020

e14061

Background: Advances in artificial intelligence (AI) continue to expand capabilities within the healthcare domain, particularly in the discipline of oncology. Watson For Oncology (WfO) is an AI-enabled clinical decision support system that presents potential therapeutic options for cancer-treating physicians. The objectives of this study were to identify non-user physicians’ expectations, perceived challenges and benefits of WfO use in Brazil. Methods: The study took place at Instituto do Câncer do Ceará (ICC), a Brazilian oncology hospital that implemented WfO in December 2017, but not all physicians adopted the tool. Physicians who had not used WfO (n = 5) were recruited through purposive sampling identified with the assistance of local research personnel. Semi-structured interviews were conducted in Portuguese and later de-identified and transcribed into English. A thematic analysis of interview data based on grounded theory by two members of the research team with extensive experience in qualitative data analysis was conducted. Results: Non-user physicians had positive perceptions about WfO, along with several concerns and uncertainties. They expected that WfO would be easy to learn, useful, and helpful. Physicians perceived that WfO would provide a more standardized approach to treatment than care without it. They also believed that WfO would play a supportive and not a substitute role in care especially for complex cases in which the physicians had more in-depth knowledge of a patient and already had an established patient-provider relationship. Physicians did expect WfO use to negatively impact productivity, specifically through longer office times per patient because of the need to enter data and review recommendations. Physicians questioned whether the use of WfO would negatively impact their autonomy and role in providing care. Finally, physicians also questioned whether the treatment suggested by WfO would fit the social context of a low-middle income country such as Brazil with limited technological and economic resources. Conclusions: The implementation of US-developed AI technologies, such as WfO, should be further explored in different social and economic contexts. Physician concerns about productivity and autonomy need to be assessed and addressed in AI implementation; one strategy is to leverage previous lessons learned from electronic health record (EHR) implementations. This study is a critical step in understanding potential user perspectives in adopting a new AI tool in different social contexts.
Using oral morphine-equivalent values to track the analgesic effect of treatments in clinical trials.
Authors: Raymond Stephen A , Hutchin John F , Degboe Arnold , Faulkner Kenneth G
Published: May 2020

e14075

Background: Patient reported pain is subjective, requiring individuals to grade the feeling of pain as “high” or “low”, typically using a numeric scale. As a supplement to self-reported pain, monitoring the dosage and type of treatment needed to keep pain at a tolerable or comfortable level has been suggested. However, differences in treatments can complicate the ability to compare analgesic effects. One approach to this problem uses a 30 mg oral dose of morphine as a standard to quantify the relative analgesic strength of different pain medications. Methods: For use in clinical research, we developed an application on a handheld device for logging opioid consumption and supporting automated determination of oral morphine equivalents (OME) for commonly prescribed analgesics. We reviewed the literature to prepare a comprehensive database of OMEs for more than 450 commonly prescribed pain medications. From treatment logs (time, dose, and route of administration), the application determines a daily “OME Score” in standardized units of mg of oral morphine. Results: As prescriptions change, the daily OME Score gives a comparable measure to track the utilization of diverse analgesic medications. OME Scores can be categorized using the Analgesic Quantification Algorithm (AQA scale) developed by Chung (1). The AQA scale has eight categories and provides 5 gradations for patients in advanced pain who receive strong opioids, each gradation reflecting adoubling in the daily OME Score. The impact of a therapy on pain may be considered clinically significant depending on the changes in self-reported pain and AQA scores related to the treatment. Conclusions: We have developed a method and associated application for easily logging the time, dose, and route of administration of analgesic treatments. This information can be used to compute the daily OME Score for a wide variety of analgesics. The daily OME score can then be tracked and used to report the AQA score alongside self-reported pain for evaluating pain relief in clinical studies. Reference: 1) Chung KC, Barlev A, Braun AH, Qian Y, Zagari M. Pain Medicine 2014; 15:225-232.
Informed consent in phase I clinical trials: Implications for trends in design.
Authors: Frankel Paul Henry , Groshen Susan G.
Published: May 2020

