Distribution of Islamic banks over six countries.
\r\n\t
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In 2015 he obtained an international patent for a prosthesis for inguinal hernioplasty and standardized a new surgical technique for the application of the aforementioned method. He has presented his works at around 200 national and international congresses.',coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"239365",title:"M.D.",name:"Angelo",middleName:null,surname:"Guttadauro",slug:"angelo-guttadauro",fullName:"Angelo Guttadauro",profilePictureURL:"https://mts.intechopen.com/storage/users/239365/images/system/239365.jpeg",biography:'Dr Angelo Guttadauro is a researcher at the University of Milan-Bicocca and first level manager at the U.O.C. of General Surgery at the Zucchi Clinical Institutes of Monza. He is the founder and head of the \\"Hernia Center\\" of Monza-Brianza. He has participated in research projects of the Ministry of University and Scientific and Technological Research. 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Thus they are contributing to wealth distribution by effective allocating of financial resources. The Literature Review have evaluated the efficiency of Islamic banks performance as compare their counterpart conventional banks performance .there are some studies investigate the impact of international financial crisis 2008 on Islamic banks, they had demonstrated that Islamic banks were stable and highly governance by local supervisory authorities, in order to avoid the same mistakes of American banks. Therefore, early identification of weak banks ranks is very important to monitory supervisors. As we know the majority of the Gulf Council Countries (GCC) nations are Muslims, so that Islamic finance is very important field in these countries should be study and develop as a result of this perception there are some studies have conducted in Islamic banks fields in GCC, such as [1, 2] (Vijaya Kumar and Hameedah Sayani, 2015), but they have used distress models like Z score, CAMEL, to evaluate the performances of Islamic banks as compare to conventional banks in GCC. So that there is no clear consensus about the relation between CAMEL dimensions ratios and Z score of Islamic bank,this gab is filled by this study There are twenty Full-fledge Islamic banks In Gulf Council Countries (GCC). The oldest one is Qatar International Islamic Bank (QIIB) in Qatar, which was establish in 1956, followed by Al Rajhi Bank established in 1957 in Saudi Arabia, the youngest one is Bank Nizwa in Oman established in 2013. See (Tables 1 and 2).
\nS | \nCountry | \nNumber of banks | \nBank list | \nWhen bank has been established | \n
---|---|---|---|---|
1 | \nSaudi Arabia kingdom | \n4 | \nAl Rajhi Bank | \n1957 | \n
Bank Albilad | \n2004 | \n|||
Aljazeera Bank | \n1975 | \n|||
Alinma Bank | \n2006 | \n|||
2 | \nUnited Arab emirates | \n4 | \nDubai Islamic Bank (DIB) (1) | \n1975 | \n
Abu Dhabi Islamic Bank (ADIB) (2) | \n1997 | \n|||
Emirates Islamic bank (3) | \n1975 | \n|||
Sharjah Islamic bank (4) | \n1976 | \n|||
3 | \nBahrain Kingdom | \n3 | \nBahrain Islamic Bank (BISB) (1) | \n1979 | \n
Al Salam Bank (2) | \n2006 | \n|||
Arabic Bank Corporation (Bank ABC) (3) | \n1980 | \n|||
4 | \nQatar | \n4 | \nQatar International Islamic Bank(QIIB) (1) | \n1956 | \n
Qatar Islamic Bank(QIsB) (2) | \n1982 | \n|||
Baraw Bank (3) | \n2007 | \n|||
Masraf Al Rayan (4) | \n2006 | \n|||
5 | \nKuwaiti | \n3 | \nKuwait Finance house | \n1977 | \n
Boubyan Bank | \n2004 | \n|||
Warba Bank | \n2009 | \n|||
6 | \nOman | \n2 | \nAlizz Bank | \n2012 | \n
Bank Nizwa | \n2013 | \n|||
Total | \n20 banks | \n\n | \n |
Distribution of Islamic banks over six countries.
