ABSTRACT
Aims/Introduction
The coronary physiology and prognosis of patients with different hemoglobin A1c (HbA1c) levels after percutaneous coronary intervention (PCI) are currently unknown. The aim of this study was to assess the effect of different levels of HbA1c control on coronary physiology in patients who underwent PCI for coronary heart disease combined with type 2 diabetes mellitus by quantitative flow ratio (QFR).
Materials and Methods
Patients who successfully underwent PCI and completed 1‐year coronary angiographic follow up were enrolled, clinical data were collected, and QFR at immediate and 1‐year follow up after PCI was retrospectively analyzed. A total of 257 patients (361 vessels) were finally enrolled and divided into the hemoglobin A1c (HbA1c)‐compliance group (103 patients, 138 vessels) and non‐HbA1c‐compliance group (154 patients, 223 vessels) according to the HbA1c cut‐off value of 7%. We compared the results of QFR analysis and clinical outcomes between the two groups.
Results
At 1‐year follow up after PCI, the QFR was significantly higher (0.94 ± 0.07 vs 0.92 ± 0.10, P = 0.019) and declined less (0.014 ± 0.066 vs 0.033 ± 0.095, P = 0.029) in the HbA1c‐compliance group. Meanwhile, the incidence of physiological restenosis was lower in the HbA1c‐compliance group (2.9% vs 8.5%, P = 0.034). Additionally, the target vessel revascularization rate was lower in the HbA1c‐compliance group (6.8% vs 16.9%, P = 0.018). Furthermore, HbA1c ≥7% (OR 2.113, 95% confidence interval 1.081–4.128, P = 0.029) and QFR decline (OR 2.215, 95% confidence interval 1.147–4.277, P = 0.018) were independent risk factors for target vessel revascularization.
Conclusion
Patients with well‐controlled HbA1c levels have better coronary physiological benefits and the incidence of adverse clinical outcome events might be reduced.
Keywords: HbA1c, Percutaneous coronary intervention, Quantitative flow ratio
Short abstract
This retrospective study investigated the relationship between the different levels of hemoglobin A1c and coronary physiology, focus on the effect of hemoglobin A1c on coronary physiology and short‐term poor prognosis in patients with coronary heart disease combined with type 2 diabetes mellitus.
INTRODUCTION
The incidence of diabetes, as a major risk factor for coronary heart disease (CHD), is increasing dramatically 1 . Up to 55% of people with diabetes have CHD2. CHD is one of the common cardiovascular complications in diabetes patients, and it is also the main cause of cardiovascular death in diabetes patients 3 . Hemoglobin A1c (HbA1c) is the most important indicator of glucose control in patients with diabetes. Mechanistically, HbA1c is a late‐stage glycosylation complex formed by the slow, non‐enzymatic reaction of glucose and hemoglobin in the body 4 . Poor blood glucose control can lead to an increase in HbA1c, and an increase in HbA1c can also reflect the body's hyperglycemia. In addition, HbA1c might be an important prognostic indicator for patients with CHD.
Percutaneous coronary intervention (PCI) is the main means of treating CHD, and in‐stent restenosis is one of the adverse consequences of coronary stent implantation in patients with CHD, and it is also one of the main difficulties in the treatment of CHD 5 . Patients with CHD combined with type 2 diabetes mellitus have an increased risk of in‐stent restenosis after PCI compared with patients with CHD alone 6 . Meanwhile, mean plasma glucose is closely related to in‐stent restenosis 7 . Recent studies have shown that after PCI in patients with type 2 diabetes, the higher the HbA1c at follow up, the higher the incidence of in‐stent restenosis 8 .
Fractional flow reserve (FFR) is now recognized as the gold standard for evaluating whether coronary stenosis causes myocardial ischemia in patients with CHD, as well as for evaluating revascularization and physiological follow up of coronary stenosis. However, FFR has many limitations, such as the need to rely on pressure guidewires, the need to induce congestion with drugs, such as adenosine, the complicated operation and the expensive price limit of its clinical application 9 , 10 . Quantitative flow ratio (QFR) is an innovative technology for calculating FFR based on angiography. By transmitting coronary angiography images to a QFR detector, it can quickly obtain calculation results similar to FFR and realize rapid coronary physiology assessment during PCI 11 . Many previous studies have shown that QFR is feasible and important in evaluating coronary physiology 12 , 13 , 14 , 15 .
However, for patients with CHD combined with type 2 diabetes mellitus, the impact of strict glycemic control on post‐PCI and prognosis after successful coronary revascularization remains controversial 16 . From a functional perspective, there is still a lack of evidence regarding the relationship between HbA1c control levels and coronary artery physiology. Therefore, the present study aimed to investigate the effect of different HbA1c levels on coronary physiology after PCI by QFR.
