Abstract
Aim: Patients with chronic limb-threatening ischemia (CLTI) have a high bleeding risk (HBR) and mortality rate. The 2-year life expectancy is an important factor in deciding the appropriate treatment strategy. This study aimed to assess the influence of HBR on the prognosis of patients with CLTI.
Methods: A total of 259 patients with CLTI who underwent endovascular therapy (EVT) (mean age, 76.2 years; male, 62.9%) between January 2018 and December 2019 were evaluated. The Academic Research Consortium for HBR (ARC-HBR) criteria were applied to each patient, and the ARC-HBR scores were calculated. The cut-off score for predicting all-cause mortality within two years was derived using a survival classification and regression tree (CART) model. Causes of death and the association between ARC-HBR scores and major bleeding events within two years were also investigated.
Results: Based on the CART model, patients were divided into three groups (low HBR score 0–1.0, 48 patients; moderate HBR score 1.5–3.0, 176 patients; and high HBR score ≥ 3.5, 35 patients). During the study period, 82 patients (39.6%) died due to cardiac (n=23) and non-cardiac causes (n=59). All-cause mortality increased significantly with increasing ARC-HBR scores. Cox multivariate analysis revealed a significant association between high ARC-HBR scores and the risk of all-cause mortality within two years. Major bleeding events increased significantly with increasing ARC-HBR scores.
Conclusions: The ARC-HBR score could predict 2-year mortality in patients with CLTI who underwent EVT. Thus, this score can help determine the best revascularization strategy for patients with CLTI.
Introduction
Recently, the incidence of symptomatic lower extremity artery disease (LEAD) has been increasing because of the advancing age of societies worldwide1, 2). Although endovascular therapy (EVT) is regarded as a minimally invasive and effective intervention for patients with LEAD, it has been found to be associated with high bleeding risk (HBR) in this patient population3, 4). Previous studies have reported an association between the Academic Research Consortium for HBR (ARC-HBR) criteria and patients with LEAD4). In this regard, HBR has been observed to be particularly common in patients with chronic limb-threatening ischemia (CLTI)4). Additionally, patients with CLTI face a high risk of mortality5, 6). Therefore, current guidelines on CLTI management recommend7) conducting a 2-year life expectancy assessment in order to determine the most appropriate revascularization strategy for patients with CLTI8, 9). However, to date, no studies have been performed on the relationship between HBR and prognosis in patients with CLTI.
Aim
This study aimed to apply the ARC-HBR criteria to patients with CLTI and assess the impact of HBR on mortality within two years of EVT.
Methods
Ethics Statements
This study was approved by the medical ethics committees of each hospital and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients or their relatives before initiating EVT.
Study Design and Population
Data of patients with LEAD of the lower limbs who underwent EVT between January 2018 and December 2019 were retrieved from the reseArcherS In next GenerAtion of endovasculaR therapeUtics (ASIGARU) peripheral artery disease (PAD) registry database. The details of the registry have been described previously10).
This study included patients aged ≥ 20 years who were diagnosed with CLTI, defined as ischemic rest pain with confirmatory hemodynamic studies, diabetic foot ulcer or any other lower limb ulceration present for at least two weeks, or gangrene involving any portion of the lower limb or foot with stenotic or occlusive lesions of the lower limb arteries7). Patients who underwent EVT for acute limb ischemia and repair of traumatic complications or aortic dissection, as well as those who received hybrid therapy (open surgical procedure and EVT), were excluded from the study. Furthermore, data from the first intervention were used for patients who underwent multiple EVTs during the study period. Finally, 259 consecutive patients were included in the analysis.
Using the Wound, Ischemia, and foot Infection (WIfI) grading system, all included limbs were classified into one of the three categories (wound, ischemia, or foot infection), and each category was graded from 0 to 3 11). After grading, the scores of each category were combined to obtain the final WIfI grade11).