S | \nBank name | \nEstablishment date | \nAge | \n
---|---|---|---|
1 | \nQatar International Islamic Bank (QIIB) | \n1956 | \n64 | \n
2 | \nAl Rajhi Bank | \n1957 | \n63 | \n
3 | \nAljazeera Bank | \n1975 | \n45 | \n
4 | \nDubai Islamic Bank (DIB) | \n1975 | \n45 | \n
5 | \nEmirates Islamic bank | \n1975 | \n45 | \n
6 | \nSharjah Islamic bank | \n1976 | \n44 | \n
7 | \nKuwait Finance house | \n1977 | \n43 | \n
8 | \nBahrain Islamic Bank (BISB) | \n1979 | \n41 | \n
9 | \nArabic Bank Corporation (Bank ABC) | \n1980 | \n40 | \n
10 | \nQatar Islamic Bank (QIsB) | \n1982 | \n38 | \n
11 | \nAbu Dhabi Islamic Bank (ADIB) | \n1997 | \n23 | \n
12 | \nBoubyan Bank | \n2004 | \n16 | \n
13 | \nBank Albilad | \n2004 | \n16 | \n
14 | \nAlinma Bank | \n2006 | \n14 | \n
15 | \nAl Salam Bank | \n2006 | \n14 | \n
16 | \nMasraf Al Rayan | \n2006 | \n14 | \n
17 | \nBaraw Bank | \n2007 | \n13 | \n
18 | \nWarba Bank | \n2009 | \n11 | \n
19 | \nAlizz Islamic Bank | \n2012 | \n8 | \n
20 | \nBank Nizwa | \n2013 | \n7 | \n
Establishment date of Islamic banks in GCC.
Source the researcher from banks sites information.
The researcher collects financial data from banks sites, and General Economic development indicators (GDP, Inflation rate, Exchange rate) for each country of GCC from World Bank site. Then all financial data with local currency was converted in to dollar, even exchange rate. After that Z score is calculated for each bank within the study period and it regressed with independent variables including CAMEL ratios as internal factors of the studied banks, and GDP, inflation rate and exchange rate as external factors. The results of multiple linear regression show the best ratios that can be used as indicators of CAMEL Dimensions ratio.
\nThere is no clear consensus in previous studies on GCC, which investigated the impact of the CMELS model ratios as internal factors of the bank on Z score as soundness indicator. Also there is no clear consensus in previous studies on GCC about impact of GDP, Inflation rate,exchange rate as external economic factors on Z score as a soundness of Islamic banks.
\nThe main objective of this study is to fill the gap in selecting the best ratios of CAMEL Dimensions indicators, that can measure bank’s soundness.
\nThe research follows survey method to search sample consist of five full-fledge Islamic banks worked in GCC as population. But each a selected bank its age less than 10 years will excluded, because its experience cannot able it to achieve competitiveness. The total of full-fledge Islamic banks in GCC are 20 banks; they are distributed over 6 countries (see Tables 1 and 2).
\nWhile population is homogenies (because the Islamic banks in GCC are homogenies) the researcher ranked these banks according to their establishment date (see Table 2), in order to use simple random sample with lottery method using serial number as assigned number to give equal chance for each bank, but each selected bank its age less than ten year should be excluded,because the period of study extend to fourteen years.Thus The age of selected bank Alizz bank is excluded because its age less than 10 years. Then start from the beginning and Al Salam Bank was chosen. After that the researcher examines normality distribution to ensure that this sample represents the population figures show histogram normality test results, are (Figures 1–3) moreover researcher employed one sample Kolmogorov–Smirnov test (K-S test) (Table 3) ensured that distribution of the sample is normal. Which allow researcher to used parametric test (linear regression).
\nNormality distribution of Islamic banks names.
Normality distribution of financial years.
Normality test of the country names.
One-Sample Kolmogorov–Smirnov Test | \n|||||
---|---|---|---|---|---|
\n | Bank name | \nCountry name | \nCurrency name | \nFinancial year | \n|
N | \n168 | \n168 | \n168 | \n168 | \n|
Normal Parameters\na\n\n,\n\nb\n\n | \nMean | \n6.49 | \n3.50 | \n3.51 | \n2012.50 | \n
Std. Deviation | \n3.448 | \n1.713 | \n1.716 | \n4.043 | \n|
Most Extreme Differences | \nAbsolute | \n.100 | \n.143 | \n.147 | \n.092 | \n
Positive | \n.098 | \n.143 | \n.143 | \n.092 | \n|
Negative | \n−.100 | \n−.143 | \n−.147 | \n−.092 | \n|
Test Statistic | \n.100 | \n.143 | \n.147 | \n.092 | \n|
Asymp. Sig. (2-tailed) | \n.000\nc\n\n | \n.000\nc\n\n | \n.000\nc\n\n | \n.001\nc\n\n | \n
Normality test result.
