Dataset Information

Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting-Role of Academic and Hybrid Modeling.

ABSTRACT: Non-small cell lung cancer is a devastating disease and with the advent of targeted therapies and molecular testing, the decision-making process has become complex. While established guidelines and pathways offer some guidance, they are difficult to utilize in a busy community practice and are not always implemented in the community. The rationale of the study was to identify a cohort of patients with lung adenocarcinoma at a City of Hope community site (n = 11) and utilize their case studies to develop a decision-making framework utilizing fast-and-frugal tree (FFT) heuristics. Most patients had stage IV (N = 9, 81.8%) disease at the time of the first consultation. The most common symptoms at initial presentation were cough (N = 5, 45.5%), shortness of breath (N = 3, 27.2%), and weight loss (N = 3, 27.2%). The Eastern Cooperative Oncology Group (ECOG) performance status ranged from 0-1 in all patients in this study. Distribution of molecular drivers among the patients were as follows: EGFR (N = 5, 45.5%), KRAS (N = 2, 18.2%), ALK (N = 2, 18.2%), MET (N = 2, 18.2%), and RET (N = 1, 9.1%). Seven initial FFTs were developed for the various case scenarios, but ultimately the decisions were condensed into one FFT, a molecular stage IV FFT, that arrived at accurate decisions without sacrificing initial information. While these FFT decision trees may seem arbitrary to an experienced oncologist at an academic site, the simplicity of their utility is essential for community practice where patients often do not get molecular testing and are not assigned proper therapy.

SUBMITTER: Salgia R

PROVIDER: S-EPMC7356888 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting-Role of Academic and Hybrid Modeling.

Salgia Ravi R Mambetsariev Isa I Tan Tingting T Schwer Amanda A Pearlstein Daryl P DP Chehabi Hazem H Baroz Angel A Fricke Jeremy J Pharaon Rebecca R Romo Hannah H Waddington Thomas T Babikian Razmig R Buck Linda L Kulkarni Prakash P Cianfrocca Mary M Djulbegovic Benjamin B Pal Sumanta K SK

Journal of clinical medicine 20200616 6

Non-small cell lung cancer is a devastating disease and with the advent of targeted therapies and molecular testing, the decision-making process has become complex. While established guidelines and pathways offer some guidance, they are difficult to utilize in a busy community practice and are not always implemented in the community. The rationale of the study was to identify a cohort of patients with lung adenocarcinoma at a City of Hope community site (n = 11) and utilize their case studies to ...[more]

PMID: 32560187

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Project description:BackgroundThe contribution of patients' non-medical characteristics to individual physicians' decision-making has attracted considerable attention, but little information is available on this topic in the context of collective decision-making. Medical decision-making at cancer centres is currently carried out using a collective approach, at MultiDisciplinary Team (MDT) meetings. The aim of this study was to determine how patients' non-medical characteristics are presented at MDT meetings and how this information may affect the team's final medical decisions.DesignObservations were conducted at a French Cancer Centre during MDT meetings at which non-standard cases involving some uncertainty were discussed from March to May 2014. Physicians' verbal statements and predefined contextual parameters were collected with a non-participant observational approach. Non numerical data collected in the form of open notes were then coded for quantitative analysis. Univariate and multivariate statistical analyses were performed.ResultsIn the final sample of patients' records included and discussed (N = 290), non-medical characteristics were mentioned in 32.8% (n = 95) of the cases. These characteristics corresponded to demographics in 22.8% (n = 66) of the cases, psychological data in 11.7% (n = 34), and relational data in 6.2% (n = 18). The patient's age and his/her "likeability" were the most frequently mentioned characteristics. In 17.9% of the cases discussed, the final decision was deferred: this outcome was positively associated with the patients' non-medical characteristics and with uncertainty about the outcome of the therapeutic options available.LimitationsThe design of the study made it difficult to draw definite cause-and-effect conclusions.ConclusionThe Social Representations approach suggests that patients' non-medical characteristics constitute a kind of tacit professional knowledge that may be frequently mobilised in physicians' everyday professional practice. The links observed between patients' attributes and the medical decisions made at these meetings show that these attributes should be taken into account in order to understand how medical decisions are reached in difficult situations of this kind.

Project description:Previous studies have reported promising differences in the quality of kernels from farmers' maize populations collected in a Portuguese region known to produce maize-based bread. However, several limitations have been identified in the previous characterizations of those populations, such as a limited set of quality traits accessed and a missing accurate agronomic performance evaluation. The objectives of this study were to perform a more detailed quality characterization of Portuguese farmers' maize populations; to estimate their agronomic performance in a broader range of environments; and to integrate quality, agronomic, and molecular data in the setting up of decision-making tools for the establishment of a quality-oriented participatory maize breeding program. Sixteen farmers' maize populations, together with 10 other maize populations chosen for comparison purposes, were multiplied in a common-garden experiment for quality evaluation. Flour obtained from each population was used to study kernel composition (protein, fat, fiber), flour's pasting behavior, and bioactive compound levels (carotenoids, tocopherols, phenolic compounds). These maize populations were evaluated for grain yield and ear weight in nine locations across Portugal; the populations' adaptability and stability were evaluated using additive main effects and multiplication interaction (AMMI) model analysis. The phenotypic characterization of each population was complemented with a molecular characterization, in which 30 individuals per population were genotyped with 20 microsatellites. Almost all farmers' populations were clustered into the same quality-group characterized by high levels of protein and fiber, low levels of carotenoids, volatile aldehydes, ?- and ?-tocopherols, and breakdown viscosity. Within this quality-group, variability on particular quality traits (color and some bioactive compounds) could still be found. Regarding the agronomic performance, farmers' maize populations had low, but considerably stable, grain yields across the tested environments. As for their genetic diversity, each farmers' population was genetically heterogeneous; nonetheless, all farmers' populations were distinct from each other's. In conclusion, and taking into consideration different quality improvement objectives, the integration of the data generated within this study allowed the outline and exploration of alternative directions for future breeding activities. As a consequence, more informed choices will optimize the use of the resources available and improve the efficiency of participatory breeding activities.

Dataset Information

Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting-Role of Academic and Hybrid Modeling.

Publications

Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting-Role of Academic and Hybrid Modeling.

Similar Datasets

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