Trends in end-of- life (EOL) systemic oncologic treatment in contemporary clinical practice: Insights from real-world data.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 253-253
Author(s):  
Maureen Canavan ◽  
Xiaoliang Wang ◽  
Mustafa Ascha ◽  
Rebecca A. Miksad ◽  
Timothy N Showalter ◽  
...  

253 Background: Among patients with cancer, receipt of systemic oncolytic therapy near the end-of-life (EOL) does not improve outcomes and worsens patient and caregiver experience. Accordingly, the ASCO/NQF measure, Proportion Receiving Chemotherapy in the Last 14 Days of Life, was published in 2012. Over the last decade there has been exponential growth in high cost targeted and immune therapies which may be perceived as less toxic than traditional chemotherapy. In this study, we identified rates and types of EOL systemic therapy in today’s real-world practice; these can serve as benchmarks for cancer care organizations to drive improvement efforts. Methods: Using data from the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database we included patients who died during 2015 through 2019, were diagnosed after 2011, and who had documented cancer treatment. We identified the use of aggressive EOL systemic treatment (including, chemotherapy, immunotherapy, and combinations thereof) at both 30 days and 14 days prior to death. We estimated standardized EOL rates using mixed-level logistic regression models adjusting for patient and practice-level factors. Year-specific adjusted rates were estimated in annualized stratified analysis. Results: We included 57,127 patients, 38% of whom had documentation of having received any type of systemic cancer treatment within 30 days of death (SD: 5%; range: 25% - 56%), and 17% within 14 days of death (SD: 3%; range: 10% - 30%). Chemotherapy alone was the most common EOL treatment received (18% at 30 days, 8% at 14 days), followed by immunotherapy (± other treatment) (11% at 30 days, 4% at 14 days). Overall rates of EOL treatment did not change over the study period: treatment within 30 days (39% in 2015 to 37% in 2019) and within 14 days (17% in 2015 to 17% in 2019) of death. However, the rates of chemotherapy alone within 30 days of death decreased from 24% to 14%, and within 14 days, from 10% to 6% during the study period. In comparison, rates for immunotherapy with chemotherapy (0%-6% for 30 days, 0% -2% for 14 days), and immunotherapy alone or with other treatment types (4%-13% for 30 days, 1%-4% for 14 days) increased over time for both 30 and 14 days. Conclusions: End of life systemic cancer treatment rates have not substantively changed over time despite national efforts and expert guidance. While rates of traditional chemotherapy have decreased, rates of costly immunotherapy and targeted therapy have increased, which has been associated with higher total cost of care and overall healthcare utilization. Future work should examine the drivers of end-of-life care in the era of immune-oncology.

2020 ◽  
Vol 13 ◽  
pp. 175628642092268 ◽  
Author(s):  
Francesco Patti ◽  
Andrea Visconti ◽  
Antonio Capacchione ◽  
Sanjeev Roy ◽  
Maria Trojano ◽  
...  

Background: The CLARINET-MS study assessed the long-term effectiveness of cladribine tablets by following patients with multiple sclerosis (MS) in Italy, using data from the Italian MS Registry. Methods: Real-world data (RWD) from Italian MS patients who participated in cladribine tablets randomised clinical trials (RCTs; CLARITY, CLARITY Extension, ONWARD or ORACLE-MS) across 17 MS centres were obtained from the Italian MS Registry. RWD were collected during a set observation period, spanning from the last dose of cladribine tablets during the RCT (defined as baseline) to the last visit date in the registry, treatment switch to other disease-modifying drugs, date of last Expanded Disability Status Scale recording or date of the last relapse (whichever occurred last). Time-to-event analysis was completed using the Kaplan–Meier (KM) method. Median duration and associated 95% confidence intervals (CI) were estimated from the model. Results: Time span under observation in the Italian MS Registry was 1–137 (median 80.3) months. In the total Italian patient population ( n = 80), the KM estimates for the probability of being relapse-free at 12, 36 and 60 months after the last dose of cladribine tablets were 84.8%, 66.2% and 57.2%, respectively. The corresponding probability of being progression-free at 60 months after the last dose was 63.7%. The KM estimate for the probability of not initiating another disease-modifying treatment at 60 months after the last dose of cladribine tablets was 28.1%, and the median time-to-treatment change was 32.1 (95% CI 15.5–39.5) months. Conclusion: CLARINET-MS provides an indirect measure of the long-term effectiveness of cladribine tablets. Over half of MS patients analysed did not relapse or experience disability progression during 60 months of follow-up from the last dose, suggesting that cladribine tablets remain effective in years 3 and 4 after short courses at the beginning of years 1 and 2.


Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 875
Author(s):  
Kerri Beckmann ◽  
Hans Garmo ◽  
Ingela Franck Lissbrant ◽  
Pär Stattin

Real-world data (RWD), that is, data from sources other than controlled clinical trials, play an increasingly important role in medical research. The development of quality clinical registers, increasing access to administrative data sources, growing computing power and data linkage capacities have contributed to greater availability of RWD. Evidence derived from RWD increases our understanding of prostate cancer (PCa) aetiology, natural history and effective management. While randomised controlled trials offer the best level of evidence for establishing the efficacy of medical interventions and making causal inferences, studies using RWD offer complementary evidence about the effectiveness, long-term outcomes and safety of interventions in real-world settings. RWD provide the only means of addressing questions about risk factors and exposures that cannot be “controlled”, or when assessing rare outcomes. This review provides examples of the value of RWD for generating evidence about PCa, focusing on studies using data from a quality clinical register, namely the National Prostate Cancer Register (NPCR) Sweden, with longitudinal data on advanced PCa in Patient-overview Prostate Cancer (PPC) and data linkages to other sources in Prostate Cancer data Base Sweden (PCBaSe).


