Risk factors and establishment of nomogram model for cardiac insufficiency combined with arteriosclerosis obliterans

doi:10.11958/20220020

Abstract

Abstract:

Objective To investigate the risk factors of arteriosclerosis obliterans (ASO) in patients with cardiac insufficiency, and to construct and verify the nomogram risk prediction model. Methods A total of 319 patients with cardiac insufficiency were selected. According to whether there was lower extremity arterial stenosis or occlusion in the lower extremity, patients were divided into the ASO positive group (n=161) and the ASO negative group (n=158). Gender, age, history of smoking, history of drinking, history of hypertension, New York heart association (NYHA) cardiac function grade, triglycerides (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), homocysteine (HCY), glycosylated hemoglobin (HbA1c), N-terminal pro-brain natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT), ankle-brachial index (ABI) and left ventricular ejection fraction (LVEF) were collected after admission. Logistic regression was used to analyze the risk factors of ASO in patients with cardiac insufficiency, so as to establish and verify the nomogram risk prediction model. Results Compared with the ASO negative group, the ASO positive group had higher male ratio, high proportion of hypertension, higher smoking rate, high proportion of NYHA cardiac function grade Ⅱ-Ⅲ, higher levels of HbA1c, TG, TC, LDL-C and HCY, and lower ABI (P<0.05). Logistic regression analysis showed that male,hypertension, smoking, NYHA cardiac function grade Ⅱ-Ⅲ,higher levels of TG, TC, LDL-C, and HCY were independent risk factors for ASO in patients with cardiac insufficiency. The nomogram risk prediction model constructed on this basis was verified by Bootstrap internal verification. The Hosmer-Lemeshow test showed that the model had good goodness of fit (χ²=0.602,P>0.05). The C-index of the model was 0.856 (95%CI: 0.815-0.896). Spearman correlation analysis showed that LVEF was positively correlated with ABI (r_s=0.228,P<0.01). NYHA cardiac function grade, NT-proBNP and hs-cTnT were negatively correlated with ABI (r_s=-0.296, -0.303 and -0.268, P<0.01). Conclusion Male, hypertension, smoking, NYHA cardiac function grade Ⅱ-Ⅲ, higher levels of TG, TC, LDL-C and HCY are independent risk factors for ASO in patients with cardiac insufficiency. The established nomogram prediction model could effectively assess the risk of ASO in patients with cardiac insufficiency.

Key words: arteriosclerosis obliterans, lower extremity, Logistic models, risk factors, nomograms, cardiac insufficiency

CLC Number:

R543.5

Figures/Tables 4

Tab.1 Comparison of basic data and clinical indicators between the two groups of patients

Tab.2 Logistic regression analysis of ASO in patients with cardiac insufficiency

Fig.1 A nomogram model for predicting the risk of ASO in patients with cardiac insufficiency

Fig.2 Calibration curve of nomogram model for predicting ASO in patients with cardiac insufficiency

