Dataset Information

Influence of socioeconomic status on immediate breast reconstruction rate, patient information and involvement in surgical decision-making.

ABSTRACT: BACKGROUND:Immediate breast reconstruction (IBR) rates in breast cancer differ between healthcare regions in Sweden. This is not explained by regional differences in patient age distribution or tumour characteristics, but by differences in patient-reported information and patient involvement in the decision-making process. As socioeconomic status may play a significant role in surgical decision-making, its potential associations with IBR rates were analysed. METHODS:Women who had undergone therapeutic mastectomy for primary breast cancer in Sweden in 2013 were included in the analysis. Tumour and treatment data were retrieved from the Swedish National Breast Cancer Register, and socioeconomic background data from the Central Bureau of Statistics Sweden. Postal questionnaires regarding information about reconstruction and perceived involvement in the preoperative decision-making process had been sent out in a previous survey. RESULTS:In addition to regional differences, lower tumour and nodal category, independent factors increasing the likelihood of having IBR for the 3131 women in the study were living without a registered partner, having current employment and high income per household. Patient-reported perceived preoperative information (odds ratio (OR) 12·73, 95 per cent c.i. 6·03 to 26·89) and the feeling of being involved in the decision-making process (OR 2·56, 1·14 to 5·76) remained strong independent predictors of IBR despite adjustment for socioeconomic factors. Importantly, responders to the survey represented a relatively young and wealthy population with a lower tumour burden. CONCLUSION:Several socioeconomic factors independently influence IBR rates; however, patient-reported information and involvement in the surgical decision-making process remain independent predictors for the likelihood of having IBR.

SUBMITTER: Frisell A

PROVIDER: S-EPMC7093785 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Influence of socioeconomic status on immediate breast reconstruction rate, patient information and involvement in surgical decision-making.

Frisell A A Lagergren J J Halle M M de Boniface J J

BJS open 20200131 2

<h4>Background</h4>Immediate breast reconstruction (IBR) rates in breast cancer differ between healthcare regions in Sweden. This is not explained by regional differences in patient age distribution or tumour characteristics, but by differences in patient-reported information and patient involvement in the decision-making process. As socioeconomic status may play a significant role in surgical decision-making, its potential associations with IBR rates were analysed.<h4>Methods</h4>Women who had ...[more]

PMID: 32003544

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Project description:BackgroundSurgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.MethodsCochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsRequirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.ConclusionTraditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Dataset Information

Influence of socioeconomic status on immediate breast reconstruction rate, patient information and involvement in surgical decision-making.

Publications

Influence of socioeconomic status on immediate breast reconstruction rate, patient information and involvement in surgical decision-making.

Similar Datasets

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