Chronic Kidney Disease and Its Impact on a Prothrombotic State in Patients with Atrial Fibrillation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patients
2.2. Laboratory Tests
2.3. Statistical Analysis
3. Results
3.1. Patients
3.2. Markers of a Prothrombotic State
3.3. Association with Inflammation
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Bellasi, A.; Di Lullo, L.; Di Iorio, B. Chronic Kidney Disease: The Silent Epidemy. J. Clin. Med. 2019, 8, 1795. [Google Scholar] [CrossRef] [Green Version]
- Zeng, W.T.; Sun, X.T.; Tang, K.; Mei, W.Y.; Liu, L.J.; Xu, Q.; Cheng, Y.J. Risk of thromboembolic events in atrial fibrillation with chronic kidney disease. Stroke 2015, 46, 157–163. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Piccini, J.P.; Stevens, S.R.; Chang, Y.; Singer, D.E.; Lokhnygina, Y.; Go, A.S.; Patel, M.R.; Mahaffey, K.W.; Halperin, J.L.; Breithardt, G.; et al. Renal dysfunction as a predictor of stroke and systemic embolism in patients with nonvalvular atrial fibrillation: Validation of the R(2)CHADS(2) index in the ROCKET AF (Rivaroxaban Once-daily, oral, direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation) and ATRIA (AnTicoagulation and Risk factors In Atrial fibrillation) study cohorts. Circulation 2013, 127, 224–232. [Google Scholar] [CrossRef] [Green Version]
- Fu, S.; Zhou, S.; Luo, L.; Ye, P. R2(GFR)CHADS2 and R2(GFR)CHA2DS2VASc schemes improved the performance of CHADS2 and CHA2DS2VASc scores in death risk stratification of Chinese older patients with atrial fibrillation. Clin. Interv. Aging 2017, 12, 1233–1238. [Google Scholar] [CrossRef] [PubMed]
- O’Brien, E.C.; Holmes, D.N.; Thomas, L.; Singer, D.E.; Fonarow, G.C.; Mahaffey, K.W.; Kowey, P.R.; Hylek, E.M.; Pokorney, S.D.; Ansell, J.E.; et al. Incremental prognostic value of renal function for stroke prediction in atrial fibrillation. Int. J. Cardiol. 2019, 274, 152–157. [Google Scholar] [CrossRef]
- Banerjee, A.; Fauchier, L.; Vourc’h, P.; Andres, C.R.; Taillandier, S.; Halimi, J.M.; Lip, G.Y. Renal impairment and ischemic stroke risk assessment in patients with atrial fibrillation: The Loire Valley Atrial Fibrillation Project. J. Am. Coll. Cardiol. 2013, 61, 2079–2087. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Khan, A.A.; Lip, G.Y.H. The prothrombotic state in atrial fibrillation: Pathophysiological and management implications. Cardiovasc. Res. 2019, 115, 31–45. [Google Scholar] [CrossRef] [Green Version]
- Undas, A. Altered fibrin clot properties and fibrinolysis in patients with atrial fibrillation: Practical implications. Europace 2020, 22, 185–194. [Google Scholar] [CrossRef]
- Matusik, P.T.; Matusik, P.S.; Kornacewicz-Jach, Z.; Malecka, B.; Zabek, A.; Undas, A. Elevated NT-proBNP is associated with unfavorably altered plasma fibrin clot properties in atrial fibrillation. Int. J. Cardiol. 2017, 243, 244–250. [Google Scholar] [CrossRef]
