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J Thorac Cardiovasc Surg 2005;129:730-739
© 2005 The American Association for Thoracic Surgery

Surgery for Congenital Heart Disease

Diagnostic power of aortic elastic properties in young patients with Marfan syndrome

^a Department of Pediatric Cardiology, Innsbruck Medical University, Innsbruck, Austria
^b Research Group for Biomedical Data Mining, Institute for Information Systems, University for Health Sciences, Medical Informatics and Technology, Innsbruck, Austria
^c Division of Metabolism and Molecular Pediatrics, University Children’s Hospital, Zurich, Switzerland
^d Institute of Medical Genetics, University of Zurich, Schwerzenbach, Switzerland
^e Institute of Medical Biology and Human Genetics, Innsbruck Medical University, Innsbruck, Austria
^f Department of Ophthalmology, Innsbruck Medical University, Innsbruck, Austria
^g Department of Orthopedics, Innsbruck Medical University, Innsbruck, Austria
^h Department of Pediatric Cardiology and Congenital Heart Disease, German Heart Center, Munich, Germany

Received for publication March 4, 2004; revisions received June 21, 2004; accepted for publication July 8, 2004.

^_* Address for reprints: Daniela Baumgartner, MD, Department of Pediatric Cardiology, Innsbruck Medical University, Anichstr 35, A-6020 Innsbruck, Austria (E-mail: Daniela.Baumgartner{at}aon.at).

	Abstract

Top Abstract Methods Results Discussion Conclusions References

 
BACKGROUND: In patients with Marfan syndrome, progressive aortic dilation implicates a still-unpredictable risk of life-threatening aortic dissection and rupture. We sought to quantify aortic wall dysfunction noninvasively, determine the diagnostic power of various aortic parameters, and establish a diagnostic model for the early detection of aortic abnormalities associated with Marfan syndrome.

METHODS: In 19 patients with Marfan syndrome (age, 17.7 ± 9.5 years) and 19 age- and sex-matched healthy control subjects, computerized ascending and abdominal aortic wall contour analysis with continuous determination of aortic diameters was performed out of transthoracic M-mode echocardiographic tracings. After simultaneous oscillometric blood pressure measurement, aortic elastic properties were determined automatically.

RESULTS: The following ascending aortic elastic parameters showed statistically significant differences between the Marfan group and the control group: (1) decreased aortic distensibility (P < .001), (2) increased wall stiffness index (P < .01), (3) decreased systolic diameter increase (P < .01), and (4) decreased maximum systolic area increase (P < .001). The diagnostic power of all investigated parameters was tested by single logistic regression models. A multiple logistic regression model including solely aortic parameters yielded a sensitivity of 95% and a specificity of 100%.

CONCLUSIONS: In young patients with Marfan syndrome, a computerized image-analyzing technique revealed decreased aortic elastic properties expressed by parameters showing high diagnostic power. A multiple logistic regression model including merely aortic parameters can serve as useful predictor for Marfan syndrome.

Ambras Castle, Innsbruck. Top left to bottom right: D. Baumgartner, C. Baumgartner, Mátyás, Steinmann, Löffler-Ragg, Schermer, Schweigmann, Baldissera, Frischhut, Hess, Hammerer

Marfan syndrome (MFS; Online Mendelian Inheritance in Man #154700) is an autosomal dominant connective tissue disorder caused by mutations in the gene encoding fibrillin-1 (FBN1), with highly variable clinical manifestations in the musculoskeletal, ocular, and cardiovascular systems.^1,2 Dilatation of the aortic root predisposes the subject to aortic dissection and rupture or severe regurgitation and heart failure.³ Diseases of the aorta account for 80% of known causes of death.³ Before life-threatening complications, alterations of aortic elastic properties due to defective FBN1 can be characterized by the terms of elasticity or compliance, distensibility, stiffness index, and pulse wave velocity.^4–7

The aim of this study was to investigate aortic elasticity and assess its abnormality in patients with MFS by means of a standardized, semiautomated, and noninvasive method. This technique is appropriate for determining the course of aortic elasticity during follow-up investigations. All aortic parameters were implemented in single logistic regression models to test their diagnostic power. To further increase sensitivity and specificity, we searched for a multiple logistic regression model able to serve as an appropriate diagnostic marker for MFS. To localize aortic elastic dysfunction, we suggest visualization of ascending (AscAo) and descending aortic (DescAo) diameter changes by a vector loop.