ARC-HBR Criteria
First, the ARC-HBR criteria were assessed for each patient. The major criteria included severe or end-stage chronic kidney disease (CKD) (defined as an estimated glomerular filtration rate ＜30 mL/min/1.73 m2), liver cirrhosis with portal hypertension, active malignancy, severe anemia (defined as hemoglobin ＜11 g/dL), moderate or severe thrombocytopenia (defined as platelet count ＜10×104/µL), prior spontaneous intracranial hemorrhage, spontaneous bleeding requiring hospitalization or transfusion during the past six months, chronic bleeding diathesis, use of anticoagulant medications (excluding vascular protection doses12)), non-deferrable major surgery while using dual antiplatelet therapy, and recent major surgery or major trauma within 30 days of EVT. The minor criteria were age ≥ 75 years, moderate CKD (defined as estimated glomerular filtration rate of 30–59 mL/min/1.73 m2), mild anemia (defined as hemoglobin of 11–12.9 g/dL for males and 11–11.9 g/dL for females), prior ischemic stroke, history of spontaneous bleeding requiring hospitalization or transfusion, and long-term use of steroids13).
Subsequently, to assess the number of ARC-HBR criteria, the HBR categories were modified, and the ARC-HBR score was calculated by allocating 1 point for each major criterion and 0.5 points for each minor criterion, as described in previous studies14, 15).
Study Endpoints
The cut-off ARC-HBR score was investigated using a survival classification and regression tree (CART) model to predict mortality within two years of EVT. The primary endpoint of this study was all-cause mortality within two years. The causes of death and predictive factors of all-cause mortality were also assessed.
The secondary outcome was a major bleeding event, defined as Bleeding Academic Research Consortium (BARC) type 3 or type 5 bleeding within two years. BARC type 3 bleeding includes overt bleeding combined with a decrease in hemoglobin level of ≥ 3 g/dL, any transfusion with overt bleeding, bleeding requiring surgical intervention, bleeding requiring intravenous vasoactive agents, and intracranial bleeding; BARC type 5 bleeding is considered fatal (both probable and definite)16).
Medication and Procedure-Related Protocols
Drugs were administered before and after EVT according to the local hospital policies and at the physicians’ discretion. Aspirin, clopidogrel, and prasugrel were used as antiplatelet therapy. Dual antiplatelet therapy comprised of aspirin plus thienopyridine. On the other hand, warfarin or a direct oral anticoagulant (dabigatran, edoxaban, apixaban, or rivaroxaban) was used as anticoagulant therapy. The operator determined the access site and device based on the EVT procedure performed. In all cases, an intraarterial or intravenous bolus of heparin was administered as an anticoagulant after sheath placement during EVT. The heparin dose was adjusted based on individual hospital policy and the operator’s discretion to achieve an activated clotting time of 200–300 s. Furthermore, the choice of the EVT device was based on the discretion of the operator and the patient’s background, lesion characteristics, and interventional results.
Follow-Up Protocol
The follow-up interval and use of modality were at the discretion of the physicians and dependent on each hospital’s policies. Typical practice is every 2–4 weeks until the wound heals for patients with ulcer or gangrene, and then every 3 months as long as possible with the measurement of the ankle-brachial index, skin perfusion pressure, and duplex ultrasound if needed. Clinical data were collected during hospital visits or by telephone interviews with the patients and their relatives. Reintervention was clinically indicated by recurrent symptoms or delayed wound healing.
Statistical Analysis
Continuous variables were expressed as mean±standard deviation (SD) or median and interquartile range (IQR, 25–75%). Categorical variables were expressed as numbers and percentages. Comparisons of clinical, lesional, and procedural characteristics and medications were performed using the chi-squared test for categorical covariates. One-way analysis of variance was performed to compare continuous normally distributed covariates (expressed as mean±SD). The Kruskal–Wallis test was performed to compare continuous non-normally distributed variables (expressed as median with IQR) according to the HBR score. Risk stratification was carried out by dividing the patients into subgroups using a survival CART model17). We calculated the most significant classification using the machine learning method and expressed the results as a regression tree model. Event-free survivals (Kaplan–Meier curves) were compared using the log-rank test. The Cox regression analysis was performed to determine the independent predictors of all-cause mortality within two years after EVT, using predictors associated with these events (p＜0.05) in the univariate analyses. Additionally, logarithmic transformation was performed for non-normally distributed variables. The results were reported as adjusted hazard ratios (HRs) with associated 95% confidence intervals (95% CI).
