在安大略省使用行政数据定义重大外科并发症:一个验证研究
J Andrew McClure1, Eric Walser2, Laura Allen2
1London Health Sciences Centre, London, Ont. (McClure, Walser, Allen, Vinden, Jones, Dubois, Vogt); ICES Western, London, Ont. (McClure, Vinden, Jones, Dubois); Department of Surgery, Schulich School of Medicine and Dentistry, Western University, London, Ont. (Walser, Vinden, Dubois, Vogt); Department of Anesthesia & Perioperative Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ont. (Jones); Department of Epidemiology & Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ont. (Jones, Dubois) andrew.mcclure@lhsc.on.ca.
卫生行政数据可以很准确地识别主要的手术并发症. 这项研究验证了捕获这些不良事件的算法,为未来的研究显示了希望.
科学领域:
- 医疗信息学 医疗信息学
- 手术结果研究研究.
- 数据验证数据验证
背景情况:
- 手术并发症是研究中的关键结果,但在使用行政数据时往往缺乏验证.
- 设计用于从卫生行政数据中捕获手术并发症的算法存在有限的验证.
研究的目的:
- 使用卫生行政数据评估用于捕获主要手术并发症的算法的诊断性能.
- 评估行政数据在识别术后并发症时的准确性.
主要方法:
- 对270名接受高风险选择性整体手术的患者进行了回顾性研究.
- 将行政数据算法与临床医生从医疗记录中抽象的数据进行比较.
- 使用灵敏度,特异性,正预测值 (PPV),负预测值 (NPV) 和准确度进行评估.
主要成果:
- 管理数据算法实现了72%的灵敏度,80%的特异性,82%的PPV,70%的NPV和76%的准确性,用于主要手术并发症的复合结果.
- 55%的患者经历了至少一个主要并发症,根据图表审计.
- 诊断性能各不相同,对几个个别并发症的准确性不佳.
结论:
- 卫生行政数据可以有效地捕获具有足够灵敏度和特异性的重大手术并发症的复合指标.
- 需要进一步的研究来开发精确的算法来识别特定的手术并发症.
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