Institute for Health Informatics.
Department of Medicine.
AMIA Annu Symp Proc. 2022 Feb 21;2021:1234-1243. eCollection 2021.
Acute kidney injury (AKI) is potentially catastrophic and commonly seen among inpatients. In the United States, the quality of administrative coding data for capturing AKI accurately is questionable and needs to be updated. This retrospective study validated the quality of administrative coding for hospital-acquired AKI and explored the opportunities to improve the phenotyping performance by utilizing additional data sources from the electronic health record (EHR). A total of34570 patients were included, and overall prevalence of AKI based on the KDIGO reference standard was 10.13%, We obtained significantly different quality measures (sensitivity.-0.486, specificity:0.947, PPV.0.509, NPV:0.942 in the full cohort) of administrative coding from the previously reported ones in the U.S. Additional use of clinical notes by incorporating automatic NLP data extraction has been found to increase the AUC in phenotyping AKI, and AKI was better recognized in patients with heart failure, indicating disparities in the coding and management of AKI.
急性肾损伤(AKI)是一种潜在的灾难性疾病,在住院患者中很常见。在美国,用于准确捕捉 AKI 的行政编码数据的质量存在疑问,需要进行更新。这项回顾性研究验证了医院获得性 AKI 的行政编码质量,并探讨了通过利用电子健康记录(EHR)中的其他数据源来提高表型性能的机会。共纳入 34570 例患者,根据 KDIGO 参考标准,AKI 的总体患病率为 10.13%。我们获得了与美国先前报告的质量指标(全队列中的灵敏度为-0.486,特异性为 0.947,PPV 为 0.509,NPV 为 0.942)显著不同的质量指标。通过合并自动自然语言处理数据提取,额外使用临床记录发现,在表型 AKI 方面,AUC 增加,心力衰竭患者的 AKI 识别更好,表明 AKI 的编码和管理存在差异。