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心率和呼吸频率变异性能否改善对危重症患者拔管结局的预测?

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

作者信息

Seely Andrew J E, Bravi Andrea, Herry Christophe, Green Geoffrey, Longtin André, Ramsay Tim, Fergusson Dean, McIntyre Lauralyn, Kubelik Dalibor, Maziak Donna E, Ferguson Niall, Brown Samuel M, Mehta Sangeeta, Martin Claudio, Rubenfeld Gordon, Jacono Frank J, Clifford Gari, Fazekas Anna, Marshall John

出版信息

Crit Care. 2014 Apr 8;18(2):R65. doi: 10.1186/cc13822.

Abstract

INTRODUCTION

Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown.

METHODS

We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models.

RESULTS

Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively.

CONCLUSIONS

Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01237886. Registered 13 October 2010.

摘要

引言

长时间通气和拔管失败与伤害增加及成本上升相关。在自主呼吸试验(SBT)期间,心率和呼吸频率变异性(HRV和RRV)对预测拔管失败的附加价值尚不清楚。

方法

我们纳入了721例患者,进行一项多中心(12个地点)、前瞻性、观察性研究,评估拔管失败风险的临床估计值、SBT期间记录的生理指标、拔管前最后一次SBT之前及期间记录的HRV和RRV,以及拔管结果。由于方案或技术违规或数据质量差,我们排除了287例患者。根据心电图和二氧化碳描记图波形计算变异性指标(97项HRV,82项RRV),随后使用连续个体化多器官变异性分析(CIMVA™)软件进行自动清理和变异性分析。采用重复随机子抽样结合训练、验证和测试来推导和比较预测模型。

结果

在434例有高质量数据的患者中,51例(12%)拔管失败。两项HRV指标和八项RRV指标显示与拔管失败有统计学显著关联(P<0.0041,错误发现率5%)。使用SBT期间RRV的五个单变量逻辑回归模型的总体平均值得出拔管失败概率(称为WAVE评分),显示出最佳预测能力。通过重复随机子抽样和测试,该模型的平均受试者工作特征曲线下面积(ROC AUC)为0.69,高于心率(0.51)、快速浅呼吸指数(RBSI;0.61)和呼吸频率(0.63)。基于所有数据推导WAVE模型后,训练集表现表明,该模型应用于传统上被认为是高风险的患者时,其预测能力增强:对于RSBI>105且被认为有高失败风险的患者,WAVE评分>0.5时,拔管失败风险分别增加3.0倍(95%置信区间(CI)1.2至5.2)和3.5倍(95%CI 1.9至5.4)。

结论

(拔管前SBT期间)HRV和RRV改变与拔管失败显著相关。使用最后一次SBT期间RRV的预测模型在所有患者中提供了最佳预测准确性,与临床印象或RBSI结合时准确性提高。该模型需要一个验证队列来评估准确性和普遍性。

试验注册

ClinicalTrials.gov NCT01237886。2010年10月13日注册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebcb/4057494/29609843e871/cc13822-1.jpg

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