Factors that Influence Growth Rates of Abdominal Aortic Aneurysms. Analysis of a Mexican Cohort

Introduction

Abdominal Aortic Aneurysms (AAA) are defined as an increase in 50% (or 1.5 times) on the normal abdominal aortic diameter, typically designated as a diameter of 3 centimeters (cm) or larger.^1,2 In terms of screening, the most recent recommendations from US Preventive Services Task Force state that it should be performed in 65-75 year old men who have ever smoked as women have been reported to have a significantly lower prevalence of AAA. ¹ Additionally, duplex ultrasonography is the preferred method for screening as Computed Tomography (CT) is associated with high radiation doses. ¹ In contrast to classical recommendations, AAA growth is recently believed to be influenced by several clinical and epidemiological factors, as Behr Andersen and colleagues ³ reported in their analysis on protein concentration and porosity of the intraluminal thrombus, where high levels of plasma proteins in the intraluminal thrombus correlated with an accelerated growth.

AAA surgical repair is indicated when a diameter of 5.5 cm or greater is documented, as the risk of rupture is thought to be greater than the surgical risk. ¹ Contrary to guidelines’ recommendations on surgical indications regarding AAA, several series^4,5 have demonstrated that over 20 to 40% of patients that present with a Ruptured AAA would not make it into the screening age, hence, undertaking new research protocols could lead us to a better understanding of this pathology, improving current thresholds for screening in order to avoid this complication.

The objective of our study was to analyze risk factors associated with changes in AAA diameters in a tertiary care facility in Mexico City.

Methods

An observational, retrospective study where we analyzed the entirely of patients in which an infrarenal AAA was incidentally reported in a Contrast Enhanced Computed Tomography from 2014 to 2021. We then selected patients that underwent AAA diagnosis and had at least 1 follow-up Computed Tomography a year apart (for reasons other than AAA follow up). Afterwards, we divided them by groups depending on the diagnosis of type 2 diabetic mellitus and pharmacological history (diabetic vs non-diabetic, metformin vs non-metformin intake and statin vs non-statin intake). AAA measurements were taken from the radiology report and measured a second time by a vascular surgery resident in order to confirm measurements. AAA maximum diameter (Dmax) was taken from the radiology official report (the maximal external cross-sectional measurement in any plane but perpendicular to any curvature in the aorta). Measurements were made using our institutional software (Carestream Vue Motion TM). We compared pre and post follow-up AAA largest diameter by the aforementioned groups using paired t-tests. When performing a multivariate analysis, we used the median growth rate (0.22 mm/month) of our studied sample in order to identify independent variables associated with an increased (≥0.22 mm/month) or decreased (<0.22 mm/month) growth rate. We obtained the independent association of variables potentially related to time and increase in mm below the median value, vs ≥0.22 mm as dependent variable. Independents variables tested were (a)age of the patient at diagnosis (b) Body Mass Index (BMI) and (c) Type 2 Diabetes Mellitus (DM2) as a dichotomous value indicating presence/absence. Growth rate was obtained by subtracting subsequent diameter measurements to previous ones and using a month as the unit of time elapsed between measurements, for each patient. Categorical data were presented as frequencies and non-categorical data were presented as means and medians depending on data distribution. Measures of dispersion were used accordingly. Statistical analysis was performed on Stata 17. Ethics committee approval was obtained from the Institutional Ethics Committee. Approval number: 2530-18-20-1.

Results

A total of 181 patients were found with AAA; out of the latter, 72 (39.77%) patients had a follow-up CT at least a year apart. Mean follow-up period was 18 months. In Table 1 we present our patients baseline characteristics. In regard to radiological analysis of AAA, supra-renal and infra-renal diameters were measured and distribution was evidenced to be non-parametric (Shapiro-wilk test P = 0.00). In regard to the latter, 105 (63.20%) patients had mural thrombus and 63 (37.90%) had some degree of atherosclerosis, in addition, median follow up interval was 30 months (IQR 16-55 months). In terms of AAA diameters in our sample, median Dmax was 39.8 mm (IQR 33.6-55.7 mm) in the pre-follow up scan whilst median Dmax was 41.2 mm (IQR 34.4-51.02 mm) in the follow up scan.

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Table 1.

Patients Baseline Characteristics (n = 72).

Baseline characteristics	Mean	SD
Age	75 years	±9.05 years
	N(72)	%
Men	52	72.2
Type 2 diabetes mellitus	23	13.7
Metformin intake	21	12.5
Statin intake	42	58.3

Results of the comparison between pre and post follow up AAA Dmax by groups were as followed; in the non-diabetic group, mean Dmax was 42.13 (±12.55) vs 45.33 (±14.38) P = 0.02. In the diabetic group, mean Dmax was 35.12 mm (±35.24 mm) whilst mean follow up diameter was 36.32 mm (±5.93) P = 0.01. In the metformin-intake group no difference was found when comparing mean AAA Dmax through time (36.12 mm ± 7.04 vs 37.00 mm ± 4.51, P = 0.57), in contrast, a statistically significant difference was found in the non-metformin intake group (42.05 ± 12.54 vs 45.34 ± 12.06; P = 0.02) (Figure 1). No significant difference was evidenced in the statin-intake or non-statin intake groups regarding AAA largest diameter through follow-up. To test if monthly growth rate could be predicted, a Poisson regression was developed considering age, BMI, DM2, and the AAA Dmax at diagnosis. Independent associations and equivalent analysis as an adjusted model are presented in Figure 2.

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Figure 1.

(A, B) Contrast tomographic angiography axial views of a 70-year-old male with type 2 diabetes mellitus on Metformin. The aneurysmal lesion in the abdominal aorta has remained stable with minimal growth during 65 months of follow up (A, B).

