Abstract
Background:
Antiplatelet therapy (APT) after percutaneous coronary intervention (PCI) prevents ischemic events with increased risk of bleeding. Little is known about the relationship between hypoalbuminemia and bleeding risk in patients receiving APT after PCI. This study investigated the association between serum albumin level and bleeding events in this population.
Methods and Results:
We enrolled 438 consecutive patients who were prescribed dual APT (DAPT; aspirin and thienopyridine) beyond 1 month after successful PCI without adverse events. The patients were divided into 3 groups according to serum albumin tertile: tertile 1, ≤3.7 g/dL; tertile 2, 3.8–4.1 g/dL; and tertile 3, ≥4.2 g/dL. Adverse bleeding events were defined as Bleeding Academic Research Consortium criteria types 2, 3, and 5. During the median follow-up of 29.5 months, a total of 30 adverse bleeding events were observed. Median duration of DAPT was 14 months. The tertile 1 group had the highest risk of adverse bleeding events (event-free rate, 83.1%, 94.3% and 95.8%, respectively; P<0.001). On Cox proportional hazards modeling, serum albumin independently predicted adverse bleeding events (HR, 0.10, 95% CI: 0.027–0.39, P=0.001, for tertile 3 vs. tertile 1).
Conclusions:
Decreased serum albumin predicted bleeding events in patients with APT after PCI.
Antiplatelet therapy (APT) after percutaneous coronary intervention (PCI), especially dual APT (DAPT) after stent implantation, is widely recognized as the standard therapy for preventing ischemic events, such as myocardial infarction (MI) and stent thrombosis. There has been concern, however, that the use of APT may be associated with an increased incidence of bleeding events. Longer DAPT has been shown to significantly decrease the risk of stent thrombosis and major adverse cardiovascular and cerebrovascular events (MACCE).1
More recently, Yeh et al, the DAPT Study Investigators, advocated a clinical prediction score that could stratify whether continuation of DAPT would be beneficial or harmful in individual patients.2
Nevertheless, the appropriate duration of DAPT is still controversial, and careful evaluation is required for each individual patient.
Hypoalbuminemia is strongly associated with poor prognosis in several clinical settings,3–5
including cardiovascular disease.6,7
Moreover, several studies indicated a relationship between hypoalbuminemia and bleeding risk.8,9
In contrast, little is known about the association of serum albumin level with bleeding events in patients receiving APT after PCI.
The aim of this study was therefore to investigate the ability of serum albumin level to predict bleeding events in this population.
Methods
Subjects
We evaluated 532 consecutive patients who were prescribed DAPT after successful PCI at Nagoya University Hospital between January 2011 and March 2015. DAPT was defined as the prescription of two types of antiplatelet agent in an optimal dose, that is, the combinations of aspirin (maintenance dose, 81–162 mg daily; loading dose, 162–330 mg if not received before PCI) and a thienopyridine, such as clopidogrel (maintenance dose, 75 mg daily; loading dose, 300 mg if not received before PCI); prasugrel (maintenance dose, 3.75 mg daily; loading dose, 20 mg if not received before PCI); or ticlopidine (maintenance dose, 200 mg daily). Exclusion criteria were as follows: cessation of DAPT within 1 month after PCI; and any adverse event within 1 month after PCI, including death, non-fatal MI, cerebral infarction, and adverse bleeding events. The duration of DAPT and other treatment after PCI were left to the discretion of each attending physician, based on the appropriate guidelines. Ceased DAPT was defined as termination of DAPT due to the discretion of the attending physician, which was not related to any bleeding event. Patients who had ceased DAPT were then treated either with aspirin alone, thienopyridine alone, or without APT. This study was approved by the local ethics committee and was conducted in accordance with the ethics principles stated in the Declaration of Helsinki. Written informed consent was obtained from all patients.