- Matusik, P.T.; Malecka, B.; Lelakowski, J.; Undas, A. Association of NT-proBNP and GDF-15 with markers of a prothrombotic state in patients with atrial fibrillation off anticoagulation. Clin. Res. Cardiol. 2020, 109, 426–434. [Google Scholar] [CrossRef] [Green Version]
- Drabik, L.; Wolkow, P.; Undas, A. Fibrin Clot Permeability as a Predictor of Stroke and Bleeding in Anticoagulated Patients with Atrial Fibrillation. Stroke 2017, 48, 2716–2722. [Google Scholar] [CrossRef] [PubMed]
- Drabik, L.; Konieczynska, M.; Undas, A. Clot Lysis Time Predicts Stroke During Anticoagulant Therapy in Patients with Atrial Fibrillation. Can. J. Cardiol. 2020, 36, 119–126. [Google Scholar] [CrossRef] [PubMed]
- Gackler, A.; Rohn, H.; Lisman, T.; Benko, T.; Witzke, O.; Kribben, A.; Saner, F.H. Evaluation of hemostasis in patients with end-stage renal disease. PLoS ONE 2019, 14, e0212237. [Google Scholar] [CrossRef] [PubMed]
- Undas, A.; Kolarz, M.; Kopec, G.; Tracz, W. Altered fibrin clot properties in patients on long-term haemodialysis: Relation to cardiovascular mortality. Nephrol. Dial. Transpl. 2008, 23, 2010–2015. [Google Scholar] [CrossRef] [Green Version]
- Sagripanti, A.; Cozza, V.; Baicchi, U.; Camici, M.; Cupisti, A.; Barsotti, G. Increased thrombin generation in patients with chronic renal failure. Int. J. Clin. Lab. Res. 1997, 27, 72–75. [Google Scholar] [CrossRef]
- Lau, Y.C.; Hardy, L.J.; Philippou, H.; Blann, A.D.; Lip, G.Y. Altered fibrin clot structure in patients with atrial fibrillation and worsening renal function. Thromb. Haemost. 2016, 116, 408–409. [Google Scholar] [CrossRef] [Green Version]
- Mortberg, J.; Blomback, M.; Wallen, A.; He, S.; Jacobson, S.H.; Spaak, J. Increased fibrin formation and impaired fibrinolytic capacity in severe chronic kidney disease. Blood Coagul. Fibrinolysis 2016, 27, 401–407. [Google Scholar] [CrossRef]
- Shashar, M.; Francis, J.; Chitalia, V. Thrombosis in the uremic milieu—Emerging role of “thrombolome”. Semin Dial. 2015, 28, 198–205. [Google Scholar] [CrossRef] [Green Version]
- Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009, 150, 604–612. [Google Scholar] [CrossRef]
- Glowicki, B.; Matusik, P.T.; Plens, K.; Undas, A. Prothrombotic State in Atrial Fibrillation Patients with One Additional Risk Factor of the CHA2DS2-VASc Score (Beyond Sex). Can. J. Cardiol. 2019, 35, 634–643. [Google Scholar] [CrossRef]
- Matusik, P.T.; Prior, S.M.; Butenas, S.; Malecka, B.; Lelakowski, J.; Undas, A. Association of cardiac troponin I with prothrombotic alterations in atrial fibrillation. Kardiol. Pol. 2018, 76, 1106–1109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chua, S.K.; Shyu, K.G.; Lu, M.J.; Hung, H.F.; Cheng, J.J.; Chiu, C.Z.; Lin, C.H.; Chao, H.H.; Lo, H.M. Renal dysfunction and the risk of postoperative atrial fibrillation after cardiac surgery: Role beyond the CHA2DS2-VASc score. Europace 2015, 17, 1363–1370. [Google Scholar] [CrossRef] [PubMed]
