PDF(Pubmed)
Abstract：
This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.
摘要：
本研究旨在通过使用心率变异性（HRV）数据来开发重症监护病房（ICU）入院的预测模型。这项回顾性病例对照研究使用了两个数据集（急诊科[ED]入住ICU的患者，和未入住ICU的手术室患者）来自单一的学术三级医院。使用R-峰-R-峰(R-R)间隔每5分钟测量HRV度量。我们开发了一个广义线性混合模型来预测ICU入院并评估受试者工作特征曲线（AUC）下的面积。根据系数计算具有95%置信区间(CI)的赔率比(OR)。我们分析了610名（ICU：122;非ICU：488）患者，影响ICU入院几率的因素包括糖尿病史（OR[95％CI]：3.33[1.71-6.48]）；较高的心率（OR[95％CI]：每10个单位增加3.40[2.97-3.90]）；连续R-R间隔差异的均方根较高（RMSSD；OR[95％CI]：每10个R-单位增加1.36[1.22-1.51]，RR（OR每10个单位增加0.68[0.60-0.78]）。最终模型的AUC为0.947(95%CI:0.906-0.987)。开发的模型有效地预测了ED和手术室混合人群中的ICU入院情况。