MATERIALS AND METHODS
Study design
A total of 257 consecutive patients who successfully underwent PCI stenting and had coronary angiography follow up 1 year after PCI at Fujian Provincial Hospital, Fuzhou, China, between January 2016 and December 2021 were recruited. Based on HbA1c levels at 1‐year follow up after PCI, patients were divided into two groups: HbA1c‐compliance group (HbA1c <7%, n = 103) and non‐HbA1c‐compliance group (HbA1c ≥7%, n = 154). Baseline data, coronary physiology findings and clinical outcome endpoint events were compared between the two groups to assess the impact of different HbA1c levels on coronary physiology in patients with CHD combined with type 2 diabetes mellitus treated with PCI. This study was approved by the Ethics Committee of Fujian Provincial Hospital (No. K 2022‐09‐105).
Inclusion criteria were as follows: (1) age ≥18 years; (2) diagnosis of stable angina, unstable angina or post‐acute phase of myocardial infarction (onset ≥72 h); (3) QFR calculation inclusion criteria were met (the presence of at least 1 lesion with visual diameter stenosis between 50–90%; visual reference lumen diameter ≥2.0 mm); and (4) all patients were discharged from the hospital after standard drug therapy according to the clinical guidelines at the time. Exclusion criteria were as follows: (1) the determination of HbA1c is affected by anemia due to various reasons; (2) acute phase of myocardial infarction (onset <72 h); (3) missing clinical data; and (4) QFR calculation exclusion criteria (no “blank movie” was acquired before contrast injection; only 1 lesion with visual diameter stenosis <50% or >90% and thrombolysis in myocardial infarction grade <3; reference lumen diameter <2 mm; target lesion vessel involved in myocardial bridge; target lesion vessel involved in coronary artery bypass grafting; target lesion vessel severely overlapped or distorted for QFR calculation; poor coronary angiography image quality).
We carried out retrospective QFR computation for all recruited patients, and collected relevant clinical data post‐PCI and at 1‐year follow up. Type 2 diabetes mellitus was diagnosed according to World Health Organization criteria 17 and previous medical history.
QFR and quantitative coronary angiography computation
QFR computation was carried out according to standard procedures by two trained operators who were unknown to the clinical data through the AngioPlus system (Pulse Medical Imaging Technology Shanghai, Shanghai, China). The target vessel frames are those in which the lesion is most narrowly exposed, the entire vessel is clearly visualized, the lesion does not overlap and the motion artifacts are small. At the same time, the system can automatically detect the vessel segments of interest for coronary angiography and manually modify them if they do not match. The principle of selection is that the vessel segment of interest should contain all lesions and have sufficient length (≥20 mm). In addition, the system automatically detects the main and branch contours of the vessels of interest for coronary angiography and adjusts them appropriately if needed. Check whether the reference lumen automatically generated by the system is reasonable, ensure that the reference lumen of the normal vessel segment fits with the actual lumen and check whether the μ value is within a reasonable range (0.7–1.3). Finally, the QFR report is automatically generated. The QFR report provides information on the flow velocity of the target vessel, angiographic microvascular resistance (AMR) and quantitative coronary angiography, which includes diameter stenosis (DS%), area stenosis (AS%), minimal lumen diameter (MLD) and late lumen loss (LLL) parameters. LLL is defined as the difference between the MLD immediately after PCI and the MLD at the 1‐year post‐PCI follow up to reflect the degree of coronary restenosis after PCI.
Data collection and follow up
Clinical information related to initial and follow‐up visits is collected by querying the hospital medical record system. The hospital laboratory is responsible for testing serum biochemical indicators, such as hemoglobin A1c (HbA1c), fasting glucose, N‐terminal pro‐brain natriuretic peptide, cardiac troponin I, serum creatinine, low‐density lipoprotein cholesterol, leukocyte count, hemoglobin and platelet count. Left ventricular ejection fraction was measured by echocardiography. We then calculated the body mass index and triglyceride glucose index (TyG). Body mass index was defined as weight (kg) / height squared (m). TyG index was calculated using the formula Ln [triglyceride (mg/dL) × fasting plasma glucose (mg/dL) / 2] 18 . Renal insufficiency was defined as glomerular filtration rate <60 mL/min at the time of first hospitalization. Types of CHD include stable angina, unstable angina, ST‐segment elevation myocardial infarction and non‐ST‐segment elevation myocardial infarction. The diagnosis of myocardial infarction is based on the fourth universal definition of myocardial infarction.