	Methods

Top Abstract Methods Results Discussion Conclusions References

 
Patients and control subjects
Forty-seven people with suspected MFS were investigated at the Departments of Pediatric Cardiology, Ophthalmology, and Orthopedics and at the Institute of Medical Biology and Human Genetics, Innsbruck Medical University, according to a standardized protocol. Nineteen of these, whose diseases were diagnosed as MFS according to the Ghent criteria⁸ and who were younger than 40 years, comprised the study group (3 males and 16 females; mean age, 17.7 ± 9.5 years). Clinical characteristics are shown in Tables 1 and 2. Physical features were documented according to the consensus of 2 physicians (D.B. and J.L.-R.). Before the investigation, no patient received a ß-blocker, angiotensin-converting enzyme inhibitor, or calcium antagonist or had a history of aortic dissection or aortic surgery. Nineteen age- and sex-matched healthy subjects constituted the control group. Two of them were healthy relatives of patients with MFS. A group of 35 people totally different from the study population, including 16 patients with MFS and 19 healthy controls, served as validation group for the logistic regression analysis. The mean age of this group was 14.2 ± 8.0 years and ranged from 0 to 36 years. The study complied with the Declaration of Helsinki. The protocol was approved by the institutional committee on human research. All subjects gave informed consent.

Echocardiographic evaluation
All echocardiographic examinations were performed by 1 investigator (D.B.) in the left decubitus position with commercially available equipment (System Five; GE Vingmed Ultrasound, Horten, Norway). M-mode tracings of the aorta were obtained according to published criteria¹⁰ by using 2-dimensional guidance at 4 different levels: level 1, annulus (parasternal short-axis view); level 2, sinuses of Valsalva; level 3, proximal AscAo 10 to 20 mm distal to the sinotubular junction (both parasternal long-axis views); and level 4, descending abdominal aorta just proximal to the branching off of the celiac trunk (abdominal paramedian long-axis view). Attention was paid to setting the line of sight exactly perpendicular to the long axis of the aorta in views showing the largest aortic diameters. Sharp endothelial lines were used as additional indicators for the line of sight to cut the central line of the aorta. Aortic dilatation was determined with standard nomograms.¹⁰

For automated and standardized calculation of aortic diameters, we developed suitable software. First, M-mode tracings of the AscAo (level 3) and DescAo (level 4) of at least 5 heart cycles were loaded into the program. To find the inner aortic wall contours, an image-processing algorithm ran on the M-mode images. Out of the determined aortic edge map, AscAo and DescAo outlines were calculated throughout the heart cycles (Figure 1, left). In some images with a suboptimal signal-to-noise ratio, minor manual corrections of aortic wall contours had to be performed. Interobserver reproducibility, calculated as the standard deviation of the differences between measurements and expressed as the percentage of the mean of the measurements, was determined after re-evaluation of randomly selected images by a second investigator blinded to the initial results. According to the usual aortic diameter measurements with the leading edge technique,¹⁰ the automatically detected inner diameter of the aorta was enlarged by the anterior aortic wall thickness. Time-diameter curves of 5 heart cycles were generated, based on the aortic wall contours. They showed a time resolution of approximately 6 ms per pixel and a spatial resolution of 0.2 mm per pixel. The curves were averaged and slightly smoothed by a digital low-pass filter (Butterworth; degree 2) to eliminate the digitalization noise (Figure 1, right). Out of time-diameter curves and averaged threefold blood pressure measurements, which were taken at the right arm oscillometrically (Dinamap; GE Healthcare, Slough, United Kingdom) immediately before M-mode registration, aortic elastic parameters were estimated automatically.

