Development and validation of the MLR-based nomogram for predicting short-term adverse events in patients with acute uncomplicated type B aortic intramural hematoma
10.3760/cma.j.cn112148-20241009-00588
- VernacularTitle:基于MLR的急性非复杂性B型主动脉壁内血肿近期不良预后预测模型建立与验证
- Author:
Yasong WANG
1
;
Xuan WU
1
;
Yue WANG
1
;
Tienan ZHOU
1
;
Dongyuan SUN
1
;
Xue LIU
1
;
Xiaozeng WANG
1
Author Information
1. 解放军北部战区总医院心血管内科 寒地心血管病全国重点实验室，沈阳 110016
- Publication Type:Journal Article
- Keywords:
Aortic diseases;
Intramural hematoma;
Monocyte to lymphocyte ratio;
Prediction model;
Inflammation response;
Prognosis
- From:
Chinese Journal of Cardiology
2025;53(2):128-135
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a nomogram based on the monocyte-to-lymphocyte ratio (MLR) for predicting the risk of aortic-related adverse events within 30 days in patients with acute uncomplicated type B aortic intramural hematoma.Methods:This single-center retrospective cohort study screened consecutive patients with acute uncomplicated type B aortic intramural hematoma treated at the Emergency and Cardiovascular Medicine Departments of the General Hospital of the Northern Theater Command from April 2018 to April 2024. Patients were divided into two groups based on the optimal MLR cut-off value for predicting aortic-related adverse events: low MLR and high MLR group. MLR was defined as the ratio of monocytes to lymphocytes. Aortic-related adverse events were defined as a composite of aortic-related death or aortic intramural hematoma progression (including aortic dissection and penetrating aortic ulcers) within 30 days. The receiver operating characteristic (ROC) curve identified the optimal MLR cut-off value. Multivariate logistic regression was used to identify independent predictors of aortic-related adverse events within 30 days, based on which nomogram models were constructed: the clinical characteristics model and the clinical characteristics-MLR model. The DeLong test was used to evaluate the diagnostic performance of different risk models. The additional predictive value of MLR was assessed using the net reclassification index (NRI) and integrated discrimination improvement (IDI).Results:A total of 332 patients were included, of whom 217 were male (65.4%), with an average age of (64.3±9.4) years. A total of 107 aortic-related adverse events occurred during the 30-day follow-up period. The optimal cut-off value for MLR was 0.529. There were 189 cases in the low MLR group (MLR<0.529) and 143 cases in the high MLR group (MLR≥0.529). The rate of aortic-related adverse events was higher in the high MLR group compared to the low MLR group (44.1% (63/143) vs. 23.3% (44/189), P<0.001), mainly due to a higher rate of progression to aortic dissection (9.8% (14/143) vs. 1.1% (2/189), P<0.001) and penetrating aortic ulcers (31.5% (45/143) vs. 20.6% (39/189), P=0.025). Multivariate analysis identified diabetes ( OR=0.25, 95% CI 0.08-0.78, P=0.017), anemia ( OR=3.45, 95% CI 1.28-9.27, P=0.014), maximum descending aorta diameter ( OR=1.08, 95% CI 1.02-1.15, P=0.007), ulcer-like projections ( OR=4.04, 95% CI 2.26-7.24, P<0.001), and MLR ( OR=6.61, 95% CI 2.50-17.46, P<0.001) as independent predictors of aortic-related adverse events during the 30-day follow-up period. The clinical characteristics model includes diabetes, anemia, ulcer-like projections and maximum diameter of the descending aorta, and the clinical characteristics-MLR model includes the above clinical characteristics and MLR. The results of the DeLong test showed that the clinical characteristic-MLR model demonstrated a higher area under the ROC curve compared to the clinical characteristic model alone (0.784 (95% CI 0.736-0.841) vs. 0.742 (95% CI 0.691-0.788), P=0.031). The continuous NRI was 0.461 (95% CI 0.237-0.685, P<0.001) and the IDI was 0.077 (95% CI 0.043-0.112, P<0.001), indicating that the inclusion of the MLR in the model significantly improved the predictive accuracy. Conclusion:The integration of MLR with other clinical characteristics improves the early identification of high-risk patients with acute uncomplicated type B aortic intramural hematoma, optimizing clinical decisions and improving patient outcomes.
