Construction and validation of predictive models for major adverse cardiovascular events in patients with acute coronary syndrome
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摘要:目的
基于心率变异性(HRV)建立列线图模型，并评估急性冠状动脉综合征(ACS)患者主要不良心血管事件(MACEs)的发生风险。
方法回顾性纳入确诊ACS患者322例作为建模集，另选择164例ACS患者作为验证集，中位随访时间为23.5个月。将建模集分为MACEs组30例和无MACEs组292例，分别采用单因素分析与多因素Cox回归分析筛选危险因素，并建立列线图模型。采用受试者工作特征(ROC)曲线、校准曲线与决策曲线验证模型，以及采用Kaplan-Meier生存曲线进行风险分层。
结果建模集与验证集的性别、年龄、ACS类型、靶血管直径狭窄率、支架置入、MACEs发生率等一般资料比较，差异有统计学意义(P < 0.05)。单因素分析发现, MACEs组年龄大于无MACEs组, ST段抬高型心肌梗死(STEMI)和既往心肌梗死病史例数占比及入院肌酸激酶同工酶(CK-MB)、肌钙蛋白I(cTnI)、B型利钠肽(BNP)和靶血管直径狭窄率、标准化高频(HF)值高于无MACEs组, HRV指标中标准化低频(LF)值、标准化LF/HF低于无MACEs组，差异有统计学意义(P < 0.05)。多因素Cox回归分析显示，年龄≥65岁(HR=1.425, 95%CI: 1.124~1.758, P=0.001)、既往心肌梗死病史(HR=1.326, 95%CI: 1.102~1.659, P=0.003)和标准化LF/HF < 1.32(HR=2.203, 95%CI: 1.568~2.659, P < 0.001)是ACS患者随访2年MACEs的独立危险因素。R软件建立列线图模型，总分200分。ROC曲线显示，列线图模型预测建模集1年与2年MACEs的曲线下面积(AUC)分别为0.845和0.902, 预测验证集1年与2年MACEs的AUC分别为0.802和0.856。校准曲线与决策曲线均显示，模型有较好的吻合度和净获益比。Kaplan-Meier生存曲线显示，建模集与验证集1年和2年高风险(＞80分)累积MACEs发生率大于中风险(60~80分)、低风险(< 60分)，其中低风险最低(P < 0.05)。
结论ACS患者入院早期检测HRV有利于准确评估介入干预后中长期随访MACEs的发生风险。ACS患者年龄、既往心肌梗死病史联合标准化LF/HF建立的列线图模型在评估预后风险分层中具有重要应用潜力。
Abstract:ObjectiveTo establish a nomograph model based on heart rate variability (HRV) and evaluate the risk of major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS).
MethodsA total of 322 patients with confirmed ACS were retrospectively included as the modeling set, and 164 patients with ACS were selected as the validation set. The median follow-up time was 23.5 months. The modeling set was divided into MACEs group(n=30) and non-MACEs group(n=292). The risk factors were screened by single-factor comparison and multi-factor Cox regression analysis respectively, and the nomogram model was established. Receiver operator characteristic (ROC) curve, calibration curve and decision curve were used to verify the model, and Kaplan-Meier survival curve was used for risk stratification.