View larger version (68K): [in this window] [in a new window]   Figure 1. Semiautomated aortic wall contour analysis (white lines) in M-mode images of the ascending (top left) and descending (bottom left) aorta of patient 5. Arrows indicate the beginning and end of 1 heart cycle. Averaged time-diameter curves of the ascending (top right) and descending (bottom right) aorta are shown.

Systolic diameter increase was calculated as

(1)

where D_s is systolic (maximum) and D_d is end-diastolic (minimum) aortic diameter. Cross-sectional (CS) aortic distensibility and stiffness index were estimated as previously described^6,11:

(2)

(3)

where A_s is systolic and A_d is end-diastolic area and P_s is systolic and P_d is diastolic blood pressure (mm Hg). Area A was determined as (D/2)² · .

MSAI was defined as the maximum systolic slope of the area-time curve A(t) normalized to A_d:

(4)

Integrals of the AscAo and DescAo area-time curves normalized to the corresponding end-diastolic area—defined as aortic integral ratios—show in which aortic segment elasticity is reduced more severely.

(5)

where A(t) the is aortic area-time curve and HC is the heart cycle.

The vectoraortography visualizes the vector loop of the relative AscAo and DescAo diameter changes during the heart cycle. The rotating vector can be characterized by its magnitude and phase:

(6)

(7)

where D(t) is the aortic diameter-time curve.

Statistics
Data are expressed as mean ± SD and, in Figure 2, A as mean ± 95% confidence interval. Quantitative variables were compared by means of unpaired Student t tests and Mann-Whitney U tests, respectively. The relation between continuous variables was tested by linear regression analysis. Single and multiple logistic regression models were developed to estimate the diagnostic power of the aortic parameters. The conditional probability for the presence of MFS is denoted by the equation where z indicates the logit of the model. The effect of each model parameter is given by its odds ratio. All statistical analyses were performed with the software package SPSS 11.0 (SPSS Inc, Chicago, Ill).

View larger version (20K): [in this window] [in a new window]   Figure 2. Vectoraortography. A, Vector loops represent relative ascending (AscAo) and descending (DescAo) aortic diameter changes (D(t)/Dd) during the heart cycle. The 95% confidence interval at 0, 200, 400, and 600 ms is denoted by thin lines. The loop of the Marfan (MFS) patients is smaller and a little steeper than that of the control group. This shows the reduction of aortic elasticity predominantly present in the AscAo of MFS patients. Arrows indicate the vectors’ maximum magnitude in the MFS and the control groups. Loops are not closed because of varying cycle lengths of individuals. Beyond 600 ms cycle length, data were not included. B, The MFS group was divided into 4 subgroups according to the aortic integral ratio. The vectors of the total MFS group, the MFS subgroups, and the control group are shown. *, , and indicate the members of family 1, 2, and 3 (Table 1).

	Results

Top Abstract Methods Results Discussion Conclusions References

Aortic dimensions and calculation of elastic parameters
Echocardiographic aortic findings of the Marfan group and the control group are shown in Tables 1 and 2. Diastolic aortic root (P < .001) and diastolic AscAo diameter (P = .007) were significantly increased in the MFS group, whereas the difference of DescAo diameters between groups did not reach statistical significance. All 4 investigated elastic parameters demonstrated reduced aortic elastic properties in MFS patients (Table 2 and Figure 2): AscAo systolic diameter increase (42% of control group), CS distensibility (47%), and MSAI (51%) were significantly diminished in the Marfan group. The stiffness index, as being inversely related to distensibility, was markedly increased (182% of control group). Four MFS patients (patients 12–14 and 18; Table 1) revealed an AscAo diameter decrease during systole; in these cases, CS distensibility and MSAI were set to 0, and stiffness index could not be calculated. Note that both patients without aortic root dilatation (patients 7 and 18; Table 1) showed a decreased AscAo distensibility and a reduced DescAo distensibility of 1 SD. In the DescAo of MFS patients, we observed less systolic diameter increase, CS distensibility, and MSAI; the stiffness index was markedly greater than in the control group. The differences were smaller than in the AscAo (Table 2). In 3 of 5 adult MFS patients, in whom elective prosthetic aortic root replacement was indicated at or 1 year after the initial investigation (patients 12, 14, and 17 out of the study group [Table 1] and 2 patients out of the validation group), AscAo distensibility and MSAI were 0. Because of systolic diameter decrease, aortic stiffness index could not be calculated. In the remaining 2 of the 5 operated patients, AscAo distensibility was strongly decreased (12 and 27 kPa^–1 · 10^–3). However, 1 MFS patient (patient 18) showed an AscAo 0 distensibility without aortic root dilatation.