All statistical analyses were performed using SPSS version 23 (SPSS Inc., Chicago, IL, USA) and R version 3.5.1 (The R Project for Statistical Computing [http://www.R-project.org/]). CART models were developed using the rpart package. All p-values were two-tailed, and p＜0.05 was considered statistically significant for all analyses.
Results
Study Population and Machine Learning Using the CART Model
A total of 259 patients with CLTI were examined during the study period (Fig.1A). The numbers and prevalence of individual ARC-HBR criteria are summarized in Fig.1B and Fig.1C. Severe CKD and anemia were the most common observations among the 11 major criteria, whereas age ≥ 75 years was the most frequent observation among the six minor criteria.
The distribution of HBR scores in this study is shown in Fig.2A. Using the CART model, the difference in all-cause mortality in patients with CLTI who underwent EVT was discovered to be most significant between patients with an HBR score ＜1.25 and those with an HBR score ≥ 1.25. Since the CART model further divided patients with an HBR score of ≥ 1.25 into two groups (HBR score ＜3.25 and ≥ 3.25; Fig.2B), the patients were ultimately classified into three groups according to their HBR scores: HBR score=0–1.0, HBR score=1.5–3.0, and HBR score ≥ 3.5.
Table 1 shows the baseline patient characteristics of the study population. The mean age of the patients was 76.2 years (SD, ±10.3 years), and 62.9% were male (according to administrative data). Diabetes mellitus and CKD were observed in 65.6% and 78.0% of the patients, respectively. Regarding patient characteristics, significant differences between the groups were observed in age, clinical frailty scale score, prior intracranial bleeding, renal failure, atrial fibrillation, serum hemoglobin level, platelet count, and serum albumin level.
Table 1. Baseline patient characteristics, lesion and procedure characteristics, and post procedure medications
|
Overall |
HBR score: ≥ 3.5 |
HBR score: 1.5-3.0 |
HBR score: 0-1.0 |
p value
|
| n = 259
|
n = 35
|
n = 176
|
n = 48
|
|
| -Patient clinical characteristics
|
|
|
|
|
|
| Age, years |
76.2±10.3 |
78.0±11.3 |
76.7±10.1 |
73.4±9.7 |
0.083 |
| Age ≥ 75 years, n (%)
|
150 (57.9) |
27 (77.1) |
107 (60.8) |
16 (33.3) |
＜0.001 |
| Male, n (%)
|
163 (62.9) |
20 (57.1) |
112 (63.6) |
31 (64.6) |
0.74 |
| BMI, kg/m2
|
21.3 (18.8, 23.6) |
20.5 (17.7, 22.9) |
21.2 (18.7, 23.6) |
22.1 (19.5, 25.2) |
0.064 |
| BMI ＜18.5kg/m2, n (%)
|
56 (21.8) |
9 (25.7) |
40 (23.0) |
7 (14.6) |
0.38 |
| CFS |
|
|
|
|
0.019 |
| CFS 1-3, n (%)
|
46 (17.8) |
5 (14.3) |
29 (16.5) |
12 (25.0) |
|
| CFS 4-5, n (%)
