Electronic algorithmic prediction of central vascular catheter use.
Electronic algorithmic prediction of central vascular catheter use.
复制标题
中心血管导管使用的电子算法预测。
DOI：
10.1086/649015
复制
发表时间：
2010
影响因子：
4.5
通讯作者：
CentersforDiseaseControlandPreventionEpicenters
中科院分区：
文献类型：
--
作者：
Hota,Bala;Harting,Brian;Weinstein,RobertA;Lyles,RosieD;Bleasdale,SusanC;Trick,William;CentersforDiseaseControlandPreventionEpicenters
目的利用电子病历中的数据，开发住院患者中心血管导管存在的预测算法。这些算法可用于测量设备利用率和临床决策支持规则。设计标准。设置小约翰·H·斯特罗格，JR，库克县医院，伊利诺伊州芝加哥的一家拥有464张床位的公立医院。参与者2005年5月31日至2006年6月26日入住医疗重症监护病房的患者(推导数据集，2005年5月31日至2005年9月28日；验证数据集，2005年9月29日至2006年6月28日)。方法从电子病历中收集每个患者的协变量；结果变量为中央血管装置的存在。利用导数集和广义估计方程建立了多变量模型。使用验证集对三个模型进行了验证，每个模型都有不断增加的数据库要求。结果虽然Charlson评分和重症监护病房停留时间在所有模型中都是显著的预测因素，但表明使用或存在中心线的因素也很重要。由算法模型得出的设备利用率与使用手动采样获得的设备利用率一样准确。结论自动计算中心血管导管的使用是可行和准确的，提供的估计值在统计学上类似于使用人工监测获得的估计值。对中心血管导管使用的预测建模可能使血液感染的自动监测成为可能，并加强重要的预防干预措施，例如及时移除不必要的中央管道。
ObjectiveTo develop prediction algorithms for the presence of a central vascular catheter in hospitalized patients with use of data present in an electronic health record. Such algorithms could be used for measurement of device utilization rates and for clinical decision support rules.DesignCriterion standard.SettingJohn H. Stroger, Jr, Hospital of Cook County, a 464-bed public hospital in Chicago, Illinois.ParticipantsPatients admitted to the medical intensive care unit from May 31, 2005 through June 26, 2006 (derivation data set, May 31, 2005-September 28, 2005; validation data set, September 29, 2005-June 28, 2006).MethodsCovariates were collected from the electronic medical record for each patient; the outcome variable was presence of a central vascular device. Multivariate models were developed using the derivation set and the generalized estimating equation. Three models, each with increasing database requirements, were validated using the validation set. Device utilization ratios and performance characteristics were calculated.ResultsAlthough Charlson score and duration of intensive care unit stay were significant predictors in all models, factors that indicated use or presence of a central line were also important. Device utilization rates derived from the algorithmic models were as accurate as those obtained using manual sampling.ConclusionsAutomated calculation of central vascular catheter use is both feasible and accurate, providing estimates statistically similar to those obtained using manual surveillance. Prediction modeling of central vascular catheter use may enable automated surveillance of bloodstream infections and enhance important prevention interventions, such as timely removal of unnecessary central lines.