Developing and validating a nomogram for prediction coronary heart disease in patients with chronic obstructive pulmonary disease

doi:10.21203/rs.3.rs-285814/v1

Download PDF

Research Article

Developing and validating a nomogram for prediction coronary heart disease in patients with chronic obstructive pulmonary disease

https://doi.org/10.21203/rs.3.rs-285814/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Previous studies indicate that there is an association between chronic obstructive pulmonary disease (COPD) and coronary heart disease (CHD). Herein,we established and further validated a novel nomogram for predicting CHD in patients with COPD.

Methods

In total,we assessed 421 patients with COPD admitted in the Second Affiliated Hospital of Nanchang University between January 2017 to August 2019 .Univariate and multivariate analyses were used to develop a nomogram model containing variables which we screened,testing the discriminative ability and calibration of the nomogram by C-index,area under the curve(AUC) and calibration plots. Decision curve analysis was applied to evaluate the benefit of the screeing model.Further, we conducted an internal validation by using the bootstrapping validation.

Results

Multivariate analysis shows that arterial thrombosis,prealbumin,albumin and estimated glomerular filtration rate are independent factors in predicting the risk of CHD.The prediction model exhibited discriminative ability with a C-index of 0.882 (95% CI: 0.848–0.916), and the AUC at 0.878(95% CI: 0.843–0.913).The nomogram demonstrated efficient calibration and clinical applicability when deciding on interventions with a CHD possibility threshold of 7% .During the interval validation, it could still attain a C-index value of 0.869.

Conclusion

The nomogram can predict the risk of CHD in patients with COPD,with a high discriminative ability and potential clinical applicability.

Cardiac & Cardiovascular Systems

Pulmonology

Coronary heart disease

chronic obstructive pulmonary disease

nomogram

ROC

Chronic obstructive pulmonary disease(COPD) is an obstructive lung disease,characterized by airflow limitation,complicated by accelerated atherosclerosis and significantly increased risk of incident Coronary heart disease(CHD) [1–3]. Accumulated epidemiological evidence shows that COPD is linked to increased risk of CHD,which causes increased health burden in terms of prolonged hospitalization,costs,and mortality [3–5]. Coronary heart disease(CHD) is the leading cause of death worldwide,characterized by myocardial dysfunction and/or organic lesions due to insufficent blood and coronary artery stenosis [6–7]. Existing evidence indicates that coronary heart disease (CHD) may develop without typical symptoms for decades, early identification of vulnerable CHD will be very difficult, which makes it very critical to identify patients with high risk of CHD [8]. Whilst acknowledging Coronary angiography as the golden standard for coronary heart disease (CHD) diagnosis,patients have missed effective interventions due to its invasive and cannot be used as routine examinations, resulting in adverse clinical outcomes [6].

Recent studies have revealed that many clinical variables, as among them estimated glomerular filtration rate, Fibrinogen and SBP can serve as early diagnostic tools for coronary heart disease [9–11]. However,other findings argue that these variables were unable to accurately predict all patients with CHD.

Besides widely helping clinicians determine diagnosis and treatment strategies, a nomogram has been recommended in disease managing protocols. As an objective in our study, we developed a nomogram to assess the risk of CHD in patients with COPD, which would further help clinicians speedily diagnose high-risk patients, and administer treatment.

2.1 Patients

Our study conformed to the Helsinki Declaration ,and obtained approval from the Ethics Committee of the Second Affiliated Hospital of Nanchang University.All researchers obtained certification of procedures for lancing the fingertip and blood drawing.766 patients with COPD, diagnosed according to current Global Initiative for Chronic Obstructive Lung Disease guidelines based on clinical history and spirometric criteria (FEV1/FVC ratio < 0.7), were recruited by using research databases and hospital records between January 2017 and August 2019.

