DIFFICULT DECISION-MAKING AT THE END OF LIFE: STOPPING ORAL PALLIATIVE ANTICANCER TREATMENT. A SYSTEMATIC LITERATURE REVIEW AND NARRATIVE SYNTHESIS

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

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A Systematic Literature Review of Multi-Criteria Decision-Making Methods for Sustainable Selection of Insulation Materials in Buildings

Sustainability ◽

10.3390/su13020737 ◽

2021 ◽

Vol 13 (2) ◽

pp. 737

Author(s):

Indre Siksnelyte-Butkiene ◽

Dalia Streimikiene ◽

Tomas Balezentis ◽

Virgilijus Skulskis

Keyword(s):

Decision Making ◽

Literature Review ◽

Systematic Literature Review ◽

Energy Demand ◽

Energy Performance ◽

Future Research ◽

Multi Criteria Decision Making ◽

Insulation Materials ◽

Advantages And Disadvantages ◽

Selection Of

The European Commission has recently adopted the Renovation Wave Strategy, aiming at the improvement of the energy performance of buildings. The strategy aims to at least double renovation rates in the next ten years and make sure that renovations lead to higher energy and resource efficiency. The choice of appropriate thermal insulation materials is one of the simplest and, at the same time, the most popular strategies that effectively reduce the energy demand of buildings. Today, the spectrum of insulation materials is quite wide, and each material has its own specific characteristics. It is recognized that the selection of materials is one of the most challenging and difficult steps of a building project. This paper aims to give an in-depth view of existing multi-criteria decision-making (MCDM) applications for the selection of insulation materials and to provide major insights in order to simplify the process of methods and criteria selection for future research. A systematic literature review is performed based on the Search, Appraisal, Synthesis and Analysis (SALSA) framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. In order to determine which MCDM method is the most appropriate for different questions, the main advantages and disadvantages of different methods are provided.

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A Systematic Literature Review of Supply Chain Decision Making supported by the Internet of Things and Big Data Analytics

Computers & Industrial Engineering ◽

10.1016/j.cie.2020.107076 ◽

2020 ◽

pp. 107076

Author(s):

Martijn Koot ◽

Martijn R.K. Mes ◽

Maria E. Iacob

Keyword(s):

Decision Making ◽

Big Data ◽

Supply Chain ◽

Internet Of Things ◽

Literature Review ◽

Systematic Literature Review ◽

Data Analytics ◽

Big Data Analytics ◽

The Internet ◽

The Internet Of Things

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Antibiotic Use at the End-of-Life in Patients with Advanced Dementia: A Systematic Literature Review

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.627 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s120-s120

Author(s):

Alexandre Marra ◽

Mireia Puig-Asensio ◽

Eli Perencevich

Keyword(s):

Literature Review ◽

Cohort Studies ◽

End Of Life ◽

Systematic Literature Review ◽

End Of Life Care ◽

Final Analysis ◽

Antibiotic Use ◽

Life Care ◽

Advanced Dementia ◽

Days Of Therapy

Background: Improving the use of antibiotics across the care continuum will be necessary as we strive to protect our patients from antimicrobial resistance. One potential target for antimicrobial stewardship is during end-of-life care of patients with advanced dementia. We aimed to perform a systematic literature review measuring the burden of antibiotic use during end-of-life care in patients with dementia. Methods: We searched PubMed, CINAHL, and Embase through July 2019 for studies with the following inclusion criteria in the initial analysis: (1) end-of-life patients (ie, dementia, cancer, organ failure, frailty or multi-morbidity); (2) antibiotic use in the end-of-life care; with the final analysis restricted to (3) patients with advanced dementia. Only randomized controlled trials (RCTs) and cohort studies were included. Results: Of the 93 full-text articles, 17 studies (18.3%) met the selection criteria for further analysis. Most of the included studies were retrospective (n = 8) or prospective (n = 8) cohort studies. These studies in combination included 2,501 patients with advanced dementia. Also, 5 studies (698 patients, [27.9%]) were restricted to patients with Alzheimer’s disease. In 5 studies in which data were available, fewer than one-quarter of patients (19.9%, 498) with advanced dementia were referred to palliative care. In 12 studies >50% of patients received antibiotics during the end-of-life period. Also, 15 studies did not report the duration of antimicrobial therapy. Only 2 studies reported the antimicrobial consumption in days of therapy per 1,000 resident days. Only 6 studies studied whether the use of antibiotics was associated with beneficial outcomes (survival or comfort), and none of them evaluated potential adverse effects associated with antibiotic use. Conclusions: There are significant gaps in the literature surrounding antimicrobial use at the end of life in patients with advanced dementia. Future studies are needed to evaluate the benefits and harms of using antibiotics for patients during end-of-life care in this patient population.Acknowledgement. We thank Jennifer Deberg from Hardin Library for the Health Sciences, University of Iowa Libraries on the search methods.Disclosures: NoneFunding: None

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Multiple Criteria Decision Making for the Achievement of the UN Sustainable Development Goals: A Systematic Literature Review and a Research Agenda

Sustainability ◽

10.3390/su13084129 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4129

Author(s):

Manuel Sousa ◽

Maria Fatima Almeida ◽

Rodrigo Calili

Keyword(s):

Decision Making ◽

Sustainable Development ◽

Literature Review ◽

Systematic Literature Review ◽

Research Agenda ◽

Sustainable Development Goals ◽

Multiple Criteria Decision Making ◽

Multiple Criteria ◽

2030 Agenda ◽

Development Goals

Multiple-criteria decision making (MCDM) methods have been widely employed in various fields and disciplines, including decision problems regarding Sustainable Development (SD) issues. The main objective of this paper is to present a systematic literature review (SLR) on MCDM methods supporting decisions focusing on the achievement of UN Sustainable Development Goals (SDGs) and the implementation of the 2030 Agenda for Sustainable Development in regional, national, or local contexts. In this regard, 143 published scientific articles from 2016 to 2020 were retrieved from the Scopus database, selected and reviewed. They were categorized according to the decision problem associated with SDGs issues, the MCDM methodological approach, including the use (or not) of fuzzy set theory, sensitivity analysis, and multistakeholder approaches, the context of MCDM applications, and the MCDM classification (if utility-based, compromise, multi-objective, outranking, or other MCDM methods). The widespread adoption of MCDM methods in complex contexts confirms that they can help decision-makers solve multidimensional problems associated with key issues within the 2030 Agenda framework. Besides, the state-of-art review provides an improved understanding of this research field and directions for building a research agenda for those interested in advancing the research on MCDM applications in issues associated with the 2030 Agenda framework.

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