ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Coronary Artery Disease
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1681170
This article is part of the Research TopicAdvancements in Understanding and Managing Residual Risk of Coronary Artery DiseasesView all articles
Prognostic Value of the Atherogenic Index of Plasma in Patients with Acute Coronary Syndrome Without Standard Modifiable Risk Factors: A Machine Learning-based Cohort Study
Provisionally accepted- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Background: Patients without any standard modifiable cardiovascular risk factors (SMuRF-less) who develop ACS, tend to have poor outcomes. However, the prognostic value of atherogenic index of plasma (AIP) in these patients is unclear. Therefore, we investigated the association between AIP and adverse outcomes in SMuRF-less patients with ACS. Methods: This study retrospectively enrolled 722 SMuRF-less patients with ACS receiving PCI at Beijing Anzhen Hospital from March 2017 to March 2018. Three patient-groups were formed using AIP tertiles. The primary outcome, MACCE was a composite of all-cause mortality, non-fatal myocardial infarction (MI), unplanned revascularization, and non-fatal ischemic stroke. Association between AIP levels and MACCE risk was examined using RCS analysis. Prognostic value of AIP levels for MACCE was assessed using multivariable Cox regression models and machine learning approaches. Results: During follow-up of the 722 patients (median age, 60 years [interquartile range, 53–67]; female, 29.8%; median follow-up duration, 59 months), 168 (23.3%) developed MACCE. The RCS results showed linear association of progressively increasing MACCE risk with increasing AIP levels. In multivariable Cox regression analysis, significantly higher MACCE risk occurred with the highest AIP tertile than with the lowest (HR 2.03, 95% CI 1.34–3.08; P < 0.001). Elevated AIP level was associated with higher risks of all-cause death (HR 3.49, 95% CI 1.09–11.23; P = 0.036); non-fatal MI (HR 3.02, 95% CI 1.08– 8.48; P = 0.035); and unplanned revascularization (HR 2.18, 95% CI 1.34–3.52; P < 0.001). As a continuous variable, higher AIP levels were significantly associated with increased risks of MACCE (HR 2.95, 95% CI 1.74–4.98; P<0.001), all-cause mortality (HR 6.80, 95% CI 1.85–24.96; P = 0.003), non-fatal MI (HR 3.58, 95% CI 1.08–11.86; P = 0.037), and unplanned revascularization (HR 2.84, 95% CI 1.55–5.19; P<0.001). Machine-learning models incorporating AIP levels improved outcome prediction. At 48 months, the gradient boosting machine model achieved the highest AUC (0.796; 95% CI: 0.703–0.889), while complementary assessments showed that the random survival forest model provided the greatest net clinical benefit and demonstrated excellent calibration. Conclusion: Among SMuRF-less patients with ACS undergoing PCI, AIP level was identified as an independent predictor of clinical prognosis.
Keywords: Atherogenic index of plasma, standard modifiable cardiovascular risk factors, acutecoronary syndrome, Major adverse cardiovascular and cerebrovascular events, prognosis
Received: 07 Aug 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Chen, Liu, Sun, Zhang, Cheng and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yujing Cheng, 13426481193@163.com
Yujie Zhou, azzyj12@163.com
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