Determining Processes of Cardiovascular Care Relevant to Complex Patients

描述(由申请人提供)：护理患有复杂慢性疾病的人的临床医生既面临着相互竞争的需求，又缺乏关于这一不同群体的最佳护理过程的证据。那些在新的癌症诊断后面临预防冠心病(CHD)的临床医生必须考虑癌症预后、心血管风险状况、总体发病率负担以及患者的目标、偏好和价值观。为了帮助为这些决定提供信息，我们建议使用分析流行病学方法来研究所有(超过30,000名)科罗拉多州凯撒永久(Kpco)成年成员的历史队列，这些成员在1999年至2007年期间被初步诊断为癌症。在这个队列中，我们将a)根据癌症预后、总体发病率和它们之间的相互作用来评估冠心病一级、二级和‘三级’预防的特定组成部分的目标的实现情况，以及b)评估这些CHD预防干预措施(在相关的子队列中)在接受CHD事件和各种原因死亡的综合情况下的相对有效性以及达到这些干预措施的时间。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冠心病预防干预目标的实现；b)发病率和癌症预后的特定层次将影响这些干预措施对冠心病预后的有效性。在评估这些预防策略对冠心病预后的影响时，我们将研究高血压、糖尿病、高脂血症和既往冠心病并存的护理过程。我们将使用线性回归模型和COX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)对冠心病预防结果的影响。COX比例风险模型将被用来评估预防措施的有效性，以及时获得由预后/发病率定义的各阶层的CHD综合结果。最后，我们将描述哪些预防措施对CHD结果的哪些组成部分影响最大。来自这项调查的信息将为在患有各种疾病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相一致。 公共卫生相关性：相关性如果我们发现冠心病预防干预措施的某些组成部分在不同发病率水平上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指导方针，以及改变临床医生参与共享决策的时间的补偿策略。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，为各种护理领域的个性化决策提供信息。

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.