Li Guowei, Holbrook Anne, Delate Thomas, Witt Daniel M, Levine Mitchell Ah, Thabane Lehana
Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada.
Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada St. Joseph's Hospital, McMaster University, Hamilton, Ontario, Canada Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
BMJ Open. 2015 Nov 5;5(11):e009518. doi: 10.1136/bmjopen-2015-009518.
Clinical prediction rules have been validated and widely used in patients with atrial fibrillation (AF) to predict stroke and major bleeding. However, these prediction rules were not developed in the same population, and do not provide the key information that patients and prescribers need at the time anticoagulants are being considered-what is the individual patient-specific risk of both benefit (decreased stroke) and harm (increased major bleeding). In this study, our primary objective is to develop and validate a prediction model for patients' individual combined benefit and harm outcomes (stroke, major bleeding and neither event) with and without warfarin therapy. Our secondary outcome is all-cause mortality.
We will use data from the Kaiser Permanente Colorado (KPCO) anticoagulation management databases and electronic medical records. Patients with a primary or secondary diagnosis during an ambulatory KPCO medical office visit, emergency department visit, or inpatient stay between 1 January 2005 and 31 December 2012 with no AF diagnosis in the previous 180 days will be included. Patients' demographic characteristics, laboratory data, comorbidities, warfarin medication data and concurrent use of medication will be used to construct the prediction model. For primary outcomes (stroke with no major bleeding, and major bleeding with no stroke), we will perform polytomous logistic regression to develop a prediction model for patients' individual combined benefit and harm outcomes, taking neither event group as the reference group. As regards death, we will use Cox proportional hazards regression analysis to build a prediction model for all-cause mortality.
This study has been approved by the KPCO Institutional Review Board and the Hamilton Integrated Research Ethics Board. Results from this study will be published in a peer-reviewed journal electronically and in print. The prediction models may aid in patient-physician shared decision-making when they are considering warfarin therapy.
临床预测规则已在心房颤动（AF）患者中得到验证并广泛应用于预测中风和大出血。然而，这些预测规则并非在同一人群中制定，且未提供患者和处方者在考虑使用抗凝剂时所需的关键信息——即个体患者受益（中风风险降低）和危害（大出血风险增加）的具体风险。在本研究中，我们的主要目标是开发并验证一个预测模型，用于评估使用和未使用华法林治疗的患者个体的综合受益和危害结果（中风、大出血以及无任何事件）。我们的次要结果是全因死亡率。
我们将使用凯撒永久医疗集团科罗拉多分部（KPCO）的抗凝管理数据库和电子病历数据。纳入2005年1月1日至2012年12月31日期间在KPCO门诊、急诊科就诊或住院时被初次或二次诊断且前180天内无AF诊断的患者。患者的人口统计学特征、实验室数据、合并症、华法林用药数据及同时使用的药物将用于构建预测模型。对于主要结局（无大出血的中风，以及无中风的大出血），我们将进行多分类逻辑回归，以开发一个针对患者个体综合受益和危害结果的预测模型，将无任何事件组作为参照组。关于死亡，我们将使用Cox比例风险回归分析来构建全因死亡率的预测模型。
本研究已获得KPCO机构审查委员会和汉密尔顿综合研究伦理委员会的批准。本研究结果将以电子和印刷形式发表在同行评审期刊上。这些预测模型在患者和医生考虑华法林治疗时可能有助于共同决策。
