迈向急性综合征的预防：住院期间对高危患者进行电子识别。
Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission.
作者信息
Ahmed A, Thongprayoon C, Pickering B W, Akhoundi A, Wilson G, Pieczkiewicz D, Herasevich V
机构信息
Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic , Rochester.
Institute for Health Informatics, University of Minnesota , Minneapolis.
出版信息
Appl Clin Inform. 2014 Jan 22;5(1):58-72. doi: 10.4338/ACI-2013-07-RA-0045. eCollection 2014.
BACKGROUND
Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predisposing conditions at the time of hospital admission.
METHODS
This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS predisposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction.
RESULTS
Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%.
CONCLUSION
A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.
背景
在患者入住重症监护病房之前识别急性呼吸窘迫综合征（ARDS）的高危患者对于预防和治疗至关重要。本研究的目的是确定一种自动算法在医院入院时识别选定的ARDS诱发条件的性能。
方法
这项前瞻性队列研究的二次分析纳入了2010年1月1日至12月31日期间入院的3005例患者。通过应用于机构电子病历数据库的一系列查询，开发了用于五种ARDS诱发条件（败血症、肺炎、误吸、急性胰腺炎和休克）的自动算法。该自动算法在1562例患者的推导队列中得出并完善，随后在1443例患者的独立队列中进行验证。将识别ARDS风险因素的自动算法的敏感性、特异性以及阳性和阴性预测值与另外两种独立的数据提取策略进行比较，包括手动数据提取和ICD-9编码搜索。参考标准定义为ICD-9编码、自动和手动数据提取之间的一致性。
结果
与参考标准相比，自动算法在识别败血症（95%对56%）、误吸（63%对42%）、急性胰腺炎（100%对70%）、肺炎（93%对62%）和休克（77%对41%）病例时，除败血症和肺炎外，敏感性高于手动数据提取（败血症特异性90%对98%，肺炎特异性95%对99%）。使用自动算法识别这五种急性病症的阳性预测值范围从肺炎的65%到急性胰腺炎的91%，而自动算法的阴性预测值范围从99%到100%。
结论
基于规则的电子数据提取可以在医院入院时可靠且准确地识别有ARDS风险的患者。
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