Background: Coronary heart disease (CHD) is the leading cause of death in the world. There are some decision-making conflicts in the management of chest pain, treatment methods, stent selection, and other aspects due to the unstable condition of CHD in the treatment stage. Although using decision aids to facilitate shared decision-making (SDM) contributes to high-quality decision-making, it has not been evaluated in the field of CHD. This review systematically assessed the effects of SDM in patients with CHD.
Methods: We conducted a systematic review and meta-analysis of randomized controlled trials of SDM interventions in patients with CHD from database inception to 1 June 2022 (PROSPERO [Unique identifier: CRD42022338938]). We searched for relevant studies in the PubMed, Embase, Cochrane Library, Web of Science, CNKI, and Wan Fang databases. The primary outcomes were knowledge and decision conflict. The secondary outcomes were satisfaction, patient participation, trust, acceptance, quality of life, and psychological condition.
Results: A total of 8244 studies were retrieved. After screening, ten studies were included in the analysis. Compared with the control group, SDM intervention with patient decision aids obviously improved patients' knowledge, decision satisfaction, participation, and medical outcomes and reduced decision-making conflict. There was no significant effect of SDM on trust.
Conclusions: This study showed that SDM intervention in the form of decision aids was beneficial to decision-making quality and treatment outcomes among patients with CHD. The results of SDM interventions need to be evaluated in different environments.
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http://dx.doi.org/10.31083/j.rcm2408246 | DOI Listing |
Eur J Pain
February 2025
Center for Mathematics, Computation and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil.
Background And Objective: Non-invasive neuromodulation techniques (NIN), such as transcranial Direct Current Stimulation (tDCS) and repetitive Transcranial Magnetic Stimulation (rTMS), have been extensively researched for their potential to alleviate pain by reversing neuroplastic changes associated with neuropathic pain (NP), a prevalent and complex condition. However, treating NP remains challenging due to the numerous variables involved, such as different techniques, dosages and aetiologies. It is necessary to provide insights for clinicians and public healthcare managers to support clinical decision-making.
View Article and Find Full Text PDFBiometrics
January 2025
Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States.
In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Hepatobiliary Pancreatic Surgery Department, Huadu District People's Hospital of Guangzhou, Guangzhou, China.
Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.
Objective: To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.
Front Artif Intell
January 2025
School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
Traditional Chinese medicine (TCM) has long utilized tongue diagnosis as a crucial method for assessing internal visceral condition. This study aims to modernize this ancient practice by developing an automated system for analyzing tongue images in relation to the five organs, corresponding to the heart, liver, spleen, lung, and kidney-collectively known as the "five viscera" in TCM. We propose a novel tongue image partitioning algorithm that divides the tongue into four regions associated with these specific organs, according to TCM principles.
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