CHD was defined as death from CHD, myocardial infarction (by WHO MONICA criteria), or angiographic stenosis (involving at least 50% of a major coronary artery). Recruitment of COPD + CHD patients was based on the followong inclusion criteria: (1)patients aged above 18 years, (2) patients who met both the standard diagnostic criteria for COPD and CHD. In addition,COPD patients without CHD met the following inclusion criteria: (1)had a minimum age of 18 years,(2)fulfilled the diagnostic criteria of COPD was confirmed for all patients.Exclusion criteria include: (1) absence of laboratory assessment data, (2) patients diagnosed with active lung diseases other than COPD, (3) patients previously diagnosed CHD or valvular heart disease, (4) patients taking any drug with antibiotic treatments(༜3 months before study period) and corticosteroids or drugs that affect the cardiovascular system.

2.2 Data Collection

All the clinical and laboratory data of patients were obtained from electronic medical records,including age, gender, smoking and drinking status, hypertension, diabetes mellitus, hyperlipidemia, chronic kidney disease, body mass index.Laboratory variables included blood lipid testing, neutrophils, albumin, eGFR, creatinine, glycated hemoglobin, and fasting blood glucose.

2.3 Statistical Analysis

For data analysis, we used SPSS 22.0 (Chicago, IL, USA) and R statistical software (version 3.6.3, http://www.r-project.org). The chi-square or Fishers exact test was used to evaluate differences in categorical variables, while the Mann–Whitney U test or Students t-test was used for continuous variables.To identify the independent factors associated with COPD + CHD group,multiple logistic regression analysis was performed. Using the above clinical predictors, we used R software to develop a nomogram predicting the risk of CHD in COPD patients.

For discriminative ability, we assessed the model using C-index and the area under the curve(AUC) of ROC curves.Calibration curves were analyzed by the predicted nomogram and the actual probability of the occurrence of CHD.The decision curve was used for analysis in the COPD group.Moreover,the clinical effectiveness of the CHD nomogram was determined by quantifying the net benefit at different threshold probabilities.We performed bootstrap verification (1,000 bootstrap resampling) on the CHD nomogram to calculate the relative corrected C-index. Two-sided p < 0.05 was generally considered statistically significant.

All 421 eligible patients enrolled in the cohort were assigned into COPD and COPD + CHD groups (337 males ; mean age 73.12 ± 8.55 years [range 40–97 years]). All data for the two groups including demographic, clinical, and laboratory test data are presented in Table 1.