ResultsThere were statistically significant differences in gender, age, ACS type, target vessel diameter stenosis rate, stent placement and incidence of MACEs between the modeling set and the validation set (P < 0.05). Univariate comparison showed that the age of the MACEs group was significantly higher, the proportion of ST-segment elevation myocardial infarction (STEMI) and history of myocardial infarction, admission creatine kinase isoenzyme (CK-MB), troponin I (cTnI) and B-type natriuretic peptide (BNP), target vessel diameter stenosis rate and standardized high frequency (HF) value were significantly higher, the standardized low frequency (LF) value in HRV index and the standardized LF/HF were significantly lower than those in the non-MACEs group (P < 0.05). Multivariate Cox regression analysis showed that age ≥65 years (HR=1.425; 95%CI, 1.124 to 1.758; P=0.001) and previous history of myocardial infarction (HR=1.326; 95%CI, 1.102 to 1.659; P=0.003) and standardized LF/HF < 1.32 (HR=2.203; 95%CI, 1.568 to 2.659; P < 0.001) were independent risk factors for MACEs in ACS patients followed up for two years. R software was used to establish a nomogram model, with a total score of 200 points. ROC showed that the area under the curve (AUC) of the 1-year and 2-year MACEs predicted by the nomogram model was 0.845 and 0.902, respectively, and the AUC of 1-year and 2-year MACEs for the prediction validation set was 0.802 and 0.856, respectively. Both the calibration curve and the decision curve showed that the model had good consistency and net benefit ratio. Kaplan-Meier survival curve showed that the cumulative incidence of MACEs at 1-year and 2-year high-risk (≥80 points) in modeling set and validation set was higher than that at medium-risk (60 to 80 points) and low-risk (< 60 points), and low-risk was the lowest (P < 0.05).
ConclusionEarly detection of HRV in ACS patients is beneficial to accurately assess the risk of MACEs in the medium- and long-term follow-up after intervention. The age of ACS patients, past history of MI combined with standardized LF/HF map model has important application potential in assessing prognostic risk stratification.
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图 2 ROC曲线验证列线图模型预测MACEs的区分度
A: 建模集1年MACEs的AUC; B: 建模集2年MACEs的AUC; C: 验证集1年MACEs的AUC; D: 验证集2年MACEs的AUC。
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图 3 校准曲线验证列线图模型预测MACEs的吻合度
A: 建模集1年MACEs的发生概率与实际发生率; B: 建模集2年MACEs的发生概率与实际发生率; C: 验证集1年MACEs的发生概率与实际发生率; D: 验证集2年MACEs的发生概率与实际发生率。
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图 4 决策曲线验证列线图模型预测MACEs的净获益比
A: 建模集1年MACEs的净获益比; B: 建模集2年MACEs的净获益比; C: 验证集1年MACEs的净获益比; D: 验证集2年MACEs的净获益比。
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图 5 Kaplan-Meier曲线比较建模集与验证集不同风险组的累积MACEs发生率
A: 建模集1年累积MACEs发生率; B: 建模集2年累积MACEs发生率; C: 验证集1年累积MACEs发生率; D: 验证集2年累积MACEs发生率。
表 1 建模集与验证集的一般临床资料比较(x±s)[n(%)]
