Abstract
Background: Percutaneous coronary intervention (PCI) is a good option for patients with ischemic cardiomyopathy (ICM) at high operative risk. However, evidence shows little benefit of PCI, potentially due to heterogeneity in ICM. Here, we applied latent class analysis (LCA) to clinical data to characterize ICM phenotypes based on clinical features and to assess differences in clinical outcomes.
Methods and Results: LCA was performed on data from 492 patients with a left ventricular (LV) ejection fraction <50% who underwent PCI. Primary outcomes included all-cause mortality and heart failure (HF) hospitalization. The optimal number of clinical phenotypes was 3. Phenotype 1 (n=101) was characterized by severe chronic kidney disease and a high frequency of hemodialysis. Phenotype 2 (n=192) included men with early-onset ICM, a high frequency of lifestyle-related diseases, and a high body mass index. Phenotype 3 (n=199) included older adults with a high prevalence of atrial fibrillation, moderate/severe mitral regurgitation, and high B-type natriuretic peptide levels. The risk of combined all-cause mortality and HF hospitalization was significantly lower for Phenotype 2 than the other phenotypes. LV reverse remodeling (LVRR) was associated with a lower incidence of the primary outcome in Phenotype 3, but not in the other phenotypes.
Conclusions: We identified differences in clinical outcomes and LVRR across clinical ICM phenotypes after PCI, suggesting distinct underlying mechanisms across ICM phenotypes that may benefit from targeted treatments.
Heart failure (HF) is a common disease, with its prevalence expected to increase in developed countries.1 Despite recent advances in the treatment strategies for chronic HF, patient outcomes remain suboptimal, highlighting the unmet need to identify alternative therapeutic targets.2–4 Reversible ischemia and myocardial infarction (MI) in coronary artery disease (CAD) are a few of the most common etiologies of HF,5–7 and the role of revascularization in addition to medical therapy in managing HF remains unclear.
For patients with ischemic cardiomyopathy (ICM), although coronary artery bypass grafting (CABG) is considered the standard treatment, with evidence that it improves clinical outcomes,8 PCI is also a good option for patients at high operative risk or high age. However, the most recent randomized controlled trial noted that PCI did not affect the clinical outcome or improve left ventricular ejection fraction (LVEF) or quality of life compared with optimal medical therapy (OMT).9 One reason why PCI did not result in better clinical outcomes than OMT could be that CAD with left ventricular (LV) dysfunction represents patient heterogeneity regarding comorbidity, myocardial condition, and the location and severity of coronary vessel stenosis. Therefore, the impact of PCI differs depending on the patient.
Latent class analysis (LCA) identifies groups of individuals with similar clinical profiles and has been used to identify, characterize, and validate disease phenotypes. LCA assumes the existence of subgroups, or latent classes, within a population, explaining patterns of association between clinical features and categorizing subgroups based on prevalent feature patterns. Therefore, LCA has been used to develop diagnostic criteria for complex diseases, identify disease subgroups for risk stratification, and determine the likelihood of treatment response.10–12 Therefore, in the present study we applied LCA to both continuous and categorical data to examine the clinical phenotypes of patients in a cohort with LV dysfunction who underwent PCI, and observed differences in clinical outcomes after PCI for each phenotype.
Methods
Study Population
This multicenter real-world observational study enrolled 516 patients with LVEF <50% estimated through echocardiography within 180 days before the patient underwent PCI for revascularization to treat stable CAD between January 2016 and December 2018 at 7 different hospitals in Japan and for whom follow-up echocardiography was available. Of these patients, 1 was undergoing cardiac surgery and 23 had a history of implantable cardioverter defibrillator (ICD) implantation and cardiac resynchronization therapy with a defibrillator; all these patients were excluded from the study. Thus, 492 patients were included in the study.
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Research Ethics Committee of Osaka University Hospital. The requirement for informed consent was waived due to the retrospective nature of the study.
