老年慢性心力衰竭恶化的风险模型构建及NT-proBNP的预测价值探讨

陈红, 赵敬东, 李兴升. 老年慢性心力衰竭恶化的风险模型构建及NT-proBNP的预测价值探讨[J]. 临床心血管病杂志, 2024, 40(3): 199-206. doi: 10.13201/j.issn.1001-1439.2024.03.008
引用本文: 陈红, 赵敬东, 李兴升. 老年慢性心力衰竭恶化的风险模型构建及NT-proBNP的预测价值探讨[J]. 临床心血管病杂志, 2024, 40(3): 199-206. doi: 10.13201/j.issn.1001-1439.2024.03.008
CHEN Hong, ZHAO Jingdong, LI Xingsheng. A nomogram to predict the risk of worsening chronic heart failure in the elderly and the predictive value of NT-proBNP[J]. J Clin Cardiol, 2024, 40(3): 199-206. doi: 10.13201/j.issn.1001-1439.2024.03.008
Citation: CHEN Hong, ZHAO Jingdong, LI Xingsheng. A nomogram to predict the risk of worsening chronic heart failure in the elderly and the predictive value of NT-proBNP[J]. J Clin Cardiol, 2024, 40(3): 199-206. doi: 10.13201/j.issn.1001-1439.2024.03.008

老年慢性心力衰竭恶化的风险模型构建及NT-proBNP的预测价值探讨

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    通讯作者: 李兴升,E-mail:ptlxs@163.com
  • 中图分类号: R541.6

A nomogram to predict the risk of worsening chronic heart failure in the elderly and the predictive value of NT-proBNP

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  • 目的 构建并验证老年慢性心力衰竭恶化(WHF)的风险预测列线图,并探讨列线图与N末端B型利钠肽前体(NT-proBNP)联合预测WHF的价值。方法 回顾性纳入2018年1月—2022年6月在我院连续就诊的老年WHF患者(WHF组,572例),另纳入同期于我院门诊规律随访的老年慢性心力衰竭稳定期患者561例(Non-WHF组)。比较WHF组与Non-WHF组临床指标的差异。运用LASSO回归联合多因素logistic回归模型构建老年WHF风险预测列线图。由内部验证集评估列线图的可行性,采用受试者工作特征曲线(ROC曲线)、校准曲线和决策曲线(DCA)评价列线图的区分度、校准度及临床实用性。最后通过对比ROC曲线下面积(AUC)、整体鉴别指数(IDI)及净重新分类指数(NRI)来评估列线图、NT-proBNP及其联合使用在老年WHF人群中的预测价值。结果 入院心率、合并心房颤动、左心室射血分数(LVEF)、红细胞分布宽度(RDW)、肌酐(Scr)、血清尿素氮/白蛋白比值(BAR)是WHF发生的独立危险因素(均P<0.05);根据以上6个变量绘制相应的可视化列线图,列线图在训练集和验证集中的AUC分别为0.830(95%CI:0.802~0.858)、0.834(95%CI:0.792~0.876)。DCA提示该列线图的校准度好,临床实用性强。经ROC曲线分析,列线图、NT-proBNP及其联合预测老年WHF的AUC值分别为0.830、0.890和0.924。相对于NT-proBNP,列线图和NT-proBNP联合预测的IDI、NRI分别为0.073、0.065。结论 包含6个预测变量(入院心率、合并心房颤动、LVEF、RDW、Scr和BAR)的列线图可用于预测老年慢性心力衰竭患者WHF发生风险。此列线图与NT-proBNP联合评估更有助于识别高风险的老年WHF人群,协助医务工作者制定临床决策。
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  • 图 1  研究设计流程

    Figure 1.  The flow diagram of study design

    图 2  训练集特征变量筛选

    Figure 2.  Selection of variables in the training set

    图 3  老年慢性心衰患者WHF发生风险的列线图预测模型

    Figure 3.  The Nomogram to predict the risk of WHF in the elderly with chronic heart failure

    图 4  评价列线图区分度的ROC曲线

    Figure 4.  ROC curves for evaluating the distinction of the nomogram

    图 5  评价列线图精确度的校准曲线

    Figure 5.  The calibration curves for estimating the accuracy of the nomogram

    图 6  评价列线图临床实用性的决策分析曲线

    Figure 6.  The DCA curves for assessing the practicability of the nomogram

    表 1  WHF组与Non-WHF组的临床特征比较

    Table 1.  Clinical characteristics between the WHF group and Non-WHF group 例(%), X±S, M(P25, P75)