References 22

[1]	SAENZ-PIPAON G, MARTINEZ-AGUILAR E, ORBE J, et al. The role of circulating biomarkers in peripheral arterial disease[J]. Int J Mol Sci, 2021, 22(7):3601. doi: 10.3390/ijms22073601.
[2]	FOWKES F G, RUDAN D, RUDAN I, et al. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010:a systematic review and analysis[J]. Lancet, 2013, 382(9901):1329-1340. doi: 10.1016/s0140-6736(13)61249-0.
[3]	JIRAK P, MIRNA M, WERNLY B, et al. Analysis of novel cardiovascular biomarkers in patients with peripheral artery disease[J]. Minerva Med, 2018, 109(6):443-450. doi: 10.23736/s0026-4806.18.05628-8.
[4]	ERASO L H, FUKAYA E, MOHLER E R 3^rd, et al. Peripheral arterial disease,prevalence and cumulative risk factor profile analysis[J]. Eur J Prev Cardiol, 2014, 21(6):704-711. doi: 10.1177/2047487312452968.
[5]	AMROCK S M, ABRAHAM C Z, JUNG E, et al. Risk factors for mortality among individuals with peripheral arterial disease[J]. Am J Cardiol, 2017, 120(5):862-867. doi: 10.1016/j.amjcard.2017.05.057.
[6]	DING N, SANG Y, CHEN J, et al. Cigarette smoking, smoking cessation, and long-term risk of 3 major atherosclerotic diseases[J]. J Am Coll Cardiol, 2019, 74(4):498-507. doi: 10.1016/j.jacc.2019.05.049.
[7]	MAKOWSKI L, FELD J, KÖPPE J, et al. Sex related differences in therapy and outcome of patients with intermittent claudication in a real-world cohort[J]. Atherosclerosis, 2021, 325:75-82. doi: 10.1016/j.atherosclerosis.2021.03.019.
[8]	KOU M, DING N, BALLEW S H, et al. Conventional and novel lipid measures and risk of peripheral artery disease[J]. Arterioscler Thromb Vasc Biol, 2021, 41(3):1229-1238. doi: 10.1161/atvbaha.120.315828.
[9]	LIU M, FAN F, LIU B, et al. Joint effects of plasma homocysteine concentration and traditional cardiovascular risk factors on the risk of new-onset peripheral arterial disease[J]. Diabetes Metab Syndr Obes, 2020, 28(13):3383-3393. doi: 10.2147/dmso.s267122.
[10]	REHMAN T, SHABBIR M A, INAM-UR-RAHEEM M, et al. Cysteine and homocysteine as biomarker of various diseases[J]. Food Sci Nutr, 2020, 8(9):4696-4707. doi: 10.1002/fsn3.1818.
[11]	STALLING P, ENGELBERTZ C, LÜDERS F, et al. Unmet medical needs in intermittent claudication with diabetes and coronary artery disease-A "real-world" analysis on 21197 PAD patients[J]. Clin Cardiol, 2019, 42(6):629-636. doi: 10.1002/clc.23186.
[12]	ALTHOUSE A D, ABBOTT J D, FORKER A D, et al. Risk factors for incident peripheral arterial disease in type 2 diabetes:results from the bypass angioplasty revascularization investigation in type 2 diabetes (BARI 2D) trial[J]. Diabetes Care, 2014, 37(5):1346-1352. doi: 10.2337/dc13-2303.
[13]	TAKAHARA M. Diabetes mellitus and lower extremity peripheral artery disease[J]. JMA J, 2021, 4(3):225-231. doi: 10.31662/jmaj.2021-0042.
[14]	LU Y, BALLEW S H, TANAKA H, et al. 2017 ACC/AHA blood pressure classification and incident peripheral artery disease:the atherosclerosis risk in communities (ARIC) study[J]. Eur J Prev Cardiol, 2020, 27(1):51-59. doi: 10.1177/2047487319865378.
[15]	ADAY A W, MATSUSHITA K. Epidemiology of peripheral artery disease and polyvascular disease[J]. Circ Res, 2021, 128(12):1818-1832. doi: 10.1161/CIRCRESAHA.121.318535.
[16]	ROSOFF D B, DAVEY SMITH G, MEHTA N, et al. Evaluating the relationship between alcohol consumption, tobacco use, and cardiovascular disease:A multivariable Mendelian randomization study[J]. PLoS Med, 2020, 17(12):e1003410. doi: 10.1371/journal.pmed.1003410.
[17]	WAKABAYASHI I, SOTODA Y. Alcohol drinking and peripheral arterial disease of lower extremity[J]. Nihon Arukoru Yakubutsu Igakkai Zasshi, 2014, 49(1):13-27.
[18]	DANIELUK A, CHLABICZ S. Automated measurements of ankle-brachial index:A narrative review[J]. J Clin Med, 2021, 10(21):5161. doi: 10.3390/jcm10215161.
[19]	KREMERS B, WÜBBEKE L, MEES B, et al. Plasma biomarkers to predict cardiovascular outcome in patients with peripheral artery disease:A systematic review and meta-analysis[J]. Arterioscler Thromb Vasc Biol, 2020, 40(9):2018-2032. doi: 10.1161/atvbaha.120.314774.
[20]	KIM K H, VALLABHAJOSYULA S, RHA S W, et al. Initial diastolic dysfunction is a powerful predictor of 5-year mortality in peripheral arterial disease patients undergoing percutaneous transluminal angioplasty[J]. Heart Vessels, 2021, 36(10):1514-1524. doi: 10.1007/s00380-021-01823-0.
[21]	FATEMI S, ACOSTA S, GOTTSÄTER A, et al. Copeptin,B-type natriuretic peptide and cystatin C are associated with incident symptomatic PAD[J]. Biomarkers, 2019, 24(6):615-621. doi: 10.1080/1354750X.2019.1631886.
[22]	JANUS S E, HAJJARI J, AL-KINDI S G. High sensitivity troponin and risk of incident peripheral arterial disease in chronic kidney disease (from the chronic renal insufficiency cohort[CRIC] study[J]. Am J Cardiol, 2020, 125(4):630-635. doi: 10.1016/j.amjcard.2019.11.019.