The primary clinical outcome is major adverse cardiovascular and cerebrovascular events (MACCE), which includes any type of recurrent myocardial infarction, stroke, cardiac death and target vessel revascularization. The incidence of MACCE within 1 year was recorded through medical record queries. Physiological restenosis was defined as QFR <0.8 at 1‐year follow up after successful PCI. Recurrent myocardial infarction was defined as readmission for ST‐segment elevation myocardial infarction or non‐ST‐segment elevation myocardial infarction. Target vessel revascularization was defined as secondary stent implantation at 1‐year follow‐up admission.
Statistical analysis
Statistical analyses were carried out using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). We used the Kolmogorov–Smirnov test to measure the normality of the distribution. Normally distributed continuous variables are expressed as mean ± standard, and compared using Student's t‐test. Continuous variables with non‐normal distributions are expressed as the median and interquartile range, and compared using the Mann–Whitney U‐test. Categorical variables are expressed as percentages, and compared using χ2‐tests. Variables with P‐values <0.10 in the univariable logistic regression analysis entered the multivariable model. Spearman's correlation coefficient was used for correlation analysis. Interobserver reproducibility of QFR computations was evaluated by blinded analysis of 30 vessels randomly selected from the post‐PCI and 1‐year follow‐up populations by two observers. We carried out agreement analysis and plotted Bland–Altman plots by using MedCalc (version 20.010; MedCalc Software, Ostend, Belgium). All tests were carried out using a two‐sided test, and differences between data were considered statistically significant at a P‐value <0.05.
RESULTS
A total of 323 patients (469 vessels) were recruited for this study. After exclusion according to predefined criteria, 257 patients (361 vessels) were included in the final analysis. Based on the presence or absence of diabetes, all enrolled patients were divided into the HbA1c‐compliance group (103 patients, 138 vessels) and the non‐HbA1c‐compliance group (154 patients, 223 vessels; Figure 1).
Figure 1.
Study flowchart. CABG, coronary artery bypass grafting; HbA1c, hemoglobin A1c; PCI, percutaneous coronary intervention; QFR, quantitative flow ratio; T2DM, type 2 diabetes mellitus.
Baseline characteristics
A total of 257 patients with CHD combined with type 2 diabetes mellitus were included in the present study, including 103 patients (40.1%) in the HbA1c‐compliance group and 154 patients (59.9%) in the non‐HbA1c‐compliance group. The mean age of patients in the HbA1c‐compliance group was 64.02 ± 9.72 years, including 86 men (83.5%) and 17 women (16.5%). There was no significant difference in the distribution of patients in the two groups in terms of age, body mass index, sex, smoking history, hypertension, diabetes mellitus, renal insufficiency, previous history of myocardial infarction and previous history of PCI (P > 0.05). The HbA1c‐compliance group had a shorter diabetes duration (2 [0–7] vs 5.5 [1–10], P = 0.001), lower HbA1c (6.89 ± 1.03 vs 8.14 ± 1.68, P < 0.001), lower fasting glucose (6.87 ± 2.08 vs 8.44 ± 2.94, P < 0.001) and lower TyG index (8.97 ± 0.63 vs 9.41 ± 0.65, P < 0.001). In addition, there were no significant differences in medications at discharge, except for insulin use (9.7% vs 26%, P = 0.001), which was lower in the HbA1c‐compliance group. The differences in N‐terminal pro‐brain natriuretic peptide, cardiac troponin I, serum creatinine, low‐density lipoprotein cholesterol, leukocyte count, hemoglobin, platelet count and left ventricular ejection fraction between the two groups were not statistically significant (P > 0.05). During the 1‐year follow up after PCI, the HbA1c‐compliance group had lower HbA1c levels (6.38 ± 0.36 vs 8.19 ± 1.21, P < 0.001), lower fasting glucose (5.93 ± 1.18 vs 8.60 ± 2.95, P < 0.001) and lower TyG (8.67 ± 0.57 vs 9.39 ± 0.71, P < 0.001). Similarly, there were no significant differences in medications at admission, except for insulin use (7.8% vs 24.7%, P = 0.001), which was lower in the HbA1c‐compliance group. There were no statistically significant differences in N‐terminal pro‐brain natriuretic peptide, cardiac troponin I, serum creatinine, low‐density lipoprotein cholesterol, leukocyte count, hemoglobin, platelet count and left ventricular ejection fraction between the patients at 1‐year follow up (P > 0.05; Table 1).
Table 1.
