| Literature DB >> 27412101 |
Masahiko Hara1, Yasuhiko Sakata2, Daisaku Nakatani3, Shinichiro Suna3, Masami Nishino4, Hiroshi Sato5, Tetsuhisa Kitamura6, Shinsuke Nanto7, Masatsugu Hori8, Issei Komuro9.
Abstract
OBJECTIVES: To evaluate the short-term and long-term prognostic impacts of acute phase coronary collaterals to occluded infarct-related arteries (IRA) after ST-elevation myocardial infarction (STEMI) in the percutaneous coronary intervention (PCI) era.Entities:
Keywords: Coronary collateral; Mortality; Percutaneous coronary intervention; ST-elevation myocardial infarction
Mesh:
Year: 2016 PMID: 27412101 PMCID: PMC4947770 DOI: 10.1136/bmjopen-2016-011105
Source DB: PubMed Journal: BMJ Open ISSN: 2044-6055 Impact factor: 2.692
Figure 1Patient selection flow. OACIS, Osaka Acute Coronary Insufficiency Study and STEMI, ST-elevation myocardial infarction.
Patient background
| Parameter | Overall (n=3340) | RCS-0 (n=2040) | RCS-1 (n=530) | RCS-2 (n=522) | RCS-3 (n=248) | p Value |
|---|---|---|---|---|---|---|
| Age, years | 65 (57–73) | 66 (57–74) | 64 (55–71) | 63 (55–71) | 66 (57–74) | <0.001 |
| Male, % | 76.9 | 75.8 | 80.0 | 77.6 | 78.2 | 0.197 |
| Onset to admission, hour | 2.4 (1.1–5.5) | 2.3 (1.0–5.0) | 2.3 (1.2–5.5) | 3.0 (1.5–6.7) | 3.0 (1.3–7.4) | <0.001 |
| Coronary risk factor | ||||||
| Diabetes, % | 31.6 | 31.0 | 31.7 | 32.3 | 34.6 | 0.705 |
| Hypertension, % | 58.1 | 59.1 | 58.4 | 53.5 | 59.5 | 0.147 |
| Dyslipidaemia, % | 44.2 | 40.8 | 50.4 | 47.6 | 52.1 | <0.001 |
| Smoking, % | 65.3 | 63.7 | 68.7 | 70.4 | 59.8 | 0.003 |
| Previous MI, % | 11.0 | 10.0 | 9.2 | 14.3 | 16.3 | <0.001 |
| Angina pectoris, % | 20.7 | 17.6 | 22.8 | 25.9 | 31.1 | <0.001 |
| KILLIP classification | 0.113 | |||||
| Class 1 | 82.0 | 80.9 | 83.2 | 84.8 | 82.8 | |
| Class 2 | 9.3 | 9.4 | 10.6 | 8.0 | 8.0 | |
| Class 3 | 2.5 | 2.4 | 2.1 | 2.9 | 2.5 | |
| Class 4 | 6.2 | 7.2 | 4.1 | 4.3 | 6.7 | |
| Laboratory data | ||||||
| Peak CPK, IU/L | 2957 (1637–5030) | 3225 (1722–5390) | 3074 (1895–4915) | 2514 (1449–4218) | 2277 (1093–4180) | <0.001 |
| CAG findings | ||||||
| Culprit vessel | <0.001 | |||||
| Left main trunk, % | 1.8 | 1.7 | 1.3 | 1.9 | 3.3 | |
| LAD, % | 44.0 | 42.7 | 48.3 | 45.2 | 42.7 | |
| Diagonal branch, % | 2.4 | 3.1 | 1.0 | 1.2 | 2.8 | |
| RCA, % | 41.5 | 40.1 | 43.5 | 44.8 | 41.9 | |
| LCx, % | 10.2 | 12.4 | 5.5 | 6.7 | 9.3 | |
| Graft, % | 0.1 | 0.0 | 0.4 | 0.2 | 0.0 | |
| Multivessel disease, % | 35.1 | 34.3 | 29.5 | 35.6 | 53.7 | <0.001 |
| Emergency PCI, % | 96.8 | 96.4 | 98.5 | 97.5 | 95.6 | 0.042 |