CS distensibility and MSAI values of the AscAo and DescAo of Marfan patients and control persons correlated significantly (MSAI = 0.68 x CS distensibility + 10.54; r = 0.86; P < .01).

Interobserver reproducibility, which was determined in 6 consecutive patients, was 2.6% and 3.3% for AscAo and DescAo diastolic diameter measurements and was 3.8% and 4.6% for AscAo and DescAo distensibility. Reproducibility of further aortic elastic parameters showed comparable values.

Aortic integral ratio
Mean values of the AscAo/DescAo integral ratio were similar between Marfan patients and control persons, but in the MFS group, the standard deviation was markedly increased (1.1 ± 2.1 in the MFS vs 1.1 ± 0.5 in the control group; Table 2). This ratio showed the variable extent of regional aortic elasticity alterations in the Marfan patients and, conversely, a tight relationship of AscAo and DescAo integrals in healthy control subjects.

Vectoraortography
The vector loops characterizing the relative aortic diameter changes during the heart cycle differed significantly between the MFS and the control group (Figure 2, A and Table 2). The maximum magnitude of the vector (Figure 2, A) was significantly reduced in the MFS group, and the vector’s phase at maximum magnitude (ie, the angle below the vector) showed no significant difference between groups (P = .061). Because of the high standard deviation of the phase and aortic integral ratio in the MFS group, we split the Marfan patients into 4 subgroups to distinguish among different elasticity patterns (Figure 2, B). In the first subgroup, the AscAo diameter decreased during early systole, so that phase was strongly increased (mean, 131°). In subgroup 2, phase was also increased, but AscAo diameter increased during systole. Subgroup 3 (aortic integral ratio, 0.6–1.6; ie, mean value ± 1 SD of control group) showed a mean phase (54°) roughly comparable to the control group because of similar reduction of AscAo and DescAo pulsatile diameter changes. In subgroup 4, phase was strongly reduced (mean 10°) because of decreased pulsatile diameter changes predominantly in the DescAo.

Single and multiple logistic regression analysis
All presented aortic parameters were tested separately for their diagnostic power by single logistic regression analysis (Table 3). AscAo distensibility and systolic diameter increase demonstrated the highest sensitivity (84%); the diastolic diameter of the bulbus aortae normalized to body-surface area showed the highest specificity (84%).

(P = .030; P = .028; P = .035; odds ratios: 9.901, 0.086, and 0.781; Table 3)

Subsequently, our best model z was tested on the independent validation group and showed a sensitivity of 100% and a specificity of 94.7%. Validation of the single logistic regression models also yielded comparable results to those established in the study population.

	Discussion

Top Abstract Methods Results Discussion Conclusions References

 
Our new noninvasive semiautomated M-mode echocardiographic image-segmentation technique showed reduced aortic elastic properties in children and young adults with MFS, with high accuracy and objectivity. In most published studies, aortic diameters and CS areas, which underlie the calculation of aortic elastic properties, still rely on a slow, tedious, and observer-dependent process of manual outlining, which has to be performed by expert physicians.

