1From the MedEL Directorate, St. James's Hospital, James St, Dublin 8, D08 NHY1, Ireland.
Discipline of Medical Gerontology , School of Medicine, Trinity College Dublin, College Green, Dublin 2, D02 PN40, Ireland.
QJM. 2022 Jun 7;115(6):367-373. doi: 10.1093/qjmed/hcab141.
Syncope is aetiologically diverse and associated with adverse outcomes; in older people, there is clinical overlap with complex falls presentations (i.e. recurrent, unexplained and/or injurious).
To formulate an index to predict future risk of syncope and falls in the Irish longitudinal study on ageing (TILDA).
DESIGN/METHODS: Using the frailty index methodology, we selected, from TILDA Wave 1 (2010), 40 deficits that might increase risk of syncope and falls. This syncope-falls index (SYFI) was applied to TILDA Wave 1 participants aged 65 and over, who were divided into three risk groups (low, intermediate and high) based on SYFI tertiles. Multivariate logistic regression models were used to investigate, controlling for age and sex, how SYFI groups predicted incident syncope, complex falls and simple falls occurring up to Wave 4 of the study (2016).
At Wave 1, there were 3499 participants (mean age 73, 53% women). By Wave 4, of the remaining 2907 participants, 185 (6.4%) had reported new syncope, 1077 (37.0%) complex falls and 218 (7.5%) simple falls. The risk of both syncope and complex falls increased along the SYFI groups (high risk group: odds ratio 1.88 [1.26-2.80], P = 0.002 for syncope; 2.22 [1.82-2.72], P < 0.001 for complex falls). No significant relationship was identified between SYFI and simple falls.
The 6-year incidences of falls and syncope were high in this cohort. SYFI could help identify older adults at risk of syncope and complex falls, and thus facilitate early referral to specialist clinics to improve outcomes.
晕厥的病因多种多样，并与不良结局相关；在老年人中，其临床表现与复杂跌倒（即反复发作、原因不明和/或造成损伤的跌倒）存在临床重叠。
在爱尔兰老龄化纵向研究（TILDA）中构建一个预测晕厥和跌倒未来风险的指数。
设计/方法：我们使用脆弱指数方法，从 TILDA 第 1 波（2010 年）中选择了 40 个可能增加晕厥和跌倒风险的缺陷。该晕厥-跌倒指数（SYFI）应用于 TILDA 第 1 波中年龄在 65 岁及以上的参与者，他们根据 SYFI 三分位数分为低、中、高三个风险组。使用多变量逻辑回归模型，在控制年龄和性别因素的情况下，研究 SYFI 组如何预测研究的第 4 波（2016 年）中发生的新发晕厥、复杂跌倒和单纯跌倒事件。
在第 1 波中，有 3499 名参与者（平均年龄 73 岁，53%为女性）。到第 4 波时，在其余的 2907 名参与者中，有 185 人（6.4%）报告了新发晕厥，1077 人（37.0%）发生了复杂跌倒，218 人（7.5%）发生了单纯跌倒。随着 SYFI 组的增加，晕厥和复杂跌倒的风险均增加（高风险组：晕厥的比值比为 1.88 [1.26-2.80]，P=0.002；复杂跌倒为 2.22 [1.82-2.72]，P<0.001）。SYFI 与单纯跌倒之间没有显著关系。
在该队列中，6 年内跌倒和晕厥的发生率较高。SYFI 可以帮助识别有晕厥和复杂跌倒风险的老年人，从而有助于将其及早转介至专科诊所以改善结局。
