| Literature DB >> 35743532 |
Zyta Beata Wojszel1, Łukasz Kuźma2, Ewelina Rogalska3, Anna Kurasz2, Sławomir Dobrzycki2, Bożena Sobkowicz3, Anna Tomaszuk-Kazberuk3.
Abstract
PURPOSE: Atrial fibrillation (AF) can be a valuable indicator of non-obstructive coronary artery disease (CAD) among older patients indicated for elective coronary angiography (CAG). Appropriate stratification of AF patients is crucial for avoiding unnecessary complications. The objective of this study was to identify independent predictors that can allow diagnosing obstructive CAD in AF patients over 65 years who were indicated to undergo elective CAG. PATIENTS AND METHODS: This cross-sectional study included 452 (23.9%) AF patients over 65 years old who were directed to the Department of Invasive Cardiology at the Medical University of Bialystok for elective CAG during 2014-2016. The participants had CAD and were receiving optimal therapy (median age: 73 years, interquartile range: 69-77 years; 54.6% men). The prevalence and health correlates of obstructive CAD were determined, and a multivariate logistic regression model was generated with predictors (p < 0.1). Predictive performance was analyzed using a receiver-operating characteristic (ROC) curve analysis.Entities:
Keywords: chronic coronary artery disease; geriatric patients; obstructive coronary lesions; predictive factors
Year: 2022 PMID: 35743532 PMCID: PMC9224727 DOI: 10.3390/jcm11123462
Source DB: PubMed Journal: J Clin Med ISSN: 2077-0383 Impact factor: 4.964
Figure 1Flowchart of patients’ enrollment. Abbreviations: ACS, acute coronary syndrome; AF, atrial fibrillation; CAD, coronary artery disease; CCS, chronic coronary syndrome.
Characteristics of the studied groups (N (%) or Me [IQR]).
| Total | Stenosis+ | Stenosis− | OR (95% CI) | ||
|---|---|---|---|---|---|
| N [%] | 452 (100.0) | 184 (40.7) | 268 (59.3) | ||
| Age, years | 73.0 [69.0–77.0] | 74.0 [68.0–77.0] | 73.0 [69.0–76.0] | 0.55 | 1.02 (0.98–1.05) |
| Sex, male | 247 (54.6) | 120 (65.2) | 127 (47.4) | <0.001 | 2.08 (1.41–3.06) |
| BMI, kg/m2 | 29 [26.4–33.1] | 29 [26.3–31.9] | 29 [26.6–33.3] | 0.33 | 0.98 (0.94–1.03) |
| Obesity | 161 (41.5) | 57 (37.0) | 104 (44.4) | 0.15 | 0.74 (0.48–1.11) |
| Hypertension | 388 (85.8) | 159 (86.4) | 229 (85.4) | 0.89 | 1.08 (0.63–1.86) |
| Diabetes mellitus | 114 (25.2) | 47 (25.5) | 67 (25.0) | 0.91 | 1.03 (0.67–1.59) |
| Hyperlipidemia | 223 (49.3) | 99 (53.8) | 124 (46.3) | 0.13 | 1.35 (0.93–1.97) |
| Chronic heart failure | 143 (31.6) | 51 (27.7) | 92 (34.3) | 0.15 | 0.73 (0.49–1.11) |
| Prior stroke, TIA, or thromboembolism | 96 (21.3) | 24 (13.0) | 72 (27.0) | <0.001 | 0.64 (0.50–0.82) |
| Vascular disease | 195 (42.3) | 164 (89.1) | 31 (11.6) | <0.001 | 62.4 (34.4–113.3) |
| LVEF, % | 48 [35.0–56.5] | 47.5 [36.5–57] | 48 [32.5–56] | 0.65 | 1.01 (0.99–1.03) |
| LVEF < 50% | 117 (52.0) | 44 (52.4) | 73 (51.8) | 0.93 | 1.03 (0.60–1.76) |
| AF type | |||||
| Paroxysmal | 215 (47.6) | 83 (45.1) | 132 (49.3) | 0.39 | 0.85 (0.58–1.23) |