Table 1

Characteristics of Patients in the COPD group and the COPD + CHD group.
	COPD + CHD(n = 156)	COPD(n = 265)	P
Demographic data
Age(year)	74.51 ± 7.64	72.3 ± 8.95	0.01
Gender,male,n(%)	131(84)	206(77.7)	0.122
BMI	22.35 ± 2.59	22.03 ± 2.38	0.193
Smoke,n(%)	132(84.6)	224(84.5)	0.981
Alcohol,n(%)	15(9.6)	16(6)	0.175
Hypertension,n(%)	62(39.7)	73(27.5)	0.01
AF,n(%)	18(11.5)	20(7.5)	0.168
Stroke,n(%)	8(5.1)	9(3.4)	0.383
ICH,n(%)	2(1.3)	1(0.4)	0.558
T2DM,n(%)	3(1.9)	1(0.4)	0.146
AT,n(%)	103(66)	4(1.5)	༜0.001
SBP(mmHg)	134.55 ± 16.55	132.87 ± 12.14	0.234
DBP(mmHg)	76.52 ± 9.08	75.81 ± 6.14	0.343
CKD,n(%)	3(1.9)	4(1.5)	0.713
PE,n(%)	2(1.3)	0(0)	0.137
Hyperlipidemia,n(%)	18(11.5)	18(6.8)	0.093
Serological test
NEUT#(10⁹/L)	5.6 ± 3.19	6.91 ± 4.62	0.002
Lym#(10⁹/L)	1.27 ± 0.48	1.10 ± 0.57	0.002
RDW(fl)	44.43 ± 10.74	42.58 ± 14.03	0.155
PLT(10⁹/L)	178.13 ± 77.42	180.52 ± 76.08	0.758
PA(mg/L)	158.08 ± 46.34	172.13 ± 60.13	0.012
ALB(g/L)	35.05 ± 4.06	36.01 ± 4.05	0.019
ALP(U/L)	88.87 ± 26.02	88.12 ± 56.27	0.876
CR(umol/L)	96.25 ± 56.38	89.15 ± 70.6	0.285
eGFR ml/(min・1.73m²)	78.99 ± 27.19	86.89 ± 29.23	0.007
UA(umol/L)	374.59 ± 144.26	348.38 ± 141.31	0.069
TC(mmol/L)	4.18 ± 0.98	4.07 ± 1	0.282
TG(mmol/L)	1.13 ± 0.61	1 ± 0.61	0.042
HDL-C(mmol/L)	1.18 ± 0.33	1.23 ± 0.44	0.199
LDL-C(mmol/L)	2.44 ± 0.83	2.34 ± 0.8	0.234
Hcy(umol/L)	17.5 ± 6.02	16.53 ± 5.06	0.078
HbA1c(%)	6.13 ± 0.85	6.1 ± 0.74	0.703
FPG(mmol/L)	5.59 ± 1.86	5.64 ± 2.2	0.782
Apo(a)(g/L)	1.07 ± 0.28	1.07 ± 0.34	0.962
Apo(b)(g/L)	0.76 ± 0.24	0.76 ± 0.29	0.819
Fib(g/L)	3.43 ± 1.09	3.5 ± 1.67	0.676
AF,Atrial Fibrillation; ICH,intracerebral hemorrhae; T2DM,Type two Diabetes Mellitus; AT,Arterial Thrombosis; SBP,Systolic Blood Pressure;DBP,Diastolic Blood Pressure;CKD,Chronic Kidney Disease;PE,pulmonaryembolism;NEUT#,neutrophil; Lym#,lymphocyte;RDW,Red blood cell distribution width; PLT,platelet; PA,Prealbumin; ALB,Albumin;ALP,Alkaline phosphatase; CR,Creatinine; eGFR: estimated
glomerular filtration rate; UA,Uric acid; TC, total cholesterol; TG, triglycerides; HDL-C, High-density lipoprotein cholesterol;LDL-C, low-density lipoprotein cholesterol;Hcy,homocysteine;HbA1c,Hemoglobin A1c;FPG,Fasting plasma glucose; Apo(a),Apolipoprotein(a); Apo(b),Apolipoprotein(b); LP(a),Lipoprotein(a); Fib,Fibrinogen.

In this cohort, the univariate analysis suggested that the occurrence of CHD is associated with age, hypertension, arterial thrombosis, neutrophil,lymphocyte,prealbumin, albumin, eGFR and TG (Table 1). All above factors were included in the regression model to identify the significant predictors of CHD. Results from multivariate logistic regression analysis results showed that four variables of COPD patients including arterial thrombosis, prealbumin,albumin and eGFR were independent predictors of CHD (Table 2).

Table 2

Logistic regression analysis for predicting CHD..
Variables	βcoefficent	S.E.	Wald	P	OR	95%CI
AT	4.920	0.542	82.511	༜0.001	136.950	47.375-395.885
PA	-0.005	0.002	6.151	0.013	0.995	0.992–0.997
ALB	-0.058	0.025	5.437	0.02	0.943	0.898–0.991
eGFR	-0.010	0.004	7.311	0.007	0.990	0.983–0.997
AT,Arterial Thrombosis; PA,Prealbumin;ALB,Albumin;eGFR: estimated glomerular filtration rate.

The results from logistic regression analysis among the arterial thrombosis, prealbumin, and eGFR are shown in Table 2. We developed a nomogram that contained the above significant predictive factors (Fig. 1).

