Zwartkruis Victor W, Groenewegen Amy, Rutten Frans H, Hollander Monika, Hoes Arno W, van der Ende M Yldau, van der Harst Pim, Cramer Maarten Jan, van der Schouw Yvonne T, Koffijberg Hendrik, Rienstra Michiel, de Boer Rudolf A
University Medical Center Groningen, University of Groningen, Department of Cardiology, Groningen, the Netherlands.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Prev Med. 2020 Sep;138:106143. doi: 10.1016/j.ypmed.2020.106143. Epub 2020 May 27.
Cardiovascular disease (CVD) often goes unrecognized, despite symptoms frequently being present. Proactive screening for symptoms might improve early recognition and prevent disease progression or acute cardiovascular events. We studied the diagnostic value of symptoms for the detection of unrecognized atrial fibrillation (AF), heart failure (HF), and coronary artery disease (CAD) and developed a corresponding screening questionnaire. We included 100,311 participants (mean age 52 ± 9 years, 58% women) from the population-based Lifelines Cohort Study. For each outcome (unrecognized AF/HF/CAD), we built a multivariable model containing demographics and symptoms. These models were combined into one 'three-disease' diagnostic model and questionnaire for all three outcomes. Results were validated in Lifelines participants with chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Unrecognized CVD was identified in 1325 participants (1.3%): AF in 131 (0.1%), HF in 599 (0.6%), and CAD in 687 (0.7%). Added to age, sex, and body mass index, palpitations were independent predictors for unrecognized AF; palpitations, chest pain, dyspnea, exercise intolerance, health-related stress, and self-expected health worsening for unrecognized HF; smoking, chest pain, exercise intolerance, and claudication for unrecognized CAD. Area under the curve for the combined diagnostic model was 0.752 (95% CI 0.737-0.766) in the total population and 0.757 (95% CI 0.734-0.781) in participants with COPD and DM. At the chosen threshold, the questionnaire had low specificity, but high sensitivity. In conclusion, a short questionnaire about demographics and symptoms can improve early detection of CVD and help pre-select people who should or should not undergo further screening for CVD.
心血管疾病（CVD）常常未被识别，尽管症状常常存在。对症状进行主动筛查可能会改善早期识别，并预防疾病进展或急性心血管事件。我们研究了症状对于未被识别的心房颤动（AF）、心力衰竭（HF）和冠状动脉疾病（CAD）检测的诊断价值，并开发了一份相应的筛查问卷。我们纳入了基于人群的生命线队列研究中的100311名参与者（平均年龄52±9岁，58%为女性）。对于每个结局（未被识别的AF/HF/CAD），我们构建了一个包含人口统计学和症状的多变量模型。这些模型被合并为一个针对所有三个结局的“三病”诊断模型和问卷。结果在患有慢性阻塞性肺疾病（COPD）和糖尿病（DM）的生命线参与者中得到验证。1325名参与者（1.3%）被识别出患有未被识别的CVD：AF患者131名（0.1%），HF患者599名（0.6%），CAD患者687名（0.7%）。除年龄、性别和体重指数外，心悸是未被识别的AF的独立预测因素；心悸、胸痛、呼吸困难、运动不耐受、与健康相关的压力以及自我预期的健康恶化是未被识别的HF的预测因素；吸烟、胸痛、运动不耐受和跛行是未被识别的CAD的预测因素。联合诊断模型在总人群中的曲线下面积为0.752（95%CI 0.737 - 0.766），在患有COPD和DM的参与者中为0.757（95%CI 0.734 - 0.781）。在选定的阈值下，问卷的特异性较低，但敏感性较高。总之，一份关于人口统计学和症状的简短问卷可以改善CVD的早期检测，并有助于预先筛选出应该或不应该接受进一步CVD筛查的人群。
