澳大利亚医院获得并发症算法的性能,用于检测医院发病的血液感染
Leon J Worth1, Stephanie J Curtis1, Rhonda L Stuart2
1Department of Infectious Diseases, The Alfred and School of Translational Medicine, Monash University, Melbourne, Australia.
医院获得并发症 (HAC) 算法用于检测医院发病的血液感染 (HO-BSI) 具有较低的积极预测值 (PPV). 删除未指定的败血症代码显著改善了PPV,提高了HO-BSI的诊断准确性.
科学领域:
- 医疗保健的质量和安全.
- 传染病监测 传染病监测 传染病监测 传染病监测
- 医疗信息学 医疗信息学
背景情况:
- 澳大利亚医疗保健安全和质量委员会制定了一份可能可预防的16种医院获得并发症 (HAC) 清单.
- 使用国际疾病分类 (ICD) 代码创建了一个算法来检测这些HAC.
- 这种算法用于诊断医院发病的血液感染 (HO-BSI) 的性能需要评估.
研究的目的:
- 评估BSI HAC算法在识别医院发病的血液感染 (HO-BSI) 的性能.
- 评估算法的正预测值 (PPV) 和负预测值 (NPV).
- 探索对算法的修改,以提高其诊断准确度.
主要方法:
- 从五家澳大利亚医院 (2016年7月 - 2017年6月) 提取了入院患者护理的行政记录.
- 应用了BSI HAC算法来识别潜在的HO-BSI.
- 盲目审查员使用参考监视定义随机选择和审查事件.
- 计算PPV和NPV,以及测试的算法修改.
主要成果:
- BSI HAC算法显示PPV为0.28 (95% CI,0.23-0.34) 的低值,NPV为1.00 (95% CI,0.98-1.00) 的高值.
- "败血症,未指明"和"新生儿细菌败血症,未指明"的代码是频繁的触发因素,但具有非常低的PPV (0.06和0.03).
- 删除这些未指定的败血症代码将算法的PPV提高到0.53 (95% CI,0.45-0.62).
结论:
- 现有的HAC算法在准确识别医院发病的血液感染方面表现不佳.
- 包括"未指明的败血症"ICD代码在该算法的低正预测值中起到了重要作用.
- 通过删除这些代码来修改算法,可以大大提高其检测HO-BSI的准确性.
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