临床资料 建模集(n=322) 验证集(n=164) t/χ2 P 性别 男 188(58.4) 89(54.3) 0.751 0.386 女 134(41.6) 75(45.7) 年龄/岁 65.6±6.7 64.3±6.6 0.568 0.524 ACS类型 STEMI 122(37.9) 62(37.8) < 0.001 0.986 NSTEMI 200(62.1) 102(62.2) 靶血管直径狭窄/% 93.5±6.7 94.8±7.2 0.789 0.263 支架置入数目 1.3±0.3 1.4±0.5 0.325 0.667 支架长度/mm 18.9±4.5 17.6±4.4 0.821 0.302 主要不良心血管事件 30(9.3) 18(11.0) 0.336 0.562 新发心力衰竭 8(2.5) 4(2.4) 靶血管重建 5(1.6) 3(1.8) 心肌梗死再发 15(4.7) 10(6.1) 心源性死亡 2(0.6) 1(0.6) ACS: 急性冠状动脉综合征; STEMI: ST段抬高型心肌梗死; NSTEMI: 非ST段抬高型急性心肌梗死。 
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表 2 建模集MACEs组与无MACEs组临床资料比较(x±s)[n(%)][M(P25, P75)]
临床资料 无MACEs组(n=292) MACEs组(n=30) Z/t/χ2 P 性别 男 171(58.6) 17(56.7) 0.040 0.841 女 121(41.4) 13(43.3) 年龄/岁 61.9±7.3 69.2±8.3 5.869 < 0.001 ACS类型 STEMI 103(35.2) 19(63.3) 9.102 0.003 NSTEMI 189(64.7) 11(36.7) 高血压 109(37.3) 11(36.7) 0.005 0.943 糖尿病 62(21.2) 5(16.7) 0.344 0.557 既往心肌梗死病史 44(15.1) 9(30.0) 4.411 0.036 靶血管直径狭窄/% 91.6±7.9 95.2±8.3 0.902 0.124 支架置入数目 1.2±0.2 1.3±0.3 0.425 0.565 支架长度/mm 17.6±6.9 20.2±8.3 0.659 0.324 CK-MB/(U/L) 128(56, 402) 345(126, 562) -1.526 < 0.001 肌钙蛋白I/(ng/L) 2.6(0.5, 4.5) 5.6(1.6, 8.8) -1.124 < 0.001 B型利钠肽/(pg/mL) 106(45, 256) 345(102, 525) -1.632 < 0.001 空腹血糖/(mmol/L) 8.2±1.6 8.5±1.9 0.757 0.302 总胆固醇/(mmol/L) 5.5±1.3 5.3±1.2 0.565 0.428 低密度脂蛋白/(mmol/L) 3.3±0.4 3.4±0.5 0.326 0.602 平均心率/(次/min) 78.5±6.5 80.2±7.9 0.825 0.426 窦性心律RR间期/ms 112.6±25.4 115.7±26.9 0.854 0.263 SDANN/ms 78.9±10.2 81.2±13.2 0.653 0.602 均方根连续差/ms 26(13, 42) 33(15, 46) -0.658 0.452 Pnn50 4.5(3.6, 5.5) 4.2(3.3, 5.2) 0.263 0.754 标准化低频 59.8±12.3 52.5±9.5 5.324 < 0.001 标准化高频 40.2±6.3 45.8±6.6 5.023 < 0.001 标准化低频/标准化高频 1.52±0.43 1.13±0.25 5.564 < 0.001 ACS: 急性冠状动脉综合征; STEMI: ST段抬高型心肌梗死; NSTEMI: 非ST段抬高型急性心肌梗死; CK-MB: 肌酸激酶同工酶; SDANN: NN间期的标准差平均值; Pnn50: 相邻正常RR间期差异大于50 ms的时间占NN间期总数。
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表 3 ACS患者MACEs危险因素的Cox回归分析
因素 β Wald P HR 95%CI 年龄≥65岁 0.625 6.023 0.001 1.425 1.124~1.758 既往心肌梗死病史 0.524 4.859 0.003 1.326 1.102~1.659 标准化LF/HF < 1.32 0.989 15.524 < 0.001 2.203 1.568~2.659 常数项 -0.235 3.256 0.013 — —
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[1] HE Y N, LI Y P, YANG X G, et al. The dietary transition and its association with cardiometabolic mortality among Chinese adults, 1982-2012: a cross-sectional population-based study[J]. Lancet Diabetes Endocrinol, 2019, 7(7): 540-548. doi: 10.1016/S2213-8587(19)30152-4
[2] 符会妮, 华朋铎, 韦雪峰, 等. 心肺运动试验与运动平板试验及动态心电图在评价心肌缺血中价值对比[J]. 中国循证心血管医学杂志, 2020, 12(11): 1348-1351, 1356. doi: 10.3969/j.issn.1674-4055.2020.11.16 [3] FALTER M, BUDTS W, GOETSCHALCKX K, et al. Accuracy of apple watch measurements for heart rate and energy expenditure in patients with cardiovascular disease: cross-sectional study[J]. JMIR Mhealth Uhealth, 2019, 7(3): e11889. doi: 10.2196/11889
[4] WANG J, LIU W, CHEN H Q, et al. Novel insights into the interaction between the autonomic nervous system and inflammation on coronary physiology: a quantitative flow ratio study[J]. Front Cardiovasc Med, 2021, 8: 700943. doi: 10.3389/fcvm.2021.700943