- Hemker, H.C.; Al Dieri, R.; De Smedt, E.; Beguin, S. Thrombin generation, a function test of the haemostatic-thrombotic system. Thromb. Haemost. 2006, 96, 553–561. [Google Scholar] [PubMed]
- Hemker, H.C.; Kremers, R. Data management in thrombin generation. Thromb. Res. 2013, 131, 3–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Siudut, J.; Grela, M.; Wypasek, E.; Plens, K.; Undas, A. Reduced plasma fibrin clot permeability and susceptibility to lysis are associated with increased risk of postthrombotic syndrome. J. Thromb. Haemost. 2016, 14, 784–793. [Google Scholar] [CrossRef] [PubMed]
- Fraczek, P.; Krzysztofik, M.; Stanisz, A.; Undas, A. Clinical outcomes and plasma clot permeability and lysability in patients with venous thromboembolism on rivaroxaban: A cohort study. Pol. Arch. Intern. Med. 2019, 129, 377–385. [Google Scholar] [CrossRef] [Green Version]
- Meus, R.; Son, M.; Sobczyk, D.; Undas, A. Prothrombotic State in Patients with a Left Atrial Appendage Thrombus of Unknown Origin and Cerebrovascular Events. Stroke 2016, 47, 1872–1878. [Google Scholar] [CrossRef] [Green Version]
- Hsu, C.C.; Fu, T.C.; Huang, S.C.; Wang, J.S. High-intensity interval training recuperates capacity of endogenous thrombin generation in heart failure patients with reduced ejection fraction. Thromb. Res. 2020, 187, 159–165. [Google Scholar] [CrossRef]
- Brophy, D.F.; Martin, E.J.; Gehr, T.W.; Carr, M.E., Jr. Enhanced anticoagulant activity of enoxaparin in patients with ESRD as measured by thrombin generation time. Am. J. Kidney Dis. 2004, 44, 270–277. [Google Scholar] [CrossRef]
- Vaziri, N.D.; Gonzales, E.C.; Wang, J.; Said, S. Blood coagulation, fibrinolytic, and inhibitory proteins in end-stage renal disease: Effect of hemodialysis. Am. J. Kidney Dis. 1994, 23, 828–835. [Google Scholar] [CrossRef]
- Undas, A. How to Assess Fibrinogen Levels and Fibrin Clot Properties in Clinical Practice? Semin Thromb. Heamost. 2016, 42, 381–388. [Google Scholar] [CrossRef] [PubMed]
- Sjoland, J.A.; Sidelmann, J.J.; Brabrand, M.; Pedersen, R.S.; Pedersen, J.H.; Esbensen, K.; Standeven, K.F.; Ariens, R.A.; Gram, J. Fibrin clot structure in patients with end-stage renal disease. Thromb. Haemost. 2007, 98, 339–345. [Google Scholar] [PubMed]
- Pignatelli, P.; Menichelli, D.; Pastori, D.; Violi, F. Oxidative stress and cardiovascular disease: New insights. Kardiol. Pol. 2018, 76, 713–722. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Coppolino, G.; Leonardi, G.; Andreucci, M.; Bolignano, D. Oxidative Stress and Kidney Function: A Brief Update. Curr. Pharm. Des. 2018, 24, 4794–4799. [Google Scholar] [CrossRef]