Baseline demographic and clinical characteristics of patients
| HbA1c‐compliance (n = 103) | Non‐HbA1c‐compliance (n = 154) | P‐value | |
|---|---|---|---|
| Age (years) | 64.02 ± 9.72 | 63.28 ± 9.99 | 0.557 |
| BMI (kg/m2) | 24.82 ± 2.90 | 24.87 ± 2.91 | 0.914 |
| Male, n (%) | 86 (83.5%) | 118 (76.6%) | 0.182 |
| Smoking history, n (%) | 49 (47.6%) | 85 (55.2%) | 0.231 |
| Hypertension, n (%) | 76 (73.8%) | 107 (69.5%) | 0.455 |
| Diabetes, n (%) | 103 (100%) | 154 (100%) | ‐ |
| Renal insufficiency, n (%) | 3 (2.9%) | 5 (3.2%) | 0.880 |
| Previous MI, n (%) | 8 (7.8%) | 9 (5.8%) | 0.543 |
| Previous PCI, n (%) | 22 (21.4%) | 29 (18.8%) | 0.618 |
| Diabetes duration (years) | 2 (0–7) | 5.5 (1–10) | 0.001 |
| HbA1c (%) | 6.89 ± 1.03 | 8.14 ± 1.68 | <0.001 |
| Fasting glucose (mmol/L) | 6.87 ± 2.08 | 8.44 ± 2.94 | <0.001 |
| NT‐proBNP (pg/mL) | 185.20 (68.84–592.20) | 152.50 (41.35–548.90) | 0.323 |
| cTnI (ng/mL) | 0.017 (0.006–0.519) | 0.026 (0.006–0.674) | 0.323 |
| Scr (μmol/L) | 80.93 ± 25.06 | 76.90 ± 22.46 | 0.179 |
| LDL‐C (mmol/L) | 2.63 ± 1.07 | 2.69 ± 1.07 | 0.640 |
| Leukocyte (×109/L) | 7.36 ± 2.29 | 7.62 ± 2.26 | 0.373 |
| Hb (g/L) | 136.95 ± 14.54 | 138.83 ± 16.68 | 0.353 |
| PLT (×109/L) | 217.59 ± 48.98 | 229.13 ± 58.13 | 0.098 |
| LVEF (%) | 56.94 ± 7.27 | 58.32 ± 5.78 | 0.132 |
| TyG | 8.97 ± 0.63 | 9.41 ± 0.65 | <0.001 |
| Medications at discharge | |||
| Antiplatelet agent, n (%) | 103 (100%) | 154 (100%) | ‐ |
| Statin, n (%) | 103 (100%) | 154 (100%) | ‐ |
| ACEI/ARB, n (%) | 75 (72.8%) | 109 (70.8%) | 0.723 |
| Insulin, n (%) | 10 (9.7%) | 40 (26%) | 0.001 |
| α‐Glucosidase inhibitor, n (%) | 59 (57.3%) | 86 (55.8%) | 0.820 |
| Insulin secretagogues, n (%) | 46 (44.7%) | 85 (55.2%) | 0.098 |
| Metformin, n (%) | 61 (59.2%) | 92 (59.7%) | 0.934 |
| DPP‐4 inhibitor, n (%) | 5 (4.9%) | 10 (6.5%) | 0.583 |
| Insulin sensitizer, n (%) | 4 (3.9%) | 8 (5.2%) | 0.767 |
| GLP‐1 RA, n (%) | 1 (1%) | 9 (5.8%) | 0.054 |
| SGLT2i, n (%) | 0 (0%) | 5 (3.2%) | 0.085 |
| Type of coronary heart disease | |||
| Stable angina, n (%) | 1 (1.0%) | 3 (1.9%) | 0.535 |
| Unstable angina, n (%) | 64 (62.1%) | 91 (59.1%) | 0.625 |
| STEMI, n (%) | 24 (23.3%) | 30 (19.5%) | 0.461 |
| NSTEMI, n (%) | 14 (13.6%) | 30 (19.5%) | 0.219 |
| 1‐year follow up | |||
| HbA1c(%) | 6.38 ± 0.36 | 8.19 ± 1.21 | <0.001 |
| Fasting glucose(mmol/L) | 5.93 ± 1.18 | 8.60 ± 2.95 | <0.001 |
| NT‐proBNP (pg/mL) | 123.00 (65.67–359.40) | 100.24 (54.34–178.80) | 0.070 |
| cTnI (ng/mL) | 0.008 (0.001–0.015) | 0.006 (0.006–0.014) | 0.642 |
| Scr (μmol/L) | 82.25 ± 21.01 | 81.15 ± 22.47 | 0.693 |
| LDL‐C (mmol/L) | 1.96 ± 0.95 | 2.08 ± 0.98 | 0.344 |
| Leukocyte (×109/L) | 6.71 ± 1.75 | 7.09 ± 1.75 | 0.090 |
| Hb (g/L) | 134.63 ± 15.87 | 137.33 ± 17.26 | 0.206 |
| PLT (×109/L) | 214.30 ± 48.79 | 225.83 ± 54.15 | 0.083 |
| LVEF (%) | 57.45 ± 6.77 | 59.03 ± 5.24 | 0.064 |
| TyG | 8.67 ± 0.57 | 9.39 ± 0.71 | <0.001 |
| Medications at admission | |||
| Antiplatelet agent, n (%) | 103 (100%) | 154 (100%) | – |
