背景:很少有研究使用标准化护理记录和系统化医学临床术语命名法(SNOMEDCT)来确定临床恶化的预测因素。
目的:本研究旨在标准化使用SNOMEDCT的COVID-19患者的护理文件记录,并通过标准化护理记录确定COVID-19患者临床恶化的预测因素。
方法:在本研究中,分析了226例COVID-19患者的57,558份护理报告。其中,来自稳定(对照)组的207名患者的45,852份陈述和来自恶化(病例)组的19名患者的11,706份陈述,他们在7天内被转移到重症监护病房。数据是在2019年12月至2022年6月之间收集的。这些护理声明使用2022年11月30日发布的SNOMEDCT国际版进行标准化。在57,558个陈述中占前90%的260个独特护理陈述被选择为映射源,并由2位具有5年以上SNOMEDCT映射经验的专家根据其含义映射到SNOMEDCT概念中。确定与患者病情恶化相关的护理陈述的主要特征,使用随机森林算法,并为护理问题或结局以及与护理程序相关的陈述选择最佳超参数。此外,进行了logistic回归分析,以确定确定COVID-19患者临床恶化的特征。
结果:所有护理陈述都在语义上映射到SNOMEDCT概念以进行临床发现,带有明确上下文的\“\”情况,\"和\"procedure\"层次结构。作图结果的评分者间可靠性为87.7%。随机森林计算的最重要特征是氧饱和度低于参考范围,“\”呼吸困难,\"\"呼吸急促,临床发现中的“和”咳嗽“,“和”氧疗,脉搏血氧饱和度监测,“\”体温测量,“\”医生的通知,“”和“感染控制隔离教育”中的“程序”。“其中,“临床发现”中的“呼吸困难”和“食物饮食不足”增加了临床恶化风险(呼吸困难:比值比[OR]5.99,95%CI2.25-20.29;食物饮食不足:OR10.0,95%CI2.71-40.84),以及“程序”中的“氧疗”和“医生通知”也增加了COVID-19患者临床恶化的风险(氧疗:OR1.89,95%CI1.25-3.05;医生通知:OR1.72,95%CI1.02-2.97)。
结论:该研究使用SNOMEDCT来表达和标准化护理陈述。Further,它揭示了标准化护理记录作为患者临床恶化的预测变量的重要性.
BACKGROUND: Few studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.
OBJECTIVE: This
study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records.
METHODS: In this
study, 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to the intensive care unit within 7 days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International Edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on their meaning by 2 experts with more than 5 years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.
RESULTS: All nursing statements were semantically mapped to SNOMED CT concepts for \"clinical finding,\" \"situation with explicit context,\" and \"procedure\" hierarchies. The interrater reliability of the mapping results was 87.7%. The most important features calculated by random forest were \"oxygen saturation below reference range,\" \"dyspnea,\" \"tachypnea,\" and \"cough\" in \"clinical finding,\" and \"oxygen therapy,\" \"pulse oximetry monitoring,\" \"temperature taking,\" \"notification of physician,\" and \"education about isolation for infection control\" in \"procedure.\" Among these, \"dyspnea\" and \"inadequate food diet\" in \"clinical finding\" increased clinical deterioration risk (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84), and \"oxygen therapy\" and \"notification of physician\" in \"procedure\" also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).
CONCLUSIONS: The
study used SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.