Department of Cardiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
Vall d'Hebron Research Institute, Vall d'Hebron Hospital, Autonomous University of Barcelona, Barcelona, Spain.
PLoS One. 2022 Apr 21;17(4):e0266955. doi: 10.1371/journal.pone.0266955. eCollection 2022.
Atrial fibrillation (AF) remain a prevalent undiagnosed condition frequently encountered in primary care.
We aimed to find the parameters that optimize the diagnostic accuracy of pulse palpation to detect AF. We also aimed to create a simple algorithm for selecting which individuals would benefit from pulse palpation and, if positive, receive an ECG to detect AF.
Nurses from four Cardiology outpatient clinics palpated 7,844 pulses according to a randomized list of arterial territories and durations of measure and immediately followed by a 12-lead ECG, which we used as the reference standard. We calculated the sensitivity and specificity of the palpation parameters. We also assessed whether diagnostic accuracy depended on the nurse's experience or on a list of clinical factors of the patients. With this information, we estimated the positive predictive values and false omission rates according to very few clinical factors readily available in primary care (age, sex, and diagnosis of heart failure) and used them to create the algorithm.
The parameters associated with the highest diagnostic accuracy were palpation of the radial artery and classifying as irregular those palpations in which the nurse was uncertain about pulse regularity or unable to palpate pulse (sensitivity = 79%; specificity = 86%). Specificity decreased with age. Neither the nurse's experience nor any investigated clinical factor influenced diagnostic accuracy. We provide the algorithm to select the ≥40 years old individuals that would benefit from a pulse palpation screening: a) do nothing in <60 years old individuals without heart failure; b) do ECG in ≥70 years old individuals with heart failure; c) do radial pulse palpation in the remaining individuals and do ECG if the pulse is irregular or you are uncertain about its regularity or unable to palpate it.
Opportunistic screening for AF using optimal pulse palpation in candidate individuals according to a simple algorithm may have high effectiveness in detecting AF in primary care.
心房颤动（AF）仍然是一种普遍存在的未诊断病症，在初级保健中经常遇到。
我们旨在找到优化脉动触诊诊断 AF 准确性的参数。我们还旨在创建一种简单的算法，用于选择哪些个体将从脉动触诊中受益，如果阳性，则接受心电图以检测 AF。
来自四个心脏病门诊的护士根据动脉区域和测量持续时间的随机列表进行了 7844 次脉搏触诊，然后立即进行 12 导联心电图检查，我们将其作为参考标准。我们计算了触诊参数的敏感性和特异性。我们还评估了诊断准确性是否取决于护士的经验或患者的临床因素列表。有了这些信息，我们根据初级保健中容易获得的很少的临床因素（年龄、性别和心力衰竭诊断）估计了阳性预测值和假漏诊率，并使用它们创建了算法。
与最高诊断准确性相关的参数是触诊桡动脉和将那些护士不确定脉搏节律或无法触诊脉搏的触诊归类为不规则（敏感性=79%；特异性=86%）。特异性随年龄降低。护士的经验或任何调查的临床因素都没有影响诊断准确性。我们提供了一种选择≥40 岁个体的算法，这些个体将受益于脉搏触诊筛查：a）在没有心力衰竭的<60 岁个体中不做任何操作；b）在有心力衰竭的≥70 岁个体中做心电图；c）在其余个体中进行桡动脉脉搏触诊，如果脉搏不规则或您不确定其节律或无法触诊，则进行心电图。
根据简单的算法，在候选个体中使用最佳脉搏触诊进行 AF 的机会性筛查可能在初级保健中具有很高的检测 AF 的有效性。