Data Collection
Body mass index (BMI) was calculated as body weight divided by height squared (kg/m2). Hypertension (HT) was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, and/or the use of hypertensive drugs. Diabetes mellitus (DM) was defined as fasting plasma glucose concentration >126 mg/dL and/or glycosylated hemoglobin concentration ≥6.5% (National Glycohemoglobin Standardization Program), and/or the use of any anti-diabetic agent. Dyslipidemia was defined as low-density lipoprotein cholesterol ≥140 mg/dL, high-density lipoprotein cholesterol (HDL-C) ≤40 mg/dL, triglycerides ≥150 mg/dL, and/or the use of any medication for dyslipidemia. Current smoking was defined as active smoking habit at the time of PCI. Blood samples were obtained from all patients, in the morning after fasting in the case of elective PCI, or at the time of arrival in the emergency room for urgent PCI. Estimated glomerular filtration rate (eGFR) was calculated using the equation for Japanese subjects recommended by the Japanese Society of Nephrology: eGFR (mL/min/1.73 m2)=194×SCr−1.094×age−0.287×0.739 (if female).10
Left ventricular ejection fraction (LVEF) was calculated on echocardiography using the Teichholz formula or the modified Simpson method.11,12
Serum Albumin
Serum albumin was measured on modified bromocresol purple assay using Aqua Auto Kainos (Kainos, Tokyo, Japan), with a Hitachi LST 008 automated analyzer (Hitachi High-Technologies, Tokyo, Japan). Patients were divided into 3 groups according to serum albumin tertile: tertile 1, ≤3.7 g/dL; tertile 2, 3.8–4.1 g/dL; and tertile 3, ≥4.2 g/dL.
Clinical Outcomes
Follow-up data were taken from a period starting 1 month after PCI and were obtained from medical records or via telephone interview. The primary endpoint was the incidence of adverse bleeding events during the follow-up period. Bleeding events were evaluated according to the Bleeding Academic Research Consortium (BARC) criteria.13
Adverse bleeding events were defined as types 2, 3, and 5 of the BARC criteria. We also investigated the incidence of MACCE, which were defined as a composite of all-cause death, non-fatal MI, and cerebral infarction.
Statistical Analysis
Normally distributed continuous variables are expressed as mean±SD. Non-normally distributed continuous variables are expressed as median (IQR). Categorical variables are expressed as number (percentage). Continuous variables were compared using one-way analysis of variance (ANOVA; between 3 groups with normal distribution), Student’s t-test (2 groups with normal distribution), Kruskal-Wallis test (3 groups with non-normal distribution), or the Mann-Whitney U-test (2 groups with non-normal distribution). Categorical variables were compared using chi-squared test or Fisher’s exact test. Event-free survival curve for adverse bleeding events was estimated using the Kaplan-Meier method and compared using log-rank test. The independent predictors of adverse bleeding events and MACCE were analyzed using Cox proportional hazards models. To estimate about adverse bleeding events, the variables with significant difference on univariate analysis and those with an important association with bleeding risk in previous studies were entered into the multivariate model, as follows: age, gender, BMI, HT, eGFR, hemoglobin and serum albumin. We also carried out analysis according to DAPT status (continued vs. ceased DAPT) using the same multivariate model. As for MACCE, the variables that were significantly different on univariate analysis were entered into the multivariate model, such as age, BMI, LVEF, eGFR, HDL-C, hemoglobin and serum albumin. C index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess whether the accuracy of predicting adverse bleeding events would improve after adding the tertiles of albumin into the baseline model, which consisted of gender, BMI, HT, eGFR and hemoglobin, as proposed by DeLong et al14
and Pencina et al.15
P<0.05 was considered statistically significant. Statistical analysis was performed using SPSS version 18.0 for Windows (SPSS, Chicago, IL, USA) and R version 2.13.1 with the PredictABEL and pROC packages (R Development Core Team 2011, Vienna, Austria).
Results
As shown in
Figure 1, in the present study, we finally enrolled 438 patients who continued DAPT after successful PCI beyond 1 month without adverse events. The number of patients with continued DAPT was 211 (48.2%), and of those with ceased DAPT was 227 (51.8%). In the ceased DAPT group, aspirin alone was continued in 188 patients, thienopyridine alone in 35, and no APT in 4 (2 patients were prescribed oral anticoagulant [OAC] alone, and 2 patients had hematologic disease). Follow-up was concluded at 31 January 2016. Before then, 3 patients were lost to follow-up, therefore they were censored at the point of dropping out.
