ONCOPRE: A new chemotherapy benefit prediction model to assist treatment decision making.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 126-126
Author(s):  
Dimas Yusuf ◽  
Maria Yi Ho ◽  
Winson Y. Cheung
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Colon Cancer ◽  
Adjuvant Treatment ◽  
Real World ◽  
Treatment Decision ◽  
Real World Data ◽  
World Data ◽  
Treatment Decision Making ◽  
Mutational Status

126 Background: Clinical decision support tools (CDSTs) can help physicians make complex treatment decisions and inform care. For colon cancer, CDSTs such as Adjuvant! Online and Numeracy were widely used to estimate the effects of adjuvant treatment and guide conversations with patients. Existing CDSTs, however, do not consider more contemporary predictive and prognostic factors, such as microsatellite instability (MSI), BRAF mutational status, or the presence of additional high risk clinical or pathological features (HRFs), in their assessment of outcomes. Current CDSTs are also not optimized for handheld devices. Methods: We developed ONCOPRE, which is an adjuvant chemotherapy benefit calculator for colon cancer that addresses the limitations of current CDSTs. Based on a comprehensive review of epidemiological data and results of landmark trials, ONCOPRE was devised to predict 5 year colon cancer recurrence and death. To validate ONCOPRE, we compared its predictions with those generated by existing CDSTs as well as real-world data from 7 tertiary cancer centers across Canada. Results: ONCOPRE is able to predict 5-year DFS and OS of patients with colon cancer based on age, sex, TNM status, and contemporary risk factors such as MSI status, BRAF mutations, and other HRFs. ONCOPRE’s predictions compare favorably with real-world data and predictions from other CDSTs. ONCOPRE’s predictions are typically more optimistic than historical outcomes, and this likely reflects the fact that current day colon cancer patients experience better prognosis with the use of modern therapy and improved supportive care. These attributes make ONCOPRE a potentially new benchmark among CDSTs that can reliably predict colon cancer outcomes. Conclusions: ONCOPRE ( http://www.oncopre.com/ ) represents a new CDST that can assist in adjuvant treatment decision-making and patient counseling. We make the case that the next generation of CDSTs in oncology must take into account more contemporary clinical, biochemical, and genetic risk factors since these elements significantly affect outcomes. The ONCOPRE platform serves as a potential model on which to develop prediction tools for other forms of cancers.

Oncopre: A new chemotherapy benefit prediction algorithm to assist treatment decision making.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 705-705
Author(s):  
Dimas Yusuf ◽  
Maria Yi Ho ◽  
Hagen F. Kennecke ◽  
Winson Y. Cheung
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Colon Cancer ◽  
Adjuvant Treatment ◽  
Real World ◽  
Treatment Decision ◽  
Prediction Algorithm ◽  
Real World Data ◽  
World Data ◽  
Treatment Decision Making

705 Background: Clinical decision support tools (CDSTs) can help physicians make complex treatment decisions and inform care. For colon cancer, CDSTs such as Adjuvant! Online and Numeracy were widely used to estimate the effects of adjuvant treatment and guide conversations with patients. Existing CDSTs, however, do not consider more contemporary predictive and prognostic factors, such as microsatellite instability (MSI), BRAF mutational status, or the presence of additional high risk clinical or pathological features (HRFs), in their assessment of outcomes. Current CDSTs are also not optimized for handheld devices. Methods: We developed ONCOPRE, which is an adjuvant chemotherapy benefit calculator for colon cancer that addresses the limitations of current CDSTs. Based on a comprehensive review of epidemiological data and results of landmark trials, ONCOPRE was devised to predict 5-year colon cancer recurrence and death. To validate ONCOPRE, we compared its predictions with those generated by existing CDSTs as well as real-world data from 7 tertiary cancer centers across Canada. Results: ONCOPRE is able to predict 5-year DFS and OS of patients with colon cancer based on age, sex, TNM status, and contemporary risk factors such as MSI status, BRAF mutations, and other HRFs. ONCOPRE’s predictions compare favorably with real-world data and predictions from other CDSTs. ONCOPRE’s predictions are typically more optimistic than historical outcomes, and this likely reflects the fact that current day colon cancer patients experience better prognosis with the use of modern therapy and improved supportive care. These attributes make ONCOPRE a potentially new benchmark among CDSTs that can reliably predict colon cancer outcomes. Conclusions: ONCOPRE ( http://www.oncopre.com/beta/ ) represents a new CDST that can assist in adjuvant treatment decision-making and patient counseling. We make the case that the next generation of CDSTs in oncology must take into account more contemporary clinical, biochemical, and genetic risk factors since these elements significantly affect outcomes. The ONCOPRE platform serves as a potential model on which to develop prediction tools for other forms of cancers.

Real-world treatment patterns in patients with advanced (stage III–IV) ovarian cancer in the USA and Europe

2020 ◽  
Vol 16 (15) ◽  
pp. 1013-1030
Author(s):  
Jennifer P Hall ◽  
Jane Chang ◽  
Rebecca Moon ◽  
Olivia Higson ◽  
Katherine Byrne ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Real World ◽  
Parp Inhibitor ◽  
Advanced Ovarian Cancer ◽  
Treatment Decision ◽  
Stage Iii ◽  
Real World Data ◽  
World Data ◽  
Treatment Decision Making ◽  
The Usa

Aim: To analyze real-world data relating to treatment decision-making in stage III–IV ovarian cancer (OC). Materials & methods: Real world data were collected from a survey of physicians and their patients (n = 2413) across Europe and the USA in 2017–2018. Results: 49% had stage IVb disease. 39, 54 and 7% of patients received first-line (1L), second-line, or 7% third-line or later treatment. In the 1L (ongoing or completed), 93% received platinum-containing regimens, 26% bevacizumab-containing regimens and 1% a PARP inhibitor-containing regimen. In 1L maintenance treatment, 81% received bevacizumab, 17% platinum-containing treatments and 6% a PARP inhibitor. Conclusion: The most common 1L treatment for advanced ovarian cancer was platinum-containing chemotherapy. Of those receiving 1L maintenance therapy, 70–99% (across countries) received targeted therapy.