    变量 训练集 验证集
    WHF组(404例) Non-WHF组(392例) P WHF组(168例) Non-WHF组(169例) P
    年龄/岁 79.47±7.72 78.19±9.24 0.772 80.60±10.22 79.67±8.08 0.021
    男性 199(49.26) 175(44.64) 0.135 92(54.76) 65(38.46) 0.007
    心率/(次/min) 87.81±22.18 78.48±14.55 <0.001 87.56±22.19 78.34±16.66 <0.001
    收缩压/mmHg 130.78±24.91 133.45±21.54 0.105 131.86±23.70 135.76±21.71 0.118
    舒张压/mmHg 78.11±15.73 77.00±13.50 0.928 77.20±13.88 77.35±14.01 0.136
    冠心病 256(63.37) 286(72.96) 0.004 112(66.67) 123(72.78) 0.199
    高血压 249(61.63) 287(73.21) 0.030 98(58.33) 115(68.05) <0.001
    肺源性心脏病 119(29.46) 106(27.04) 0.094 57(33.93) 36(21.30) 0.232
    心肌病 45(11.14) 17(4.34) 0.001 11(6.55) 5(2.96) 0.083
    心房颤动 188(46.53) 121(30.87) <0.001 93(55.36) 47(27.81) <0.001
    共病数/种 2.47±1.07 2.44±0.94 0.391 2.49±1.02 2.31±0.99 0.384
    LAD/mm 42.11±7.43 39.60±6.94 <0.001 42.85±6.72 39.35±6.61 0.001
    LVEDD/mm 51.75±10.28 47.49±6.87 <0.001 51.01±9.47 47.65±8.72 <0.001
    LVEF/% 56.25±16.58 66.00±11.52 <0.001 56.74±16.59 64.31±12.34 <0.001
    Hb/(g/L) 117.75±24.68 122.51±24.72 <0.001 116.65±25.22 122.91±26.87 0.028
    WBC/(×109/L) 6.63 (5.26,8.40) 6.06 (4.88,7.91) 0.178 6.51 (5.31,8.62) 6.39 (5.05,8.00) 0.053
    RDW/fL 48.0 (45.10,52.30) 45.5 (43.00,47.90) <0.001 47.5 (44.90,51.38) 45.3 (43.40,47.60) <0.001
    PDW/fL 15.90 (13.20,16.40) 15.10 (12.33,16.30) 0.318 15.65 (12.40,16.40) 15.60 (12.65,16.40) 0.274
    K+/(mmol/L) 4.07 (3.76,4.45) 3.99 (3.71,4.31) <0.001 4.15 (3.74,4.62) 3.95(3.62,4.30) 0.034
    Na+/(mmol/L) 139.1 (136.1,141.2) 140.0 (137.1,141.7) 0.050 138.7 (135.8,141.3) 139.9 (137.2,142.1) 0.063
    Scr/(μmol/L) 96.80 (73.98,138.85) 77.20 (62.53,100.23) <0.001 91.25 (68.70,140.05) 77.00 (63.65,98.00) <0.001
    BAR/(mg/g) 7.15 (5.08,10.55) 5.34 (4.04,6.77) <0.001 7.08 (4.52,10.59) 5.03(3.69,6.34) <0.001
    NT-proBNP/(ng/L) 5520.0 (2 482.3,9963.8) 925.5 (366.8,1 800.0) <0.001 4 756.5 (2 319.0,9 291.0) 1 077.0 (424.0,2 042.0) <0.001
    下载: 导出CSV

    表 2  训练集WHF发生风险的多因素logistic回归分析结果

    Table 2.  Multivariate logistic regression analysis of WHF risk in the elderly in the training set

    自变量 回归系数 标准误 Wald χ2 P OR 95%CI
    心房颤动 0.797 0.179 4.461 <0.001 2.22 1.56~3.15
    心率 0.025 0.005 4.821 <0.001 1.03 1.02~1.04
    LVEF -0.052 0.006 -8.286 <0.001 0.95 0.94~0.96
    RDW 0.104 0.017 6.07 <0.001 1.11 1.07~1.15
    Scr 0.009 0.003 3.611 0.008 1.01 1.00~1.02
    BAR 0.127 0.036 3.498 <0.001 1.14 1.06~1.22
    常量 -5.842 0.995 -5.870 0.001 - -
    下载: 导出CSV