Mean age was 69.2±10.1 years, and 80.4% of the subjects were male. Mean serum albumin was 3.9±0.5 g/dL. Among these patients, 72.6% had HT, 43.8% had DM, and 23.8% presented with acute MI. Median duration of DAPT was 14 months in all patients, 10 months after implantation of bare metal stents, and after 20 months in the drug-eluting stent (DES) group. An OAC, such as warfarin or direct OAC, was prescribed in 8.0% of patients at 1 month after PCI.
Table 1
lists the baseline characteristics according to serum albumin tertile. Patients in tertile 1 were significantly older and had a lower prevalence of dyslipidemia, and a higher prevalence of hemodialysis and history of congestive heart failure. LVEF, eGFR, and hemoglobin were significantly lower in tertile 1.
Table 1.
Baseline Subject Characteristics vs. Serum Albumin Tertile
| Variables |
Total (n=438) |
Serum albumin |
P value |
Tertile 1 (n=140)
≤3.7 g/dL |
Tertile 2 (n=131)
3.8–4.1 g/dL |
Tertile 3 (n=167)
≥4.2 g/dL |
| Demographics |
| Male |
352 (80.4) |
106 (75.7) |
103 (78.6) |
143 (85.6) |
0.078 |
| Age (years) |
69.2±10.1 |
72.5±9.7 |
70.7±9.1 |
65.2±9.8 |
<0.001 |
| BMI (kg/m2) |
23.6±3.6 |
22.6±3.7 |
23.4±3.4 |
24.5±3.5 |
<0.001 |
| Hypertension |
318 (72.6) |
105 (75.0) |
98 (74.8) |
115 (68.9) |
0.39 |
| Dyslipidemia |
330 (75.3) |
92 (65.7) |
101 (77.1) |
137 (82.0) |
0.004 |
| Diabetes mellitus |
192 (43.8) |
62 (44.3) |
62 (47.3) |
68 (40.7) |
0.52 |
| Current smoker |
110 (25.1) |
31 (22.1) |
27 (20.6) |
52 (31.1) |
0.071 |
| Hemodialysis |
22 (5.0) |
18 (12.9) |
3 (2.3) |
1 (0.6) |
<0.001 |
| Prior PCI |
100 (22.8) |
33 (23.6) |
26 (19.8) |
41 (24.6) |
0.61 |
| Prior MI |
49 (11.2) |
17 (12.1) |
11 (8.4) |
21 (12.6) |
0.48 |
| Prior CHF |
41 (9.4) |
26 (18.6) |
9 (6.9) |
6 (3.6) |
<0.001 |
| Prior cerebral infarction |
56 (12.8) |
25 (17.9) |
17 (13.0) |
14 (8.4) |
0.047 |
| LVEF (%) |
60.8±11.7 |
58.6±12.0 |
61.2±12.3 |
62.4±10.8 |
0.019 |
| Indication for PCI |
|
|
|
|
0.26 |
| Stable AP |
259 (59.1) |
78 (55.7) |
82 (62.6) |
99 (59.3) |
|
| Unstable AP |
75 (17.1) |
32 (22.9) |
19 (14.5) |
24 (14.4) |
|
| Acute MI |
104 (23.8) |
30 (21.4) |
30 (22.9) |
44 (26.3) |
|
| Continuous DAPT |
211 (48.2) |
73 (52.1) |
65 (49.6) |
73 (43.7) |
0.31 |
| OAC at 1 month |
35 (8.0) |
11 (7.9) |
12 (9.2) |
12 (7.2) |
0.82 |
| Type of procedure |
|
|
|
|
0.14 |
| Bare metal stent |
256 (58.4) |
84 (60.0) |
84 (64.1) |
88 (52.7) |
|
| Drug eluting stent |
162 (37.0) |
51 (36.4) |
44 (33.6) |
67 (40.1) |
|
| Other |
20 (4.6) |
5 (3.6) |
3 (2.3) |
12 (7.2) |
|
| Laboratory data |
| eGFR (mL/min/1.73 m2) |
64.1 (51.3–77.3) |
52.1 (38.5–69.1) |
65.1 (54.1–77.5) |
70.3 (59.5–82.1) |