The area under the ROC curve showed a significant discriminatory power by the predictive nomogram(AUC = 0.878; 95%CI:0.843–0.913) (Fig. 2).The prediction model demonstrated an effective accuracy for predicting CHD, with a C-index of 0.882 (95% CI: 0.848–0.916) in the cohort and 0.869 in the bootstrap validations.

The predictive nomogram demonstrated good calibration between prediction and observation in the cohort (Fig. 3).

The decision curve implied that if the threshold probability of a patient and a doctor is ༞7 and ༜98%, then applying this nomogram to predict CHD risk is more effective than either treat all-patients scheme or the treat-none scheme (Fig. 4).

Cardiovascular comorbidities,CHD in particular,is prevalent in patients with COPD [12]. The presence of CHD in COPD negatively impacts on symptoms,hospitalizations and mortality [13]. Early identify patients at high risk of CHD to help COPD patients for clinical treatments or alternative management strategies. However, there is no existing model for early or accurate diagnosis of CHD from COPD patients.

In this work, multivariate logistic regression analysis suggested that arterial thrombosis, prealbumin ,albumin and eGFR were associated with CHD in COPD patients. Arterial thrombosis is the most severe consequence of atherosclerosis,thromboticcardiovascular diseases,including myocardial infarction and stroke,are the leading cause of death in developed countries [14-15]. Notably,endothelial injury is the initiating factor of atherosclerosis,accelerating atherosclerotic plaque formation, and contributes to plaque rupture and thrombosis [16-17]. Previous findings reported that endothelial dysfunction is a common phenomenon in patients with COPD and chronic coronary heart disease [18]. In our study, patients with arterial thrombosis (lower-limb artery thrombosis and carotid thrombosis) accounted for 66% of cases with both COPD and CHD.

eGFR is a sensitive index for evaluating renal filtration function. First, kidney disease caused by reduced levels of eGFR may cause endothelial dysfunction, which is considered to be one of the initial mechanisms causing atherosclerosis [19-20]. Besides, inflammation and oxidative stress may further aggravate the progression of atherosclerosis [19,21-22]. Previously, several studies indicated that low eGFR level is an independent risk factor in CVD patients [23-24]. Our findings corroborate with previous research, showing that eGFR significantly predicted the risk of CHD in patients with COPD.

Prealbumin(PA), a plasma protein involved in the transport of thyroxine and retinol ,has been reported to vital in the clinical evaluation of liver function, inflammatory reaction, and nutritional status [25]. Previously, a few studies reported that PA levels in patients with CHD are reduced and independently related to the coronary stenosis severity in patients with Acute coronary syndrome [26-27]. Similar to PA,reducing of albumin led to a significant progression of atherosclerosis. A possible mechanism is albumin as an anti-inflammatory cytokin that it could selective-inhibits the production of TNF-α [28-29]. However,a follow-up study by Weigenberg et al. in 1997 showed that in men which over 64 years old, serum albumin levels were not significantly associated with the incidence of 5-year CHD [30]. Here,we found that PA and ALB is decreased in patients with CHD. Nevertheless, little information exists on the correlation of PA/ALB with CHD among the Chinese population,further prospective study is essential to elucidate this relationship.

As far as we are aware, this is the first study to develop and validate a nomogram for predicting the risk of CHD among COPD patients.We use real-world data,based on four feasible and easily available variables which aided in assessing the risk of CHD in patients with COPD. The predictive nomogram performed good discrimination with a C-index for 0.882 and AUC for 0.878.In the interval validation could still be reached 0.869 with high C-index. These results demonstrated that the nomogram exhibited a well-discriminated and satisfactory accuracy in estimating the risk of CHD amongst COPD patients.

Limitations

There were some limitations of the study, first, this was a single-center study, and thus,the cohort is not representative of all Chinese COPD patients. Future studies should note that it is important to increase the number of patients by enrolling from other Chinese hospitals. Besides,despite validating the nomogram using the same population,it must be validated externally with a wider range of patients.