| Final TIMI 3, % | 85.8 | 84.9 | 85.4 | 87.8 | 89.6 | 0.127 |
| CABG, % | 1.7 | 1.6 | 1.2 | 1.7 | 3.7 | 0.064 |
| Medication at discharge | ||||||
| ACEI, % | 55.3 | 53.5 | 56.8 | 62.4 | 50.9 | 0.002 |
| ARB, % | 23.8 | 24.1 | 25.8 | 18.7 | 27.7 | 0.016 |
| β-Blocker, % | 48.7 | 49.0 | 50.3 | 45.0 | 51.3 | 0.271 |
| Ca-blocker, % | 16.8 | 17.9 | 15.0 | 15.3 | 15.6 | 0.297 |
| Statin, % | 41.8 | 40.9 | 42.0 | 42.0 | 47.3 | 0.334 |
| Diuretics, % | 29.0 | 29.7 | 28.8 | 26.9 | 28.6 | 0.664 |
Categorical variables are presented as percentage and continuous variables are presented as the median (25–75 percentiles).
ACEI, ACE inhibitor; ARB, angiotensin receptor blocker; CABG, coronary artery bypass graft; CAG, coronary angiography; CPK, creatine kinase; LAD, left anterior descending artery; LCx, left circumflex artery; PCI, percutaneous coronary intervention; RCA, right coronary artery; TIMI, thrombolysis in myocardial infarction.
Predictors of development of collaterals
| Univariable | Multivariable (stepwise) | |||
|---|---|---|---|---|
| OR (95% CI) | p Value | adjusted OR (95% CI) | p Value | |
| Age, per 10 years | 0.87 (0.82 to 0.92) | <0.001 | 0.85 (0.79 to 0.91) | <0.001 |
| Male, % | 1.18 (0.99 to 1.40) | 0.052 | – | – |
| Onset to admission, hour | 1.03 (1.02 to 1.04) | <0.001 | 1.04 (1.02 to 1.05) | <0.001 |
| Coronary risk factor | ||||
| Diabetes | 1.07 (0.92 to 1.24) | 0.390 | – | – |
| Hypertension | 0.91 (0.78 to 1.05) | 0.174 | 0.87 (0.74 to 1.02) | 0.081 |
| Dyslipidaemia | 1.43 (1.24 to 1.65) | <0.001 | 1.31 (1.12 to 1.53) | <0.001 |
| Smoking | 1.19 (1.03 to 1.38) | 0.020 | – | – |
| Previous MI | 1.30 (1.04 to 1.62) | 0.021 | 1.20 (0.93 to 1.54) | 0.155 |
| Angina pectoris | 1.61 (1.36 to 1.91) | <0.001 | 1.61 (1.34 to 1.94) | <0.001 |
| Culprit vessel | ||||
| LCx | 1 | reference | 1 | reference |
| LAD | 1.99 (1.53 to 2.60) | <0.001 | 2.18 (1.64 to 2.92) | <0.001 |
| RCA | 2.01 (1.55 to 2.64) | <0.001 | 2.14 (1.61 to 2.87) | <0.001 |
| Other | 1.36 (0.88 to 2.08) | 0.158 | 1.58 (0.98 to 2.52) | 0.059 |
| Multivessel disease | 1.11 (0.95 to 1.28) | 0.179 | 1.15 (0.97 to 1.35) | 0.103 |
Collaterals were divided into 2 variables (absent=RCS 0 or present=RCS 1–3).
Multivariable model was selected with stepwise method based on Akaike Information Criteria. Same results were obtained with decrease/increase and increase/decrease stepwise models.
LAD, left anterior descending artery; LCx, left circumflex artery; RCA, right coronary artery.