Test distribution is Normal.
Calculated from data.
Lilliefors Significance Correction.
Source the researcher from data analysis.
Then secondary data were collected from annual reports of studied banks, and General Economic development indicators (GDP, Inflation rate, Exchange rate) for each country of GCC from World Bank site.
\nMultiple linear regressions is used to investigate causal relation between CAMEL ratios and Z score, also it used to discover the impact of economic factors (GDP,Inflation rate, exchange rate) on Z score of Islamic banks.
\nThe structure of the paper as following: Section Two provides research design, Section Three briefly reviews the literature on the financial distress concept, measurement, and Section Four specifies the model and indicates the sources of data and setting up the statistical methodology used in the study. Section Five, contains the main findings of the study, their analyses and assessments. The final section contains conclusions and policy implications, recommendation, and limitations.
\nContribution of this study representing in a recommendation for amendment of Camels rating model should be constructing as following:
\nTotal liabilities to Total Assets ratio + Total loan to T. assets +Share market price+ net loan to Total Assets +Earning Per share+ provision Non-performing loan/Gross loan.
\nThere are several studies conducted in financial distress field, but there is no agreed of a formal definition of financial distress [3]. The absence of a formal definition of financial distress puts into questions on the validity of researches conducted within the domain. Different measures of standards would categorize non distressed firms as distressed and vice versa; thus, without a formal definition of financial distress, it would be very difficult to address this problem [3, 4]. Categorized financial distressed into three, namely: 1- event-oriented, 2- process-oriented, and [5] Technical.
\nIn the first category (event oriented), financial distress is mostly associated with terms such as default, failure and bankruptcy [4, 5]. Explained that Four terms mostly used interchangeably are default, failure, insolvency and bankruptcy; even though these terms are often used interchangeably, formally each of them presents a different definition:
\nFailure, moreover, means that the realized rate of return on invested capital is significantly lower than prevailing rates on similar investments it should be noted that a company may have had an economic failure for many years, yet never failed to meet its obligations.
\nInsolvency, furthermore, is another term depicting negative firm performance, and is generally used in a more technical fashion; whereas technical insolvency may be a temporary condition although it is often the immediate cause of bankruptcy. [5] also defines that insolvency in bankruptcy sense is a condition where total liabilities exceed a fair value of total assets rendering the net worth of the firm negative.
\nDefault distress can be technical and/or legal and always involve the debtor-creditor relationship [5]. Technical default takes place when the debtor violates a condition of an agreement with a creditor, and can be grounds for legal action [5]. Bankruptcy may be understood as a formal process where a firm announces in court that it has gone bankrupt followed by the petition to liquidate its assets or to undergo a recovery program [6]. As for the second category, financial distress is defined as a process; this definition helps in understanding financial distress as a phenomenon in constructing a comprehensive theory of financial distress [3, 4] stated that financial distress is a process situated between solvent and insolvent, and considered as a condition where the company experiences low cash flow and losses without being insolvent.
\nThe third category defines financial distress through indicators used by various financial distress prediction models [3]. Though still criticized by many, the use of ratios in many financial distress prediction models is to produce results relating to the likelihood of financial distress and default within a company [3]. In general, ratios that measure profitability, liquidity and insolvency are commonly used in predicting financial distress, despite not knowing which one is the most significant [7]. Poor management has always been the core reason behind financial distress within companies [8]. Several non-internal factors, such as high interest rates, bad industrial performance, competition on the international level etc. may contribute to the occurrence of financial distress within a company [8]; conducted a research regarding the potential of financial distress within banks in UAE. In the research, [9] identifies several factors that are greatly relevant to financial distress, such as cost to income ratio as well as equity to asset ratio and non-performing loan ratio. Macroeconomic factors, on the other hand, do not play a significant role. [10] demonstrated that financial distress is considered as the financial problems faced by an entity that prevents it from independently meeting its obligations, thus resulting in the requirement for external aid to be able to continue operating either by means of a merger, acquisition, intervention by a consumer protection authority or public aid, with the most serious case of financial distress being bankruptcy.