Study Definitions
We typically followed the current guidelines when patients were referred for revascularization.13 Revascularization via PCI was considered for patients with stable angina who had angiographically significant stenosis (≥75% diameter stenosis of a major epicardial artery segment or ≥50% diameter stenosis in the left main coronary artery or functionally significant stenosis). Incomplete revascularization was defined as the state in which ≥1 clinically significant lesions of a major coronary artery vessel (>2.5 mm in diameter) remain untreated. Echocardiographic variables were analyzed by experienced sonographers following the American Society of Echocardiography.14 LVEF was typically assessed using the Teichholz method, although the modified Simpson’s rule was used if abnormal LV wall motion was present. Hypertension was defined as systolic blood pressure >140 mmHg, diastolic blood pressure >90 mmHg, or medical treatment for hypertension on admission. Diabetes was defined as HbA1c >6.5% (NGSP) or on medical treatment for diabetes. Dyslipidemia was defined as a low-density lipoprotein cholesterol >140 mg/dL or on medical treatment for dyslipidemia.
Outcomes of Interest
Clinical events were confirmed using information from electronic health records. This was based on a review of primary diagnoses documented in each discharge summary during the follow-up period. The primary endpoint of the analysis was a composite of all-cause mortality and hospitalization for HF. The events included in this analysis were all-cause mortality, hospitalization for HF, sudden death, MI, target lesion revascularization (TLR), target vessel revascularization (TVR), and non-TVR. LV reverse remodeling (LVRR) was defined as an improvement in LVEF ≥10% after PCI relative to baseline, as estimated through transthoracic echocardiography.15 Follow-up days were calculated from the date on which PCI was performed. Follow-up echocardiography was performed at 317 days (interquartile range [IQR] 220–382 days) after PCI.
Phenotyping by LCA
Variables were selected before the LCA. Missing variables, one-sided variables that were highly correlated, variables with little variability, and variables that appeared heuristically irrelevant to the analysis were excluded. Finally, the following 18 variables were selected: age, sex (male), body weight, hypertension, dyslipidemia, diabetes, smoking, previous MI, atrial fibrillation (AF), LVEF, moderate or severe mitral regurgitation (MR), hemoglobin level, estimated glomerular filtration rate (eGFR), angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) use, β-blocker use, mineralocorticoid receptor antagonist use, left anterior descending lesions, and chronic total occlusion lesions. Of these variables, continuous variables, such as age (<65, 65–70, 70–75, 75–80, and >80 years), body weight (<50, 50–60, 60–70, and >70 kg), LVEF (<30%, 30–40%, and 40–50%), hemoglobin (<10, 10–13, 13–16, and >16 g/dL in men; <10, 10–12, 12–14.5, and >14.5 g/dL in women), and eGFR (<15, 15–30, 30–45, 45–60, and >60 mL/min/1.73 m2) were categorized and used in the analysis. Patients on hemodialysis were included in the eGFR category <15 mL/min/1.73 m2. An LCA based on the 18 selected variables was performed, and the phenotypes of CAD with LV dysfunction were identified using maximum likelihood estimation. The optimal number of phenotypes was determined based on the Bayesian information criterion, which is minimized when the number of classes is 3, and the optimal number of classes generated using LCA was determined to be 3 (Supplementary Table 1). To measure classification performance, 100 bootstrap samplings were performed, and the adjusted Rand index (ARI) was compared with class numbers 2, 3, 4, and 5. The ARI was maximized when the number of classes was 3 (Supplementary Table 1).
Statistical Analysis
Categorical variables are presented as counts and percentages, whereas continuous variables are presented as the mean ± SD or median with IQR depending on the normality of their distribution. Continuous variables were compared between the groups using analysis of variance or the Kruskal-Wallis test depending on data distribution. Categorical variables were compared among 3 groups using the Chi-squared test or Fisher’s exact test, as appropriate. The presence or absence of LVRR was compared among the 3 phenotypes. Prognoses in different phenotypes and those stratified based on the presence or absence of LVRR in each phenotype were assessed using Kaplan-Meier survival curves and log-rank tests. All tests were 2-sided with a 5% significance level. Statistical analyses were performed using R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org).
Results
Clinical Characteristics
This study enrolled 492 patients from 7 hospitals in Japan between January 2016 and December 2018. The median age was 73 years, and 79.1% of patients were male. The median eGFR was 51.1 mL/min/1.73 m2, and 14.8% of patients were on hemodialysis. Overall, 57.0%, 39.0%, and 17.3% of patients had diabetes, a previous MI, and a history of AF, respectively (Table 1).