<0.001 |
| T-chol (mg/dL) |
177±42 |
171±45 |
173±37 |
186±43 |
0.003 |
| Triglycerides (mg/dL) |
122 (85–169) |
99 (74–138) |
123 (89–154) |
143 (96–200) |
<0.001 |
| HDL-C (mg/dL) |
45±12 |
42±13 |
45±12 |
46±11 |
0.013 |
| LDL-C (mg/dL) |
104±36 |
102±37 |
100±31 |
108±37 |
0.12 |
| Hemoglobin A1c (%) |
6.4±1.0 |
6.4±1.1 |
6.4±1.1 |
6.4±0.9 |
0.73 |
| Hemoglobin (g/dL) |
13.1±1.9 |
11.6±1.7 |
13.2±1.6 |
14.2±1.5 |
<0.001 |
| Serum albumin (g/dL) |
3.9±0.5 |
3.3±0.4 |
4.0±0.1 |
4.4±0.2 |
<0.001 |
| Type of DAPT |
|
|
|
|
0.17 |
| Aspirin+clopidogrel |
406 (92.7) |
130 (92.9) |
123 (93.9) |
153 (91.6) |
|
| Aspirin+prasugrel |
12 (2.7) |
1 (0.7) |
3 (2.3) |
8 (4.8) |
|
| Aspirin+ticlopidine |
20 (4.6) |
9 (6.4) |
5 (3.8) |
6 (3.6) |
|
| Medication after PCI |
| RAS inhibitor |
294 (67.1) |
91 (65.0) |
90 (68.7) |
113 (67.7) |
0.80 |
| β-blockers |
189 (43.2) |
77 (55.0) |
51 (38.9) |
61 (36.5) |
0.003 |
| CCB |
188 (42.9) |
50 (35.7) |
63 (48.1) |
75 (44.9) |
0.097 |
| Statins |
383 (87.4) |
114 (81.4) |
115 (87.8) |
154 (92.2) |
0.018 |
| Anti-diabetic drugs |
161 (36.8) |
53 (37.9) |
49 (37.4) |
59 (35.3) |
0.89 |
Data given as median (IQR), n (%) or mean±SD. AP, angina pectoris; BMI, body mass index; CCB, calcium-channel blockers; CHF, congestive heart failure; DAPT, dual antiplatelet therapy; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MI, myocardial infarction; OAC, oral anticoagulant; PCI, percutaneous coronary intervention; RAS, renin-angiotensin system; T-chol, total cholesterol.
During the median follow-up of 29.5 months, adverse bleeding events occurred in 30 patients and MACCE in 50 patients.
Table 2
lists patient characteristics according to adverse bleeding events. There was no significant difference between the 2 groups in terms of indication for PCI, continuous use of DAPT, use of OAC, type of procedure, or medication after PCI. On event-free survival analysis, the tertile 1 group had the highest risk of adverse bleeding events (event-free rate, 83.1%, 94.3% and 95.8%, respectively; P<0.001,
Figure 2) and MACCE (event-free rate, 71.2%, 88.3% and 95.0%, respectively; P<0.001, data not shown). Cox proportional hazards modeling is shown in
Table 3. After adjustment for confounding factors, serum albumin level independently predicted adverse bleeding events (tertile 3 vs. tertile 1: hazard ratio [HR], 0.10; 95% CI: 0.027–0.39, P=0.001; tertile 2 vs. tertile 1: HR, 0.34; 95% CI: 0.14–0.83, P=0.018). In contrast, serum albumin tended to be associated with MACCE (tertile 3 vs. tertile 1: HR, 0.38; 95% CI: 0.13–1.09, P=0.073; tertile 2 vs. tertile 1: HR, 0.79; 95% CI: 0.37–1.73, P=0.56, data not shown).
Table 2.