- Matusik, P.T. Biomarkers and Cardiovascular Risk Stratification. Eur. Heart J. 2019, 40, 1483–1485. [Google Scholar] [CrossRef]
- Martens, R.J.; Kimenai, D.M.; Kooman, J.P.; Stehouwer, C.D.; Tan, F.E.; Bekers, O.; Dagnelie, P.C.; van der Kallen, C.J.; Kroon, A.A.; Leunissen, K.M.; et al. Estimated Glomerular Filtration Rate and Albuminuria Are Associated with Biomarkers of Cardiac Injury in a Population-Based Cohort Study: The Maastricht Study. Clin. Chem. 2017, 63, 887–897. [Google Scholar] [CrossRef]
- Lee, K.N.; Choi, J.I.; Kim, Y.G.; Boo, K.Y.; Kim, D.Y.; Choi, Y.Y.; Choi, H.Y.; Kim, D.H.; Lee, D.I.; Roh, S.Y.; et al. Comparison of Renal Function Estimation Formulae for Dosing Direct Oral Anticoagulants in Patients with Atrial Fibrillation. J. Clin. Med. 2019, 8, 34. [Google Scholar] [CrossRef] [Green Version]
- Scicchitano, P.; Tucci, M.; Bellino, M.C.; Cortese, F.; Cecere, A.; De Palo, M.; Massari, F.; Caldarola, P.; Silvestris, F.; Ciccone, M.M. The Impairment in Kidney Function in the Oral Anticoagulation Era. A Pathophysiological Insight. Cardiovasc. Drugs 2020, 10.1007/s10557-020-07004-x. [Google Scholar] [CrossRef]
- Joppa, S.A.; Salciccioli, J.; Adamski, J.; Patel, S.; Wysokinski, W.; McBane, R.; Al-Saffar, F.; Esser, H.; Shamoun, F. A Practical Review of the Emerging Direct Anticoagulants, Laboratory Monitoring, and Reversal Agents. J. Clin. Med. 2018, 7. [Google Scholar] [CrossRef] [Green Version]
- Park, J.; Lee, S.R.; Choi, E.K.; Kwon, S.; Jung, J.H.; Han, K.D.; Cha, M.J.; Ko, S.B.; Oh, S.; Lip, G.Y.H. Effectiveness and Safety of Direct Oral Anticoagulant for Secondary Prevention in Asians with Atrial Fibrillation. J. Clin. Med. 2019, 8. [Google Scholar] [CrossRef] [Green Version]
- Wetmore, J.B.; Roetker, N.S.; Yan, H.; Reyes, J.L.; Herzog, C.A. Direct-Acting Oral Anticoagulants Versus Warfarin in Medicare Patients with Chronic Kidney Disease and Atrial Fibrillation. Stroke 2020, 2364–2373. [Google Scholar] [CrossRef] [PubMed]
- Drabik, L.; Matusik, P.T.; Undas, A. The ORBIT bleeding score is associated with lysis and permeability of fibrin clots. Kardiol. Pol. 2019, 77, 1182–1185. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tolat, A.; Lippman, N. Update on the Non-Pharmacological Management of Stroke Prevention in Patients with Atrial Fibrillation. J. Clin. Med. 2018, 7, 32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Drabik, L.; Wolkow, P.; Undas, A. Denser plasma clot formation and impaired fibrinolysis in paroxysmal and persistent atrial fibrillation while on sinus rhythm: Association with thrombin generation, endothelial injury and platelet activation. Thromb. Res. 2015, 136, 408–414. [Google Scholar] [CrossRef]