    表 3  列线图、NT-proBNP及其联合使用在老年WHF人群中的预测价值

    Table 3.  Predictive value of the nomogram, NT-proBNP, and their combination in the elderly WHF population

    项目 AUC 95%CI 灵敏度 特异度 最大约登指数
    列线图 0.830 0.802~0.858 0.792 0.719 0.511
    NT-proBNP 0.890 0.877~0.909 0.849 0.786 0.635
    列线图+NT-proBNP 0.924 0.907~0.942 0.854 0.811 0.665
    下载: 导出CSV
  • [1]

    Greene SJ, Bauersachs J, Brugts JJ, et al. Worsening Heart Failure: Nomenclature, Epidemiology, and Future Directions: JACC Review Topic of the Week[J]. J Am Coll Cardiol, 2023, 81(4): 413-424. doi: 10.1016/j.jacc.2022.11.023

    [2]

    Butler J, Djatche LM, Sawhney B, et al. Clinical and Economic Burden of Chronic Heart Failure and Reduced Ejection Fraction Following a Worsening Heart Failure Event[J]. Adv Ther, 2020, 37(9): 4015-4032. doi: 10.1007/s12325-020-01456-1

    [3]

    Ambrosy AP, Parikh RV, Sung SH, et al. Analysis of Worsening Heart Failure Events in an Integrated Health Care System[J]. J Am Coll Cardiol, 2022, 80(2): 111-122. doi: 10.1016/j.jacc.2022.04.045

    [4]

    Butler J, Yang M, Manzi MA, et al. Clinical Course of Patients With Worsening Heart Failure With Reduced Ejection Fraction[J]. J Am Coll Cardiol, 2019, 73(8): 935-944. doi: 10.1016/j.jacc.2018.11.049

    [5]

    孔洪. 重视慢性心力衰竭恶化的患者管理[J]. 临床心血管病杂志, 2021, 37(4): 289-292. doi: 10.13201/j.issn.1001-1439.2021.04.001

    [6]

    中华医学会老年医学分会心血管疾病学组, 《老年慢性心力衰竭诊治中国专家共识》编写组. 老年人慢性心力衰竭诊治中国专家共识(2021)[J]. 中华老年医学杂志, 2021, 40(5): 550-561. doi: 10.3760/cma.j.issn.0254-9026.2021.05.002

    [7]

    中国医师协会检验医师分会心血管专家委员会. B型利钠肽及N末端B型利钠肽前体实验室检测与临床应用中国专家共识[J]. 中华医学杂志, 2022, 102(35): 2738-2754. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYZ201304005.htm

    [8]

    王华, 梁延春. 中国心力衰竭诊断和治疗指南2018[J]. 中华心血管病杂志, 2018, 46(10): 760-789. https://www.cnki.com.cn/Article/CJFDTOTAL-LCYW201910003.htm

    [9]

    Writing Committee, Maddox TM, Januzzi JL Jr, et al. 2021 Update to the 2017 ACC Expert Consensus Decision Pathway for Optimization of Heart Failure Treatment: Answers to 10 Pivotal Issues About Heart Failure With Reduced Ejection Fraction: A Report of the American College of Cardiology Solution Set Oversight Committee[J]. J Am Coll Cardiol, 2021, 77(6): 772-810. doi: 10.1016/j.jacc.2020.11.022

    [10]

    Riley RD, Ensor J, Snell K, et al. Calculating the sample size required for developing a clinical prediction model[J]. BMJ, 2020, 368: m441.

    [11]

    Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD): the TRIPOD statement[J]. Ann Intern Med, 2015, 162(1): 55-63. doi: 10.7326/M14-0697

    [12]

    Li C, Chen J, Qin G. Partial Youden index and its inferences[J]. J Biopharm Stat, 2019, 29(2): 385-399. doi: 10.1080/10543406.2018.1535502

    [13]

    Pencina MJ, Sr DRB, Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models[J]. Stat Med, 2012, 31(2): 101-13. doi: 10.1002/sim.4348

    [14]

    Ferrari R, Fox K. Heart rate reduction in coronary artery disease and heart failure[J]. Nat Rev Cardiol, 2016, 13(8): 493-501. doi: 10.1038/nrcardio.2016.84

    [15]

    Alalwan MA, Al-Ohaid F, Alhajjaj HM, et al. Stroke Prevention Therapy and Prevalence of Risk Factors Among Patients With Atrial Fibrillation at King Fahad University Hospital in Al Khobar: A Retrospective, Single-Center Study[J]. Cureus, 2021, 13(1): e12493.