The study developed a relatively accurate nomogram that can help clinicians assess the risk of CHD in patients with COPD, this will advance the practical clinical decisions made by the clinicians and further boost their management interventions.

Acknowledgements

This study did not receive any specific funding from public, commercial or non-profit sector funding agencies. We wish to thank all investigators who enrolled their patients and the physicians and nurses who cared for them.

Author Contributions

(I) Conception and design: Jing-song Xu; (II) Administrative support: Jing-song Xu; (III) Provision of study materials or patients: Xiao-Xian Cai, Jun-Lin Chen; (IV) Collection and assembly of data: Yun YU; (V) Data analysis and interpretation: Yun YU, Jing-song Xu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors

Data availability

The datasets are available from the corresponding author on reasonable request.

Funding

The authors received no specific funding for this work.

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The procedures of this study were approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University, and informed consents were obtained from all patients.

Consent for publication

All authors consent to the publication of this study.

[1] Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, et al(2017) Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Lung Disease 2017 Report: GOLD Executive Summary. Respirology 22(3):575-601.

[2] Feary JR, Rodrigues LC, Smith CJ, Hubbard RB, Gibson JE(2010) Prevalence of major comorbidities in subjects with COPD and incidence of myocardial infarction and stroke: a comprehensive analysis using data from primary care. Thorax 65:956–962.

[3] Mannino DM, Thorn D, Swensen A, Holguin F(2008) Prevalence and outcomes of diabetes, hypertension and cardiovascular disease in COPD. Eur Respir J 32:962–969.

[4] Sidney S, Sorel M, Quesenberry CP, Jr, DeLuise C, Lanes S, Eisner MD(2005) COPD and incident cardiovascular disease hospitalizations and mortality: Kaiser Permanente Medical Care Program. Chest 128:2068–2075.

[5] Agarwal S, Rokadia H, Senn T, Menon V (2014) Burden of cardiovascular disease in chronic obstructive pulmonary disease. Am J Prev Med 47:105–114.

[6] Neumann F, et al (2019) 2018 ESC/EACTS Guidelines On Myocardial Revascularization. Eur. Heart J 40:87–165.

[7] Bansilal S, Castellano JM, Fuster V (2015) Global burden of CVD: focus on secondary prevention of cardiovascular disease. Int J Cardiol 201 Suppl 1:S1-7.

[8] Laukkanen JA, Mäkikallio TH, Rauramaa R, Kurl S (2009) Asymptomatic ST-segment depression during exercise testing and the risk of sudden cardiac death in middle-aged men: a population-based follow-up study. Eur Heart J 30(5):558-565.

[9] Ryoo JH, Kim SG, Suh BS, Kim DI, Park SK (2011) Relationship between chronic kidney disease and risk of coronary heart disease in Korean men. J Korean Med Sci 26(6):753-758.

[10] Vasan RS (2006) Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 16;113(19):2335-2362.

[11] Kario K, Saito I, Kushiro T, Teramukai S, Tomono Y, Okuda Y, Shimada K (2016) Morning Home Blood Pressure Is a Strong Predictor of Coronary Artery Disease: The HONEST Study. J Am Coll Cardiol 5;67(13):1519-1527.

[12] Sin DD, Man SFP (2003) Why are patients with chronic obstructive pulmonary disease at increased risk of cardiovascular diseases? The potential role of systemic inflammation in chronic obstructive pulmonary disease. Circulation 107(11):1514–1519.

[13] Holguin F, Folch E, Redd SC, Mannino DM (2005) Comorbidity and mortality in COPD-related hospitalizations in the United States, 1979 to 2001. Chest 128(4):2005–2011.

[14] Wang Y, Gao H, Shi C, Erhardt PW, Pavlovsky A, A Soloviev D, Bledzka K, Ustinov V, Zhu L, Qin J, Munday AD, Lopez J, Plow E, Simon DI (2017) Leukocyte integrin Mac-1 regulates thrombosis via interaction with platelet GPIbα. Nat Commun 30;8:15559.