- Marin, F.; Roldan, V.; Climent, V.E.; Ibanez, A.; Garcia, A.; Marco, P.; Sogorb, F.; Lip, G.Y. Plasma von Willebrand factor, soluble thrombomodulin, and fibrin D-dimer concentrations in acute onset non-rheumatic atrial fibrillation. Heart 2004, 90, 1162–1166. [Google Scholar] [CrossRef] [Green Version]
Variables | Total, n = 502 | eGFR Categories | p Value | eGFR Categories | p Value | ||||
---|---|---|---|---|---|---|---|---|---|
≥ 60 mL/min/1.73 m2, n = 322 | 15–59 mL/min/1.73 m2, n = 180 | ≥ 90 mL/min/1.73 m2, n = 108 | 60–89 mL/min/1.73 m2, n = 214 | 30–59 mL/min/1.73 m2, n = 93 | 15–29 mL/min/1.73 m2, n = 87 | ||||
Demographics | |||||||||
Age (years) | 66.0 (60.0–73.0) | 63.0 (58.0–70.0) | 71.0 (64.0–76.0) | <0.0001 | 60.0 (55.3–64.0) | 65.0 (59.0–72.0) | 71.0 (64.0–74.0) | 71.0 (65.0–77.0) | <0.0001 |
Male sex, n (%) | 257 (51.2) | 166 (51.6) | 91 (50.6) | 0.83 | 83 (76.9) | 83 (38.8) | 32 (34.4) | 59 (67.8) | 0.20 |
BMI (kg/m2) | 27.9 (25.0– 31.0) | 28.0 (24.7–31.4) | 27.8 (25.4–30.6) | 0.97 | 28.1 (25.2–31.1) | 27.7 (24.6–31.5) | 28.7 (25.6–32.5) | 27.0 (24.9–29.3) | 0.04 |
Type of AF and Risk Scores | |||||||||
Permanent AF, n (%) | 140 (27.9) | 70 (21.7) | 70 (38.9) | <0.0001 | 38 (35.2) | 32 (15.0) | 23 (24.7) | 47 (54.0) | <0.0001 |
Paroxysmal AF, n (%) | 198 (39.4) | 156 (48.4) | 42 (23.3) | 40 (37.0) | 116 (54.2) | 42 (45.2) | 0 (0.0) | ||
Persistent AF, n (%) | 164 (32.7) | 96 (29.8) | 68 (37.8) | 30 (27.8) | 66 (30.8) | 28 (30.1) | 40 (46.0) | ||
CHA2DS2-VASc score | 3.0 (2.0–4.0) | 2.0 (1.0–4.0) | 3.0 (2.0–5.0) | <0.0001 | 2.0 (1.0–3.0) | 2.0 (2.0–4.0) | 3.0 (2.0–5.0) | 3.0 (2.0–4.0) | <0.0001 |
R-CHA2DS2-VASc score | 3.0 (2.0–5.0) | 2.0 (1.0–4.0) | 4.0 (3.0–6.0) | <0.0001 | 2.0 (1.0–3.0) | 2.0 (2.0–4.0) | 4.0 (3.0–6.0) | 4.0 (3.0–5.0) | <0.0001 |
R2-CHA2DS2-VASc score | 4.0 (2.0–5.0) | 2.0 (1.0–4.0) | 5.0 (4.0–7.0) | <0.0001 | 2.0 (1.0–3.0) | 2.0 (2.0–4.0) | 5.0 (4.0–7.0) | 5.0 (4.0–6.0) | <0.0001 |
Comorbidities and CVD Risk Factors | |||||||||
Hypertension, n (%) | 265 (52.8) | 165 (51.2) | 100 (55.6) | 0.35 | 52 (48.1) | 113 (52.8) | 49 (52.7) | 51 (58.6) | 0.18 |
Heart failure / LV dysfunction, n (%) | 156 (31.1) | 94 (29.2) | 62 (34.4) | 0.22 | 21 (19.4) | 73 (34.1) | 42 (45.2) | 20 (23.0) | 0.26 |
Diabetes mellitus, n (%) | 110 (21.9) | 59 (18.3) | 51 (28.3) | 0.009 | 17 (15.7) | 42 (19.6) | 21 (22.6) | 30 (34.5) | 0.002 |
Previous MI, n (%) | 101 (20.1) | 59 (18.3) | 42 (23.3) | 0.18 | 20 (18.5) | 39 (18.2) | 23 (24.7) | 19 (21.8) | 0.31 |
History of smoking, n (%) | 152 (30.3) | 111 (34.5) | 41 (22.8) | 0.006 | 52 (48.1) | 59 (27.6) | 26 (28.0) | 15 (17.2) | <0.0001 |
Variables | Total, n = 502 * | eGFR Categories | p Value | eGFR Categories | p Value | ||||
---|---|---|---|---|---|---|---|---|---|
≥ 60 mL/min/1.73 m2, n = 322 | 15–59 mL/min/1.73 m2, n = 180 | ≥ 90 mL/min/1.73 m2, n = 108 | 60–89 mL/min/1.73 m2, n = 214 | 30–59 mL/min/1.73 m2, n = 93 | 15–29 mL/min/1.73 m2, n = 87 | ||||