Table 1.
Baseline Characteristics Across Clinical Phenotypes Identified Using Latent Class Analysis
| |
Phenotype 1 |
Phenotype 2 |
Phenotype 3 |
P value |
| No. patients |
101 |
192 |
199 |
|
| Age (years) |
69.00 [61.00–76.00] |
67.00 [60.00–72.00] |
79.00 [75.00–83.00] |
<0.001 |
| Male sex |
92 (91.1) |
186 (96.9) |
111 (55.8) |
<0.001 |
| BMI (kg/m2) |
22.31 [20.50–25.59] |
24.60 [22.18–26.45] |
21.21 [19.33–23.59] |
<0.001 |
| Diabetes |
84 (83.2) |
99 (51.6) |
97 (48.7) |
<0.001 |
| Hypertension |
85 (84.2) |
136 (70.8) |
150 (75.4) |
0.042 |
| Dyslipidemia |
67 (66.3) |
176 (91.7) |
161 (80.9) |
<0.001 |
| Smoking |
24 (23.8) |
77 (40.1) |
20 (10.1) |
<0.001 |
| Hemodialysis |
70 (69.3) |
0 (0.0) |
3 (1.5) |
<0.001 |
| History of MI |
25 (24.8) |
97 (50.5) |
70 (35.2) |
<0.001 |
| History of PCI |
47 (46.5) |
85 (44.3) |
88 (44.2) |
0.918 |
| History of CABG |
11 (10.9) |
24 (12.5) |
14 (7.0) |
0.185 |
| AF |
11 (10.9) |
26 (13.5) |
48 (24.1) |
0.004 |
| Medications used |
| ACEi/ARB |
42 (41.6) |
126 (65.6) |
116 (58.3) |
<0.001 |
| β-blocker |
60 (59.4) |
148 (77.1) |
127 (63.8) |
0.002 |
| Calcium blocker |
45 (44.6) |
51 (26.6) |
51 (25.6) |
0.001 |
| MRA |
1 (1.0) |
59 (30.7) |
68 (34.2) |
<0.001 |
| Diuretic |
39 (38.6) |
83 (43.2) |
122 (61.3) |
<0.001 |
| SGLT2 inhibitor |
1 (1.0) |
18 (9.4) |
6 (3.0) |
0.002 |
| Statin |
45 (44.6) |
162 (84.4) |
143 (71.9) |
<0.001 |
| Aspirin |
98 (97.0) |
185 (96.4) |
189 (95.0) |
0.648 |
| P2Y12 inhibitor |
97 (96.0) |
183 (95.3) |
191 (96.0) |
0.934 |
| VKA |
7 (6.9) |
23 (12.0) |
16 (8.0) |
0.264 |
| DOAC |
5 (5.0) |
17 (8.9) |
33 (16.6) |
0.004 |
Unless indicated otherwise, data are given as the median [interquartile range] or n (%). ACEi, angiotensin-converting enzyme inhibitor; AF, atrial fibrillation; ARB, angiotensin receptor blocker; BMI, body mass index; CABG, coronary artery bypass grafting; DOAC, direct oral anticoagulant; MI, myocardial infarction; MRA, mineralocorticoid receptor antagonist; PCI, percutaneous coronary intervention; SGLT2, sodium-glucose cotransporter 2; VKA, vitamin K antagonist.
Identified Phenotypes
The optimal number of clinical phenotypes was 3. Patients’ baseline characteristics, laboratory data and lesion characteristics, and echocardiographic parameters across the 3 phenotypes are presented in Tables 1–3, respectively. Patients were subcategorized into 3 phenotype groups. Phenotype 1 (n=101; 20.5%) had a high prevalence of severe chronic kidney disease (CKD), with a high frequency of hemodialysis and diabetes (Table 1). Phenotype 2 (n=192; 39.0%) primarily comprised males with early-onset MI and was characterized by a high frequency of lifestyle-related diseases, including dyslipidemia, smoking, previous MI, and a high body mass index (Table 1). Phenotype 3 (n=199; 40.4%) comprised older people; in addition, the proportion of women was higher in this group, with a high prevalence of AF (Table 1). Moderate/severe MR was more frequent in Phenotype 3 (Table 3), and B-type natriuretic peptide (BNP) and N-terminal pro BNP levels were higher in Phenotype 3 than in Phenotype 2 (Table 2). No significant differences were found among the 3 phenotypes in the frequency of PCI for a left main trunk lesion, PCI for multivessel disease, and the presence of incomplete revascularization (Table 2). Phenotypes 1, 2, and 3 were labeled according to the group characteristics as “severe CKD,” “early onset and lifestyle-related diseases,” and “older age and congestion,” respectively.