[5] 王萍, 龙滔, 邵江. 两种血清标志物联合24 h动态心电图对老年冠心病并发无症状心肌缺血的诊断价值[J]. 中华老年心脑血管病杂志, 2020, 22(9): 934-937. https://www.cnki.com.cn/Article/CJFDTOTAL-LNXG202009011.htm [6] WANG J, LIU C Z, GUO F D, et al. Deceleration capacity improves prognostic accuracy of relative increase and final coronary physiology in patients with non-ST-elevation acute coronary syndrome[J]. Front Cardiovasc Med, 2022, 9: 848499. doi: 10.3389/fcvm.2022.848499
[7] ZHENG Y Y, WU T T, GAO Y, et al. A novel ABC score predicts mortality in non-ST-segment elevation acute coronary syndrome patients who underwent percutaneous coronary intervention[J]. Thromb Haemost, 2021, 121(3): 297-308. doi: 10.1055/s-0040-1718411
[8] WANG L, CONG H L, ZHANG J X, et al. Triglyceride-glucose index predicts adverse cardiovascular events in patients with diabetes and acute coronary syndrome[J]. Cardiovasc Diabetol, 2020, 19(1): 80. doi: 10.1186/s12933-020-01054-z
[9] KIMURA K, KIMURA T, ISHIHARA M, et al. JCS 2018 guideline on diagnosis and treatment of acute coronary syndrome[J]. Circ J, 2019, 83(5): 1085-1196. doi: 10.1253/circj.CJ-19-0133
[10] 卢喜烈, 段扬, 朱金秀, 等. 18导动态心电图专家共识[J]. 中国心血管病研究, 2021, 19(9): 773-776. https://www.cnki.com.cn/Article/CJFDTOTAL-XXGZ202109002.htm [11] CHEW D P, HYUN K, MORTON E, et al. Objective risk assessment vs standard care for acute coronary syndromes: a randomized clinical trial[J]. JAMA Cardiol, 2021, 6(3): 304-313. doi: 10.1001/jamacardio.2020.6314
[12] DUAN S P, WANG J, YU F, et al. Enrichment of the postdischarge GRACE score with deceleration capacity enhances the prediction accuracy of the long-term prognosis after acute coronary syndrome[J]. Front Cardiovasc Med, 2022, 9: 888753. doi: 10.3389/fcvm.2022.888753
[13] CATAI A M, PASTRE C M, GODOY M F, et al. Heart rate variability: are You using it properly Standardisation checklist of procedures[J]. Braz J Phys Ther, 2020, 24(2): 91-102. doi: 10.1016/j.bjpt.2019.02.006
[14] MOHANTA S K, PENG L, LI Y F, et al. Neuroimmune cardiovascular interfaces control atherosclerosis[J]. Nature, 2022, 605(7908): 152-159. doi: 10.1038/s41586-022-04673-6
[15] MVNZEL T, SCHMIDT F P, STEVEN S, et al. Environmental noise and the cardiovascular system[J]. J Am Coll Cardiol, 2018, 71(6): 688-697. doi: 10.1016/j.jacc.2017.12.015
[16] HELDEWEG M L, LIU N, KOH Z X, et al. A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain[J]. Crit Care, 2016, 20(1): 179. doi: 10.1186/s13054-016-1367-5
[17] RAJA J M, ELSAKR C, ROMAN S, et al. Apple watch, wearables, and heart rhythm: where do we stand[J]. Ann Transl Med, 2019, 7(17): 417. doi: 10.21037/atm.2019.06.79
[18] TURAKHIA M P, DESAI M, HEDLIN H, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the Apple Heart Study[J]. Am Heart J, 2019, 207: 66-75. doi: 10.1016/j.ahj.2018.09.002
[19] 郭继鸿, 王思让, 谭学瑞, 等. 动态心电图报告规范专家共识(2019)[J]. 实用心电学杂志, 2019, 28(6): 381-386. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXD201906001.htm -
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