|
91 (35.1) |
8 (22.8) |
60 (34.1) |
23 (47.9) |
|
| CFS 6~, n (%)
|
122 (47.1) |
22 (62.9) |
87 (49.4) |
13 (27.1) |
|
| Prior heart failure, n (%)
|
65 (25.1) |
11 (31.4) |
44 (25.0) |
10 (20.8) |
0.55 |
| Prior MI, n (%)
|
33 (12.7) |
6 (17.1) |
25 (14.2) |
2 (4.2) |
0.13 |
| Prior PCI, n (%)
|
84 (32.4) |
12 (34.3) |
61 (34.7) |
11 (22.9) |
0.30 |
| Prior CABG, n (%)
|
34 (13.1) |
7 (20.0) |
24 (13.6) |
3 (6.3) |
0.18 |
| Prior ischemic stroke, n (%)
|
69 (26.6) |
11 (31.4) |
48 (27.3) |
10 (20.8) |
0.53 |
| Prior intracranial bleeding, n (%)
|
11 (4.2) |
5 (14.3) |
6 (3.4) |
0 (0.0) |
0.004 |
| Prior PAD, n (%)
|
91 (35.1) |
16 (45.7) |
60 (34.1) |
15 (31.3) |
0.35 |
| Dyslipidemia, n (%)
|
146 (56.4) |
15 (42.9) |
105 (59.7) |
26 (54.2) |
0.18 |
| Diabetes mellitus, n (%)
|
170 (65.6) |
22 (62.9) |
119 (67.6) |
29 (60.4) |
0.61 |
| Hypertension, n (%)
|
201 (77.6) |
25 (71.4) |
143 (81.3) |
33 (68.8) |
0.12 |
| Current smoking, n (%)
|
35 (13.5) |
1 (2.9) |
25 (14.2) |
9 (18.8) |
0.10 |
| Dialysis, n (%)
|
135 (52.1) |
29 (82.9) |
97 (55.1) |
9 (18.8) |
＜0.001 |
| CKD, n (%)
|
202 (78.0) |
35 (100) |
146 (83.0) |
21 (43.8) |
＜0.001 |
| Atrial fibrillation, n (%)
|
69 (26.6) |
18 (51.4) |
49 (27.8) |
2 (4.2) |
＜0.001 |
| Ejection fraction, % |
60.0 (47.0, 66.0) |
57.2 (44.0, 66.0) |
60.0 (46.8, 66.0) |
63.0 (51.2, 67.0) |
0.37 |
| Ejection fraction ＜40%, n (%)
|
37 (15.7) |
7 (21.2) |
24 (14.6) |
6 (15.4) |
0.64 |
| -Laboratory data at presentation
|
|
|
|
|
|
| Creatinine, mg/dL |
3.18 (0.90, 6.16) |
5.02 (4.08, 6.14) |
3.50 (0.95, 6.56) |
0.90 (0.65, 1.26) |
＜0.001 |
| e GFR, ml/min/1.73m2
|
14.5 (6.9, 55.6) |
8.2 (6.1, 11.0) |
12.6 (6.8, 50.6) |
61.8 (43.4, 80.5) |
＜0.001 |
| Hemoglobin, g/dL |
11.2 (10.2, 12.8) |
10.2 (9.2, 10.5) |
11.1 (10.2, 12.8) |
13.0 (12.0, 13.5) |
＜0.001 |
| Hemoglobin ＜11g/dL, n (%)
|
117 (45.2) |
33 (94.3) |
80 (45.5) |
4 (8.3) |
＜0.001 |
| Platelet, ×104/μL
|
21.0 (16.9, 26.7) |
15.7 (12.4, 21.6) |
21.2 (17.1, 26.8) |
24.2 (18.2, 28.3) |
＜0.001 |
| Platelet ＜10×104/μL, n (%)
|
13 (5.0) |
6 (17.1) |
7 (4.0) |
0 (0.0) |
0.001 |
| Albumin, g/dL |
3.4 (2.9, 3.7) |
3.1 (2.8, 3.5) |
3.3 (2.8, 3.7) |
3.7 (3.4, 4.0) |
＜0.001 |
| Albumin ＜3.5g/dL, n (%)
|
141 (54.4) |
25 (71.4) |
101 (57.4) |
15 (31.3) |
0.001 |
| -Rutherford category
|
|
|
|
|
0.12 |
| 4, n (%)
|
43 (16.6) |
5 (14.3) |
25 (14.2) |
13 (27.1) |
|
| 5, n (%)
|
156 (60.2) |
24 (68.6) |
104 (59.1) |
28 (58.3) |
|
| 6, n (%)
|
60 (23.2) |
6 (17.1) |
47 (26.7) |
7 (14.6) |
|
| -Lesion location
|
|
|
|
|
0.36 |
| Aortoiliac, n (%)
|
21 (8.1) |
1 (2.9) |
14 (8.0) |
6 (12.5) |
|
| Femoropopliteal, n (%)
|
385 (32.8) |