In adults with MFS, automated border detection has been used to measure aortic diameters out of transesophageal aortic images.¹² We used 2-dimensional guided transthoracic M-mode echocardiographic aortic diameter measurements, which showed good correlation with 2-dimensional echocardiographically obtained values.¹⁰ Two-dimensional guidance is indispensable for correct diameter measurements out of M-mode echocardiographic images, especially for displaying the largest aortic diameter and for finding an axis strictly perpendicular to the long axis of the aorta.¹⁰ In contrast to continuous aortic measurements out of 2-dimensional echocardiographic or magnetic resonance imaging sequences, M-mode echocardiography enables us to measure aortic diameters over 5 heart cycles with twofold to fivefold higher time resolution out of merely 1 to 2 images. In children and young adults, images of high quality can be obtained in most cases. However, accurate image acquisition with a high signal-to-noise ratio is essential for appropriate computerized contour finding.

Aortic root dilatation, a major criterion of MFS,⁸ was shown to be present in 89% of our patients and was reported in 61.5% to 84% of adults^1,13–16 and in 42.5% to 76% of children aged 0.25 to 18 years.^15,17 AscAo dilatation was present in 42% of our patients and has been reported in 54% of adults¹⁵ and in 45% of children (age, 0.5–18 years).¹⁵ Because aortic dilatation evolves during childhood and adolescence, serial evaluations of aortic dimensions may be necessary to clearly demonstrate the presence and progression of aortic dilatation.¹⁸ Because aortic root growth is of prognostic value for the occurrence of aortic complications,¹⁸ objective diameter measurements will enhance the accuracy of results.

The representation of time-diameter curves gives us an optical impression of aortic diameter changes during the heart cycle (Figure 1, right). The vectoraortography—a compaction of time-diameter relations of 2 aortic segments in 1 diagram—and the aortic integral ratio allow us to distinguish different patterns of aortic stiffening within the MFS group (Figure 2). In 4 patients with considerable aortic root dilatation, the AscAo anteroposterior diameter decreased during systole, which—to our knowledge—has never been described before (patients 12–14 and 18; mean end-diastolic aortic root diameter, 42.0 ± 7.5 mm vs 34.9 ± 8.6 mm in the total MFS group; mean end-diastolic AscAo diameter, 34.0 ± 5.4 mm vs 26.2 ± 7.5 mm in the total MFS group; Figure 2, B, subgroup 1). As we observed by echocardiography in a few patients with excellent quality of AscAo 2-dimensional images, the AscAo seemed to bump against an anterior structure (probably the sternum) during its systolic anterior movement; the aortic CS area for a short time deviated from its circular shape toward an elliptic shape. Therefore, the aortic wall of these patients is exposed to increased shear stress. AscAo distensibility and MSAI were set to 0, and AscAo stiffness index could not be calculated. Patients with predominant loss of DescAo elasticity (subgroup 4) may resemble those who are at risk for aneurysm or dissection of the DescAo.^19,20 Our technique can thus serve as a valuable noninvasive tool for assessing the descending abdominal aorta.

Simultaneous diameter and blood pressure registration is essential for exact calculation of elastic parameters. Simultaneous diameter and pressure registration at the same aortic site is impossible if elastic parameters are determined noninvasively. However, close correlation of invasive and noninvasive determination of AscAo distensibility has been demonstrated.⁵ Nevertheless, aortic valve competence and normal left ventricular systolic function are basic requirements for the interpretation of calculated aortic elastic parameters.