[15] Shindo J, Ishibashi T, Kijima M, et al (2001) Increased plasminogen activator inhibitor-1 and apolipoprotein (a) in coronary atherectomy specimens in acute coronary syndromes. Coron Artery Dis 12: 573–579.

[16] Schultz A, Lavie L, Hochberg I, Beyar R, Stone T, Skorecki K, Lavie P, Roguin A, Levy AP (1999) Interindividual heterogeneity in the hypoxic regulation of VEGF: significance for the development of the coronary artery collateral circulation. Circulation 3;100(5):547-552.

[17] Park C, Guallar E, Linton JA, Lee DC, Jang Y, Son DK, Han EJ, Baek SJ, Yun YD, Jee SH (2013) Samet JM. Fasting glucose level and the risk of incident atherosclerotic cardiovascular diseases. Diabetes Care 36(7):1988-1993.

[18] Karoli NA, Rebrov AP (2019) Endothelial dysfunction in patients with chronic obstructive pulmonary disease in combination with coronary heart disease. Ter Arkh 29;91(3):22-26.

[19] Schiffrin EL, Lipman ML, Mann JF (2007) Chronic kidney disease: effects on the cardiovascular system. Circulation 3;116(1):85-97.

[20] Amann K, Wanner C, Ritz E (2006) Cross-talk between the kidney and the cardiovascular system. J Am Soc Nephrol 17(8):2112-2119.

[21] Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al (2003) American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 28;108(17):2154-2169.

[22] Vaziri ND, Dicus M, Ho ND, Boroujerdi-Rad L, Sindhu RK (2003) Oxidative stress and dysregulation of superoxide dismutase and NADPH oxidase in renal insufficiency. Kidney Int 63(1):179-185.

[23] Manjunath G, Tighiouart H, Ibrahim H, MacLeod B, Salem DN, Griffith JL, et al (2003) Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol 41(1):47–55.

[24] Weiner DE, Gaussoin SA, Nord J, Auchus AP, Chelune GJ, Chonchol M, et al (2017) SPRINT Study Research Group. Cognitive Function and Kidney Disease: Baseline Data From the Systolic Blood Pressure Intervention Trial (SPRINT). Am J Kidney Dis 70(3):357-367.

[25] Yang X, Lyakurwa F, Xie H, Chen J, Li X, Qiao X, Cai X (2017) Different binding mechanisms of neutral and anionic poly-/perfluorinated chemicals to human transthyretin revealed by In silico models. Chemosphere 182:574-583.

[26] Yeo Y, Byun SW, Lee JY, Min WK, Park JS, Kim SB (2009) Lack of association between small dense low-density lipoprotein levels and coronary artery disease in chronic hemodialysis patients. Am J Nephrol 30(3):310-314.

[27] Zhang C, Liu P, Xia K, Fang H, Jiang M, Xie Q, Yu Z, Yang T (2017) Association of Serum Prealbumin with Angiographic Severity in Patients with Acute Coronary Syndrome. Med Sci Monit. 23:4041-4049.

[28] John D, Rory C, Paul A, et al. Association of fibrinogen (1998) C- reactive protein, albumin, or leukocyte count with coronary heart disease. JAMA 279: 1477 - 1482.

[29] Garcia-Martinez R, Caraceni P, Bernardi M, Gines P, Arroyo V, Jalan R (2013) Albumin: pathophysiologic basis of its role in the treatment of cirrhosis and its complications. Hepatology 58(5):1836-46.

[30] Weijenberg MP, Feskens EJ, Souverijn JH, Kromhout D (1997) Serum albumin, coronary heart disease risk, and mortality in an elderly cohort. Epidemiology. 8(1):87-92.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Developing and validating a nomogram for prediction coronary heart disease in patients with chronic obstructive pulmonary disease

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials And Methods

2.1 Patients

2.3 Statistical Analysis

3. Results

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1