| Statin, n (%) | 103 (100%) | 154 (100%) | – |
| ACEI/ARB, n (%) | 57 (55.3%) | 79 (51.3%) | 0.525 |
| Insulin, n (%) | 8 (7.8%) | 38 (24.7%) | 0.001 |
| α‐Glucosidase inhibitor, n (%) | 49 (47.6%) | 61 (39.6%) | 0.206 |
| Insulin secretagogues, n (%) | 36 (35%) | 70 (45.5%) | 0.094 |
| Metformin, n (%) | 46 (44.7%) | 79 (51.3%) | 0.297 |
| DPP‐4 inhibitor, n (%) | 5 (4.9%) | 8 (5.2%) | 0.903 |
| Insulin sensitizer, n (%) | 3 (2.9%) | 10 (6.5%) | 0.199 |
| GLP‐1 RA, n (%) | 1 (1%) | 5 (3.2%) | 0.407 |
| SGLT2i, n (%) | 0 (0%) | 3 (1.9%) | 0.277 |
ACEI, angiotensin‐converting‐enzyme inhibitor; ARB, angiotensin II receptor blocker; BMI, body mass index; cTnI, cardiac troponin I; DPP‐4 inhibitor, dipeptidyl peptidase 4 inhibitors; GLP‐1 RA, glucagon like peptide‐1 receptor agonists; Hb, hemoglobin; HbA1c, hemoglobin A1c; LDL‐C, low‐density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MI, myocardial infarction; NSTEMI, non‐ST‐segment elevation myocardial infarction; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; PCI, percutaneous coronary intervention; PLT, platelet; Scr, serum creatinine; SGLT2i, sodium‐glucose cotransporter‐2 inhibitors; STEMI, ST‐segment elevation myocardial infarction; TyG, triglyceride‐glucose index.
QFR and quantitative coronary angiography analysis
The differences in QFR, AMR, flow velocity, DS%, AS% and MLD in the immediate post‐PCI between the two groups were not statistically significant (P > 0.05; Figure 2a). However, during the 1‐year follow up after PCI, QFR was significantly higher in the HbA1c‐compliance group than in the non‐HbA1c‐compliance group (0.94 ± 0.07 vs 0.92 ± 0.10, P = 0.019; Figure 2b). In addition, the decline in QFR was smaller in the HbA1c‐compliance group compared with the non‐HbA1c‐compliance group (0.014 ± 0.066 vs 0.033 ± 0.095, P = 0.029; Figure 3). Meanwhile, the incidence of physiological restenosis (QFR <0.8) was lower in the HbA1c‐compliance group (2.9% vs 8.5%, P = 0.034). DS% and AS% were significantly lower in the HbA1c‐compliance group than in the non‐HbA1c‐compliance group (27.92 ± 10.41 vs 31.53 ± 12.96, P = 0.006; 46.99 ± 13.66 vs 51.43 ± 15.60, P = 0.006).The MLD in the HbA1c‐compliance group at the 1‐year follow up after PCI was lower than that in the non‐HbA1c‐compliance group (2.12 ± 0.59 vs 1.98 ± 0.61, P = 0.042). There was no statistical difference in flow velocity, LLL and AMR between the two groups at the 1‐year follow up after PCI (P > 0.05; Table 2). In addition, multivariable logistic regression analysis identified HbA1c ≥7% (OR 2.113, 95% confidence interval 1.081–4.128, P = 0.029) and QFR decline (OR 2.215, 95% confidence interval 1.147–4.277, P = 0.018) as independent risk factors for target vessel revascularization (Table 3).
Figure 2.
Quantitative flow ratio (QFR) analysis process. (a) Immediate postoperative QFR; (b) 1‐year follow‐up QFR.
Figure 3.
Quantitative flow ratio (QFR) decline in the (a) hemoglobin A1c‐compliance group and (b) non‐hemoglobin A1c‐compliance group. PCI, percutaneous coronary intervention.
Table 2.
