Grogan Eric L, Morris John A, Norris Patrick R, France Daniel J, Ozdas Asli, Stiles Renée A, Harris Paul A, Dawant Benoit M, Speroff Theodore
VA Quality Scholars Program, Vanderbilt University, Nashville, TN, USA.
Ann Surg. 2004 Sep;240(3):547-54; discussion 554-6. doi: 10.1097/01.sla.0000137143.65540.9c.
To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death.
Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation.
Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data.
Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set.
CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.
确定使用密集数据采集来测量以5分钟为间隔的心率波动（标准差）是否能预测死亡。
自20世纪初以来，评估危重症患者生命体征的基本方法变化不大。我们此前在该领域的工作已经证明了密集采样数据的实用性，尤其是在患者整个住院期间的心率波动，对于预测死亡和延长通气时间具有重要意义。
在30个月内，前瞻性地收集并存档了1316名创伤重症监护病房患者的约1.2亿个心率数据点。数据每1至4秒采样一次，存储在关系数据库中，与结局数据相关联，并进行去识别处理。连续计算5分钟间隔内的心率标准差（CVRD，与心脏波动相关的功能障碍）。在一组测试患者（N = 923）上建立了纳入年龄和损伤严重程度评分的逻辑回归模型，并在前瞻性分析中对一个不同的验证集（N = 393）的重症监护病房数据的前24小时进行分析。
在测试集中，CVRD的分布因生存情况而异。在验证集中对该模型进行前瞻性评估，受试者工作曲线下面积为0.81，敏感性和特异性分别为70.1%和80.0%。在验证集中，CVRD最早可在24小时内预测死亡。
CVRD可识别出高死亡概率的患者亚组。在住院的前24小时内即可预测死亡。我们假设CVRD是自主神经系统功能障碍的替代指标。