\nThere are various detection models that have been constructed in CBs [11, 12] grouped the models into the following families of techniques: (i) statistical techniques, (ii) neural networks, (iii) case-based reasoning, (iv) decision trees, (iv) operational research, (v) evolutionary approaches, (vi) rough set based techniques, (vii) other techniques subsuming fuzzy logic, supporting vector machine and isotonic separation and (viii) soft computing subsuming seamless hybridization of all the above-mentioned techniques. Based on these methods, various authors came out with various research findings mentioned in the following section literature review.
\nFound that stock market information can be used to estimate leading indicators of bank financial distress [13]. Had selected 64 European banks [13] pacified a logit early warning model, designed for European banks, which tests if market based indicators add predictive value to models relying on accounting data [14] also study the robustness of the link between market information and financial downgrading in the light of the safety net and asymmetric information hypotheses Other of their results show that the accuracy of the predictive power depends on the extent to which bank liabilities are market traded [15]. Conducted a research to use the financial data to identify changes in bank conditions. They used the call-report data to predict deterioration in condition as measured by changes in two main factors. The call report data could be used to construct non statistical early-warning models that mimic the examination process. The two main factors are the CAMEL ratings, and the role of off-site monitoring in the banking examination process. Off-site monitoring is an alternative method for on-site monitoring system in a bank using the financial ratios. There are twenty two commonly used financial ratios selected [16]. Each ratio is included because it provides insight into a dimension of the financial condition of the sample banks that is reflected in the actual composite CAMEL rating. The ratios generally are similar to those used in previous early-warning failure-prediction models. Fifty eight samples of banks in the US were chosen. They used logit regression and logit analysis ratio. They found five financial ratios that are significant as follows:
Asset quality indicators: defined as non-performing loans and leases divided by primary capital;
Liquidity-type ratios: loans plus securities/total sources of funds;
Liquidity-type ratios: volatile liabilities/total sources of funds;
Primary capital/average assets;
Current-quarter ratio: nonperforming-loan ratio. For the Shari’ah compliance, the CAMEL ratings should be assessed. This CAMEL rating consists of elements from Capital adequacy, Asset quality, Management, Earnings and Liquidity [17].
[18] Stated that the CAMEL ratings generally assess overall soundness of the banks, and identify and/or predict different risk factors that may contribute to turn the banks into a problem or failed banks. These banks tend to perform the FFS. Bangladesh Banks have included an additional key point of “Sensitivity to market risk” to be the CAMELS. However, [18)] has recommended the CAMELS Rating Framework to be the CAMELSS in order to comprehend the Islamic Banking that is “Shari’ah Rating”. In line with [18].
\n[18] Stated that Recommendation on the “Shari’ah Rating” is the Ethical Identity Index (EII) [18]. Said that EII is the Shari’ah compliance determination identified by the existence of discrepancy between the communicated (based on information disclosed in the annual reports) and ideal (disclosure of information deemed vital based on the Islamic ethical business framework), which was termed by [19] as Ethical Identities Index (EII) [19]. Examined seven Islamic Banks over a three-year period of longitudinal survey in the Arabian Gulf region [19]. Found that six out of seven Islamic Banks suffered from disparity between the “communicated” and “ideal” ethical identities. They demonstrated that: From both functions of the CAMELSS and EII, they could ensure the Shari’ah compliance in the IBs. They have Recommend that False Financial Statements (FFS) detection model in CBs could be applied similarly by adding this Shari’ah compliance control variable.
\n[20] used a simple stress test method, including three stress test areas: profitability stress test, capital stress test and liquidity stress test. His results showed that in term of profitability, Islamic banks in Indonesia are immune from losses if the default rate (Non-Performing Loan) is less than 8.5%. If the industry can improve the profit margin, the resistance will be higher. In term of capital position, by assuming loss given default (LGD) is constant at 40%, the industry will not go bankrupt if probability of default (PD) is less than 9%. If the PD is more than 9%, total expected loss is more than available capital.