Quantitative flow ratio and quantitative coronary angiography data analysis results
| HbA1c‐compliance (vessels = 138) | Non‐HbA1c‐compliance (vessels = 223) | P‐value | |
|---|---|---|---|
| Target vessel | |||
| LAD, n (%) | 61 (44.2%) | 94 (42.2%) | 0.702 |
| LCX, n (%) | 37 (26.8%) | 56 (25.1%) | 0.720 |
| RCA, n (%) | 40 (29.0%) | 73 (32.7%) | 0.455 |
| Post‐PCI | |||
| QFR | 0.95 ± 0.03 | 0.95 ± 0.03 | 0.407 |
| AMR (mmHg × s/cm) | 3.12 ± 0.63 | 3.25 ± 0.59 | 0.064 |
| FV (cm/s) | 13.55 ± 5.34 | 13.21 ± 5.66 | 0.571 |
| DS (%) | 25.25 ± 7.90 | 25.51 ± 6.17 | 0.737 |
| AS (%) | 43.49 ± 11.36 | 44.19 ± 9.10 | 0.541 |
| MLD (mm) | 2.20 ± 0.60 | 2.15 ± 0.56 | 0.374 |
| 1‐year follow up | |||
| QFR | 0.94 ± 0.07 | 0.92 ± 0.10 | 0.019 |
| AMR (mmHg × s/cm) | 2.87 ± 0.61 | 2.99 ± 0.63 | 0.090 |
| FV (cm/s) | 14.01 ± 4.97 | 13.60 ± 5.59 | 0.478 |
| DS (%) | 27.92 ± 10.41 | 31.53 ± 12.96 | 0.006 |
| AS (%) | 46.99 ± 13.66 | 51.43 ± 15.60 | 0.006 |
| MLD (mm) | 2.12 ± 0.59 | 1.98 ± 0.61 | 0.042 |
| LLL † (mm) | 0.09 ± 0.57 | 0.16 ± 0.69 | 0.246 |
| QFR <0.8 ‡ , n (%) | 4 (2.9%) | 19 (8.5%) | 0.034 |
| ∆QFR § | 0.014 ± 0.066 | 0.033 ± 0.095 | 0.029 |
AMR, angiographic microvascular resistance; AS, area stenosis; DS, diameter stenosis; FV, flow velocity; LAD, left anterior descending artery; LCX, left circumflex artery; LLL, late lumen loss; MLD, minimal lumen diameter; PCI, percutaneous coronary intervention; QCA, quantitative coronary angiography; QFR, quantitative flow ratio; RCA, right coronary artery.
LLL was defined as the difference in MLD between post‐PCI and follow up.
QFR <0.8 was defined as the functional restenosis.
∆QFR = post‐PCI QFR – follow‐up QFR.
Table 3.
Univariable and multivariable logistic regression analysis of target revascularization factors.
|
Univariable OR (95% CI) |
P‐value |
Multivariable OR (95% CI) |
P‐value | |
|---|---|---|---|---|
| Age >60 years | 0.590 (0.330–1.056) | 0.076 | 0.573 (0.317–1.038) | 0.066 |
| Male | 1.406 (0.654–3.019) | 0.383 | ||
| Smoking history | 1.639 (0.906–2.967) | 0.103 | ||
| Hypertension | 1.283 (0.657–2.508) | 0.465 | ||
| Diabetes duration | 1.011 (0.968–1.057) | 0.611 | ||
| Renal insufficiency | 0.454 (0.058–3.562) | 0.452 | ||
| 1‐year follow‐up | ||||
| LDL‐C ≥1.8mmol/L | 1.316 (0.734–2.361) | 0.357 | ||
| HbA1c ≥7% | 2.231 (1.150–4.328) | 0.018 | 2.113 (1.081–4.128) | 0.029 |
| QFR decline † | 2.134 (1.117–4.079) | 0.022 | 2.215 (1.147–4.277) | 0.018 |
| Insulin | 1.047 (0.510–2.148) | 0.901 | ||
| TyG | 1.301 (0.862–1.962) | 0.210 | ||
HbA1c, hemoglobin A1c; LDL‐C, low‐density lipoprotein cholesterol; QFR, quantitative flow ratio; TyG, triglyceride‐glucose index.
QFR decline, follow‐up QFR< post‐PCI QFR.
Clinical outcomes
During the 1‐year follow up after PCI, 38 cases of MACCE occurred in the present study, and the incidence of MACCE was not statistically different between the two groups (9.7% vs 18.2%, P = 0.061), but the target vessel revascularization rate was significantly lower in the HbA1c‐compliance group (6.8% vs 16.9%, P = 0.018). For the incidence of cardiac death, stroke and recurrent myocardial infarction, there was no significant difference between the two groups (Table 4).
Table 4.
Clinical outcomes at 1‐year follow‐up.
| HbA1c‐compliance (n = 103) | Non‐HbA1c‐compliance (n = 154) | P‐value | |
|---|---|---|---|
| MACCE, n (%) | 10 (9.7%) | 28 (18.2%) | 0.061 |
| Cardiac death, n (%) | 0 | 0 | – |
| Recurrent myocardial infarction, n (%) | 3 (2.9%) | 4 (2.6%) | 0.879 |
| Stroke, n (%) | 0 | 0 | – |
| Target vessel revascularization, n (%) | 7 (6.8%) | 26 (16.9%) | 0.018 |
MACCE, major adverse cardiovascular and cerebrovascular events.
