Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet. aSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination. A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity. MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

中文翻译：

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利用信号复杂性预测动脉瘤性蛛网膜下腔出血后的结果：一项前瞻性两中心队列研究

信号复杂性（即熵）描述了系统内的有序程度。低生理信号复杂性预示着多种疾病的不利结果，并被认为反映了心/脑血管系统刚性的增加，导致（或反映）自动调节失败。动脉瘤性蛛网膜下腔出血（aSAH）会引发一系列复杂的全身和脑后遗症。在aSAH中，熵的值尚未确定。来自 2 个前瞻性队列（苏黎世——衍生队列，亚琛——验证队列）的 aSAH 患者被纳入其中。评估动脉血压、颅内压、心率及其导数的多尺度熵 (MSE)，并与 12 个月时的二分法（1-4 与 5-8）或顺序结果（GOSE——扩展格拉斯哥结果量表）进行比较使用单变量和多变量（根据年龄、世界神经外科医生联合会分级、改良 Fisher (mFisher) 分级、迟发性脑梗塞进行调整）和序数方法（比例赔率逻辑回归/滑动二分法）。使用自举法对多变量逻辑回归模型进行内部验证，并通过评估校准和辨别力进行外部验证。总共分析了 330 名（衍生：241 名，验证：89 名）aSAH 患者。 MSE 下降与不良结果的可能性较高相关，与协变量和分析方法无关。多变量调整逻辑回归模型经过良好校准，在验证队列中进行评估时仅显示歧视性略有下降。序数分析显示其影响是线性的。根据初始严重程度调整结果定义时，MSE 仍然有效。 MSE 指标以及生理信号的复杂性是 12 个月结果的独立、内部和外部有效的预测因子。将高频生理数据纳入临床结果预测的一部分可以实现精确、个性化的结果预测。这项研究的结果值得进一步调查造成复杂性的原因及其与重要且可能可预防的并发症（包括血管痉挛和迟发性脑缺血）的关联。