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Figure 2.

Schematic representation of factors associated to Growth rate in mm, presented in a Poisson Regression Model for discontinuous or count numeric dependent variables reporting coefficients.

To test if a growth rate ≥0.22 mm could be predicted solely by AAA Dmax at diagnosis, alone or considering coexisting factors such as age, BMI, DM2, smoking or hypertension a logistic regression model was performed. The model explanation proportion as pseudo r2 was of 0.25 without smoking or hypertension, hence, as the model was well fitted we opted on performing independent logistic regressions considering the aforementioned (Table 2). Diabetes did not independently predict Growth rate ≥0.22 mm, but when considered along with other studied factors it became a significant predictor (Table 3).

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Table 2.

Factors Associated to Growth Rate ≥0.22 mm Presented in a Model and Independent Logistic Regression for Binary Dependent Variables Reporting Odds Ratio.

Growth ≥0.22 mm	Odds ratio	Standard error	z	P > z	95% conf. Interval
Logistic regression model
Age	0.99	.02	−0.19	0.847	.96	1.03
BMI	0.99	.05	−0.09	0.92	.90	1.10
DM2	3.35	2.21	1.78	0.076	.88	12.32
Mayor IRD at diagnosis	1.14	0.04	3.48	0.001	1.06	1.21
Independent association
Hypertension	2.12	.91	1.7	0.08	.91	4.92
Smoking antecedent	1.75	1.49	94	0.66	.32	9.30

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Table 3.

Factors Associated to Growth Rate ≥0.22 mm Presented in Logistic Regression for Binary Dependent Variables Reporting Odds Ratio.

Growth ≥0.22 mm	Odds ratio	Standard error	z	P	95% confidence intervals
BMI	0.98	0.02	−0.90	0.367	0.94	1.02
DM2	5.52	4.54	2.08	0.037	1.10	27.64
Age	1.14	0.044	3.49	0.000	1.06	1.23
Mayor IRD at diagnosis	0.97	0.059	−0.47	0.637	0.86	1.09

IRD: Infra-renal diameter; DM2: Diabetes Mellitus Type 2; BMI: Body Mass Index. Model Log likelihood = -33.27; Pseudo R2 = 0.25; Post-estimation Hosmer-Lemeshow = 0.3757; hypertension and smoking were dropped from analysis due to collinearity.

AAA Dmax at diagnosis correlated with significantly increased growth rate (coeff = 0.06, P < 0.05); diabetes associated with monthly growth rate and as model, studied factors explained a variance of 0.38.

Discussion

There are multiple studies in the current literature which intend to describe different variables which may influence AAA growth rates; reported by most of the authors as mm/year, ^6-10 yet, important limitations arise when it comes to analyzing these studies, particularly that most assume these rates to be linear and normally distributed; which can be evidenced after reading the results reported as mean mm grown in a year. Recent work performed by authors such as Siika and collaborators ¹¹ appears to confirm linear and continuous growth, yet, as the authors claim, only analyzing maximal aneurysm diameter fails to take into account other clinical factors besides AAA biomechanics. The latter appears to be complementary to the work performed by Brady et al, ¹² where they evidenced that AAA growth accelerates as the aneurysm enlarges; a finding replicated in our analysis. In short, AAA growth remains linear and continuous, yet, accelerated growth periods appear to exist; as confirmed by recent studies ¹³ and shown in the present work.

Diabetes and metformin intake have been widely discussed recently for their recent role in AAA growth, ¹⁴ as such, in our work we evidenced that DM2 appeared to predict a growth rate over the 50th percentile and an increased growth rate evidenced in the poisson regression, yet, the metformin-intake group reported no significant differences in AAA Dmax through follow-up; which may explain the association between DM2 and a decreased growth rate. As such, a decreased growth rate of AAA appears to be secondary to the pleiotropic effects attributed to metformin-intake on several age-related diseases.^15,16 In contrast to recent findings that statin-intake slows AAA growth rates, ¹⁷ in our analysis no difference was found between groups.

Latin-American countries have always been thought to have a lower AAA prevalence, ¹⁸ yet, in a recent multi-center study performed by our group ¹⁹ a prevalence between 4%–4.6% was reported; which suggests an important rate of under-diagnosis.

In the past decade a decrease in incidence and mortality secondary to AAA has been reported in the literature. ²⁰ The latter, while widely seen in many developed western countries, is not replicated in Mexico, as we evidenced in our nation-wide analysis where we described an increase in AAA attributed deaths. ²¹ Additionally, up to 60% of the aforementioned were secondary to AAA rupture, which confirms the need for a greater number of vascular surgery specialists in order to improve early diagnosis and management.

Conclusions

Initial Dmax appears to be an accurate predictor of an increased growth rate, as such, growth rates seem to behave in a non-linear matter, as growth velocity may increase in proportion to the AAA Dmax. The aforementioned must be taken into account when attempting to perform an accurate follow-up in order to prevent AAA rupture.

AAA growth pathophysiology appears to be a novel and poorly understood field with many new and exciting research being currently underway, as such, new efforts need to be made in Latin America and developing countries to understand this not-so-rare disease in order to provide better care for our patients.

Limitations

Since this study is of a retrospective nature, we acknowledge all limitations inherent to this type of design. Additionally, limitations regarding measurements as they were performed by different radiologists although reviewed by vascular surgeons from our department.

Contrast Enhanced Computed Tomographies (CT) in our study were not all CT Angiographies, hence we acknowledge the related limitations. Follow-up intervals are different in our study which could impact our results.