Determining Processes of Cardiovascular Care Relevant to Complex Patients

描述（由申请人提供）：临床医生谁照顾的人与复杂的慢性疾病面临着竞争的需求和缺乏证据的最佳过程中照顾这个异质性的人口。在新的癌症诊断后，面临预防冠心病（CHD）决策的临床医生必须考虑癌症预后、心血管风险状态、发病率的总体负担以及患者的目标、偏好和价值观。为了帮助告知这些决定，我们建议使用分析流行病学方法来研究所有（超过30，000）凯萨永久科罗拉多（KPCO）的成年成员谁收到了癌症的初步诊断在1999年至2007年期间的历史队列。在该队列中，我们将a）评估CHD的一级、二级和“三级”预防的特定组成部分的目标实现情况，作为癌症预后、总体发病率和它们之间相互作用的函数，和B）评估这些CHD预防干预措施（在相关子队列中）在收到CHD事件和全因死亡率的复合事件和时间时的相对有效性。我们假设：a）总体发病率、癌症预后以及它们之间的相互作用将影响CHD预防干预目标的实现;和B）发病率和癌症预后的特定分层将改变这些干预对CHD结果的有效性。在评估这些预防策略对冠心病的影响时，我们将研究高血压、糖尿病、高脂血症和既存冠心病合并症的护理过程。我们将使用线性回归模型和考克斯比例风险模型来评估癌症预后和其他发病率评分（及其相互作用）对CHD预防结局的影响。将使用考克斯比例风险模型评估预防措施在按预后/发病率定义的分层中达到CHD复合结局时间的有效性。最后，我们将描述哪些预防措施最影响CHD结局的哪些组成部分。本调查的信息将为在具有一系列发病率的患者中使用这些特定预防干预措施提供建议，以便使这些建议与承认复杂患者的优先事项，时间和资源的证据基础一致。 公共卫生相关性：相关性如果我们发现不同发病率水平的冠心病预防干预措施的某些组成部分的相对有效性存在差异，政策影响可能包括为复杂患者的护理提供信息的指南制定，以及参与共同决策的临床医生时间的报销策略的变化。更重要的是，研究结果将为复杂的患者及其临床医生提供额外的信息，以告知整个护理范围内的个性化决策。

DESCRIPTION (provided by applicant): Clinicians who care for persons with complex chronic medical conditions face both competing demands and a lack of evidence regarding best processes of care for this heterogeneous population. Those clinicians facing decisions about prevention of coronary heart disease (CHD) following a new diagnosis of cancer must take into account cancer prognosis, cardiovascular risk status, overall burden of morbidity, and the patient's goals, preferences and values. To help inform such decisions, we propose to use analytic epidemiology methods to study an historical cohort of all (over 30,000) Kaiser Permanente Colorado (KPCO) adult members who received an initial diagnosis of cancer during the period 1999 to 2007. In this cohort, we will a) assess the attainment of goals for specific components of primary, secondary, and 'tertiary' prevention of CHD as a function of cancer prognosis, overall morbidity and the interaction between them, and b) assess the comparative effectiveness of these CHD prevention interventions (in relevant sub-cohorts) on receipt of, and time to, a composite of CHD events and all-cause mortality. We hypothesize that: a) Overall morbidity, cancer prognosis, and the interaction between them will affect attainment of goals of preventive interventions for CHD; and b) specific strata of morbidity and cancer prognosis will modify the effectiveness of these interventions on the CHD outcomes. In evaluating these prevention strategies on CHD outcomes, we will study the processes of care for the comorbidities of hypertension, diabetes, hyperlipidemia, and pre-existing CHD. We will use linear regression models and Cox proportional hazard models to assess the impact of the cancer prognosis and other morbidity scores (and their interactions) on the CHD prevention outcomes. A Cox proportional hazards model will be used to assess the effectiveness of the prevention measures on time to the CHD composite outcome across strata defined by prognosis/morbidity. Finally, we will describe which prevention measures most influence which components of the CHD outcomes. Information from this investigation will inform recommendations for the use of these specific preventive interventions in patients with a range of morbidities in order to make these recommendations congruent with an evidence base that acknowledges complex patients' priorities, time, and resources. PUBLIC HEALTH RELEVANCE: Relevance if we find differences in the comparative effectiveness of certain components of coronary heart disease prevention interventions across different levels of morbidity, policy implications could range from informing guideline development for the care of complex patients, to changes in reimbursement strategies for clinician time involved in shared decision-making. More importantly, findings will provide complex patients and their clinicians with additional information to inform personalized decisions across a spectrum of care.