Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Sci Rep. 2021 Oct 8;11(1):20076. doi: 10.1038/s41598-021-99581-6.
While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87-99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.
虽然血清乳酸水平是预测脓毒症危重症患者临床预后不良的指标,但许多患者的血清乳酸水平正常。更好地了解这种差异可能有助于区分脓毒症表型,并为脓毒症病理生理学提供线索。利用了三个重症监护病房数据集。确定了疾病严重程度评分最高四分位数的成年脓毒症患者。为每个数据集构建了逻辑回归、随机森林和偏最小二乘模型。报告了区分第 1 天血清乳酸正常/高的患者的特征。为了排除组间差异是由于复苏前高乳酸血症的潜在混杂因素引起的,对第 2 天的分析进行了重复。在纳入的 4861 例患者中,47%的患者乳酸水平正常。尽管生理表型相当,但血清乳酸水平正常的患者 28 天死亡率(17%)低于高乳酸水平的患者(40%)。虽然性能在数据集之间存在差异,但逻辑回归始终表现最佳(接受者操作特征曲线下面积 87-99%)。与血清乳酸正常最相关的变量是血清碳酸氢盐、氯和肺部疾病,而血清钠、AST 和肝脏疾病与高血清乳酸相关。未来的研究应证实这些发现并确定潜在的病理生理机制,从而理清关联和因果关系。