Reproducibility of QFR analysis
The Spearman's correlation coefficient of the QFR measurements between the two observers showed a strong correlation (r = 0.729, P < 0.0001), whereas the Bland–Altman plot showed good agreement of the QFR measurements between the observers (mean difference 0.003 ± 0.018; Figure 4).
Figure 4.
Reproducibility of quantitative flow ratio (QFR) analysis. Two blinded investigators analyzed QFR for (a) correlation and (b) agreement.
DISCUSSION
The main results of the present study were as follows: From a functional perspective, patients with well‐controlled HbA1c levels have a better coronary physiological benefit. Also, the incidence of physiological restenosis and target vessel revascularization was lower in the HbA1c‐compliance group, indicating that patients with well‐controlled HbA1c levels had better clinical outcomes. In addition, HbA1c is an independent risk factor for target vessel revascularization. HbA1c should be controlled at <7% in patients with CHD combined with type 2 diabetes mellitus to play a role in preventing the development of cardiovascular complications. The present study is the first to confirm the importance of strict HbA1c management in patients with CHD combined with type 2 diabetes mellitus for changes in coronary physiology and the occurrence of cardiovascular adverse events by QFR assessment.
CHD combined with diabetes mellitus is very common in clinic 2 , and the two diseases can promote each other, and accelerate the development and progression of coronary artery lesions. HbA1c can accurately reflect the glycemic control of diabetes patients in the recent 2–3 months, and it can more accurately screen diabetes patients at risk compared with traditional blood glucose indicators 19 . Meanwhile, HbA1c, as the most important glycosylation end product, can cause damage to vascular endothelial cells through biochemical and molecular pathways 20 , thus changing coronary hemodynamics and accelerating the progression of coronary atherosclerosis in diabetes patients, which in turn affects the physiological status of coronary arteries and increases the incidence of adverse cardiovascular events. In addition, different HbA1c levels present different degrees of CHD, and poor glycemic control is an important risk factor for the development of CHD 21 .
According to the consensus requirements of the European Society of Cardiology/European Association for the Study of Diabetes in 2019, the control target of HbA1c in diabetes patients should be <7% 16 . Although all diabetes patients in the present study optimized the hypoglycemic regimen, only 40.1% of patients reached the target value of HbA1c, which might be related to their poor compliance and poor diet control. Furthermore, HbA1c can be an important predictor of CHD prognosis, and previous studies have shown that each 1% decrease in HbA1c reduces the risk of all diabetes‐related endpoints, the risk of diabetes‐related death by 21% and the risk of myocardial infarction by 14% 22 . In the present study, the target vessel revascularization rate was lower in the HbA1c‐compliance group than in the non‐HbA1c‐compliance group at 1‐year follow‐up (6.8% vs 16.9%, P = 0.018), and the most important factor for target vessel revascularization was in‐stent restenosis.
The present results showed that both groups were able to achieve satisfactory immediate postoperative QFR after successful PCI (0.95 ± 0.03 vs 0.95 ± 0.03, P = 0.407). However, at 1‐year follow up after PCI, the QFR was significantly higher (0.94 ± 0.07 vs 0.92 ± 0.10, P = 0.019) and declined less (0.014 ± 0.066 vs 0.033 ± 0.095, P = 0.029) in the HbA1c‐compliance group. At the same time, the increase of DS% and AS% in the HbA1c‐compliance group was smaller than that in the non‐HbA1c‐compliance group at 1‐year follow up, which suggested that patients in the non‐HbA1c‐compliance group had more rapid progression of coronary lumen lesions, resulting in more significant coronary lumen stenosis, which might be an important reason for the decline in QFR at follow up. Studies have demonstrated that QFR reduction is closely related to myocardial ischemia, and a QFR reduction of 0.01 or 0.05 can increase the risk of myocardial ischemia by 1.1 or 2.14 times 23 , 24 .
Although the differences in QFR values between the two groups appear to be small, they may carry some clinical significance. In addition, LLL is an indicator for evaluating the degree of coronary restenosis 25 . The present study results showed that there was no significant difference in LLL between the HbA1c‐compliance group and the non‐HbA1c‐compliance group, suggesting that patients in the two groups had similar coronary restenosis at 1‐year follow up. However, previous studies have shown that the incidence of in‐stent restenosis is low in patients with HbA1c <7% after PCI in patients with CHD 8 . In the present study, there was no significant difference in LLL between the two groups, which might be due to the short follow‐up time.