Patient Characteristics vs. Adverse Bleeding Events (BARC 2, 3, 5)
| Variables |
Adverse bleeding events |
P value |
| No (n=408) |
Yes (n=30) |
| Demographics |
| Male |
331 (81.1) |
21 (70.0) |
0.14 |
| Age (years) |
68.9±10.1 |
73.5±8.5 |
0.016 |
| BMI (kg/m2) |
23.6±3.6 |
23.1±4.1 |
0.32 |
| Hypertension |
298 (73.0) |
20 (66.7) |
0.45 |
| Dyslipidemia |
306 (75.0) |
24 (80.0) |
0.54 |
| Diabetes mellitus |
178 (43.6) |
14 (46.7) |
0.75 |
| Current smoker |
100 (24.5) |
10 (33.3) |
0.28 |
| Hemodialysis |
21 (5.1) |
1 (3.3) |
0.66 |
| Prior PCI |
96 (23.5) |
4 (13.3) |
0.20 |
| Prior MI |
48 (11.8) |
1 (3.3) |
0.16 |
| Prior CHF |
40 (9.8) |
1 (3.3) |
0.24 |
| Prior cerebral infarction |
49 (12.0) |
7 (23.3) |
0.073 |
| LVEF (%) |
60.9±11.8 |
59.5±11.5 |
0.53 |
| Indication for PCI |
|
|
0.66 |
| Stable AP |
242 (59.3) |
17 (56.7) |
|
| Unstable AP |
71 (17.4) |
4 (13.3) |
|
| Acute MI |
95 (23.3) |
9 (30.0) |
|
| Continuous DAPT |
193 (47.3) |
18 (60.0) |
0.18 |
| OAC at 1 month |
31 (7.6) |
4 (13.3) |
0.26 |
| Type of procedure |
|
|
0.085 |
| Bare metal stent |
233 (57.1) |
23 (76.7) |
|
| Drug-eluting stent |
155 (38.0) |
7 (23.3) |
|
| Other |
20 (4.9) |
0 (0.0) |
|
| Laboratory data |
| eGFR (mL/min/1.73 m2) |
64.4 (51.5–77.9) |
62.9 (48.5–75.5) |
0.38 |
| T-chol (mg/dL) |
177±42 |
184±44 |
0.37 |
| Triglycerides (mg/dL) |
122 (85–169) |
122 (80–164) |
0.89 |
| HDL-C (mg/dL) |
45±12 |
46±11 |
0.43 |
| LDL-C (mg/dL) |
103±36 |
112±37 |
0.23 |
| Hemoglobin A1c (%) |
6.4±1.0 |
6.5±1.4 |
0.56 |
| Hemoglobin (g/dL) |
13.1±1.9 |
12.6±1.8 |
0.20 |
| Serum albumin (g/dL) |
3.9±0.5 |
3.6±0.5 |
0.001 |
| Type of DAPT |
|
|
0.93 |
| Aspirin+clopidogrel |
378 (92.6) |
28 (93.3) |
|
| Aspirin+prasugrel |
11 (2.7) |
1 (3.3) |
|
| Aspirin+ticlopidine |
19 (4.7) |
1 (3.3) |
|
| Medication after PCI |
| RAS inhibitor |
275 (67.4) |
19 (63.3) |
0.65 |
| β-blocker |
177 (43.4) |
12 (40.0) |
0.72 |
| CCB |
178 (43.6) |
10 (33.3) |
0.27 |
| Statin use |
357 (87.5) |
26 (86.7) |
0.89 |
| Anti-diabetic drugs |
147 (36.0) |
14 (46.7) |
0.24 |
Data given as median (IQR), n (%) or mean±SD. BARC, Bleeding Academic Research Consortium. Other abbreviations as in Table 1.
Table 3.
