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基于右心室应变成像预测脓毒症患者的预后:列线图模型的建立与验证

Predicting prognosis of sepsis in patients based on right ventricular strain imaging development and validation of a nomogram model.

作者信息

Wang Qinxin, Chen Hongmin, Zhang Bingyi, Zhou Chang, Xing Boyuan, Li Chang, Xu Shijin, Liu Yun

机构信息

Department of Ultrasound Imaging, the First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang, China.

Department of General Practice, the First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang, China.

出版信息

Front Cardiovasc Med. 2025 Jun 4;12:1532674. doi: 10.3389/fcvm.2025.1532674. eCollection 2025.

DOI:10.3389/fcvm.2025.1532674
PMID:40535148
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12174150/
Abstract

BACKGROUND

The right ventricle (RV) plays a significant role in septic myocardial injury and associated organ dysfunction. Hence, identifying right ventricular systolic dysfunction (RVSD) early is crucial for improving outcomes in septic patients, yet current research on RVSD in sepsis remains limited.

OBJECTIVE

The study aims to identify risk factors for adverse outcomes in septic patients and construct a nomogram prediction model incorporating right ventricular strain and right ventricle-pulmonary artery coupling parameters.

METHODS

This single-center prospective study included 156 sepsis patients admitted from September 2021 to October 2024. General clinical, laboratory, and echocardiographic data were collected within 72 h of sepsis diagnosis. Prognosis was used to divide patients into two groups. Lasso regression was used to examine the baseline features of both groups. Multivariable logistic regression analysis and a nomogram were used to predict sepsis prognosis. The relationship between RVSD and 28-day mortality was examined.

RESULTS

Within 28 days, 52 of 141 sepsis patients died. Univariate analysis showed that the non-survivor cohort was older and had higher APACHE II and Sequential Organ Failure Assessment (SOFA) ratings and procalcitonin, B-type natriuretic peptide, cTnI, and lactate. RV-free wall strain (-18.9% ± 1.6% vs. -20.1% ± 1.5%,  < 0.001) and RV global strain (-18.6% ± 1.4% vs. -17.6% ± 1.0%,  < 0.001) were lower in the non-survivor group compared to the survivor cohort. PASP and RV-GS/PASP ratio significantly differed between the two groups ( < 0.05). Multivariable logistic regression analysis identified age >67 years, SOFA score ≥7.5, procalcitonin ≥5.7 ng/ml, lactate ≥3.5 mmol/L, RV-FWS ≥-19.4%, and RV-GS/PASP ≥-0.55 as independent risk factors for poor sepsis outcomes. The prognostic model using these six risk factors had an area under the curve (AUC) of 0.907 (95% CI: 0.858-0.954). Internal validation showed strong nomogram calibration with a C-index of 0.88.

CONCLUSION

The RV-GS/PASP ratio demonstrated significant prognostic utility for predicting clinical outcomes in sepsis patients. Furthermore, the nomogram model incorporating age, SOFA score, procalcitonin, lactate, and RV-FWS exhibited excellent discriminative ability, with an AUC of 0.907.

摘要

背景

右心室(RV)在脓毒症性心肌损伤及相关器官功能障碍中起重要作用。因此,早期识别右心室收缩功能障碍(RVSD)对于改善脓毒症患者的预后至关重要,但目前关于脓毒症中RVSD的研究仍然有限。

目的

本研究旨在确定脓毒症患者不良预后的危险因素,并构建一个包含右心室应变和右心室-肺动脉耦合参数的列线图预测模型。

方法

这项单中心前瞻性研究纳入了2021年9月至2024年10月收治的156例脓毒症患者。在脓毒症诊断后72小时内收集一般临床、实验室和超声心动图数据。根据预后将患者分为两组。采用Lasso回归分析两组的基线特征。采用多变量逻辑回归分析和列线图预测脓毒症预后。研究RVSD与28天死亡率之间的关系。

结果

在28天内,141例脓毒症患者中有52例死亡。单因素分析显示,非存活队列患者年龄更大,急性生理与慢性健康状况评分系统(APACHE II)和序贯器官衰竭评估(SOFA)评分更高,降钙素原、B型利钠肽(BNP)、心肌肌钙蛋白I(cTnI)和乳酸水平更高。与存活队列相比,非存活组的右心室游离壁应变(-18.9%±1.6% vs. -20.1%±1.5%,P<0.001)和右心室整体应变(-18.6%±1.4% vs. -17.6%±1.0%,P<0.001)更低。两组间肺动脉收缩压(PASP)和右心室整体应变/肺动脉收缩压(RV-GS/PASP)比值有显著差异(P<0.05)。多变量逻辑回归分析确定年龄>67岁、SOFA评分≥7.5、降钙素原≥5.7 ng/ml、乳酸≥3.5 mmol/L、右心室游离壁应变≥-19.4%和RV-GS/PASP≥-0.55为脓毒症预后不良的独立危险因素。使用这六个危险因素的预后模型的曲线下面积(AUC)为0.907(95%可信区间:0.858-0.954)。内部验证显示列线图校准良好,C指数为0.88。

结论

RV-GS/PASP比值在预测脓毒症患者临床结局方面具有显著的预后价值。此外,包含年龄、SOFA评分、降钙素原、乳酸和右心室游离壁应变的列线图模型具有出色的鉴别能力,AUC为0.907。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/992d029e9085/fcvm-12-1532674-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/c02e1ccf8398/fcvm-12-1532674-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/55dfd85023d2/fcvm-12-1532674-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/f831cca7516f/fcvm-12-1532674-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/2e8509712f81/fcvm-12-1532674-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/992d029e9085/fcvm-12-1532674-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/c02e1ccf8398/fcvm-12-1532674-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/55dfd85023d2/fcvm-12-1532674-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/f831cca7516f/fcvm-12-1532674-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/2e8509712f81/fcvm-12-1532674-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc9/12174150/992d029e9085/fcvm-12-1532674-g005.jpg

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