BACKGROUND Recent literature suggests a significant association between blood pressure variability (BPV) and postoperative outcomes after cardiac surgery. However, its outcome prediction ability remains unclear. Current prediction models use static preoperative patient factors. We explored the ability of Poincaré plots and coefficient of variation (CV) by measuring intraoperative BPV in predicting adverse outcomes. METHODS In this retrospective, observational, cohort study, 3687 adult patients (> 18 years) undergoing cardiac surgery requiring cardio-pulmonary bypass from 2008 to 2014 were included. Blood pressure variability was computed by Poincare plots and CV. Standard descriptors (SD) SD1, SD2 were measured with Poincare plots by ellipse fitting technique. The outcomes analyzed were the 30-day mortality and postoperative renal failure. Logistic regression models adjusted for preoperative and surgical factors were constructed to evaluate the association between BPV parameters and outcomes. C-statistics were used to analyse the predictive ability. RESULTS Analysis found that, 99 (2.7%) patients died within 30 days and 105 (2.8%) patients suffered from in-hospital renal failure. Logistic regression models including BPV parameters (standard descriptors from Poincare plots and CV) performed poorly in predicting postoperative 30-day mortality and renal failure [Concordance(C)-Statistic around 0.5]. They did not add any significant value to the standard STS risk score [C-statistic: STS alone 0.7, STS + BPV parmeters 0.7]. CONCLUSIONS In conclusion, BP variability computed from Poincare plots and CV were not predictive of mortality and renal failure in cardiac surgical patients. Patient comorbid conditions and other preoperative factors are still the gold standard for outcome prediction. Future directions include analysis of dynamic parameters such as complexity of physiological signals in identifying high risk patients and tailoring management accordingly.

中文翻译：

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通过血压变异性指数庞加莱图和变异系数预测心脏手术结局：一项回顾性研究。

背景技术最近的文献表明，心脏手术后血压变异性（BPV）与术后结果之间存在显着关联。但是，其结果预测能力仍不清楚。当前的预测模型使用静态的术前患者因素。我们通过测量术中BPV预测不良结局来探索庞加莱图和变异系数（CV）的能力。方法在这项回顾性观察性队列研究中，纳入了2008年至2014年间3687名（> 18岁）接受心脏手术的成人患者，这些患者需要进行心肺旁路手术。血压变异性通过Poincare图和CV计算。标准描述符（SD）SD1，SD2通过Poincare图通过椭圆拟合技术进行测量。分析的结局为30天死亡率和术后肾衰竭。构建针对术前和手术因素调整的逻辑回归模型，以评估BPV参数与结局之间的关联。使用C统计量分析预测能力。结果分析发现，有30名患者在30天内死亡，其中105名（2.8％）患有院内肾衰竭。包括BPV参数（来自Poincare图和CV的标准描述符）的逻辑回归模型在预测术后30天死亡率和肾衰竭方面效果较差[Concordance（C）-Statistics about 0.5]。他们没有为标准STS风险评分增加任何显着价值[C统计：仅STS为0.7，STS + BPV参数为0.7]。结论总之，根据Poincare图和CV计算的BP变异性不能预测心脏手术患者的死亡率和肾衰竭。患者合并症和其他术前因素仍然是预后预测的金标准。未来的方向包括对动态参数的分析，例如在识别高危患者和相应调整管理中的生理信号复杂性。