e14077

Background: Informed Consent (IC) is a critical aspect of human subjects protection. Institutional Review Boards are tasked with insuring proper IC as one aspect of protecting participants in clinical trials. Phase I trials in oncology present special issues with IC, as often neither the risks nor the benefits are well-known. This has resulted in carefully worded IC templates for Phase I studies based on the traditional use of dose-finding designs that are geared towards finding the “Maximum Tolerated Dose (MTD)”. As the definition of this term varies by study, the implication for patient risk and informed consent are rarely discussed. Methods: We reviewed Phase I designs to present options for improving the informed consent process for Phase I oncology trials. Results: Phase I studies have seen an increase in designs based on work from the early 1990s seeking a dose that results in a targeted percent of patients experiencing a “Dose Limiting Toxicity (DLT)” to define the MTD. The most common definition of a DLT is a treatment-related toxicity that results in a particularly concerning severe toxicity (grade 3 or higher) in the first cycle of therapy and the most common rate targeted (in designs that define toxicity as a goal) is 25%. In that setting, while lower doses may have a lower likelihood of DLT, higher doses or the expansion cohort are likely to have a 25% chance of DLT if the target is pursued. This information is rarely quantitatively communicated in the informed consent. Conclusions: IRBs and investigators should consider communicating through informed consent the quantitative summary of goals of the study and related risk. For example, transparency suggests conveying when the goal (target) of the study is to find the dose where there is a one in four chance of experiencing a severe adverse event in the first cycle.
Recruiting patients with advanced cancer to participate in a non-opioid intervention for chronic pain management.
Authors: Selvan Preethi , Myers-Coffman Katherine , Bryl Karolina , Lama Jasmine Tenpa , Sutton Brigette , Biondo Jacelyn , Cottone Carrie , Lacson Clarissa , Millstein Allison , Banks Joshua , Worster Brooke , Leader Amy , Bradt Joke
Published: May 2020

e14085

Background: Chronic pain related to advanced cancer is difficult to treat. In addition to traditional analgesics, non-pharmacological interventions, such as music therapy, may help alleviate pain in this population. Research studies to test the efficacy of these non-pharmacological interventions are necessary and important. However, recruiting patients with advanced cancer to participate in such interventions can be challenging. Identifying effective sources of referrals and barriers to participation may help increase recruitment rates in the future. Methods: We recruited patients with advanced cancer (stage III or IV) with chronic pain from two major hospital systems to participate in a pain management interventional study. The experimental group received individualized music therapy, while the control group received talk therapy. Participants attended six weekly sessions at the hospital and were compensated for time and travel. Recruitment methods included referrals from care teams, advertisement through flyers and posters, EMR chart reviews, and in-person recruitment at multiple infusion centers. Missing data was excluded from analyses. Chi-square tests assessed significant differences between groups. Results: Of 594 patients that were referred to the study, 7% enrolled (n = 40), 35% declined (n = 208), and 58% were ineligible (n = 346). Forty-seven percent (n = 19) of enrolled participants were female; race/ethnic distributions were the following: African American/Black (55%; n = 22), Caucasian/White (28%; n = 11), or other (17%; n = 7). The most effective recruitment method was through self-referral; of the 40 patients enrolled in the study, 9 (23%) were enrolled through this method. There were no statistically significant differences between referral source and enrollment. The top three reasons patients declined to participate included lack of interest (32%; n = 67), lack of time and/or energy (28%; n = 59), and lack of transportation (16%, n = 34). Conclusions: Although there were no differences in referral sources, more patients were enrolled through self-referral or in-person recruitment, showing the power of personal motivation and personal touch. While lack of participation due to interest, time, or energy are hard to overcome, future research should consider campaigns to reach patients who are motivated to participate. In addition to referrals, resources for in-person recruitment and transportation to help alleviate barriers to research participation should be considered.
Use of Twitter among cancer clinicians and researchers and the associations with scientific impact of its users and followers.
Authors: Cheung Winson Y. , Rigo Rodrigo , Batra Atul
Published: May 2020