Table 2.
Laboratory Data and Lesion Characteristics Across Clinical Phenotypes Identified Using Latent Class Analysis
| |
Phenotype 1 |
Phenotype 2 |
Phenotype 3 |
P value |
| No. patients |
101 |
192 |
199 |
|
| HDL-C (mg/dL) |
43.00 [36.00–56.00] |
43.00 [35.00–51.50] |
46.00 [38.50–54.00] |
0.172 |
| LDL-C (mg/dL) |
91.00 [67.00–117.00] |
95.00 [78.00–121.50] |
92.00 [75.00–112.25] |
0.319 |
| HbA1c (%) |
6.20 [5.57–6.93] |
6.20 [5.80–7.05] |
6.20 [5.70–6.80] |
0.514 |
| Creatinine (mg/dL) |
5.43 [3.00–7.50] |
0.92 [0.80–1.09] |
0.99 [0.82–1.31] |
<0.001 |
| eGFR (mL/min/1.73 m2) |
8.73 [6.24–16.89] |
63.54 [52.81–75.41] |
49.17 [33.85–60.02] |
<0.001 |
| BNP (pg/mL) |
703.50 [244.60–1,464.70] |
128.45 [61.98–319.35] |
339.55 [145.20–587.45] |
<0.001 |
| NT-proBNP (pg/mL) |
9,769.50 [2,713.50–16,356.50] |
818.95 [328.17–2,273.00] |
3,060.00 [1,060.00–7,521.00] |
<0.001 |
| Hemoglobin (g/dL) |
11.40 [10.30–12.30] |
14.05 [13.20–15.00] |
11.90 [10.70–13.20] |
<0.001 |
| PCI location |
| LAD |
47 (46.5) |
117 (60.9) |
127 (63.8) |
0.013 |
| RCA |
56 (55.4) |
78 (40.6) |
75 (37.7) |
0.011 |
| LCX |
32 (31.7) |
58 (30.2) |
50 (25.1) |
0.389 |
| LMT |
5 (5.0) |
14 (7.3) |
14 (7.0) |
0.727 |
| Multivessel disease |
29 (28.7) |
60 (31.2) |
59 (29.6) |
0.890 |
| No. lesions |
1 [1–2] |
1 [1–2] |
1 [1–2] |
0.447 |
| CTO |
8 (7.9) |
45 (23.4) |
13 (6.5) |
<0.001 |
| SYNTAX score |
14.5 [8–22.5] |
15 [9–21] |
13 [8–21] |
0.333 |
| Incomplete revascularization |
61 (60.4) |
111 (57.8) |
120 (60.3) |
0.857 |
Unless indicated otherwise, data are given as the median [interquartile range] or n (%). BNP, B-type natriuretic peptide; CTO, chronic total occlusion; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LAD, left anterior descending; LCX, left circumflex; LDL-C, low-density lipoprotein cholesterol; LMT, left main trunk; NT-proBNP, N-terminal pro B-type natriuretic peptide; PCI, percutaneous coronary intervention; RCA, right coronary artery.
Table 3.