Laboratory Tests Results | |||||||||
eGFR (ml/min/1.73 m2) | 70.0 (45.0–87.0) | 83.0 (71.0–93.0) | 34.0 (23.0–48.0) | <0.0001 | 95.0 (93.0–99.0) | 75.5 (67.0–83.0) | 48.0 (42.0–55.0) | 23.0 (21.0–25.0) | <0.0001 |
CRP (mg/L) † | 1.8 (1.1–3.1) | 1.8 (1.1–2.9) | 2.4 (1.4–3.7) | 0.002 | 2.1 (1.2–3.6) | 1.7 (1.0–2.9) | 2.4 (1.4–3.7) | 3.0 (3.0–3.0) | 0.005 |
NT-proBNP (pg/mL) | 277.0 (95.0–996.3) | 216.5 (91.8–834.3) | 396.0 (102.3–1120.0) | 0.01 | 240.5 (91.3–767.8) | 202.5 (91.5–951.3) | 349.0 (96.0–1159.0) | 425.0 (145.0–1105.0) | 0.08 |
Coagulation and Fibrinolysis Parameters | |||||||||
Fibrinogen (g/ L) † | 3.2 (2.6–3.9) | 3.1 (2.6–3.7) | 3.3 (2.6–4.1) | 0.02 | 3.2 (2.7–3.9) | 3.0 (2.5–3.7) | 3.4 (2.7–4.1) | 3.2 (2.6–3.9) | 0.05 |
PAI-1:Ag (ng/mL) | 21.4 (13.7–29.8) | 20.7 (12.9–29.6) | 23.5 (15.7–29.9) | 0.06 | 19.2 (11.0–29.0) | 21.6 (14.1–30.2) | 18.8 (13.4–27.7) | 25.2 (20.1–30.3) | 0.0004 |
ETP (nM ×min) | 1557.0 (1461.0–1682.3) | 1541.0 (1453.0–1655.5) | 1613.1 (1487.3–1782.0) | <0.0001 | 1556.0 (1485.5–1656.5) | 1527.5 (1449.5–1655.5) | 1539.0 (1448.5–1675.0) | 1729.9 (1513.0–2084.0) | <0.0001 |
Ks (×10−9 cm2) | 6.7 (6.0–7.2) | 6.8 (6.1–7.4) | 6.6 (5.9–7.1) | 0.009 | 7.0 ± 0.9 | 6.7 ± 0.8 | 6.6 ± 0.9 | 6.5 ± 0.9 | 0.001 |
CLT (min) | 98.0 (82.0–108.3) | 95.0 (80.0–108.0) | 99.0 (86.3–110.8) | 0.01 | 90.0 (79.0–101.8) | 98.5 (80.0–110.0) | 98.0 (85.0–107.0) | 103.0 (90.0–118.0) | <0.0001 |
Adjustment | Logistic Regression Models | ||
---|---|---|---|
Variable | OR (95% CI) | p Value | |
No adjustment (univariate model) | eGFR categories | <0.0001 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.51 (0.80–2.88) | 0.21 | |
eGFR 30–59 mL/min/1.73 m2 | 1.59 (0.76–3.35) | 0.22 | |
eGFR 15–29 mL/min/1.73 m2 | 8.00 (4.01–15.95) | <0.0001 | |
Fibrinogen | eGFR categories | <0.0001 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.60 (0.84–3.06) | 0.15 | |
eGFR 30–59 mL/min/1.73 m2 | 1.47 (0.69–3.12) | 0.32 | |
eGFR 15–29 mL/min/1.73 m2 | 8.14 (4.04–16.39) | <0.0001 | |
CHA2DS2-VASc score | eGFR categories | <0.0001 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.67 (0.87–3.20) | 0.13 | |
eGFR 30–59 mL/min/1.73 m2 | 1.88 (0.87–4.06) | 0.11 | |
eGFR 15–29 mL/min/1.73 m2 | 9.06 (4.44–18.46) | <0.0001 | |
CHA2DS2-VASc score risk factors * | eGFR categories | <0.0001 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.56 (0.78–3.14) | 0.21 | |
eGFR 30–59 mL/min/1.73 m2 | 1.87 (0.81–4.30) | 0.14 | |
eGFR 15–29 mL/min/1.73 m2 | 10.04 (4.54–22.20) | <0.0001 |
Adjustment | Logistic Regression Models | ||
---|---|---|---|
Variable | OR (95% CI) | p Value | |
No adjustment (univariate model) | eGFR categories | 0.27 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.43 (0.81–2.53) | 0.21 | |
