细胞外基质蛋白可改善经导管主动脉瓣置换术患者的风险预测。

Extracellular Matrix Proteins Improve Risk Prediction in Patients Undergoing Transcatheter Aortic Valve Replacement.

作者信息

Boeckling Felicitas, Rasper Tina, Zanders Lukas, Pergola Graziella, Cremer Sebastian, Mas-Peiro Silvia, Vasa-Nicotera Mariuca, Leistner David, Dimmeler Stefanie, Kattih Badder

机构信息

Institute for Cardiovascular Regeneration, Goethe University Frankfurt am Main Germany.

Department of Cardiology Goethe University Frankfurt, University Hospital Frankfurt Germany.

出版信息

J Am Heart Assoc. 2025 Mar 4;14(5):e037296. doi: 10.1161/JAHA.124.037296. Epub 2025 Feb 26.

Abstract

BACKGROUND

Cardiac fibrosis is common in patients with severe aortic stenosis and an independent predictor of death. Therefore, we examined the additional value of circulating fibrosis markers as a putative biomarker platform to identify patients with aortic stenosis undergoing transcatheter aortic valve replacement (TAVR) who are at a higher risk of death.

METHODS

In this study, 2-year survival analyses were conducted in 378 consecutive patients undergoing TAVR to evaluate the association between fibrosis marker and risk of adverse long-term outcome. Implementation of fibrosis marker into TAVR risk stratification was tested by a machine-learning algorithm.

RESULTS

Among 20 circulating fibrosis markers involved in pathological extracellular matrix remodeling, high tissue inhibitor of metalloproteinase-1 (TIMP-1) levels independently predicted risk of death in univariable (hazard ratio, 5.0 [95% CI, 2.6-9.7]; <0.001) and multivariable (adjusted hazard ratio, 2.2 [95% CI, 1.0-4.7]; =0.046) Cox regression analyses. Consequently, higher TIMP-1 levels offered a significantly higher overall prediction of reduced survival compared with the conventional Society of Thoracic Surgeons Predicted Risk of Mortality score (area under the curve, 0.753 [95% CI, 0.682-0.824] versus area under the curve, 0.656 [95% CI, 0.578-0.734]; <0.05). Applying an independent machine-learning algorithm allowed identification of a simple combination of 2 biomarkers (TIMP-1 and high-sensitivity cardiac troponin T) with superior prognostic value compared with Society of Thoracic Surgeons Predicted Risk of Mortality alone (area under the curve, 0.757 [95% CI, 0.686-0.828] versus 0.656 [95% CI, 0.578-0.34]; <0.05).

CONCLUSIONS

Circulating TIMP-1 is an independent predictor of reduced 2-year overall survival in patients undergoing TAVR. Combined with high-sensitivity cardiac troponin T, circulating TIMP-1 should be incorporated into risk stratification to identify patients undergoing TAVR who are at a higher risk of death.

摘要

背景

心脏纤维化在重度主动脉瓣狭窄患者中很常见,且是死亡的独立预测因素。因此,我们研究了循环纤维化标志物作为一种潜在生物标志物平台的附加价值,以识别接受经导管主动脉瓣置换术(TAVR)且死亡风险较高的主动脉瓣狭窄患者。

方法

在本研究中,对378例连续接受TAVR的患者进行了2年生存分析,以评估纤维化标志物与不良长期结局风险之间的关联。通过机器学习算法测试了将纤维化标志物纳入TAVR风险分层的情况。

结果

在参与病理性细胞外基质重塑的20种循环纤维化标志物中,高金属蛋白酶组织抑制剂-1(TIMP-1)水平在单变量(风险比,5.0[95%CI,2.6-9.7];P<0.001)和多变量(调整后风险比,2.2[95%CI,1.0-4.7];P=0.046)Cox回归分析中独立预测死亡风险。因此,与传统的胸外科医师协会预测死亡风险评分相比,较高的TIMP-1水平对生存率降低的总体预测显著更高(曲线下面积,0.753[95%CI,0.682-0.824]对曲线下面积,0.656[95%CI,0.578-0.734];P<0.05)。应用独立的机器学习算法能够识别出2种生物标志物(TIMP-1和高敏心肌肌钙蛋白T)的简单组合,其预后价值优于单独的胸外科医师协会预测死亡风险(曲线下面积,0.757[95%CI,0.686-0.828]对0.656[95%CI,0.578-0.34];P<0.05)。

结论

循环TIMP-1是接受TAVR患者2年总生存率降低的独立预测因素。与高敏心肌肌钙蛋白T联合使用时,循环TIMP-1应纳入风险分层,以识别接受TAVR且死亡风险较高的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3324/12132695/6a980405f662/JAH3-14-e037296-g002.jpg

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