    [16]

    Coll-Vinent B, Varona M, Martin A, et al. Association between acute heart failure and major cardiovascular events in atrial fibrillation patients presenting at the emergency department: an EMERG-AF ancillary study[J]. Eur J Emerg Med, 2021, 28(3): 210-217. doi: 10.1097/MEJ.0000000000000779

    [17]

    Carlisle MA, Fudim M, DeVore AD, et al. Heart Failure and Atrial Fibrillation, Like Fire and Fury[J]. JACC Heart Fail, 2019, 7(6): 447-456. doi: 10.1016/j.jchf.2019.03.005

    [18]

    Santhanakrishnan R, Wang N, Larson MG, et al. Atrial Fibrillation Begets Heart Failure and Vice Versa: Temporal Associations and Differences in Preserved Versus Reduced Ejection Fraction[J]. Circulation, 2016, 133(5): 484-492. doi: 10.1161/CIRCULATIONAHA.115.018614

    [19]

    Ferreira JP, Verdonschot J, Girerd N, et al. Influence of ejection fraction on biomarker expression and response to spironolactone in people at risk of heart failure: findings from the HOMAGE trial[J]. Eur J Heart Fail, 2022, 24(5): 771-778. doi: 10.1002/ejhf.2455

    [20]

    Armstrong PW, Pieske B, Anstrom KJ, et al. Vericiguat in Patients with Heart Failure and Reduced Ejection Fraction[J]. N Engl J Med, 2020, 382(20): 1883-1893. doi: 10.1056/NEJMoa1915928

    [21]

    Lund LH, Pitt B, Metra M. Left ventricular ejection fraction as the primary heart failure phenotyping parameter[J]. Eur J Heart Fail, 2022, 24(7): 1158-1161. doi: 10.1002/ejhf.2576

    [22]

    Silva Litao MK, Kamat D. Back to Basics: Red Blood Cell Distribution Width: Clinical Use beyond Hematology[J]. Pediatr Rev, 2018, 39(4): 204-209. doi: 10.1542/pir.2017-0118

    [23]

    Xanthopoulos A, Tryposkiadis K, Giamouzis G, et al. Larissa Heart Failure Risk Score: a proposed simple score for risk stratification in chronic heart failure[J]. Eur J Heart Fail, 2018, 20(3): 614-616. doi: 10.1002/ejhf.1132

    [24]

    Chen DC, Shlipak MG, Scherzer R, et al. Association of Intra-individual Differences in Estimated GFR by Creatinine Versus Cystatin C With Incident Heart Failure[J]. Am J Kidney Dis, 2022, 80(6): 762-772. doi: 10.1053/j.ajkd.2022.05.011

    [25]

    Bock JS, Gottlieb SS. Cardiorenal syndrome: new perspectives[J]. Circulation, 2010, 121(23): 2592-2600. doi: 10.1161/CIRCULATIONAHA.109.886473

    [26]

    Arques S. Human serum albumin in cardiovascular diseases[J]. Eur J Intern Med, 2018, 52: 8-12. doi: 10.1016/j.ejim.2018.04.014

    [27]

    van Veldhuisen DJ, Ruilope LM, Maisel AS, et al. Biomarkers of renal injury and function: diagnostic, prognostic and therapeutic implications in heart failure[J]. Eur Heart J, 2016, 37(33): 2577-2585. doi: 10.1093/eurheartj/ehv588

    [28]

    Kajimoto K, Sato N, Takano T, et al. eGFR and Outcomes in Patients with Acute Decompensated Heart Failure with or without Elevated BUN[J]. Clin J Am Soc Nephrol, 2016, 11(3): 405-412. doi: 10.2215/CJN.08210815

    [29]

    Lin Z, Zhao Y, Xiao L, et al. Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in critical patients with chronic heart failure[J]. ESC Heart Fail, 2022, 9(2): 1360-1369. doi: 10.1002/ehf2.13825

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出版历程
收稿日期:  2023-10-27
刊出日期:  2024-03-13

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