\n[21] focused on cutting-edge FDP models and applied them to Islamic. They had employed three models: Altman Z-Score and Altman Z-Score for service firms, and Standardized Profits method, their results indicated that there is a need for a specific financial distress mechanism for Islamic banks, as variables that are indicative of a bank’s status differ between the old Altman [7] standard and novel approaches. “Working Capital/Total Assets” was the most predictive variable for forecasting financial distress in Islamic banks. As for the Standardized Profits method, “Return On Revenue” was the most influential variable banks, they employed three models [22]. Examined, evaluated and compared the financial activities of selected Islamic and conventional banks of Pakistan for period (2003–2012.). Various parameters of CAMEL model were tested by employing simple t-test. His result showed that: there are significant differences between Islamic and conventional banks in risk-weighted credit exposures, regulatory capital, advances in proportion to asset portfolios, long-term debt paying abilities, management’s control over expenses in proportion to income, return on assets, and liquidity.
\n[23], analyzed the financial performance of three selected Islamic Banks in Bangladesh over 8 years (2007–2014), he was using Camel Rating model to evaluate banks’ performance, he demonstrated that all the selected Islamic Banks are in strong position on their composite rating system (CAMEL).
\n[24] Their study has conducted with the objective of comparing shariah compliance and traditional banks of Pakistan from performance perspective. The relative investigations were conducted by means of t.test, for the period 2010–2017. Ratios based on CAMELS approach are applied to identify the managerial and monetary performance of shariah compliance and traditional banks of Pakistan. They demonstrated that Shariah compliance banks are significantly better in managing capital adequacy, management adequacy/quality, earning ability, liquidity and sensitivity to risk as compared to their traditional counterparts [23]. Aimed to analyze the financial performance of three selected Islamic Banks (Islami Bank Bangladesh Limited, Export Import Bank of Bangladesh Limited, Shahjalal Islami Bank Limited) over a period of eight years (2007–2014) in Bangladeshi banking sectors. For this reason, CAMEL Rating Analysis approach has been conducted and it is found that all the selected Islamic Banks are in strong position on their composite rating system. [1] aimed to analyze the performance of Islamic banks and conventional banks during the crisis and after the crisis, by comparing the performance of Islamic and conventional banks based in the Gulf Cooperation Council (GCC) during the period of 2008–2011 by deploying the CAMEL testing factors, his results showed that Islamic banks possessed adequate capital structure but have recorded lower ROAE and poor management efficiency. Asset quality and liquidity for both the modes of banking system have not recorded any significant difference. [2], Directed a study on the GCC for a period of 2002–2009, to assess the factors that affect the Islamic bank and conventional banks. The study included a sample of 38 conventional banks, and 13 Islamic banks. The factors that were studied were foreign ownership, bank specific variable and macroeconomic variables. Some interesting results were found. The cost-income was found to have a negative and significant impact on banks performance for Islamic and conventional banks. Equity was found out to be important factor in maximizing the profitability of Islamic banks. The size of the banks supported the economies of scale utilizing the ROE for Islamic banks. GDP was found to be positively related, while inflation negatively related to the banks performance [25]. Aimed to evaluate the soundness of Islamic banks in the GCC for the period 2008 to 2014. Methodology- The study involves 11 listed Islamic banks based in the GCC countries of Saudi Arabia, United Arab Emirates, Qatar, Bahrain, and Kuwait [25]. Applied the CAMEL parameters, which include Capital Adequacy, Asset quality, Management, Earning and liquidity. Multivariate Z- score model is also used to ensure robustness of the results. Findings-The findings suggest that although the Islamic banks in the GCC have adequate capital, their asset quality and earning ability have deteriorated over the period of study.
\nThe applications of Altman Z score on banks have previously been researched by several researches like [26, 27], for banks in India and [28] suggested that Altman Z score is an analytical tool that may be applied in the banking industry. Additionally, [29] stated that Altman Z score has better predicting capabilities than CAEL model when predicting bankruptcy.
\nHowever, several studies indicated the inappropriateness of Altman Z score in predicting financial distress within banks. A study conducted [25] applied Altman Z score model, CAEL model and bankometer model altogether within the Bank of Papua in Indonesia. His results showed that the results of Altman Z score model in many occasions were contradicted with the results of CAEL model. Altman Z score model was initially formed from an empirical study of manufacturing companies which is very much different from banking institutions [30].
\nZ score indicator as follow: Altman, Edward (May, 2002) [31].
\nWhereas
\nZ = a proxy variable of insolvency risk.
\nX1 = working capital to Total Assets [28].
\nX2 = retained earnings to Total Assets [28].
