PCN317 - THE CANCER DRUGS FUND: KEY UNCERTAINTIES, DATA COLLECTION PLANS, ANALYTICAL METHODS AND USE OF THE SYSTEMATIC ANTI-CANCER THERAPY (SACT) REAL WORLD DATA SET

Introduction: Limited real-world studies exist on the management of relapsed/refractory (R/R) classical Hodgkin's lymphoma (cHL) patients (pts) who failed autologous stem transplant (auto-SCT) and their subsequent healthcare resource utilization (HRU) and cost. Current treatment options include chemotherapy, a second auto-SCT, allogenic (allo-) SCT, palliative care, or newer therapies like brentuximab vedotin (BV) or programmed death-1 (PD-1) blocking antibodies. Pts eligible for treatment post auto-SCT failure may consume significant resources and using real-world data may inform the place of therapy of newly approved agents. Therefore, the objectives of this study were to compare HRU and cost among R/R cHL pts who received auto-SCT by transplant success and failure. Methods: This retrospective cohort study used electronic medical record (EMR) data of US pts from a network of oncology practices, including practices affiliated with CancerLinQ, maintained in the Definitive Oncology Dataset. Eligible adult (≥18 years) pts who had a confirmed diagnosis of cHL and ≥1 R/R event that occurred between 2000 to 2019 were included. Treatment patterns included any systemic anti-cancer therapy received post auto-SCT failure. Descriptive analyses examined differences by auto-SCT success vs failure. Auto-SCT failure was defined as having a R/R event or disease progression after receipt of auto-SCT. HRU included hospitalization rates, emergency department (ED) visits, and infused supportive care drugs. Costs (inflated for 2020$) were based on matched Health Care Utilization Project coded events. HRU and costs were reported per patient per month (PPPM) from initial cHL diagnosis (first-line [1L] therapy) through the second R/R event (third-line [3L] therapy) and for 3L among a subset of pts who failed auto-SCT in second line (2L). PPPM was calculated by dividing the total HRU or cost during the observation period by the number of months of the observation period and then averaged across all pts (regardless of being flagged for a specific service). Results: A total of 157 pts (54.9%) received auto-SCT among the R/R cHL cohort (n=286). Most pts were Caucasian (77.7%) with a median age of 31 years (range: 19-73) at the first R/R event. Median length of follow-up was 11 months from the first R/R event. Nearly all pts (91.7%) received auto-SCT after the start of 2L (68.2%, n=107) and 3L (23.6%, n=37). Approximately 9.6% (n=15) also received allo-SCT in later lines. Among auto-SCT pts, 62.4% (n=98) had a transplant success vs 37.6% (n=59) with a transplant failure. Across these 59 pts, 46 (78.0%) received treatment post auto-SCT failure. Treatment post auto-SCT failure consisted of 21 different anti-cancer regimens (monotherapy or in combination) and included BV (alone or in combination) (37.3%, n=22), chemotherapy (30.5%, n=18), PD-1 therapy (alone or in combination) (6.8%, n=4), other (5.1%, n=3), and allo-SCT (1.7%, n=1). The 59 pts with auto-SCT failure primarily failed in 2L (66.1%, n=39) and 3L (27.1%, n=16). HRU and costs for the 39 pts who failed auto-SCT in 2L were substantial in 3L. Approximately 92.3% of pts had a hospitalization, 30.8% had an ED visit, and 51.3% received infused supportive care treatment in 3L. Monthly costs in 3L were high: hospitalization $3,903, ED visit $130, infused supportive care $279, anti-cancer therapy $64,572, and $69,186 total. From the start of 1L through the end of 3L, the proportion of pts with a hospitalization was significantly higher for pts who failed auto-SCT (Table). Subsequently, costs were also higher and average length of stay longer. While HRU did not differ, infused supportive care costs were higher for auto-SCT pts. No significant differences in HRU and cost were observed across the two groups for ED visits and oncology setting outpatient visits. Anti-cancer therapy costs were significantly higher for pts who failed auto-SCT. Total monthly costs were higher for pts who failed auto-SCT. Conclusion: In the real-world setting, almost 40% of R/R cHL pts failed auto-SCT. There appears to be no clear standard of care post auto-SCT failure and using real-world data may inform the place in therapy of newer therapies. The HRU and cost of managing post auto-SCT failure was substantial and highlights the significant unmet need in this population. These findings add to the scarce real-world data on treatment patterns, utilization, and cost among R/R cHL pts who receive auto-SCT. Disclosures Yang: Merck & Co, Inc.: Current Employment. Desai:Merck & Co., Inc: Current Employment, Current equity holder in publicly-traded company. Gilligan:ConcertAI: Current Employment; Merck & Co., Inc.: Research Funding. Raut:Merck & Co., Inc.: Current Employment. Nahar:Merck Sharp & Dohme, Corp., a subsididary of Merck & Co., Inc., Kenlworth, NJ, USA: Current Employment.

