PROGNOSTIC STRATIFICATION OF ACUTE CHEST PAIN PATIENTS

急性胸痛患者的预后分层

• 批准号: 3082167
• 负责人: THOMAS H LEE
• 金额: $ 6.56万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 1985
• 资助国家: 美国
• 起止时间: 1985-07-01 至 1990-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3082167
• 关键词: cardiovascular disorder epidemiology; computer assisted medical decision making; computer assisted patient care; critical care; diagnosis quality /standard; electrocardiography; emergency care; emergency health services; health care cost /financing; health care personnel performance; health care service evaluation; health care service utilization; human subject; mathematical model; medical records; myocardial infarction; patient care management; physicians; prognosis; statistics /biometry

Prognostic stratification of patients with known or suspected cardiovascular disease influences both the effectiveness and efficiency of their management. Physicians currently perform such analyses informally in a variety of settings when making triage decisions. This project will test the hypothesis that the accuracy of these analyses can be enhanced by providing information from predictive algorithms derived through multivariate analysis, leading to improvements in diagnostic performance and efficiency of resource utilization. Algorithms for the prediction of myocardial infarction and complications requiring intensive care will be derived through analysis of clinical and electrocardiographic data from 1382 previously-seen emergency room patients, including 259 with myocardial infarctions, and from an additional group of 985 patients with acute myocardial infarction seen in the Multicenter Investigation of the Limitation of Infarct Size (MILIS). Advantages and disadvantages of competing multivariate techniques will be explored. Algorithms will be tested prospectively and compared with the performance of physicians on patients from both teaching and community hospitals, with subgroup analysis examining the performance of the algorithms in different settings and the impact of baseline data on diagnostic accuracy. The impact of results of cardiac isoenzyme assays in the emergency room on the probability of myocardial infarction will be examined through techniques of multivariate analysis. Cost-effectiveness analysis will be used to determine probability ranges within which specific management courses are recommended. In an intervention phase, algorithm predictions and recommendations from cost-effectiveness analysis will be provided to physicians during the emergency ward evaluation. The impact of these interventions on diagnostic accuracy and resource utilization will be measured.

已知或怀疑的患者的预后分层 心血管疾病会影响 他们的管理。 医师目前非正式地进行此类分析 做出分类决策时各种设置。 这个项目将测试 这些分析的准确性可以通过 从通过得出的预测算法提供信息 多元分析，导致诊断性能的改善 和资源利用率的效率。 预测的算法 心肌梗塞和需要重症监护的并发症将是 通过分析临床和心电图数据得出 1382先前看到的急诊室患者，包括259例心肌 梗塞，以及另外985例急性患者的组 心肌梗死在多中心调查中 梗塞大小（MILIS）的限制。 优势和缺点 将探索竞争的多元技术。 算法将是 前瞻性测试并与医生的表现进行了比较 来自教学和社区医院的患者，以及亚组分析 在不同的设置和 基线数据对诊断准确性的影响。 结果的影响 急诊室中心脏同工酶测定的可能性 心肌梗塞将通过多元技术进行检查 分析。 成本效益分析将用于确定 特定管理课程的概率范围 受到推崇的。 在干预阶段，算法预测和 成本效益分析的建议将提供给 紧急病房评估期间的医生。 这些的影响 诊断准确性和资源利用的干预措施将是 测量。