| Literature DB >> 25175336 |
Giuseppe Asciutto1, Nuno V Dias, Ana Persson, Jan Nilsson, Isabel Gonçalves.
Abstract
BACKGROUND: The presence of echolucent carotid plaques as defined by low ultrasound grey-scale median (GSM) is associated with a higher risk of stroke and myocardial infarction. Betablockers have shown possible anti-atherosclerotic effects. The aim of the present study was to determine if there is an association between carotid plaque GSM and treatment with betablockers.Entities:
Mesh:
Substances:
Year: 2014 PMID: 25175336 PMCID: PMC4156604 DOI: 10.1186/1471-2261-14-111
Source DB: PubMed Journal: BMC Cardiovasc Disord ISSN: 1471-2261 Impact factor: 2.298
Demographics
| No beta-blockers (n = 197) | Beta-blockers (n = 153) | P | |
|---|---|---|---|
|
| 69.91 ± 8.79 | 70.79 ± 8.43 | .364† |
|
| 69% (137) | 71% (108) | .906* |
|
| 25.9 ± 3.3 | 27 ± 3.6 | .0004† |
|
| 20% (40) | 30% (46) | .035* |
|
| 58% (115) | 89% (137) | < .0001* |
|
| .017* | ||
| | 18% (35) | 22% (33) | |
| | 37% (73) | 23% (35) | |
| | 45% (89) | 56% (85) | |
|
| 85% (167) | 89% (136) | .274* |
|
| 85.6 ± 11.8 | 84.6 ± 12.2 | .320† |
|
| |||
| | 72% (141) | 63% (96) | .618† |
|
| 20% (28) | 19% (18) | |
|
| 35% (50) | 42% (40) | |
|
| 45% (63) | 40% (38) | |
|
| 25 ± 32.4 | 21.8 ± 22.6 | .945† |
|
| 32.6 ± 23.5 | 37.8 ± 25 | .036† |
Patients are grouped based on the use of beta-blockers.
Categorical variables are presented as percentages with absolute numbers between parenthesis. Continuous variables are presented as mean (standard deviation).
AF = amaurosis fugax; BMI = Body mass index; TIA = transient ischemic attack; Time to surgery = time between symptoms (if present) and operation. * = Pearson’s Chi-square; † = Mann-Whitney U test
Figure 1Box-plots showing the GSM values according to the absence or presence of a long-term treatment with betablockers at the time of surgery.
Simple linear regression analysis
| B | St. error | Beta | t | P-value | |
|---|---|---|---|---|---|
|
| .174 | .150 | .062 | 1.157 | .248 |
|
| 3.114 | 2.830 | .059 | 1.100 | .272 |
|
| -.291 | .373 | -.042 | -.779 | -.437 |
|
| -1.070 | 2.778 | -.021 | -.385 | .700 |
|
| 3.878 | 3.011 | .069 | 1.288 | .199 |
|
| 2.138 | 2.869 | .040 | .745 | .457 |
|
| |||||
| No + Yes 0/Ex 1 | -1.299 | 2.598 | -.027 | -.500 | .617 |
| No + Ex 0/Yes 1 | 2.561 | 2.810 | .049 | .912 | .363 |
|
| -1.690 | 3.810 | -.024 | -.444 | .658 |
|
| 5.177 | 2.605 | .106 | 1.987 | .048 |
GSM as dependent continuous variable.
Multiple linear regression analysis
| B | St. error | Beta | t | P-value | |
|---|---|---|---|---|---|
|
| 16.698 | 19.970 | .840 | .401 | |
|
| .249 | .174 | .089 | 1.430 | .154 |
|
| 2.830 | 2.907 | .054 | .974 | .331 |
|
| -.333 | .399 | -.048 | -.833 | .405 |
|
| -1.693 | 2.928 | -.033 | -.578 | .563 |
|
| 3.641 | 3.108 | .065 | 1.171 | .242 |
|
| -.707 | 3.125 | -.013 | -.226 | .821 |
|
| |||||
| No + Yes 0/Ex 1 | 1.592 | 3.565 | .033 | .447 | .655 |
| No + Ex 0/Yes 1 | 5.810 | 4.127 | .111 | 1.408 | .160 |
|
| -1.496 | 3.872 | -.021 | -.386 | .699 |
|
| 5.786 | 2.833 | .118 | 2.042 | .042 |
GSM as dependent continuous variable.
Simple logistic regression analysis
| B | St. error | Wald | P-value | Exp(B) | CI for Exp(B) | ||
|---|---|---|---|---|---|---|---|
| Lower bound | Upper bound | ||||||
|
| .019 | .013 | 2.380 | .123 | 1.019 | .995 | 1.045 |
|
| .568 | .237 | 5.718 | .017 | 1.765 | 1.108 | 2.811 |
|
| -.031 | .031 | .984 | .321 | .970 | .913 | 1.030 |
|
| .041 | .229 | .033 | .856 | 1.042 | .666 | 1.632 |
|
| .311 | .250 | 1.547 | .214 | 1.365 | .836 | 2.229 |
|
| .341 | .237 | 2.067 | .150 | 1.407 | .883 | 2.240 |
|
| |||||||
| No + Yes 0/Ex 1 | -.413 | -.215 | 3.682 | .055 | .662 | .434 | 1.009 |
| No + Ex 0/Yes 1 | .365 | .233 | 2.443 | .118 | 1.440 | .912 | 2.275 |
|
| -.194 | .315 | .378 | .539 | .824 | .444 | 1.528 |
|
| .550 | .218 | 6.375 | .012 | 1.734 | 1.131 | 2.657 |
GSM ≤ 30 vs > 30 as dependent categorical variable.
Multiple logistic regression analysis
| B | St. error | Wald | P-value | Exp(B) | CI for Exp(B) | ||
|---|---|---|---|---|---|---|---|
| Lower bound | Upper bound | ||||||
|
| -.874 | 1.738 | .253 | .615 | .417 | ||
|
| .025 | .015 | 2.785 | .095 | 1.025 | .996 | 1.056 |
|
| .508 | .250 | 4.131 | .042 | 1.662 | 1.018 | 2.712 |
|
| -.029 | .035 | .698 | .403 | .972 | .908 | 1.040 |
|
| -.014 | .250 | .003 | .956 | .986 | .604 | 1.610 |
|
| .291 | .266 | 1.192 | .275 | 1.338 | .793 | 2.255 |
|
| .071 | .267 | .071 | .790 | 1.074 | .636 | 1.813 |
|
| |||||||
| No + Yes 0/Ex 1 | -.204 | .303 | .454 | .500 | .815 | .450 | 1.476 |
| No + Ex 0/Yes 1 | .488 | .355 | 1.886 | .170 | 1.629 | .812 | 3.267 |
|
| -.202 | .330 | .373 | .541 | .817 | .428 | 1.561 |
|
| -.643 | .244 | 6.926 | .008 | .526 | .325 | .849 |
GSM ≤ 30 vs > 30 as dependent categorical variable.