Download Full-text

Data resource description: Systemic Anti-Cancer Therapy (SACT) Dataset

International Journal for Population Data Science ◽

10.23889/ijpds.v4i3.1193 ◽

2019 ◽

Vol 4 (3) ◽

Author(s):

Chloe Bright ◽

Sarah Lawton ◽

Stephen Benson ◽

Martine Bomb ◽

David Dodwell ◽

...

Keyword(s):

Cancer Therapy ◽

Real World ◽

Stage Iv ◽

Population Based ◽

Cancer Registration ◽

Real World Data ◽

Data Resource ◽

World Data ◽

Treatment Providers ◽

Anti Cancer

Background The Systemic Anti-Cancer Therapy (SACT) dataset is a unique source of national, real-world data reporting chemotherapy activity delivered by NHS treatment providers in England. SACT data are collected with the intent of improving disease-free or progression-free survival. Main Aim The SACT dataset aims to provide a detailed resource on systemic treatments delivered in secondary and tertiary settings in England for patients with cancer. Approach The SACT dataset is collected and curated by the National Cancer Registration and Analysis Service (NCRAS) at Public Health England. The dataset was launched in a phased implementation from April 2012. From April 2014 submission of SACT data became mandatory for all NHS treatment providers. Data are collected on the patient, tumour, regimen, treatment cycle, drug details, and treatment outcomes. The SACT dataset can be linked to other data held by NCRAS, for example the cancer registry1, Hospital Episodes Statistics2, and the national Radiotherapy Dataset to provide a complete picture of the cancer patient pathway. Results The SACT dataset is being used: for the appraisal of drugs funded through the Cancer Drugs Fund; to review the quality of cancer care as part of the National Cancer Audits; and for observational research purposes, including investigating 30-day mortality following chemotherapy3, and sociodemographic variation in the use of chemotherapy in patients with stage IV lung, oesophageal, stomach and pancreatic cancer4. The data are also released to external researchers via the Office for Data Release. Conclusion The SACT dataset is a unique resource in terms of the breadth and depth of information on chemotherapy prescribed to cancer patients. The key strengths are: the national coverage; real-world data; detailed clinical information beyond cancer registration data; and the ability to link to other cancer datasets. References Henson KE, Elliss-Brookes L, Coupland VH, Payne E, Vernon S, Rous B et al. Data Resource Profile: National Cancer Registration Dataset in England. Int J Epidemiol 2019; doi 10.1093/ije/dyz076. Herbert A, Wijlaars L, Zylbersztejn A, Cromwell D, Hardelid P. Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). Int J Epidemiol 2017; 46(4): 1093-1093i; e-pub ahead of print 2017/03/25; doi 10.1093/ije/dyx015. Wallington M, Saxon EB, Bomb M, Smittenaar R, Wickenden M, McPhail S et al. 30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study. The Lancet Oncology 2016; 17(9): 1203-1216; doi 10.1016/s1470-2045(16)30383-7. Henson KE, Fry A, Lyratzopoulos G, Peake M, Roberts KJ, McPhail S. Sociodemographic variation in the use of chemotherapy and radiotherapy in patients with stage IV lung, oesophageal, stomach and pancreatic cancer: evidence from population-based data in England during 2013-2014. Br J Cancer 2018; doi 10.1038/s41416-018-0028-7.

Download Full-text

Empirical evaluation of feature subset selection based on a real-world data set

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2004.03.005 ◽

2004 ◽

Vol 17 (3) ◽

pp. 285-288 ◽

Cited By ~ 5

Author(s):

Petra Perner ◽

Chid Apte

Keyword(s):

Real World ◽

Empirical Evaluation ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Real World Data ◽

Data Set ◽

World Data

Download Full-text

Comparison of Cycling Behavior between Keyboard-Controlled and Instrumented Bicycle Experiments in Virtual Reality

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120921850 ◽

2020 ◽

Vol 2674 (7) ◽

pp. 244-257

Author(s):

Martyna Bogacz ◽

Stephane Hess ◽

Chiara Calastri ◽

Charisma F. Choudhury ◽

Alexander Erath ◽

...