e14101

Background: Social media channels, such as Twitter, represent relatively new technology platforms for scientific users to disseminate research findings and communicate their views and interpretations to colleagues and followers. To date, the associations between the use of Twitter and the scientific impact of its users and followers are unclear. Methods: All Canadian oncologists who are full members of the American Society of Clinical Oncology were identified from the online membership directory. Users of Twitter were defined as those with an active Twitter account, as of June 2019, and posted at least one tweet within the past 12 months. Data regarding the number of tweets, likes, and followers were collected by an online search of Twitter. Scientific impact of each individual was assessed based on a user’s h-index and number of citations from Google Scholar as well as score from Research Gate. Associations were examined with summary statistics and correlation coefficients. Results: We identified 676 eligible oncologists of whom 80 (12%) and 596 (88%) currently use and do not use Twitter. Among the users, the median number (IQR) of tweets, likes, and followers were 196 (45-865), 325 (86-1,246), and 198 (89-449), respectively. The scientific impact of Twitter users versus non-users was statistically similar (see Table). Likewise, within the group of users, there were no correlations between the number of tweets, likes, and followers and the scientific impact of individuals (correlation coefficients 0.38, 0.34, and 0.41, respectively, all p > 0.05). Conclusions: Only 1 in 10 oncologists use Twitter, but those who use Twitter leveraged this technology platform frequently. There were no associations between the use of Twitter and the scientific impact of its users. Views from a minority of oncologists are represented on Twitter. Such potential bias may underscore the potential need to exercise caution when using social media for scientific knowledge exchange. Regular evaluations of new technologies are warranted to ensure the quality and rigor of their scientific content in order to reduce misrepresentation. [Table: see text]
Initial Experience with Cancer Guidelines Navigator, a tool to standardize and improve the quality of cancer care in Sub-Saharan Africa, at Ocean Road Cancer Institute in Tanzania.
Authors: Suarez Saiz Fernando Jose , Yuravlivker Leemor , Ndumbalo Jerry , Mwaiselage Julius , Siu Sadiq Maalim , Koh Wui-Jin , Dawe Natalie , Strauss Daniel
Published: May 2020

e14106

Background: The IBM Cancer Guidelines Navigator (CGN) is a digital reference system to support treatment planning that allows clinicians to enter a cancer patient’s clinical characteristics and presents the corresponding treatment options in the NCCN Harmonized Guidelines (TM) for Sub-Saharan Africa. In October 2019, Ocean Road Cancer Institute (ORCI) in Tanzania became the first site in Africa to initiate a hospital-wide implementation of the tool to help clinicians reduce cancer treatment variability by increasing adherence to standard evidence-based care. We describe training and lessons learned from system introduction. Methods: Training for clinical staff at ORCI occurred over one week and included daily one-hour lectures, followed by personalized hands-on training. A survey was administered to assess usability and use cases of the tool. Results: Thirty-one ORCI clinical and IT staff members participated in training, and 12 completed the survey. Responses indicated that the most beneficial uses for CGN were at point of care and for self-learning. Participants indicated that the top benefits of the tool were quick access to guidelines and evidence (75%) and ease of use (58%). Expanding cancer coverage (42%), offline access and better integration into the workflow (25%) were identified as areas for improvement. Post-training, ORCI implemented easier access to CGN on each computer and tablet used for consultation and care management. Conclusions: CGN is a digital reference system that is designed to support easy and efficient access to regionalized cancer-treatment guidelines for point-of-care treatment planning and education. Expansion of this program has been planned for other hospitals in Tanzania. Future studies will examine whether CGN usage affects guideline adherence.
Evaluation of cancer patients comfort with and interest in interacting with various social robot configurations in a clinical environment.
Authors: Hajdenberg Julio , Dvorak Tomas , Robinson Diane , Spencer Christopher
Published: May 2020

e14112

Background: Our group is interested in designing and programming social robots that augment our staff capabilities in performing different cognitive tasks in the clinic. This study examined patients' interest and comfort with various robot form factors, when screened for distress as part of a routine patient evaluation. Methods: 199 Patients (N) (Power > .80) were presented 7 commercially available robot forms and rated their comfort and interest in interacting with each one on a 5-point Likert scale (1 = Strongly Agree, 3 = Neutral, 5 = Strongly disagree).One sample t-tests compared the difference between the overall mean ratings and neutral point for comfort and interest. Independent samples t-test examined sex differences in overall ratings. Robust ANOVAs and post-hoc tests compared ratings across forms. Results: Results indicated significant interest and comfort in interacting with any robot tested, with men more interested and comfortable (see Table). Robust ANOVA indicated significant main effects for robot type ( Qcomfort = 31 .12; Qinteraction = 26 .52 , ps < .005) and sex ( Qcomfort = 21 .74; Qinteraction = 19 .35 , ps < .005) on comfort and interest. Post-hoc tests indicated that taller and narrower robots were least favored; however, there was no clear preferred form. Conclusions: Our findings suggest that patients are comfortable and interested in interacting with a robot, with men more comfortable and interested. Across robot forms, the taller and narrower forms were less favorable to patients. Future implementations of robotic interfaces may wish to avoid taller and narrower form factors. These data have informed the current model being tested in our clinics, which will be available for display. [Table: see text]
Mobile health applications in oncology.
Authors: Upadhyay Vivek , Landman Adam , Hassett Michael J.
Published: May 2020

