Identifying the low risk patient in surgical intensive and intermediate care units using continuous monitoring
Identifying the low risk patient in surgical intensive and intermediate care units using continuous monitoring
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DOI：
10.1016/j.surg.2017.08.022
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发表时间：
2018-04-01
期刊：
影响因子：
3.8
通讯作者：
Calland, J. Forrest
中科院分区：
文献类型：
--
作者：
Blackburn, Holly N.;Clark, Matthew T.;Calland, J. Forrest
背景资料。连续预测监测已被成功地用于预测亚临床不良事件。然而，这些模型的低值是否应该让我们放心，患者不会有不利的结果？这些模型的阴性预测值可以帮助预测患者安全出院。这项研究的目的是基于重症监护病房和中级监护病房的床边监测数据，验证危重疾病整体模型(使用先前开发的呼吸不稳定、出血和脓毒症模型)的负面预测价值。我们计算了2014年5月至2016年5月期间从外科重症监护病房和中级监护病房降级的2924名患者每15分钟(n=124,588)所有患者患3种危重疾病的相对风险。我们构建了一个集成模型来估计在重症监护病房或中级监护病房出院时降级后良好结局的概率。综合模型的输出根据重症监护病房/中间护理病房的有利和不良结果的风险对患者进行分层；接受者操作特征曲线下的面积分别为有利结果的.639/.629和不良事件的.645/.641。这些性能特征与已发表的预测再入院的模型是相称的。在对住院时间和入院服务进行调整后，总体模型仍然是一个具有统计学意义的预测因素。重症监护病房的优良率分别为76.2%和273%(下降2.8倍)和883%和33.2%(下降2.7倍)。危重疾病的整体模型预测降级和安全出院(住院)后的良好结果
Background. Continuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit.Methods. We calculated the relative risk of 3 critical illnesses for all patients every 15 minutes (n=124,588) for 2,924 patients downgraded from the surgical intensive care units and intermediate care unit between May 2014 to May 2016. We constructed an ensemble model to estimate at the time of intensive care units or intermediate care unit discharge the probability of favorable outcome after downgrade.Results. Outputs form the ensemble model stratified patients by risk of favorable and bad outcomes in both intensive care units/intermediate care unit; area under the receiver operating characteristic curve =.639/.629 respectively for favorable outcomes and .645/.641 for adverse events. These performance characteristics are commensurate with published models for predicting readmission. The ensemble model remained a statistically significant predictor after adjusting for hospital duration of stay and admitting service. The rate of favorable outcome in the highest and lowest deciles in the intensive care units were 76.2% and 273% (2.8-fold decrease) and 883% and 33.2% in the intermediate care unit (2.7-fold decrease), respectively.Conclusion. An ensemble model for critical illness predicts favorable outcome after downgrade and safe patient discharge (hospital stay