Predictors of Adverse Bleeding Events (BARC 2, 3, 5)
| Variables |
Univariate |
Multivariate |
| HR |
95% CI |
P value |
HR |
95% CI |
P value |
| Male |
0.58 |
0.26–1.26 |
0.17 |
0.60 |
0.25–1.43 |
0.25 |
| Age |
1.05 |
1.01–1.09 |
0.016 |
1.04 |
0.99–1.08 |
0.12 |
| BMI |
0.95 |
0.85–1.05 |
0.31 |
1.01 |
0.91–1.12 |
0.92 |
| Current smoking |
1.50 |
0.70–3.20 |
0.30 |
|
|
|
| Hypertension |
0.73 |
0.34–1.55 |
0.41 |
0.62 |
0.28–1.39 |
0.25 |
| Diabetes mellitus |
1.08 |
0.53–2.21 |
0.84 |
|
|
|
| eGFR |
0.99 |
0.98–1.01 |
0.27 |
1.00 |
0.98–1.02 |
0.99 |
| Hemoglobin |
0.87 |
0.72–1.05 |
0.14 |
1.20 |
0.94–1.53 |
0.15 |
| Prior PCI |
0.44 |
0.15–1.25 |
0.12 |
|
|
|
| Prior MI |
0.65 |
0.25–1.70 |
0.38 |
|
|
|
| Acute MI |
1.69 |
0.77–3.71 |
0.19 |
|
|
|
| DES use |
0.56 |
0.24–1.31 |
0.18 |
|
|
|
| Continuous DAPT |
1.66 |
0.80–3.45 |
0.17 |
|
|
|
| OAC at 1 month |
1.96 |
0.68–5.64 |
0.21 |
|
|
|
| Serum albumin (vs. tertile 1) |
|
|
0.001† |
|
|
0.002† |
| Tertile 2 |
0.41 |
0.18–0.93 |
0.034 |
0.34 |
0.14–0.83 |
0.018 |
| Tertile 3 |
0.13 |
0.037–0.42 |
0.001 |
0.10 |
0.027–0.39 |
0.001 |
†P for trend. Multivariate model includes male, age, BMI, hypertension, eGFR, hemoglobin and albumin. DES, drug eluting stent. Other abbreviations as in Tables 1,2.
Furthermore, on subgroup analysis to evaluate the relationship between incidence of adverse bleeding events and serum albumin level according to DAPT status, lowest albumin tertile independently predicted adverse bleeding events compared with the highest tertile in the continued DAPT group and ceased DAPT group after adjustment for confounding factors (adjusted HR, 0.064; 95% CI: 0.007–0.60, P=0.016; and adjusted HR, 0.074; 95% CI: 0.011–0.50, P=0.007, respectively, for tertile 3 vs. tertile 1), without significant interaction (P for interaction=0.60).
We assessed the discriminative accuracy of each predictive model for predicting adverse bleeding events (Table 4). When albumin was added to the baseline model, which consisted of gender, BMI, HT, eGFR and hemoglobin, the C index became largest compared with the baseline model alone and the baseline model plus the clinical prediction score.2
Similarly, both NRI and IDI also peaked when albumin was added to the baseline model.
Table 4.
Ability to Predict Adverse Bleeding Events (BARC 2, 3, 5)
| Models |
C index |
P value |
NRI |
P value |
IDI |
P value |
| 1. Baseline model† |
0.62 |
Ref. |
|
Ref. |
|
Ref. |
2. Baseline model, clinical prediction
score‡ |
0.65 |
0.47 |
0.015 |
0.90 |
0.0097 |
0.070 |
| 3. Baseline model, albumin tertiles |
0.76 |
0.012 |
0.32 |
0.037 |
0.043 |
0.0058 |
†Includes gender, BMI, hypertension, eGFR and hemoglobin. ‡From Yeh et al.2 IDI, integrated discrimination improvement; NRI, net reclassification improvement. Other abbreviations as in Tables 1,2.
Discussion
The major finding of this study was that serum albumin level is strongly associated with adverse bleeding events in patients receiving APT after PCI. It is already known that serum albumin predicts poor outcome, but little is known about the relationship between serum albumin level and bleeding risk. To our knowledge, this is the first study to evaluate the predictive ability of serum albumin level for long-term bleeding events in patients receiving APT after PCI.
