在患者离开手术室之前,预测手术部感染风险的实用方法
Michael S Woods1, Valerie Ekstrom2, Jonathan D Darer3
1Global Chief Medical Officer, Caresyntax Corp, Boston, USA.
Cureus
|August 21, 2023
概括
这项研究使用患者和手术数据开发了手术部位感染 (SSI) 预测模型. 手动和自动化模型都准确地识别了患有SSI风险的患者,有助于患者安全工作.
科学领域:
- 手术成果研究的研究结果.
- 在医疗保健中的预测建模.
- 患者安全和感染控制.
背景情况:
- 手术部位感染 (SSIs) 对患者安全构成重大威胁,并增加医疗保健成本.
- 早期识别患有SSI高风险的患者对于及时实施预防性干预至关重要.
- 现有的风险预测工具在手术室环境中可能缺乏准确性或实时适用性.
研究的目的:
- 开发和验证使用手术前和术后数据进行手术部位感染 (SSI) 的预测模型.
- 将手动评分算法的性能与SSI风险预测的自动化后勤回归模型进行比较.
- 确定与增加SSI风险相关的关键患者和程序特征.
主要方法:
- 对3440名通用和瘤外科手术患者进行了回顾性分析.
- 开发了两个风险评分算法:手动计数积极因素和自动化后勤回归模型.
- 模型经过训练和验证,使用手术前 (例如,手术紧急性) 和手术前 (例如,手术时间,开放手术) 数据.
主要成果:
- 自动化算法识别了关键的风险因素,包括手术的紧急性,延迟的抗生素给药,开放式手术,高风险的手术类型和手术时间.
- 自动化模型实现了0.868的曲线下面积 (AUC) 比手动得分 (AUC:0.831) 高.
- 开放式手术,手术风险,手术时间和手术紧迫性是SSI最有影响力的预测因素.
结论:
- 手动和自动SSI风险预测模型都能准确地识别高风险的患者.
- 这些模型可以在患者从手术室出院之前使用,以指导主动干预并提高患者安全.
- 这些发现支持将基于证据的SSI风险评估纳入例行外科工作流程.
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