Literature DB >> 26888989

How best to obtain consent to thrombolysis: Individualized decision-making.

Jingjing Gong1, Yan Zhang2, Jun Feng2, Weiwei Zhang2, Weimin Yin2, Xinhuai Wu2, Yanhong Hou2, Yonghua Huang1, Hongyun Liu1, Danmin Miao1.   

Abstract

OBJECTIVE: To investigate the factors that influence the preferences of patients and their proxies concerning thrombolytic therapy and to determine how best to convey information.
METHODS: A total of 613 participants were randomly assigned to a positively or negatively framed group. Each participant completed a series of surveys. We applied latent class analysis (LCA) to explore participants' patterns of choices of thrombolysis and to classify the participants into different subgroups. Then we performed regression analyses to investigate predictors of classification of the participants into each subgroup and to establish a thrombolytic decision-making model.
RESULTS: LCA indicated an optimal 3-subgroup model comprising intermediate, favorable to thrombolysis, and aversion to thrombolysis subgroups. Multiple regression analysis revealed that 10 factors predicted assignment to the intermediate subgroup and 4 factors predicted assignment to the aversion to thrombolysis subgroup compared with the favorable to thrombolysis subgroup. The χ(2) tests indicated that the information presentation format and the context of thrombolysis influenced participants' choices of thrombolysis and revealed a framing effect in different subgroups.
CONCLUSIONS: The preference for thrombolysis was influenced by the positive vs negative framing scenarios, the format of item presentation, the context of thrombolysis, and individual characteristics. Inconsistent results may be due to participant heterogeneity and the evaluation of limited factors in previous studies. Based on a decision model of thrombolysis, physicians should consider the effects of positive vs negative framing and should seek a neutral tone when presenting the facts, providing an important reference point for health persuasion in other clinical domains.
© 2016 American Academy of Neurology.

Entities:  

Mesh:

Substances:

Year:  2016        PMID: 26888989     DOI: 10.1212/WNL.0000000000002434

Source DB:  PubMed          Journal:  Neurology        ISSN: 0028-3878            Impact factor:   9.910


  5 in total

1.  Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.

Authors:  Mo Zhou; Winter Maxwell Thayer; John F P Bridges
Journal:  Pharmacoeconomics       Date:  2018-02       Impact factor: 4.981

2.  Thrombolytic Refusal Over Telestroke.

Authors:  Alicia Zha; Adriana Rosero; Rene Malazarte; Shima Bozorgui; Christy Ankrom; Liang Zhu; Michele Joseph; Alyssa Trevino; Tiffany D Cossey; Sean Savitz; Tzu Ching Wu; Amanda Jagolino-Cole
Journal:  Neurol Clin Pract       Date:  2021-06

3.  Choices Regarding Thrombolysis Are Modified by the Way to Transfer the Messages.

Authors:  Jingjing Gong; Yan Zhang; Hongyan Gao; Wei Wei; Jing Lv; Hongyun Liu; Yonghua Huang
Journal:  Front Neurol       Date:  2017-11-07       Impact factor: 4.003

4.  How to Best Convey Information About Intensive/Comfort Care to the Family Members of Premature Infants to Enable Unbiased Perinatal Decisions.

Authors:  Jingjing Gong; Wei Xiao; Hongyan Gao; Wei Wei; Weiwei Zhang; Jing Lv; Lijun Xiao; Lida Duan; Yan Zhang; Hongyun Liu; Yonghua Huang
Journal:  Front Pediatr       Date:  2018-11-16       Impact factor: 3.418

5.  Factors delaying intravenous thrombolytic therapy in acute ischaemic stroke: a systematic review of the literature.

Authors:  Angelos Sharobeam; Brett Jones; Dianne Walton-Sonda; Christian J Lueck
Journal:  J Neurol       Date:  2020-03-21       Impact factor: 4.849

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.