Echocardiographic Characteristics Across Clinical Phenotypes Identified Using Latent Class Analysis
| |
Phenotype 1 |
Phenotype 2 |
Phenotype 3 |
P value |
| No. patients |
101 |
192 |
199 |
|
| LVDd (mm) |
56.00 [52.00–60.80] |
57.00 [53.00–62.00] |
54.00 [49.00–58.00] |
<0.001 |
| LVDs (mm) |
45.00 [40.15–49.20] |
46.00 [41.00–53.00] |
43.00 [38.00–49.20] |
<0.001 |
| LVEF (%) |
42.00 [35.40–46.00] |
38.00 [31.08–45.00] |
38.00 [32.00–45.00] |
0.022 |
| LA diameter (mm) |
44.00 [39.30–49.00] |
42.75 [39.00–47.02] |
42.00 [38.00–46.00] |
0.065 |
| IVST (mm) |
10.00 [9.00–11.00] |
9.00 [8.00–10.85] |
9.00 [8.00–10.30] |
0.009 |
| LVPW (mm) |
10.00 [9.00–11.00] |
9.00 [8.00–10.35] |
9.00 [8.00–10.30] |
0.001 |
| MR |
|
|
|
<0.001 |
| None/trivial |
52 (51.5) |
129 (67.2) |
78 (39.2) |
|
| Mild |
35 (34.7) |
50 (26.0) |
74 (37.2) |
|
| Moderate |
12 (11.9) |
13 (6.8) |
32 (16.1) |
|
| Severe |
2 (2.0) |
0 (0.0) |
15 (7.5) |
|
| LVRR (%) |
28 (27.7) |
73 (38.0) |
96 (48.2) |
0.002 |
Unless indicated otherwise, data are given as the median [interquartile range] or n (%). IVST, intraventricular septum thickness; LA, left atrium; LVDd, left ventricular end-diastolic diameter; LVDs, left ventricular end-systolic diameter; LVEF, left ventricular ejection fraction; LVPW, left ventricular posterior wall thickness; LVRR, left ventricular reverse remodeling; MR, mitral regurgitation.
Clinical Outcomes and LVRR Across Phenotypes
Patients in Phenotype 2 (early onset and lifestyle-related disease) had a significantly lower risk of the combined primary endpoint (log-rank test, P<0.001), all-cause mortality alone (log-rank test, P<0.001), and hospitalization for HF alone (log-rank test, P<0.001) than those in Phenotype 1 (severe CKD) and Phenotype 3 (older age and congestion; Figure 1). Sudden death, TLR, TVR, and non-TVR were most frequently observed in Phenotype 1 (Supplementary Figure).
LVRR was achieved in 38.4% (189/492) of all patients. LVRR was more frequently observed in Phenotype 3 (Table 3), and the change in LVEF was greater in Phenotype 3 (Figure 2) than in Phenotypes 1 and 2. The baseline characteristics of patients with and without LVRR within each phenotype group are presented in Supplementary Table 2. In patients with LVRR, baseline LVEF was lower across all phenotypes (Supplementary Table 3). During the follow-up period (median 2.9 years; IQR 2.2–3.8 years), across all 3 phenotype groups, patients with LVRR had a better prognosis than those without LVRR (log-rank test, P=0.003; Figure 3). For Phenotypes 1 and 2, no significant differences in prognosis were observed between patients with and without LVRR, whereas LVRR was associated with a higher rate of the composite of all-cause mortality and hospitalization for HF in Phenotype 3 (P=0.002; Figure 3).
A summary of the characteristics of the 3 phenotypes is shown in the Central Figure.
Discussion
We extracted data for 492 patients with LV dysfunction (LVEF <50% at initial echocardiography) from consecutive patients who underwent PCI at 7 different institutions in Japan. We identified 3 phenotypes of patients with CAD with LV dysfunction based on 18 standard clinical features using LCA. We characterized the clinical outcomes, including all-cause mortality, HF admission, and the response of LV function to PCI, in the 3 phenotype groups. The main results of this study are as follows: (1) Phenotypes 1, 2, and 3 were labeled according to group characteristics as “severe CKD,” “early onset and lifestyle-related disease,” and “older age and congestion,” respectively; (2) patients with Phenotype 2 (early onset and lifestyle-related disease) had a significantly lower risk of the composite primary endpoint than those with Phenotype 1 (severe CKD) and Phenotype 3 (older age and congestion); and (3) in the entire study cohort, patients with LVRR had a significantly lower risk of the composite primary endpoint than those without LVRR. However, when examining the different phenotype groups, the decreased risk of the composite primary endpoint in patients with LVRR was observed only in Phenotype 3, which had the highest number of patients with LVRR.