Quality criteria for Real-World Data in pharmaceutical research and healthcare decision making. An Austrian Expert Consensus (Preprint)

2021 ◽  
Author(s):  
Peter Klimek ◽  
Dejan Baltic ◽  
Martin Brunner ◽  
Alexander Degelsegger-Marquez ◽  
Gerhard Garhöfer ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Research ◽  
Real World ◽  
Pharmaceutical Medicine ◽  
Pharmaceutical Research ◽  
Quality Criteria ◽  
Clinical Decision ◽  
European Medicines Agency ◽  
Real World Data ◽  
World Data

UNSTRUCTURED Real-world data (RWD) collected in routine healthcare processes and transformed to real-world evidence (RWE) has become increasingly interesting within research and medical communities to enhance medical research and support regulatory decision making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which quality RWD must meet in order to be acceptable for decision making within regulatory or routine clinical decision support. An Austrian expert group led by GPMed (Gesellschaft für Pharmazeutische Medizin, Austrian Society for Pharmaceutical Medicine) reviewed drafted guidelines, published recommendations or viewpoints to derive a consensus statement on quality criteria for RWD to be used more effectively for medical research purposes beyond registry-based studies discussed in the European Medicines Agency (EMA) guideline for registry-based studies

Prognostic impact of the inclusion of uPA/PAI-1 for adjuvant treatment decision-making in ER+/Her2− pN0 early breast cancers

2017 ◽  
Vol 165 (3) ◽  
pp. 611-621 ◽  
Author(s):  
Marie Viala ◽  
Marie Alexandre ◽  
Simon Thezenas ◽  
Pierre-Jean Lamy ◽  
Aurélie Maran-Gonzalez ◽  
...  
Keyword(s):  
Decision Making ◽  
Adjuvant Treatment ◽  
Treatment Decision ◽  
Prognostic Impact ◽  
Breast Cancers ◽  
Treatment Decision Making ◽  
Pai 1

Automated Diagnosis through Ontologies and Logical Descriptions

2013 ◽  
pp. 117-134
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra Gayo ◽  
Juan Miguel Gómez-Berbís ◽  
...  
Keyword(s):  
Decision Making ◽  
Health Systems ◽  
Real World ◽  
Information Age ◽  
Semantic Technologies ◽  
Automated Diagnosis ◽  
Real World Data ◽  
World Data ◽  
Degree Of Maturity ◽  
Automate Diagnosis

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

Group Decision-Making during Trauma Patient Resuscitation and Anesthesia

1993 ◽  
Vol 37 (4) ◽  
pp. 372-376 ◽  
Author(s):  
Colin F. Mackenzie ◽  
Richard L. Horst ◽  
David L. Mahaffey ◽  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Trauma Patient ◽  
Treatment Decision ◽  
Post Treatment ◽  
Trauma Treatment ◽  
Treatment Decision Making ◽  
Knowledge Based ◽  
Team Interactions ◽  
Video And Audio

We examined decision-making in the real-world environment of trauma patient resuscitation and anesthesia in a Level One Trauma Center. The present paper focuses on the risk factors in the trauma treatment environment that can lead to errors or misjudgments, and strategies that may be helpful in reducing these risks. Video and audio recordings were made of a number of trauma cases involving tracheal intubation, including both emergency intubations performed during resuscitation and “elective” intubations prior to surgery. Post-treatment questionnaires completed by anesthesia personnel suggested that their perceived misjudgments were primarily procedural errors caused by lack of preparation for low probability events, inadequate monitoring of available indices, or carelessness. However, video analyses of a subset of the cases by a non-participant anesthesiologist, in conjunction with examination of patient management records, not only confirmed the occurrence of such errors but also identified instances of knowledge-based errors, which caused subsequent cascades of adverse events. Video analysis also documented the shortcuts that are characteristic of emergency intubations. The post-treatment questionnaires also suggested an association between team interactions and anesthesiologist performance. To follow up on this, we transcribed and categorized verbal communications for several minutes before, during, and after intubation in a subset of cases. This analysis indicated that during emergency intubations not only was more information communicated than during elective intubations, but that there were increases specifically in the incidence of directives, comments conveying plans or strategies, and comments both seeking and offering needed information. The discussion presents a number of strategies that emerged from the present analyses for reducing the risk factors involved in trauma treatment decision-making.

scholarly journals A NovelPRKAR1B-BRAFFusion in Gastrointestinal Stromal Tumor Guides Adjuvant Treatment Decision-Making During Pregnancy

2018 ◽  
Vol 16 (3) ◽  
pp. 238-242 ◽  
Author(s):  
Lindsey M. Charo ◽  
Adam M. Burgoyne ◽  
Paul T. Fanta ◽  
Hitendra Patel ◽  
Juliann Chmielecki ◽  
...  
Keyword(s):  
Decision Making ◽  
Gastrointestinal Stromal Tumor ◽  
Adjuvant Treatment ◽  
Treatment Decision ◽  
Stromal Tumor ◽  
Treatment Decision Making
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