Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning.
Predicting the Incidence of Pressure Ulcers in the Intensive Care Unit Using Machine Learning.
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DOI：
10.5334/egems.307
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发表时间：
2019-09-05
期刊：
影响因子：
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通讯作者：
Hernandez-Boussard, Tina
中科院分区：
文献类型：
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作者：
Cramer, Eric M;Seneviratne, Martin G;Hernandez-Boussard, Tina
背景：减少重症监护病房（ICU）中院内获得性压疮已成为国际卫生系统的一项重要质量指标。利用电子健康记录（EHR）中的观察数据对ICU中压疮情况进行描述的工作有限。因此，基于EHR确定患者压疮发生风险的预后工具有限，大多数机构依靠护士计算的风险评分（如Braden评分）来识别高危患者。
方法与结果：利用一家三级ICU（MIMIC - III）中50851次入院的EHR数据，我们发现2期及以上压疮的患病率为7.8%。对于在第2天或之后记录有压疮的1690次入院情况，我们评估了在最初24小时内测量的Braden评分的预后价值。一个高危Braden评分（此处英文不完整）
BACKGROUND: Reducing hospital-acquired pressure ulcers (PUs) in intensive care units (ICUs) has emerged as an important quality metric for health systems internationally. Limited work has been done to characterize the profile of PUs in the ICU using observational data from the electronic health record (EHR). Consequently, there are limited EHR-based prognostic tools for determining a patient's risk of PU development, with most institutions relying on nurse-calculated risk scores such as the Braden score to identify high-risk patients.METHODS AND RESULTS: Using EHR data from 50,851 admissions in a tertiary ICU (MIMIC-III), we show that the prevalence of PUs at stage 2 or above is 7.8 percent. For the 1,690 admissions where a PU was recorded on day 2 or beyond, we evaluated the prognostic value of the Braden score measured within the first 24 hours. A high-risk Braden score (