| Persistent | 36 (8.0) | 14 (7.6) | 22 (8.2) | 0.82 | 0.92 (0.46–1.85) |
| Permanent | 201 (44.5) | 87 (47.3) | 114 (42.5) | 0.32 | 1.21 (0.83–1.77) |
| CKD | 181 (40.0) | 84 (45.7) | 97 (36.2) | 0.04 | 1.48 (1.01–2.17) |
| eGFR, mL/min/1.73 m2 | 66.9 [52.0–80.5] | 66.5 [49.2–80.5] | 68.2 [54.2–80.7] | 0.35 | 0.99 (0.98–1.003) |
| Anemia | 72 (15.9) | 36 (19.6) | 36 (13.4) | 0.09 | 1.57 (0.95–2.60) |
| Liver failure | 16 (3.5) | 5 (2.7) | 11 (4.1) | 0.61 | 0.65 (0.22–1.91) |
| CHA2DS2-VASc score | 4 [ | 4 [ | 3 [ | <0.001 | 2.61 (2.06–3.32) |
| CHA2DS2-VASc score ≥4 points | 259 (57.3) | 148 (80.4) | 111 (41.4) | <0.001 | 5.82 (3.75–9.01) |
| CHA2DS2-VA score | 3 [ | 4 [ | 3 [ | <0.001 | 4.20 (3.13–5.62) |
| CHA2DS2-VA score ≥4 points | 173 (38.3) | 122 (66.3) | 51 (19.0) | <0.001 | 8.37 (5.44–12.89) |
| CHADS2 score | 2 [ | 2 [ | 2 [ | 0.31 | 0.93 (0.80–1.08) |
Notes: CHA2DS2-VASc scale is composed of: C, congestive heart failure (or left ventricular systolic dysfunction); H, hypertension; A2, age 75+ years; D, diabetes mellitus; S2, prior stroke, transient ischemic attack, or thromboembolism; V, vascular disease (e.g., peripheral artery disease, myocardial infarction, aortic plaque); A, age 65–74 years; Sc, Sex category (i.e., female sex). The CHADS2 and CHA2DS2-VA scales do not take into account V/Sc and Sc, respectively. The vascular disease covers peripheral artery disease, myocardial infarction, or aortic plaque. Abbreviations: AF, atrial fibrillation; BMI, body mass index; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; IQR, interquartile range; LVEF, left ventricular ejection fraction; Me, median; n, number; OR, odds ratio; TIA, transient ischemic attack.
Risk determinants of significant coronary stenosis—direct multivariate logistic regression model.
| Variables | OR | 95% CI | OR | 95% CI | ||
|---|---|---|---|---|---|---|
| Model 1 | Model 2 | |||||
| Male | 1.80 | 1.14–2.84 | 0.01 | 1.76 | 1.13–2.74 | 0.01 |
| CHA2DS2-VA score | 3.96 | 2.96–5.31 | <0.001 | |||
| CHA2DS2-VA score ≥ 4 points | 7.54 | 4.87–11.69 | <0.001 | |||
| CKD | 1.2 | 0.76–1.88 | 0.46 | 1.32 | 0.85–2.05 | 0.22 |
| Anemia | 1.33 | 0.72–2.46 | 0.36 | 1.31 | 0.73–2.37 | 0.37 |
| Overall prediction rate | 75.0% | 75.0% | ||||
| Sensitivity | 66.3% | 66.3% | ||||
| Specificity | 81.0% | 81.0% | ||||
| Nagelkerke’s R-squared | 0.371 | 0.299 | ||||
Abbreviations: CI, confidence interval; CKD, chronic kidney disease; OR, odds ratio.
AUC for the prediction of significant stenosis.
| 95% CI | ||||
|---|---|---|---|---|
| Variables | AUC | Lower | Upper | |
| CHA2DS2-VA score | 0.79 | 0.75 | 0.84 | <0.001 |
| CHA2DS2-VA score ≥ 4 points | 0.74 | 0.69 | 0.79 | <0.001 |
| Sex, male | 0.59 | 0.54 | 0.64 | 0.001 |
| CKD | 0.55 | 0.49 | 0.60 | 0.09 |
| Anemia | 0.53 | 0.48 | 0.59 | 0.27 |
Abbreviations: AUC, area under the ROC curve; CI, confidence interval; CKD, chronic kidney disease; ROC, receiver operator characteristic.
Figure 2Predictive performance of CHA2DS2-VA score, CHA2DS2-VA score ≥ 4, and male sex—receiver-operating characteristic (ROC) curve analysis.