Early prognostication of neurological outcome by heart rate variability in adult patients with out-of-hospital sudden cardiac arrest
Early prognostication of neurological outcome by heart rate variability in adult patients with out-of-hospital sudden cardiac arrest
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DOI：
10.1186/s13054-019-2603-6
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发表时间：
2019-10-17
期刊：
影响因子：
15.1
通讯作者：
Nitta, Masakazu
中科院分区：
文献类型：
--
作者：
Endoh, Hiroshi;Kamimura, Natuo;Nitta, Masakazu
背景大多数院外心脏骤停昏迷幸存者的死亡是由于基于神经系统诊断不良和家属意愿而撤销生命维持治疗（WLST）决定所致。因此，准确的预测对于避免过早的WLST决策至关重要。然而，有针对性的温度管理（TTM）与镇静或神经肌肉阻滞对抗寒战显着影响早期的震颤。在这项研究中，我们调查了心率变异性（HRV）分析是否可以预测接受低温TTM的昏迷患者的不良神经功能结局。方法前瞻性纳入2015年1月至2017年12月期间在急诊科成功复苏并入住日本新泻大学重症监护室的院外心脏骤停成人患者。所有患者的初始格拉斯哥昏迷量表运动评分为1分，并接受低温TTM（34 ℃）。根据自主循环恢复（ROSC）后24 h内0：00至8：00 am之间的RR间期计算20个HRV相关变量（减速能力; 4个时域、3个几何域和7个频域; 5个复杂性变量）。根据ROSC后2周的格拉斯哥预后评分（GOS）将患者分为预后良好（GOS 1-2）组和预后不良（GOS 3-5）组。结果76例患者被招募并被分配到良好（n = 22）或不良（n = 54）结局组。在20个HRV相关变量中，ln极低频（ln VLF）功率、去趋势波动分析（DFA）（α 1）和多尺度熵（MSE）指数在两组之间存在显著差异（p = 0.001），单变量logistic回归分析得出的比值比（OR）具有统计学显著性（p = 0.001）。3个变量的多变量logistic回归分析确定ln VLF功率和DFA（α 1）是不良结局的显著预测因子（分别为OR = 0.436，p = 0.006和OR = 0.709，p = 0.024）。预测不良结局的In VLF功率和DFA（alpha 1）的接收器操作特征曲线下面积分别为0.84和0.82。此外，良好结局组的ln VLF功率或DFA（α 1）的最小值预测不良结局，敏感性= 61%，特异性= 100%。结论心率变异性分析可用于昏迷患者低温TTM监测。
Background Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family's intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM. Methods Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 degrees C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1-2) and poor outcome (GOS 3-5) groups. Results Seventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (alpha 1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (alpha 1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (alpha 1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (alpha 1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%. Conclusions The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.