Heterogeneous Spectrum and Clustering of CAD With LV Dysfunction
CAD with LV dysfunction is one of the most common etiologies of HF,5–7 and PCI is a necessary strategy for patients at high operative risk or advanced age. However, a randomized controlled trial demonstrated that PCI did not affect clinical outcomes or improve LVEF compared with OMT.10 Several studies reviewing ICM have concluded that surgical revascularization improves clinical prognosis at long-term follow-up, whereas evidence supports no benefit for PCI.8,9 Therefore, CABG should be the primary choice to improve clinical outcomes for ICM.16,17 Because PCI is a less invasive procedure than CABG, patients undergoing PCI are expected to exhibit a greater degree of clinical heterogeneity of CAD with LV dysfunction, which will varies in terms of comorbidities, myocardial conditions, and the location and severity of coronary vessel stenosis.18 LCA is beneficial for identifying groups of individuals with similar clinical profiles and has been used for the identification, characterization, and validation of disease phenotypes.11–13
Phenotype 1
Phenotype 1 included patients with severe CKD and a high proportion of patients on hemodialysis. Diabetes is likely the main cause of severe CKD, because Phenotype 1 includes many people with diabetes. Phenotype 1 patients had worse clinical outcomes, such as all-cause mortality, HF admission, and the lowest incidence of LVRR. Moreover, TLR, TVR, and non-TVR were most frequent in Phenotype 1. In patients with diabetes and CKD, coronary vessel stenosis is likely to diffuse and cause severe calcification.19 Therefore, CABG, rather than PCI, may be the best strategy to avoid restenosis and further MI (resulting in cardiac death) in patients with Phenotype 1. Based on the high incidence of sudden death in this study, it may be better to have a more aggressive strategy regarding the use of implantable cardioverter defibrillators or cardiac resynchronization therapy with a defibrillator in patients with Phenotype 1 to prevent sudden death. Moreover, the mechanism of LV dysfunction in CKD, especially in patients on hemodialysis, is multifactorial and has yet to be clarified. Microvascular dysfunction, metabolic disorders such as carnitine deficiency, and cardiac amyloidosis can cause LV impairment, and PCI cannot resolve these conditions.20 This complexity would cause no significant difference in clinical outcomes between patients with and without LVRR. There are many potential considerations for patients undergoing hemodialysis, such as hypotension during dialysis, hemodialysis-induced arrhythmia, and alternative renal replacement strategies. There is still a need to establish a more reliable risk stratification strategy to improve the prognosis of the patient with CKD, especially for the hemodialysis that incorporates multimodal imaging data.20
Phenotype 2
Patients in Phenotype 2 were younger than those in the other 2 groups and had a higher body mass index. In addition, Phenotype 2 had a higher proportion of men and patients with a history of MI, dyslipidemia, and smokers. Patients in Phenotype 2 had better clinical outcomes than those in the other phenotype groups, despite the lower incidence of LVRR in this group. PCI was likely to be an adequate strategy in Phenotype 2 (early onset and lifestyle-related disease; considered relatively simple ICM without severe CKD, AF, and MR compared with Phenotypes 1 and 3) because the coronary vessels may not be complex lesion and therefore the rate of TLR was low. In addition, OMT, such as ACEi/ARB and β-blockers, were most frequently prescribed because Phenotype 2 includes patients with previous MI, suggesting that OMT is essential in patients with ICM in addition to revascularization, as demonstrated in a previous study.21
Phenotype 3
Phenotype 3 patients were older than those in the other 2 groups. Phenotype 3 had the second highest proportion of female patients across the 3 phenotypes. In addition, Phenotype 3 had a higher prevalence of AF and moderate/severe MR. BNP levels were higher in patients with Phenotype 3 than in those with Phenotype 2. The poorer clinical outcomes observed in Phenotype 3 may be attributed to the higher prevalence of patients with congestive HF, despite the lower incidence of TLR, TVR, and non-TVR lesions than in Phenotypes 1 and 2. Furthermore, given the frequent occurrence of congestive HF, patients with LVRR had improved clinical outcomes compared with patients without LVRR. Therefore, in addition to guideline-directed medical therapy,22 appropriate non-pharmacological treatment strategies, such as cardiac resynchronization therapy with a defibrillator, catheter ablation procedures for arrhythmias, and transcatheter mitral valve therapy based on the viable myocardial mass, are needed to achieve LVRR in this group.23,24
Association of Baseline LVEF With LVRR
In the entire cohort, patients with LVRR had a significantly lower risk of the composite primary endpoint than those without LVRR, which aligns with previous studies that reported improved outcomes in individuals with enhanced LVEF.25,26 Notably, in patients with LVRR, baseline LVEF was lower across all phenotypes, suggesting that patients with initially lower LVEF are more likely to experience LVRR. This correlation has also been documented in previous studies.25,26 The occurrence of LVRR may be influenced by the timing of the intervention as well as the degree to which ischemia contributes to the reduction in LVEF. In patients without LVRR, although ischemia may have played a role to the reduction of LVEF, it is conceivable that the optimal window for LVRR had been missed. Further research is warranted to explore the insights gleaned from the present study.
