PDF(Pubmed)
Abstract：
UNASSIGNED: Atrial fibrillation (AF) is prone to heart failure and stroke. Early management can effectively reduce the stroke rate and mortality. Current clinical guidelines screen high-risk individuals based solely on age, while this study aims to explore the possibility of other AF risk predictors.
UNASSIGNED: A total of 18,738 elderly people (aged over 60 years old) in Chinese communities were enrolled in this study. The baseline characteristics were mainly based on the diagnosis results of electrocardiogram (ECG) machine during follow up, accompanied by some auxiliary physical examination basic data. After the analysis of both independent and combined baseline characteristics, AF risk predictors were obtained and prioritized according to the results. Independent characteristics were studied from three aspects: Chi-square test, Mann-Whitney U test and Cox univariate regression analysis. Combined characteristics were studied from two aspects: machine learning models and Cox multivariate regression analysis, and the former was combined with recursive feature elimination method and voting decision.
UNASSIGNED: The resulted optimal combination of risk predictors included age, atrial premature beats, atrial flutter, left ventricular hypertrophy, hypertension and heart disease.
UNASSIGNED: Patients diagnosed by short-time ECG machines with the occurrence of the above events had a higher probability of AF episodes, who are suggested to be included in the focus of long-term ECG monitoring or increased screening density. The incidence of risk predictors in different age ranges of AF patients suggests differences in age-specific patient management. This can help improve the detection rate of AF, standardize the management of patients, and slow down the progression of AF.
UNASSIGNED: A total of 18,738 elderly people (aged over 60 years old) in Chinese communities were enrolled in this study. The baseline characteristics were mainly based on the diagnosis results of electrocardiogram (ECG) machine during follow up, accompanied by some auxiliary physical examination basic data. After the analysis of both independent and combined baseline characteristics, AF risk predictors were obtained and prioritized according to the results. Independent characteristics were studied from three aspects: Chi-square test, Mann-Whitney U test and Cox univariate regression analysis. Combined characteristics were studied from two aspects: machine learning models and Cox multivariate regression analysis, and the former was combined with recursive feature elimination method and voting decision.
UNASSIGNED: The resulted optimal combination of risk predictors included age, atrial premature beats, atrial flutter, left ventricular hypertrophy, hypertension and heart disease.
UNASSIGNED: Patients diagnosed by short-time ECG machines with the occurrence of the above events had a higher probability of AF episodes, who are suggested to be included in the focus of long-term ECG monitoring or increased screening density. The incidence of risk predictors in different age ranges of AF patients suggests differences in age-specific patient management. This can help improve the detection rate of AF, standardize the management of patients, and slow down the progression of AF.
摘要：
未经证实：心房颤动（AF）容易发生心力衰竭和中风。早期管理可有效降低脑卒中发生率和死亡率。目前的临床指南仅根据年龄筛选高危人群，而这项研究旨在探索其他AF风险预测因子的可能性。
UNASSIGNED：共有18,738名中国社区的老年人（60岁以上）参加了这项研究。基线特征主要依据随访时心电图机的诊断结果,附有一些辅助体检的基本资料。在分析了独立和组合的基线特征之后，获得AF风险预测因子并根据结果进行优先级排序。从三个方面研究了独立特性:卡方检验,Mann-WhitneyU检验和Cox单因素回归分析。从机器学习模型和Cox多元回归分析两个方面研究了组合特征,前者结合了递归特征消除法和投票决定法。
未经评估：所得出的风险预测因子的最佳组合包括年龄，房性早搏,房扑,左心室肥厚,高血压和心脏病.
UASSIGNED：通过短时心电图机诊断为发生上述事件的患者发生房颤的概率较高，建议将其纳入长期心电图监测或增加筛查密度的重点。在不同年龄范围的房颤患者中，风险预测因子的发生率表明在特定年龄的患者管理方面存在差异。这有助于提高AF的检出率,规范患者管理，减缓房颤的进展。
UNASSIGNED：共有18,738名中国社区的老年人（60岁以上）参加了这项研究。
未经评估：所得出的风险预测因子的最佳组合包括年龄，
UASSIGNED：通过短时心电图机诊断为发生上述事件的患者发生房颤的概率较高，