e14115

Background: Over 325,000 mobile health (mhealth) applications (apps) have been developed. There has been a substantial increase in mhealth investment, with over $8.1 billion invested in digital health startups in 2018. While apps have been studied within clinical oncology, we are aware of no comprehensive evaluation of the commercial footprint of oncology-specific apps. We sought to describe the state of oncology-specific apps and highlight notable areas of development. Methods: We conducted a systemic search for oncology apps in the Apple iOS and Google Play app stores in January 2020. Search terms included “cancer,” “oncology,” “radiotherapy,” and “chemotherapy.” All apps were manually reviewed and classified by English language support, date of last update, downloads, intended audience, intended purpose, and developer type. We also compared commercially available apps with those described in a recently conducted meta-analysis of oncology-app studies. We performed descriptive statistics using RStudio V1.2.335. Results: We identified 794 oncology-specific, English-language applications, but only 257 (32%) met basic quality standards and were considered evaluable. The primary reason for exclusion was lack of a recent update. Of included apps, almost half (47%) were found in the “Medical” Store Category and the majority were free (88%). The most common intended audience was healthcare professionals (45%), with 28% being geared towards the general public and 27% being intended for patients. The intended function was education for 37%, clinical decision support (CDS) for 19%, and patient support for 18%. Only 22% of education apps and 40% of CDS apps reported any formal app content review process. Web developers created 61% of apps, scientific societies created 10%, and hospitals/healthcare organizations created just 6% (Table). The most frequently downloaded apps tended to be geared toward educating/supporting the public. Of 54 studies that utilized mobile apps in oncology identified by a recent meta-analysis, only 2 could be matched to commercially available apps from our study, suggesting a substantial divide between investigation and product dissemination. Conclusions: Our analysis of oncology-related apps in the commercial marketplace found few high-quality, up-to-date apps, and a notable absence of key oncology stakeholders in app development. Future studies should explore barriers to developing and disseminating apps designed to advance oncology care delivery. [Table: see text]
Oncology early development technology to facilitate early analysis of safety and efficacy data.
Authors: Vaca Mireya , Naumovski Louie , Soh Chang-Heok , Leal Jailene
Published: May 2020

e14116

Background: The lack of a standard data review platform for the surveillance of early oncology trials led to the formation of a cross-functional initiative with the objective of creating a data surveillance framework that would provide clinical study teams with access to near real-time data in order to be able to generate fast insights into the safety and efficacy findings of oncology early development trials so that early signal detection or clinical proof-of-concept could take place. Methods: To determine the requirements that would drive the framework, the cross-functional team first conducted user interviews with study physicians to gather information on the types of tables, graphs, and visual outputs that would be most useful to the clinical study team, as well as most useful to re-create as a standard across the early oncology portfolio, focusing at this stage, on solid tumor trials using RECIST 1.1 for the efficacy output. Once these requirements were determined, specification details, including mock-ups of the visual output and guidelines to follow, were created. Programming and development of the pilot dashboard then began using R and Spotfire as the tools of choice. Results: The resulting dashboard branded “OED Fast Insights” included a standard set of pages composed of dynamic tables and graphs organized around 3 components: study disposition, disease response, and treatment-emergent adverse events. The output closely resembled tables and graphs the clinical study team were familiar with through statistical programming output using SAS. However, leveraging the interactive functionality of Spotfire allowed the team to produce dynamic tables and graphs that could be readily filtered by different pre-specified criteria (e.g. cohort, dose level, gender, lines of therapy, best overall response, type of cancer, biomarker status, etc.) or by subject or aggregate-level views, changed by different color indication, or adjusted in other ways by the user. The dynamic aspect of spider plots, waterfall plots, and swimmer lanes were of special interest in identifying early signal detection. Conclusions: The cross-functional initiative began in 2018 and by the end of 2019 the data surveillance framework was successfully scaled and rolled-out to the entire early oncology portfolio of existing trials utilizing RECIST 1.1 solid tumor criteria. The next phase of the initiative will be to extend the framework beyond RECIST 1.1 efficacy surveillance to include hematologic cancers.