13 (37.1) |
54 (30.7) |
18 (37.5) |
|
| Infrapopliteal, n (%)
|
51 (19.7) |
4 (11.4) |
37 (21.0) |
10 (20.8) |
|
| Multisegment, n (%)
|
102 (39.4) |
17 (48.6) |
71 (40.3) |
14 (29.2) |
|
| -WIfI clinical stage
|
|
|
|
|
0.27 |
| 1, n (%)
|
15 (5.8) |
1 (2.9) |
8 (4.5) |
6 (12.5) |
|
| 2, n (%)
|
37 (14.3) |
2 (5.9) |
28 (15.9) |
7 (14.6) |
|
| 3, n (%)
|
78 (30.2) |
12 (35.3) |
52 (29.5) |
14 (29.2) |
|
| 4, m (%) |
128 (49.6) |
19 (55.9) |
88 (50.0) |
21 (43.8) |
|
| -Procedure characteristics
|
|
|
|
|
|
| Puncture under palpation, n (%)
|
106 (40.9) |
10 (28.6) |
74 (42.0) |
22 (45.8) |
0.25 |
| Multiple puncture sites, n (%)
|
48 (18.5) |
9 (25.7) |
27 (15.3) |
12 (25.0) |
0.16 |
| Antegrade access, n (%)
|
149 (57.5) |
23 (65.7) |
103 (58.5) |
23 (47.9) |
0.24 |
| Manual compression, n (%)
|
175 (67.6) |
27 (77.1) |
116 (65.9) |
32 (66.7) |
0.43 |
| -Medication at discharge
|
|
|
|
|
|
| Aspirin use, n (%)
|
169 (65.3) |
18 (51.4) |
116 (65.9) |
35 (72.9) |
0.12 |
| Thienopyridine use, n (%)
|
174 (67.2) |
22 (62.9) |
120 (68.2) |
32 (66.7) |
0.83 |
| Cilostazol use, n (%)
|
65 (25.1) |
11 (31.4) |
39 (22.2) |
15 (31.3) |
0.28 |
| DAPT use, n (%)
|
102 (39.4) |
8 (22.9) |
70 (39.8) |
24 (50.0) |
0.043 |
| Warfarin use, n (%)
|
39 (15.1) |
15 (42.9) |
23 (13.1) |
1 (2.1) |
＜0.001 |
| DOAC use, n (%)
|
25 (9.7) |
2 (5.7) |
23 (13.1) |
0 (0.0) |
0.017 |
| Statin use, n (%)
|
121 (46.7) |
15 (42.9) |
84 (47.7) |
22 (45.8) |
0.86 |
| ACE-I use, n (%)
|
16 (6.2) |
1 (2.9) |
13 (7.4) |
2 (4.2) |
0.48 |
| ARB use, n (%)
|
97 (37.6) |
7 (20.0) |
68 (38.9) |
22 (45.8) |
0.047 |
| β-blocker use, n (%)
|
100 (38.6) |
18 (51.4) |
62 (35.2) |
20 (41.7) |
0.18 |
| PPI use, n (%)
|
212 (81.9) |
28 (80.0) |
144 (81.8) |
40 (83.3) |
0.93 |
Values are numbers (%) or mean±SD. Values are also presented as median (Q1, Q3). HBR, high bleeding risk; BMI, body mass index; CFS, clinical frailty scale; MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; PAD, peripheral artery disease; CKD, chronic kidney disease; e GFR, estimated glomerular filtration rate; DAPT, dual antiplatelet therapy; TAPT, triple antiplatelet therapy; DOAC, direct oral anticoagulants; ACE-I, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; PPI proton pump inhibitor
There were no significant differences in the severity of lower limb ischemia, lesion location, WIfI clinical stage, or procedural characteristics (Table 1). Further, it was noted that dual antiplatelet therapy was frequently administered in the lower HBR score groups. On the other hand, anticoagulant therapy was frequently performed in the higher HBR score groups (Table 1).