2016 ◽  
Vol 3 (7) ◽  
pp. 160131 ◽  
Author(s):  
Daniel Smith ◽  
Mark Dyble ◽  
James Thompson ◽  
Katie Major ◽  
Abigail E. Page ◽  
...  

Humans regularly cooperate with non-kin, which has been theorized to require reciprocity between repeatedly interacting and trusting individuals. However, the role of repeated interactions has not previously been demonstrated in explaining real-world patterns of hunter–gatherer cooperation. Here we explore cooperation among the Agta, a population of Filipino hunter–gatherers, using data from both actual resource transfers and two experimental games across multiple camps. Patterns of cooperation vary greatly between camps and depend on socio-ecological context. Stable camps (with fewer changes in membership over time) were associated with greater reciprocal sharing, indicating that an increased likelihood of future interactions facilitates reciprocity. This is the first study reporting an association between reciprocal cooperation and hunter–gatherer band stability. Under conditions of low camp stability individuals still acquire resources from others, but do so via demand sharing (taking from others), rather than based on reciprocal considerations. Hunter–gatherer cooperation may either be characterized as reciprocity or demand sharing depending on socio-ecological conditions.


Author(s):  
Kai R. Larsen ◽  
Daniel S. Becker

After preparing your dataset, the business problem should be quite familiar, along with the subject matter and the content of the dataset. This section is about modeling data, using data to train algorithms to create models that can be used to predict future events or understand past events. The section shows where data modeling fits in the overall machine learning pipeline. Traditionally, we store real-world data in one or more databases or files. This data is extracted, and features and a target (T) are created and submitted to the “Model Data” stage (the topic of this section). Following the completion of this stage, the model produced is examined (Section V) and placed into production. With the model in the production system, present data generated from the real-world environment is inputted into the system. In the example case of a diabetes patient, we enter a new patient’s information electronic health record into the system, and a database lookup retrieves additional data for feature creation.


Author(s):  
Maria Hägglund ◽  
Charlotte Blease ◽  
Isabella Scandurra

Patient portals are used as a means to facilitate communication, performing administrative tasks, or accessing one’s health record. In a retrospective analysis of real-world data from the Swedish National Patient Portal 1177.se, we describe the rate of adoption over time, as well as how patterns of device usage have changed over time. In Jan 2013, 53% of all visits were made from a computer, and 38% from a mobile phone. By June 2020, 77% of all visits were made from a mobile phone and only 20% from a computer. These results underline the importance of designing responsive patient portals that allow patients to use any device without losing functionality or usability.


2019 ◽  
Author(s):  
Vincent J Major ◽  
Neil Jethani ◽  
Yindalon Aphinyanaphongs

AbstractObjectiveThe main criteria for choosing how models are built is the subsequent effect on future (estimated) model performance. In this work, we evaluate the effects of experimental design choices on both estimated and actual model performance.Materials and MethodsFour years of hospital admissions are used to develop a 1 year end-of-life prediction model. Two common methods to select appropriate prediction timepoints (backwards-from-outcome and forwards-from-admission) are introduced and combined with two ways of separating cohorts for training and testing (internal and temporal). Two models are trained in identical conditions, and their performances are compared. Finally, operating thresholds are selected in each test set and applied in a final, ‘real-world’ cohort consisting of one year of admissions.ResultsBackwards-from-outcome cohort selection discards 75% of candidate admissions (n=23,579), whereas forwards-from-admission selection includes many more (n=92,148). Both selection methods produce similar global performances when applied to an internal test set. However, when applied to the temporally defined ‘real-world’ set, forwards-from-admission yields higher areas under the ROC and precision recall curves (88.3 and 56.5% vs. 83.2 and 41.6%).DiscussionA backwards-from-outcome experiment effectively transforms the training data such that it no longer resembles real-world data. This results in optimistic estimates of test set performance, especially at high precision. In contrast, a forwards-from-admission experiment with a temporally separated test set consistently and conservatively estimates real-world performance.ConclusionExperimental design choices impose bias upon selected cohorts. A forwards-from-admission experiment, validated temporally, can conservatively estimate real-world performance.


Author(s):  
Nils Finke ◽  
Tanya Braun ◽  
Marcel Gehrke ◽  
Ralf Möller

Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.


2021 ◽  
Vol 37 (10) ◽  
pp. S79
Author(s):  
D de Verteuil ◽  
L Azzi ◽  
L Lambert ◽  
B Daneault ◽  
E Dumont ◽  
...  

2017 ◽  
Vol 20 (9) ◽  
pp. A430
Author(s):  
J Scott ◽  
C Alcorn ◽  
D Garofalo ◽  
J Montgomery

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