\nX3 = earnings before interest &tax to Total Assets.
\nX4 = Total book equity to Total liabilities [28].
\nA higher score indicates greater financial strength with a lower probability of default and vice versa.
\nThe method examines liquidity, profitability, reinvested earnings and leverage which are integrated into a single composite score. It can be used with past, current or project data as it requires no external inputs such as GDP or market price.
\nZones of discriminations:
\nZ > 2.6 -“Safe” Zone
\n1.1 < Z < 2. 6 -“Grey” Zone
\nZ < 1.1 -“Distress” Zone
\nThe concept of CAMELS’ standard, It consists of six dimensions. Each dimension can be measured through different ratios. These ratios along with their measures are1 grouped2 in Table 4.
\n\n | Symbol | \nCalculation | \nReferences | \n
---|---|---|---|
Capital Adequacy Requirement (CAR) | \nEQTA | \nTotal Equities to Total Assets ratio. | \n\n |
D/E | \nDebt-to Equity ratio | \nKaur Harsh Vineet [32] | \n|
Asset Quality (AQ) | \nTLTA | \nTotal loans to total assets Ratio. | \nMuhammad Hussain &Rukhsana KALIM [24] | \n
LLR | \nLoan Loss Reserve | \nAhsan Mohammad, 2014, Merchant [1], | \n|
NPLR | \nNon-performing loan to Total loan | \nKumar & Sayani, 2014, | \n|
Management Efficiency | \nCOSR | \ncost to income ratio | \nAhsan Mohammad, 2014, Merchant, [1],Zeitun [2] | \n
EPS | \nEarnings Per Share | \n\n | |
IETA I | \nInterest expenses / total assets ratio. | \nMuhammad Hussain & Rukhsana KALIM [24], Vijaya Kumar & Hameedah Sayani (2014), | \n|
PPE(Profit Per employ | \nProfit to employees number | \nKaur Harsh Vineet [32] | \n|
ROE | \nNet income/ net worth (T.Equities) | \nKaur Harsh Vineet [32] | \n|
Earning Quality (EQ) | \nROA | \nNet income to total assets | \n\n |
ROE | \nNet income to total equities | \nMerchant [1], Zeitun [2] | \n|
NIITA | \nNet interest income To total assets ratio | \nMuhammad Hussain & Rukhsana KALIM [24], Vijaya Kumar & Hameedah Sayani (2014) | \n|
Liquidity (LQ) | \nLATCL | \nLiquid Assets to Current liabilities | \n\n |
CATCL | \nCurrent Assets to Current liabilities | \n\n | |
NLTA | \nNet loan to total Assets | \nAhsan Mohammad, 2014, Merchant [1] | \n|
LA:TD | \nLiquid Asset/Total Deposit | \nKaur Harsh Vineet [32] | \n|
Sensitivity (S) | \nPGL | \nProvision To Gross Loan | \nMuhammad Hussain & Rukhsana KALIM [24] | \n
\n | \n | \n | \n |
Camel dimensions ratios.
Source the researcher from literature review.
Two ratios (total Equities to total Assets ratio and Debt-to Equity ratio)are Examined using step wise method Table 5 shows the result, Table 6 shows model Summary between Z score and liabilities to Assets ratio, Table 7 shows significance for each individual studied ratio As a result of Table 7, the model between Z score and capital adequacy ratios is developed as following:
\nModel | \nVariables Entered | \nVariables Removed | \nMethod | \n
---|---|---|---|
1 | \nT.Liabilities/T.Assets | \n. | \nStepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove > = .100). | \n
2 | \nEquity/ total assets ratio | \n. | \nStepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove > = .100). | \n
Model | \nR | \nR Square\nb\n\n | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.888\na\n\n | \n.789 | \n.786 | \n3.39385892 | \n
2 | \n.896\nc\n\n | \n.803 | \n.797 | \n3.30345566 | \n
Model summary.
Predictors: T.Liabilities/T.Assets
For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.