eGFR 30–59 mL/min/1.73 m2 | 1.79 (0.93–3.42) | 0.08 | |
eGFR 15–29 mL/min/1.73 m2 | 1.77 (0.91–3.42) | 0.09 | |
Fibrinogen | eGFR categories | 0.38 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.67 (0.91–3.07) | 0.10 | |
eGFR 30–59 mL/min/1.73 m2 | 1.53 (0.76–3.09) | 0.23 | |
eGFR 15–29 mL/min/1.73 m2 | 1.70 (0.83–3.47) | 0.15 | |
CHA2DS2-VASc score | eGFR categories | 0.59 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.28 (0.72–2.29) | 0.40 | |
eGFR 30–59 mL/min/1.73 m2 | 1.47 (0.75–2.91) | 0.26 | |
eGFR 15–29 mL/min/1.73 m2 | 1.56 (0.79–3.06) | 0.20 | |
CHA2DS2-VASc score risk factors * | eGFR categories | 0.24 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 1.56 (0.84–2.90) | 0.16 | |
eGFR 30–59 mL/min/1.73 m2 | 1.98 (0.94–4.17) | 0.07 | |
eGFR 15–29 mL/min/1.73 m2 | 2.02 (0.96–4.28) | 0.07 |
Adjustment | Logistic Regression Models | ||
---|---|---|---|
Variable | OR (95% CI) | p Value | |
No adjustment (univariate model) | eGFR categories | 0.001 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 2.42 (1.30–4.50) | 0.005 | |
eGFR 30–59 mL/min/1.73 m2 | 1.49 (0.70–3.15) | 0.30 | |
eGFR 15–29 mL/min/1.73 m2 | 3.61 (1.80–7.25) | 0.0003 | |
Fibrinogen | eGFR categories | 0.001 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 2.39 (1.28–4.46) | 0.006 | |
eGFR 30–59 mL/min/1.73 m2 | 1.49 (0.70–3.16) | 0.30 | |
eGFR 15–29 mL/min/1.73 m2 | 3.61 (1.80–7.25) | 0.0003 | |
CHA2DS2-VASc score | eGFR categories | 0.002 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 2.40 (1.28–4.51) | 0.007 | |
eGFR 30–59 mL/min/1.73 m2 | 1.47 (0.68–3.19) | 0.33 | |
eGFR 15–29 mL/min/1.73 m2 | 3.58 (1.76–7.28) | 0.0004 | |
CHA2DS2-VASc score risk factors * | eGFR categories | 0.01 | |
eGFR ≥ 90 mL/min/1.73 m2 | Reference | ||
eGFR 60–89 mL/min/1.73 m2 | 2.39 (1.22–4.68) | 0.01 | |
eGFR 30–59 mL/min/1.73 m2 | 1.26 (0.54–2.90) | 0.59 | |
eGFR 15–29 mL/min/1.73 m2 | 2.61 (1.20–5.70) | 0.02 |
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Matusik, P.T.; Heleniak, Z.; Papuga-Szela, E.; Plens, K.; Lelakowski, J.; Undas, A. Chronic Kidney Disease and Its Impact on a Prothrombotic State in Patients with Atrial Fibrillation. J. Clin. Med. 2020, 9, 2476. https://doi.org/10.3390/jcm9082476
Matusik PT, Heleniak Z, Papuga-Szela E, Plens K, Lelakowski J, Undas A. Chronic Kidney Disease and Its Impact on a Prothrombotic State in Patients with Atrial Fibrillation. Journal of Clinical Medicine. 2020; 9(8):2476. https://doi.org/10.3390/jcm9082476
Chicago/Turabian StyleMatusik, Paweł T., Zbigniew Heleniak, Elżbieta Papuga-Szela, Krzysztof Plens, Jacek Lelakowski, and Anetta Undas. 2020. "Chronic Kidney Disease and Its Impact on a Prothrombotic State in Patients with Atrial Fibrillation" Journal of Clinical Medicine 9, no. 8: 2476. https://doi.org/10.3390/jcm9082476