Impact of coronary collaterals on in-hospital mortality
| All study population (n=3340) | |||
|---|---|---|---|
| OR | 95% CI | p Value | |
| Univariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.54 | 0.33 to 0.82 | 0.006 |
| Rentrop 2 | 0.47 | 0.28 to 0.74 | 0.002 |
| Rentrop 3 | 1.27 | 0.79 to 1.95 | 0.303 |
| Multivariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.48 | 0.22 to 0.94 | 0.046 |
| Rentrop 2 | 0.38 | 0.17 to 0.76 | 0.010 |
| Rentrop 3 | 1.35 | 0.72 to 2.40 | 0.331 |
| Single vessel disease without previous myocardial infarction (n=1880) | |||
| Univariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.59 | 0.28 to 1.12 | 0.131 |
| Rentrop 2 | 0.33 | 0.11 to 0.76 | 0.019 |
| Rentrop 3 | 0.20 | 0.03 to 1.49 | 0.118 |
| Multivariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.60 | 0.20 to 1.5 | 0.318 |
| Rentrop 2 | 0.29 | 0.05 to 1.03 | 0.104 |
| Rentrop 3 | <0.01 | <0.01 to >10.0 | 0.987 |
Impact of coronary collaterals on in-hospital mortality was estimated by univariable and multivariable logistic regression analysis with Rentrop 0 serving as a reference. Age, gender, onset to admission time, coronary risk factors (diabetes, hypertension, dyslipidaemia, smoking, previous myocardial infarction and angina pectoris), culprit vessel, multivessel disease and emergency percutaneous coronary intervention were used as covariates in a multivariable model.
Figure 2In-hospital mortality in (A) an all study population and (B) a subgroup of single vessel disease of main coronary arteries without previous myocardial infarction. RCS, Rentrop collateral score.
Figure 3Kaplan-Meier survival estimates in (A) an all study population and (B) a subgroup of single vessel disease of main coronary arteries without previous myocardial infarction. Numbers at risk are summarised below the figure. RCS, Rentrop collateral score.
Impact of coronary collaterals on 5-year mortality
| All study population (n=3340) | |||
|---|---|---|---|
| HR | 95% CI | p Value | |
| Univariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.47 | 0.34 to 0.66 | <0.001 |
| Rentrop 2 | 0.46 | 0.33 to 0.64 | <0.001 |
| Rentrop 3 | 0.94 | 0.66 to 1.33 | 0.714 |
| Multivariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.53 | 0.34 to 0.82 | 0.004 |
| Rentrop 2 | 0.46 | 0.30 to 0.70 | <0.001 |
| Rentrop 3 | 0.98 | 0.65 to 1.48 | 0.920 |
| Single vessel disease without previous myocardial infarction (n=1880) | |||
| Univariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.45 | 0.27 to 0.74 | 0.002 |
| Rentrop 2 | 0.35 | 0.20 to 0.64 | <0.001 |
| Rentrop 3 | 0.45 | 0.18 to 1.09 | 0.077 |
| Multivariable | |||
| Rentrop 0 | 1 | – | reference |
| Rentrop 1 | 0.49 | 0.26 to 0.91 | 0.025 |
| Rentrop 2 | 0.37 | 0.17 to 0.80 | 0.011 |
| Rentrop 3 | 0.56 | 0.21 to 1.55 | 0.267 |
Impact of coronary collaterals on 5-year mortality was estimated by univariable and multivariable Cox regression analysis with Rentrop 0 serving as a reference. Age, gender, coronary risk factors (diabetes, hypertension, dyslipidaemia, smoking, previous myocardial infarction and angina pectoris), culprit vessel, multivessel disease, emergency percutaneous coronary intervention, ACE inhibitors, angiotensin receptor blocker, β-blocker, calcium-blocker, statin and diuretic usage were used as covariates in a multivariable model.
Figure 4Subgroup analysis for 5-year mortality adjusted HR of each Rentrop collateral score as compared with no collaterals. LAD, left anterior descending artery; MI, myocardial infarction; MVD, multivessel disease; RCA, right coronary artery; RCS, Rentrop collateral score; and SVD, single vessel disease of main coronary arteries.