García-Olmos Luis, Rodríguez-Salvanés Francisco, Batlle-Pérez Maurice, Aguilar-Torres Río, Porro-Fernández Carlos, García-Cabello Alfredo, Carmona Montserrat, Ruiz-Alonso Sergio, Garrido-Elustondo Sofía, Alberquilla Ángel, Sánchez-Gómez Luis María, Sánchez de Madariaga Ricardo, Monge-Navarrete Elena, Benito-Ortiz Luis, Baños-Pérez Nicolás, Simón-Puerta Amaya, López Rodríguez Ana Belén, Martínez-Álvarez Miguel Ángel, Velilla-Celma María Ángeles, Bernal-Gómez María Isabel
Multiprofessional Education Unit for Family and Community Care (South-east), Madrid, Spain.
Research Network for Chronic Diseases (Red de Investigación en Servicios de Salud en Enfermedades Crónicas/REDISSEC), Madrid, Spain.
BMJ Open. 2017 Jun 8;7(6):e014840. doi: 10.1136/bmjopen-2016-014840.
Chronic heart failure (CHF) reduces quality of life and causes hospitalisation and death. Identifying predictive factors of such events may help change the natural history of this condition.
To develop and validate a stratification system for classifying patients with CHF, according to their degree of disability and need for hospitalisation due to any unscheduled cause, over a period of 1 year.
Prospective, concurrent, cohort-type study in two towns in the Madrid autonomous region having a combined population of 1 32 851. The study will include patients aged over 18 years who meet the following diagnostic criteria: symptoms and typical signs of CHF (Framingham criteria) and left ventricular ejection fraction (EF)<50% or structural cardiac lesion and/or diastolic dysfunction in the presence of preserved EF (EF>50%).Outcome variables will be(a) Disability, as measured by the WHO Disability Assessment Schedule V.2.0 Questionnaire, and (b) unscheduled hospitalisations. The estimated sample size is 557 patients, 371 for predictive model development (development cohort) and 186 for validation purposes (validation cohort). Predictive models of disability or hospitalisation will be constructed using logistic regression techniques. The resulting model(s) will be validated by estimating the probability of outcomes of interest for each individual included in the validation cohort.
The study protocol has been approved by the Clinical Research Ethics Committee of La Princesa University Teaching Hospital (PI-705). All results will be published in a peer-reviewed journal and shared with the medical community at conferences and scientific meetings.
慢性心力衰竭(CHF)会降低生活质量,导致住院和死亡。识别此类事件的预测因素可能有助于改变这种疾病的自然病程。
建立并验证一种分层系统,用于根据心力衰竭患者在1年期间的残疾程度和因任何非计划原因住院的需求对其进行分类。
在马德里自治区的两个城镇进行前瞻性、同期队列研究,总人口为132851人。该研究将纳入年龄超过18岁且符合以下诊断标准的患者:CHF的症状和典型体征(弗明汉标准)以及左心室射血分数(EF)<50%,或在EF保留(EF>50%)的情况下存在心脏结构病变和/或舒张功能障碍。结局变量将为:(a)残疾,通过世界卫生组织残疾评估量表V.2.0问卷进行测量;(b)非计划住院。估计样本量为557例患者,其中371例用于预测模型开发(开发队列),186例用于验证(验证队列)。将使用逻辑回归技术构建残疾或住院的预测模型。通过估计验证队列中每个个体感兴趣结局的概率来验证所得模型。
该研究方案已获得拉公主大学教学医院临床研究伦理委员会的批准(PI - 705)。所有结果将发表在同行评审期刊上,并在会议和科学会议上与医学界分享。