All-Cause Mortality
During the follow-up period, 82 patients (39.6%) died within two years of EVT (Fig.3A). The Kaplan–Meier analysis of cumulative all-cause mortality according to the cut-off HBR score based on the CART model is presented in Fig.3B. All-cause mortality increased significantly with an increase in the HBR score (low HBR score; 6.8% vs. moderate HBR score; 41.6% vs. high HBR score; 80.2%, respectively; p＜0.001).
Cause of Death
Fig.4 depicts the distribution of the various causes of death observed in the study population. Cardiac and non-cardiac deaths were observed in 23 (28.0%) and 59 (72.0%) patients, respectively. The most frequent cause of death was infection (36.6%). Only one patient died because of a bleeding event (intracranial hemorrhage).
Predictors of All-Cause Mortality
The Cox multivariate regression analysis identified the following as independent predictors of all-cause mortality within two years of EVT: ARC-HBR score (moderate score: adjusted HR, 7.78; 95% CI, 1.88–32.10; p=0.005; high score: adjusted HR, 20.62; 95% CI, 4.80–88.51; p＜0.001), body mass index (BMI) (adjusted HR, 0.11; 95% CI, 0.025–0.51; p=0.005), left ventricular ejection fraction (LVEF) (adjusted HR, 0.27; 95% CI, 0.15–0.51; p＜0.001), and serum albumin level (adjusted HR, 0.062; 95% CI, 0.016–0.24; p＜0.001) (Table 2).
Table 2. Univariate and Multivariate regression analysis for the association between all-cause mortality within 2 years and clinical findings (
n = 259)
|
Univariate analysis |
Multivariate analysis |
| All-cause mortality |
| Factors for predicting |
HR |
95% CI |
p value
|
HR |
95% CI |
p value
|
| -Patient clinical characteristics
|
|
|
|
|
|
|
| ARC-HBR score |
|
|
|
|
|
|
| Low score |
1.00 (Ref.) |
|
|
1.00 (Ref.) |
|
|
| Moderate score |
6.80 |
2.13-21.75 |
0.001 |
7.78 |
1.88-32.10 |
0.005 |
| High score |
18.97 |
5.67-63.43 |
＜0.001 |
20.62 |
4.80-88.51 |
＜0.001 |
| Male |
0.69 |
0.45-1.08 |
0.10 |
0.66 |
0.41-1.07 |
0.090 |
| BMI |
0.073 |
0.018-0.30 |
＜0.001 |
0.11 |
0.025-0.51 |
0.005 |
| CFS |
|
|
|
|
|
|
| CFS 1-3 |
1.00 (Ref.) |
|
|
|
|
|
| CFS 4-5 |
1.55 |
0.73-3.32 |
0.25 |
|
|
|
| CFS 6~ |
2.81 |
1.37-5.73 |
0.005 |
|
|
|
| Prior heart failure |
1.89 |
1.19-3.00 |
0.007 |
|
|
|
| Prior MI |
2.01 |
1.15-3.52 |
0.015 |
|
|
|
| Prior PCI |
1.91 |
1.23-2.95 |
0.004 |
|
|
|
| Prior CABG |
1.82 |
1.05-3.14 |
0.033 |
|
|
|
| Prior PAD |
0.91 |
0.58-1.44 |
0.69 |
|
|