Study Strengths
The strengths of this study encompass the inclusion of a well-characterized cohort of 492 patients with CAD and LV dysfunction from 7 institutions across Japan, detailed PCI information, LVRR estimated using transthoracic echocardiography before PCI, and well-established clustering techniques. This study demonstrates the importance of clustering patients with CAD with LV dysfunction to precisely estimate prognosis and determine the best treatment strategy.
Study Limitations
This study has some limitations. First, because this was a retrospective and observational study, there is a possibility of patient selection bias. Second, as is true for studies using clustering in general, the results of this study apply only to the collected data, and the findings may change if the period or region of interest changes. Nevertheless, these results are important and suggest heterogeneity in patients with CAD with LV dysfunction. The present study included only patients who underwent PCI; therefore, we could not compare PCI, CABG, and OMT for ICM. Third, we could not ascertain the specific indications for revascularization. We could not systematically determine the receipt of various guideline-directed therapies. In addition, the assessment of LVRR could only be conducted in patients who survived until the evaluation, introducing bias into the results. Moreover, the timing of follow-up echocardiography was not specified and echocardiography was not evaluated in a core laboratory. However, 7 hospital echocardiography specialists and certified echocardiography technicians gathered several times a year for meetings to learn and share knowledge and techniques about echocardiography measurement and analytical methods. Moreover, the intraclass correlation coefficient (ICC) was calculated to assess interobserver agreement of LVEF, and the result was excellent (ICC 0.90; 95% confidence interval 0.82–0.95).
Because the analysis included patients who experienced HF hospitalization events prior to the follow-up echocardiogram, there is a possibility that these events influenced the LVRR. Finally, the present study only included Japanese patients. Therefore, to generalize the results of the present study, it is necessary to validate them using data from different populations with ICM, including those from different regions.
Conclusions
In conclusion, we identified important differences in the clinical outcomes and LVRR across the clinical phenotypes of ICM after PCI. These findings indicate distinct underlying mechanisms across clinically identifiable ICM phenotypes that may benefit from different targeted treatments.
Sources of Funding
This study did not receive any specific funding.
Disclosures
I.M. received scholarship funds from Abbott Medical Japan and Medtronic Japan. T.I. received lecture funds from Nipro and Kaneka. Y.S. is a member of Circulation Journal’s Editorial Team. The remaining authors have no conflicts of interest to disclose.
IRB Information
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Research Ethics Committee of Osaka University Hospital (Reference no. 20447).
Data Availability
The deidentified participant data, including individual participant data and summary data, will be shared on a request basis. Please contact the corresponding author directly to request data sharing. The data will become available 6 months after publication of the study results and will remain accessible for 5 years. The data will be shared with qualified researchers who provide a reasonable proposal approved by the corresponding author and Institutional Research Ethics Committee of Osaka University Hospital. A data sharing agreement must be signed to ensure the proper use of the data. The data will be available for analyses aligned with the study objectives. For any kinds of analyses, the data will be shared as Excel files via email.
Supplementary Files
Please find supplementary file(s);
https://doi.org/10.1253/circj.CJ-24-0936
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