Coronary angiography is essentially unable to visualize vessels <300 μm in diameter and, therefore, cannot directly observe the coronary microcirculation 26 . In contrast, diabetes causes damage to microvessels systemically, and the resulting heart, eye and kidney‐related complications bring enormous clinical challenges. The QFR technique is an emerging tool that allows the simultaneous detection of coronary microvascular resistance in addition to QFR. In the present study, AMR values immediately after PCI and at 1‐year follow‐up were not significantly different between the two groups, but were higher than the cut‐off value in both groups (AMR cut‐off value of 2.5 mmHg * s/cm, accuracy of 87.2%, sensitivity of 93.5% and specificity of 82.7%) 27 , suggesting a probable coronary microvascular dysfunction (CMD) in both groups. However, the presence of CMD might affect the determination of QFR values. Previous studies have shown that the presence of CMD can increase the value of FFR 28 , and as a derivative of FFR, the value of QFR is also affected by CMD, which can increase blood flow resistance and reduce pressure loss, resulting in overestimation of QFR value 29 .
AMR was high in both groups in the present study, which might have led to an underestimation of the difference in QFR values between the two groups. Over time, the damage caused by poor HbA1c management in diabetes patients to the coronary vessels becomes more severe, and QFR, AMR and quantitative coronary angiography outcomes might show more significant differences between the two groups, which will need to be further verified in subsequent studies with longer follow‐up time. In addition, there are few studies on AMR, and its accuracy needs further validation.
Although the current treatment strategy for CHD is optimized, clinical outcomes still correlate with the level of HbA1c control. There was no significant difference in the incidence of MACCE between the two groups from the short‐term follow‐up results at 1 year after PCI (9.7% vs 18.2%, P = 0.061), but the target vessel revascularization rate in the HbA1c‐compliance group was significantly lower than that in the non‐HbA1c‐compliance group (6.8% vs 16.9%, P = 0.018). In addition, if calculated by the number of vessels, the incidence of physiological restenosis was 2.9% and 8.5% in the HbA1c‐compliance group and the non‐HbA1c‐compliance group, respectively, whereas the target vessel revascularization rate was 5.1% and 11.7%, respectively. It can be seen that the target vessel revascularization rate was higher than the incidence of physiological restenosis in both groups, so a coronary physiology‐guided PCI strategy is necessary not only to reduce the number of stents implanted, but also to save medical resources and reduce medical costs 30 . In addition, multivariable logistic regression analysis showed that HbA1c ≥7% and QFR decline were independent risk factors for target vessel revascularization at 1‐year follow up after PCI.
In summary, the present study shows that patients with HbA1c control have better clinical outcomes, and once again verify the importance of strict HbA1c management in preventing cardiovascular complications. Therefore, patients with well‐controlled HbA1c levels have better coronary physiological benefits, and might have reduced incidence of adverse clinical outcome events. We should enhance the comprehensive management of glucose in patients with CHD combined with diabetes mellitus. We validate again the significance of following coronary physiology by QFR during follow up to further guide the treatment strategy after revascularization 29 .
The present study had some limitations. First, this study was a retrospective, single‐center, observational study with a small sample size, which requires further prospective, multicenter studies to verify the results. Second, not all coronary angiographic images are suitable for QFR analysis. For example, QFR calculation cannot be carried out for coronary artery problems, such as severe overlap, shortening and distortion, or for operational problems, such as poor coronary angiographic images, which might lead to potential bias. Third, the results of this study showed a slight decrease in QFR at 1‐year follow up in the non‐HbA1c‐compliance group, which might be due to the shorter follow‐up period. Although previous studies have shown that a slight decrease in QFR values can lead to an increased risk of myocardial ischemia 23 , 24 , the results should be interpreted with caution due to some systematic error in this test method. Finally, our study was limited to 1 year, but not all patients had regular examinations, and we were unable to include these patients in the analysis due to a lack of baseline data or imaging.
DISCLOSURE
The authors declare no conflict of interest.
Approval of the research protocol: This study was approved by the Ethics Committee of Fujian Provincial Hospital (Approval No. K2022‐09‐105) and was carried out in accordance with the Declaration of Helsinki.
Informed consent: N/A.
Approval date of registry and the registration no. of the study/trial: N/A.
Animal studies: N/A.
ACKNOWLEDGMENT
This work was supported by grants from the High‐Level Hospital grants from Fujian Provincial Hospital (Grant No. 2017GL‐002), and sponsored by Fujian Provincial Health Technology Project (Grant No. 2019‐CX‐11) and Startup Fund for Scientific Research by Fujian Medical University 2022 (Grant No. 2022QH1326).
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