Keyword(s):

Virtual Reality ◽

Experimental Design ◽

Data Collection ◽

Real World ◽

Head Movements ◽

Behavioral Data ◽

Experimental Setup ◽

Real World Data ◽

World Data ◽

Cycling Behavior

The use of virtual reality (VR) in transport research offers the opportunity to collect behavioral data in a controlled dynamic setting. VR settings are useful in the context of hypothetical situations in which real-world data does not exist or in situations which involve risk and safety issues making real-world data collection infeasible. Nevertheless, VR studies can contribute to transport-related research only if the behavior elicited in a virtual environment closely resembles real-world behavior. Importantly, as VR is a relatively new research tool, the best-practice with regards to the experimental design is still to be established. In this paper, we contribute to a better understanding of the implications of the choice of the experimental setup by comparing cycling behavior in VR between two groups of participants in similar immersive scenarios, the first group controlling the maneuvers using a keyboard and the other group riding an instrumented bicycle. We critically compare the speed, acceleration, braking and head movements of the participants in the two experiments. We also collect electroencephalography (EEG) data to compare the alpha wave amplitudes and assess the engagement levels of participants in the two settings. The results demonstrate the ability of VR to elicit behavioral patterns in line with those observed in the real-world and indicate the importance of the experimental design in a VR environment beyond the choice of audio-visual stimuli. The findings will be useful for researchers in designing the experimental setup of VR for behavioral data collection.

Download Full-text

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems—The Hierarchical Presenting Problem Ontology (HaPPy)

Applied Clinical Informatics ◽

10.1055/s-0039-1691842 ◽

2019 ◽

Vol 10 (03) ◽

pp. 409-420 ◽

Cited By ~ 3

Author(s):

Steven Horng ◽

Nathaniel R. Greenbaum ◽

Larry A. Nathanson ◽

James C. McClay ◽

Foster R. Goss ◽

...

Keyword(s):

Emergency Department ◽

Real World ◽

Emergency Department Patient ◽

Snomed Ct ◽

Presenting Problems ◽

Real World Data ◽

Validation Data ◽

Data Set ◽

World Data ◽

Academic Level

Objective Numerous attempts have been made to create a standardized “presenting problem” or “chief complaint” list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges. Materials and Methods We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT). Results Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set. Discussion and Conclusion We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

Download Full-text

PCN153 Application of Real World Data to Inform a Breast Cancer Decision - Analytic Model in Austria and the U.S - Preliminary Outcomes of Data Collection

Value in Health ◽

10.1016/j.jval.2012.08.2193 ◽

2012 ◽

Vol 15 (7) ◽

pp. A437

Author(s):

B. Jahn ◽

D. Stenehjem ◽

K.R. Saverno ◽

U. Rochau ◽

B. Cai ◽

...

Keyword(s):

Breast Cancer ◽

Data Collection ◽

Real World ◽

Decision Analytic Model ◽

Analytic Model ◽

Real World Data ◽

World Data ◽

The U.S

Download Full-text

Activities for Students: As the Ball Rolls: A Quadratic Investigation Using Multiple Representations

Mathematics Teacher ◽

10.5951/mt.103.1.0062 ◽

2009 ◽

Vol 103 (1) ◽

pp. 62-68

Author(s):

Kathleen Cage Mittag ◽

Sharon Taylor

Keyword(s):

Data Collection ◽

Real Time ◽

Physical Model ◽

Real World ◽

Graphing Calculator ◽

Data Sets ◽

Real World Data ◽

World Data ◽

Hands On ◽

Modeling Data

Using activities to create and collect data is not a new idea. Teachers have been incorporating real-world data into their classes since at least the advent of the graphing calculator. Plenty of data collection activities and data sets exist, and the graphing calculator has made modeling data much easier. However, the authors were in search of a better physical model for a quadratic. We wanted students to see an actual parabola take shape in real time and then explore its characteristics, but we could not find such a hands-on model.

Download Full-text