Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study.
Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study.
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DOI：
10.1371/journal.pone.0181448
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Moorman JR
中科院分区：
文献类型：
--
作者：
Moss TJ;Clark MT;Calland JF;Enfield KB;Voss JD;Lake DE;Moorman JR
绘制的生命体征和实验室结果代表了患者动态生理状态的间歇性样本，并已被用于计算早期预警分数，以识别面临临床恶化风险的患者。我们假设，将从连续心电监测测量的心肺动力学添加到间歇采样数据中，可以提高模型的预测有效性，这些模型经过训练，可以在重症监护病房(ICU)转移或意外死亡之前检测临床恶化。我们分析了一家三级医疗学术医疗中心8105名急性护理患者入院的63个患者年的心电数据。我们开发了使用生命体征、实验室结果或持续心电监测的心肺动力学来预测恶化导致ICU转院或未来24小时内意外死亡的模型，并使用所有可用的数据源对模型进行了评估。我们计算了预测效度(C-统计量)、净重分类改进以及实现附加自由度的似然比差值χ2的概率。在586例住院患者中，755次出现了主要结果(7%)。我们用事件发生前24小时内的连续心电数据分析了395例临床恶化情况。仅使用连续的心电测量结果导致C统计量为0.65，类似于仅使用实验室结果和生命体征的模型(分别为0.63和0.69)。将连续的心电测量添加到使用传统测量的模型中，使C统计量分别提高了0.01和0.07；集成所有数据源的模型的C统计量为0.73，分类净重分类改进为0.09，风险改变1个十分之一。在有和没有心肺动力学的综合模型之间的似然比χ2的差异是2158(p值：<；0.001)。来自连续心电监测的心肺动力学检测急性护理患者的临床恶化，并改善仅使用实验室结果和生命体征的传统模型的性能。
Charted vital signs and laboratory results represent intermittent samples of a patient’s dynamic physiologic state and have been used to calculate early warning scores to identify patients at risk of clinical deterioration. We hypothesized that the addition of cardiorespiratory dynamics measured from continuous electrocardiography (ECG) monitoring to intermittently sampled data improves the predictive validity of models trained to detect clinical deterioration prior to intensive care unit (ICU) transfer or unanticipated death. We analyzed 63 patient-years of ECG data from 8,105 acute care patient admissions at a tertiary care academic medical center. We developed models to predict deterioration resulting in ICU transfer or unanticipated death within the next 24 hours using either vital signs, laboratory results, or cardiorespiratory dynamics from continuous ECG monitoring and also evaluated models using all available data sources. We calculated the predictive validity (C-statistic), the net reclassification improvement, and the probability of achieving the difference in likelihood ratio χ2 for the additional degrees of freedom. The primary outcome occurred 755 times in 586 admissions (7%). We analyzed 395 clinical deteriorations with continuous ECG data in the 24 hours prior to an event. Using only continuous ECG measures resulted in a C-statistic of 0.65, similar to models using only laboratory results and vital signs (0.63 and 0.69 respectively). Addition of continuous ECG measures to models using conventional measurements improved the C-statistic by 0.01 and 0.07; a model integrating all data sources had a C-statistic of 0.73 with categorical net reclassification improvement of 0.09 for a change of 1 decile in risk. The difference in likelihood ratio χ2 between integrated models with and without cardiorespiratory dynamics was 2158 (p value: <0.001). Cardiorespiratory dynamics from continuous ECG monitoring detect clinical deterioration in acute care patients and improve performance of conventional models that use only laboratory results and vital signs.
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影响因子：
17.1
作者：
Henry, Katharine E.;Hager, David N.;Saria, Suchi
通讯作者：
Saria, Suchi
影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
--
作者：
Ben-Ari, J.;Zimlichman, E.;Sorkine, P.
通讯作者：
Sorkine, P.
影响因子：
1.3
作者：
Carrara, Marta;Carozzi, Luca;Ferrario, Manuela
通讯作者：
Ferrario, Manuela
DOI：
10.1186/cc9975
发表时间：
2011
期刊：
Critical care (London, England)
影响因子：
--
作者：
Cardoso LT;Grion CM;Matsuo T;Anami EH;Kauss IA;Seko L;Bonametti AM
通讯作者：
Bonametti AM