Medical Intensive Care Unit, Ambroise Paré Hospital, AP-HP, Boulogne-Billancourt, INSERM UMR 1018, Clinical Epidemiology Team, CESP, Paris-Saclay University, Villejuif, France.
Curr Opin Crit Care. 2021 Jun 1;27(3):290-297. doi: 10.1097/MCC.0000000000000834.
Recent studies have failed to show significant benefit from a uniform strategy, suggesting that hemodynamic management must be individually adapted in septic shock depending on different phenotypes. Different approaches that may be used to this end will be discussed.
Fluid management is a cornerstone of resuscitation, as the positive fluid balance has been associated with higher mortality and right ventricular failure. Myocardial evaluation is mandatory, as sepsis patients may present with a hyperkinetic state, left ventricular (systolic and diastolic) and/or right ventricular dysfunction, the latter being associated with higher mortality. Statistical approaches with the identification of hemodynamic clusters based on echocardiographic and clinical parameters might be integrated into daily practice to develop precision medicine. Such approaches may also predict the progression of septic shock.
Different hemodynamic phenotypes can occur at any stage of sepsis and be associated with one another. The clinician must regularly assess dynamic changes in phenotypes in septic shock patients. Statistical approaches based on machine learning need to be validated by prospective studies.
最近的研究未能显示出统一策略有显著获益，这表明脓毒性休克的血流动力学管理必须根据不同表型进行个体化调整。将讨论为此目的可能使用的不同方法。
液体管理是复苏的基石，因为正液体平衡与更高的死亡率和右心室衰竭有关。心肌评估是强制性的，因为脓毒症患者可能表现为高动力状态，左心室（收缩和舒张）和/或右心室功能障碍，后者与更高的死亡率相关。基于超声心动图和临床参数的血流动力学聚类的统计学方法可能整合到日常实践中，以开发精准医学。这些方法还可以预测脓毒性休克的进展。
不同的血流动力学表型可在脓毒症的任何阶段发生，并相互关联。临床医生必须定期评估脓毒性休克患者表型的动态变化。基于机器学习的统计方法需要前瞻性研究验证。
