基于血小板的指数对败血症死亡率的预测值
Alice Nicoleta Drăgoescu1, Adina Turcu-Stiolica2, Marian Valentin Zorilă3
1Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Biomedicines
|January 28, 2026
概括
对血小板的C-反应蛋白比率 (CPR) 有效地预测了败血症患者的早期死亡. 这种来自常规测试的低成本生物标志物有助于重症监护室 (ICU) 的风险分层.
科学领域:
- 关键护理医学 关键护理医学
- 生物标志物 生物标志物
- 败血症病理生理学
背景情况:
- 败血症和败血性休克仍然是ICU死亡的主要原因,尽管治疗方面取得了进展.
- 早期预后评估对于指导毒症管理中的临床决策至关重要.
- 血小板衍生的指数提供了潜在的低成本的炎症和凝血的指标在败血症.
研究的目的:
- 评估各种基于血小板的比率对败血症患者住院死亡率的预测性表现.
- 确定最有效的血小板衍生指数用于ICU的早期风险分层.
主要方法:
- 一项前性观察性研究招募了114名成人败血症/败血性休克患者进入三级重症监护室.
- 测量的生物标志物包括C反应蛋白与血小板比率 (CPR),血小板与淋巴细胞比率 (PLR),血小板与白细胞比率 (PWR) 和血小板与肌素比率 (PCR).
- 后勤回归和ROC分析评估了死亡率的预测准确性.
主要成果:
- 与幸存者相比,非幸存者表现出较高的CRP和CPR值和较低的血小板/淋巴细胞计数.
- 心肺复苏在区分幸存者和非幸存者方面表现出最高的准确性 (AUC 0.757).
- CPR独立预测了住院死亡率 (OR 1.98),而PLR和PWR没有.
结论:
- 心肺复苏是早期败血症死亡率的强大和临床适用预测指标,超过其他血小板指数.
- 从常规实验室获得的CPR是ICU初始风险分层的一个有价值的工具.
- 需要进一步的多中心研究来验证CPR的预后作用和整合到现有的评分系统.
相关概念视频
02:27Predicting Molecular Geometry
45.7K
VSEPR Theory for Determination of Electron Pair Geometries
45.7K
01:03Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
01:18Structure and Function of Platelets
3.5K
The cell fragments known as platelets are disc-shaped, with an average diameter of about 3 μm and a thickness of roughly 1 μm. They play a crucial role in the body's vascular clotting system, which also involves plasma proteins, blood cells, and blood vessel tissues.
Platelets are continually replenished, circulating in the bloodstream for 9-12 days before being removed by phagocytes, primarily in the spleen. A microliter of circulating blood contains between 150,000 and 450,000...
Platelets are continually replenished, circulating in the bloodstream for 9-12 days before being removed by phagocytes, primarily in the spleen. A microliter of circulating blood contains between 150,000 and 450,000...
3.5K
01:22Formation of the Platelet Plug
9.1K
The platelet phase, the second stage of hemostasis, commences around 15-20 seconds after an injury. It follows and overlaps with the vascular phase, during which blood vessels constrict to minimize blood loss.
As the injured blood vessel contracts, endothelial cells undergo contraction, revealing collagen fibers in the basement membrane and underlying connective tissue. Furthermore, the plasma membrane of endothelial cells becomes adhesive, preparing the site for platelet adhesion. Platelets...
As the injured blood vessel contracts, endothelial cells undergo contraction, revealing collagen fibers in the basement membrane and underlying connective tissue. Furthermore, the plasma membrane of endothelial cells becomes adhesive, preparing the site for platelet adhesion. Platelets...
9.1K
01:07End Point Prediction: Gran Plot
1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.2K
01:13Sensitivity, Specificity, and Predicted Value
1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
1.3K