APT after PCI is usually prescribed to prevent ischemic events, but there are concerns about an increase in bleeding events. It seemed likely that a consensus would be reached with regard to shortened duration of DAPT – especially in the era of second-generation DES.16–18
In contrast, the DAPT study showed that continuous DAPT increases not only bleeding events, but also all-cause mortality, although treatment with longer DAPT reduces the risk of stent thrombosis and MACCE.1
Therefore, the appropriate duration of DAPT is still controversial.19,20
Thus, it is very important to assess DAPT duration in individual patients, taking into consideration their clinical condition, complications, risk factors for ischemia and bleeding, and so on. More recently, a clinical prediction score has been developed to identify whether continuous DAPT is beneficial or harmful.2
This score, however, was developed using data from patients who had received DAPT for >1 year. In the present study, serum albumin independently predicted adverse bleeding events in patients receiving DAPT for >1 month after PCI. Given that little is known about predicting bleeding risk in this period, the present findings could contribute to determining the best strategy for APT after PCI.
Hypoalbuminemia occurs in several conditions, such as systemic inflammation, decrease of albumin synthesis in the liver, loss of albumin to the extravascular space, and hemodilution via increasing volume.21–23
Advancing age and sarcopenia are correlated with systemic inflammation, which is reflected by the increase of inflammatory markers such as interleukin 6.24,25
In the present study, patients with low albumin were significantly associated with older age, lower BMI, higher prevalence of heart failure, impaired renal function and decreased cholesterol, consistent with previous reports. Several studies have reported that hypoalbuminemia significantly predicts poor outcome in cardiovascular disease.6,7,26,27
Until now, however, there have been limited data on the relationship between serum albumin level and bleeding risk in long-term follow-up. The present study suggests that the evaluation of serum albumin might provide useful information to stratify the high risk patients of bleeding events.
There have been several reports on the association between hypoalbuminemia and bleeding risk in various conditions including acute ST-segment elevation MI and cardiovascular surgery.8,28–30
In contrast, the predictive ability of lower serum albumin level for long-term bleeding risk is not fully understood in patients on APT after PCI. Using the C index, NRI, and IDI, we showed that the ability to predict bleeding events was improved more by including serum albumin level than by including clinical prediction score.2
The mechanism connecting higher bleeding risk to lower albumin level is unclear. We speculate that hypoalbuminemia might be related to the vulnerability of the capillary in systemic organs, especially malnutrition associated with vitamin C and K deficiency, which leads to an increase in coagulopathy and bleeding diathesis.31–33
There were several limitations in the present study. First, it was a non-randomized observational study conducted in a single center and included a relatively small subject group. Second, we enrolled only the patients who continued the DAPT without bleeding events or MACCE beyond 1 month after PCI. This study protocol might have excluded patients who were at very high risk of future adverse events. Moreover, we could not take into account the relationship between serum albumin level and adverse events within 1 month after PCI. Third, the choice of DAPT and other medications, such as OAC, and the duration of DAPT depended on each attending physician. Fourth, we assessed serum albumin data only at baseline, and had no information on any changes in serum albumin level during the follow-up period.
In conclusion, serum albumin was strongly associated with future bleeding events in patients prescribed APT after PCI. This suggests that serum albumin, a simple screening tool measured easily in the clinical setting, could prove very valuable and convenient as a parameter for risk stratification in these patients.
Disclosures
H.I. received lecture fees from Astellas Pharma, Daiichi-Sankyo Pharma and MSD. T.M. received lecture fees from Bayel Pharmaceutical, Daiichi Sankyo, Dainippon Sumitomo Pharma, Kowa, MSD, Mitsubishi Tanabe Pharma, Nippon Boehringer Ingelheim, Novartis Pharma, Pfizer Japan, Sanofi-aventis and Takeda Pharmaceutical. T.M. received unrestricted research grant for the Department of Cardiology, Nagoya University Graduate School of Medicine from Astellas Pharma, Daiichi Sankyo, Dainippon Sumitomo Pharma, Kowa, MSD, Mitsubishi Tanabe Pharma, Nippon Boehringer Ingelheim, Novartis Pharma, Otsuka Pharma, Pfizer Japan, Sanofi-aventis, Takeda Pharmaceutical and Teijin Pharma. The other authors declare no conflict of interest.
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