Several authors have shown decreased aortic distensibility and increased aortic stiffness index in patients with MFS.^{6,7,12,21–25} Data obtained in children are rare.^6,21 Our results, which show a 50% reduced AscAo and a 30% reduced DescAo distensibility in the MFS group, compare well to published data on children⁶ and young adults^7,22 with MFS. Smaller values of mean aortic distensibility were reported in older patients,^24–27 and greater values were reported in younger children.²¹ Our data confirm this dependence of aortic distensibility on age. It is interesting to note that the patients with normal diameters of the bulbus and the AscAo also showed aortic dysfunction in terms of decreased AscAo and DescAo distensibility. Therefore, assessment of aortic dysfunction is of additional diagnostic value compared with AscAo diameter measurements. The necessity of ß-blocker therapy should be discussed in those patients. MSAI is a further elastic parameter that is easy to determine, because blood pressure measurement is not required. In our series, MSAI correlated very closely with aortic distensibility. Follow-up investigations with the presented elastic parameters could prove the efficiency of medical treatment with, eg, ß-blocking agents and may be of help in the timing of elective aortic surgery, especially in children and adolescents not presenting with excessively dilated aortic diameters that are unquestionably an indication for elective prosthetic aortic root replacement. In our opinion, an AscAo 0 distensibility can be regarded as additional argument for elective aortic surgery. More detailed clinical description was thought to be necessary to allow a genotype/phenotype correlation between patients described by other groups²⁸; our results in this relatively small MFS group, however, did not reveal a dependence of aortic distensibility on the type of FBN1 mutation (data not shown). Objective data on aortic elastic properties, together with the results of FBN1 gene mutation analysis of a greater patient population, will perhaps show certain relationships. Because FBN1 mutation analysis is still too expensive and time-consuming to be used as screening tool, our logistic regression models based on the results of only aortic parameters are an alternative approach to recognize and classify MFS. In patients with suspected MFS without aortic dilatation, they can serve as useful additional diagnostic tools to decide whether these patients should be genetically tested. Our best multiple logistic regression model showed higher sensitivity (94.7%) and specificity (100%) than the best single logistic regression models (sensitivity and specificity of 68%-84%). This validated multiple logistic regression model can predict MFS more reliably than a cardiologic investigation including only aortic diameter measurements (yielding a sensitivity of 89% in our population and 61%-84% in published patient populations).^1,13–16 It helps to decide about the necessity of time-consuming follow-up investigations, especially in patients with low suspicion of MFS and normal aortic elasticity, but does not replace ophthalmologic and orthopedic investigations, because some rare patients with MFS show no aortic involvement.^14,17 Patients with Ehlers-Danlos syndrome type IV³⁰ and thoracic aortic aneurysm² may show reduced aortic elastic properties, too, and therefore may be investigated with similar logistic regression models.

	Conclusions

Top Abstract Methods Results Discussion Conclusions References

 
In summary, we determined decreased aortic elastic properties in young patients with MFS by a standardized semiautomated image-segmentation technique that enables us to estimate AscAo and DescAo distensibility, stiffness index, and MSAI with high reproducibility. It also gives way to high-quality follow-up investigations of aortic elastic properties in patients with suspected or confirmed MFS. Vectoraortography illustrates and the aortic integral ratio quantifies the relationship of AscAo and DescAo elasticity and so may show the region at risk for severe aortic complications. Our multiple logistic regression model enables us to calculate the probability for the presence of MFS on the basis of the results of solely aortic parameters (distensibility, normalized diastolic diameters of aortic bulbus, and AscAo) and so can be used as a diagnostic tool with high predictive power. Follow-up investigations in a larger patient population will prove the efficiency of medical treatment and may determine the value of this method for the prediction of aortic dissection and rupture, so these elastic parameters may serve as additional criteria to indicate elective surgical intervention.

	Acknowledgments

 
We thank Dr Peter Oefner (Stanford University) for the initial mutational analysis of patient 13 by denaturing high-performance liquid chromatography, Dr Barbara Utermann (Innsbruck Medical University) for genetic counseling of several patients, Melanie Maudrich for laboratory assistance, and Karin Kirchner and Silvia Achenrainer for secretarial assistance.

	Footnotes

 
Supported by the Austrian Industrial Research Promotion Fund (grant HITT-10 UMIT), Wolfermann-Nägeli-Stiftung (Zurich, Switzerland), and the Swiss National Science Foundation (grant 3200-059 445/2).

	References

Top Abstract Methods Results Discussion Conclusions References

 

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