|
| Dyslipidemia |
0.75 |
0.48-1.15 |
0.19 |
|
|
|
| Diabetes mellitus |
1.00 |
0.63-1.58 |
1.00 |
|
|
|
| Hypertension |
0.82 |
0.49-1.37 |
0.45 |
|
|
|
| Current smoking |
0.37 |
0.15-0.90 |
0.029 |
|
|
|
| Atrial fibrillation |
1.63 |
1.02-2.61 |
0.042 |
|
|
|
| Ejection fraction |
0.37 |
0.21-0.66 |
0.001 |
0.27 |
0.15-0.51 |
＜0.001 |
| -Laboratory data at presentation
|
|
|
|
|
|
|
| Albumin |
0.055 |
0.018-0.17 |
＜0.001 |
0.062 |
0.016-0.24 |
＜0.001 |
| -Rutherford category
|
|
|
|
|
|
|
| 4 |
1.00 (Ref.) |
|
|
|
|
|
| 5 |
1.19 |
0.63-2.24 |
0.59 |
|
|
|
| 6 |
1.83 |
0.91-3.71 |
0.091 |
|
|
|
| -Lesion location
|
|
|
|
|
|
|
| Aortoiliac |
1.00 (Ref.) |
|
|
|
|
|
| Femoropopliteal |
1.25 |
0.48-3.27 |
0.65 |
|
|
|
| Infrapopliteal |
1.31 |
0.47-3.63 |
0.61 |
|
|
|
| Multisegment |
1.97 |
0.78-5.00 |
0.15 |
|
|
|
| -WIfI clinical stage
|
|
|
|
|
|
|
| 1, n (%)
|
1.00 (Ref.) |
|
|
|
|
|
| 2, n (%)
|
1.43 |
0.39-5.27 |
0.59 |
|
|
|
| 3, n (%)
|
2.03 |
0.61-6.71 |
0.25 |
|
|
|
| 4, m (%) |
2.36 |
0.73-7.62 |
0.15 |
|
|
|
| -Medication at discharge
|
|
|
|
|
|
|
| DAPT use |
1.07 |
0.56-2.04 |
0.83 |
|
|
|
| Statin use |
1.02 |
0.66-1.57 |
0.93 |
|
|
|
| ACE-I use |
0.87 |
0.35-2.15 |
0.76 |
|
|
|
| ARB use |
0.63 |
0.39-1.01 |
0.057 |
|
|
|
| β-blocker use |
1.51 |
0.98-2.33 |
0.064 |
|
|
|
Covariates introduced into the multivariate model were: HBR-score, sex, BMI, CFS, prior heart failure, prior MI, prior PCI, prior CABG, current smoking, atrial fibrillation, ejection fraction, and albumin. HR, hazard ratio; CI, confidence interval; other abbreviations as in Table 1.
A total of 45 major bleeding events were observed in 38 patients (17.3%) within two years of EVT (Fig.5A). Bleeding events were significantly more frequent in the higher HBR score groups (6.5% vs. 18.0% vs. 34.6%, respectively; p=0.014) (Fig.5B).
Moreover, mortality was compared between patients with and without bleeding events (Supplementary Fig.1). The mortality in patients with bleeding events was significantly higher than that in patients without bleeding events (57.1% vs. 36.6%, respectively; p=0.006).
Discussion
This study demonstrated an association between the ARC-HBR criteria and 2-year mortality in patients with CLTI after EVT. HBR with moderate and high ARC-HBR scores, as well as lower BMI, LVEF, and serum albumin levels, were identified as independent predictors of all-cause mortality within two years of EVT.
Previously, elderly age6, 18-22) and comorbidities, such as anemia20) and renal failure6, 19, 21, 22), were reported as predictive factors of mortality in patients with CLTI. Importantly, the ARC-HBR criteria include several comorbidities that can worsen the clinical outcomes. Indeed, the current study revealed that elderly patients with CKD and anemia were common among patient populations with HBR. The ARC-HBR score is a composite concept consisting of several factors, including patient and clinical characteristics. Patients with higher HBR scores might be at higher risk of both mortality and bleeding events, creating a “risk burden.” Therefore, it can be inferred that patients with higher HBR scores have a compromised baseline health status, resulting in poorer outcomes. In fact, our study revealed that patients with bleeding events had significantly higher mortality compared with those without bleeding events. This result can be attributed to the worse background of the patients with bleeding events, rather than bleeding events directly causing mortality. Although the ARC-HBR criteria were originally established to assess bleeding events after a percutaneous intervention, they can help evaluate not only bleeding events but also prognosis in patients with CLTI who are generally at HBR.
In patients with CLTI, the 2-year life expectancy is one of the most important factors in making a treatment decision (bypass or EVT). It has been reported previously that the 2-year life expectancy in patients with CLTI is greater with bypass surgery than EVT, indicating that bypass surgery is an appropriate first-line revascularization strategy for patients with a predicted life expectancy of more than two years8, 9). However, Iida et al. reported anemia and renal failure (including hemodialysis) as less favorable factors for surgical reconstruction in patients with CLTI23). Since these elements are part of the HBR criteria, using these criteria for evaluating patients with CLTI can prove to be beneficial in deciding the optimal treatment strategy. In other words, bypass surgery can be considered an appropriate management strategy for patients with a zero or low HBR score and a good vein conduit24). However, the present study only included patients with CLTI who underwent EVT and did not assess prognosis after bypass surgery. Therefore, further studies are needed to assess the relationship between patients with HBR and CLTI who underwent bypass surgery in order to improve the decision-making process for revascularization strategies.
Several cardiac-associated factors, including heart failure21), low LVEF6, 22), and a history of myocardial infarction20), have been identified as predictive factors of mortality in patients with CLTI. The current study also revealed low LVEF as one of the strongest predictors of mid-term mortality in patients with CLTI, indicating that impaired LVEF increases the risk of cardiovascular events in patients with LEAD, as previously reported25). In addition to being accepted as markers of malnutrition and frailty26), BMI and serum albumin levels have been reported as predictive factors of mortality in patients with CLTI18, 27-31). Generally, patients with a low BMI and serum albumin level are at a higher risk of mortality due to non-cardiac causes, such as malignancies and cachexia, malnutrition, depression, and immunodeficiency caused by chronic diseases32-34). Therefore, our findings highlight the impact of BMI and serum albumin levels on the mid-term mortality of patients with CLTI, especially non-cardiac death.
Despite significant advances in medical technology, patients with CLTI generally exhibit a high risk of mortality in both acute and chronic phases of the pathology. In the current study, the mortality rate of patients with CLTI was found to be as high as 40%. Previous reports have shown that patients with CLTI have a worse background compared to those with intermittent claudication35, 36). Likewise, in the present study, it was observed that patients diagnosed with CLTI tended to be elderly (average age, 76.2 years) and had high rates of other comorbidities (BMI ＜18.5 kg/m2, 22%; undergoing hemodialysis, 52%; hemoglobin ＜11 g/dL, 45%). As a result, patients with CLTI are at a higher risk of both cardiac and non-cardiac death than patients with intermittent claudication35). Thus, it is crucial to monitor patients with CLTI not only in terms of limb condition but also life expectancy.
This study had several limitations. First, this was a non-randomized retrospective observational study. Second, the study population and the number of events were relatively small, which might have resulted in low statistical power. Moreover, the current study included only Japanese patients; hence, caution should be exercised when extrapolating the results of this study to populations of other countries. Finally, the cut-off HBR score was derived from a small cohort, and the generalizability of the cut-off value was not assessed. Further validation studies are warranted to investigate the relationship between HBR and adverse events in patients with CLTI.
Conclusion
The ARC-HBR score was successful in predicting the 2-year mortality in patients with CLTI who underwent EVT. Therefore, this scoring system can be considered a useful parameter for determining the best revascularization strategy in patients with CLTI.
Conflict of Interest
The authors declare that they have no conflict of interest.
Notice of Grant Support
None.
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