Predictors: T.Liabilities/T.Assets, Equity/ total assets ratio
Source researcher from data analysis.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \nT.Liabilities/T.Assets | \n8.898 | \n.554 | \n.888 | \n16.051 | \n.000 | \n
2 | \nT.Liabilities/T.Assets | \n7.267 | \n.918 | \n.725 | \n7.918 | \n.000 | \n
Equity/ total assets ratio | \n6.586 | \n2.997 | \n.201 | \n2.197 | \n.031 | \n
Z score = 8.9 Total liabilities to Total assets ratio + 6.6 Equities to Assets ratio. Thus lesson to be learned that T. Liabilities/T. Assets has more effects on Z score than T .equities to T. assets. See the following Table 8.
\nModel | \nBeta In | \nt | \nSig. | \nPartial Correlation | \nCollinearity Statistics | \n|
---|---|---|---|---|---|---|
Tolerance | \n||||||
1 | \nEquity/ total assets ratio | \n.201\nc\n\n | \n2.197 | \n.031 | \n.257 | \n.346 | \n
It shows exclude variables. Table 8 indicates that the best ratio that can measure Capital Adequacy is debit to Equity ratio.
\nThree ratios are examined: Total loans / total assets; Loan Loss Reserve; Non-performing loan to Total loan.
\n\nTable 9 shows test method
Variables Entered/Removed\na\n\n,\n\nb\n\n | \n|||
---|---|---|---|
Model | \nVariables Entered | \nVariables Removed | \nMethod | \n
1 | \nT.Loan /T.Assets | \n. | \nStepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove > = .100). | \n
Test method.
Dependent Variable: Z Score.
Linear Regression through the Origin.
Source researcher from data analysis to Assets ratio interpret 88% of changes in Z score at significance level 0.0001.
\nTable 10 shows3 model summary between Z score and Total Loan to Total Assets, it indicates that Loan to Assets ratio interpret 88% of changes in Z score by positive causal relation = 11.45 point, at significance level 0.0001, see Tables 11–13 indicates that the best ratio can measure Assets Quality is Total Loan to Total Assets. However both provision of non-performing loan to net loans and Non-Performing Loan to Total Loan ratios are excluded because they have high multi collinearity statistics. (They are highly correlated with each other).
\nModel | \nR | \nR Square\nb\n\n | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.886\na\n\n | \n.786 | \n.783 | \n3.41686937 | \n
Model Summary between Z score and Assets Quality dimension.
Predictors: Total Loan /Total Assets
For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.
Source researcher from data analysis.
ANOVA\na\n\n,\n\nb\n\n | \n||||||
---|---|---|---|---|---|---|
Model | \nSum of Squares | \ndf | \nMean Square | \nF | \nSig. | \n|
1 | \nRegression | \n2956.514 | \n1 | \n2956.514 | \n253.235 | \n.000\nc\n\n | \n
Residual | \n805.575 | \n69 | \n11.675 | \n\n | \n | |
Total | \n3762.089\nd\n\n | \n70 | \n\n | \n | \n |
The significance of the model.
Dependent Variable: Z Score
Linear Regression through the Origin
Predictors: T.Loan /T.Assets
This total sum of squares is not corrected for the constant because the constant is zero for regression through the origin.
Source researcher from data analysis.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \nT.Loan /T.Assets | \n11.454 | \n.720 | \n.886 | \n15.913 | \n.000 | \n
Four ratios are examined including: Cost to Income; Finance Cost /Total Assets; ROE. Market price (absolute value is used). The researcher could not find employees number, so that she excluded Profit per Employee (PPE) and replaced with Market Price, however it did not use before in previous reviewed studied as efficient management indicator.
\n\nTable 14 shows the model Summary which indicates that share market price plus constant interpret 28% of Z score changes (Table 15). And Table 16 shows excluded variables (ratios) from the model between Z score and management efficiency Dimension (Table 17).
\nModel | \nR | \nR Square | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.289\na\n\n | \n.083 | \n.078 | \n4.86709490 | \n
Model Summary\nb\n between Z score and management efficiency Dimension.
Predictors: (Constant), share Market Price
Dependent Variable: Z Score
Source researcher from data analysis.
Model | \nSum of Squares | \ndf | \nMean Square | \nF | \nSig. | \n|
---|---|---|---|---|---|---|
1 | \nRegression | \n358.058 | \n1 | \n358.058 | \n15.115 | \n.000\nb\n\n | \n
Residual | \n3932.310 | \n166 | \n23.689 | \n\n | \n | |
Total | \n4290.368 | \n167 | \n\n | \n | \n |
ANOVA.\na\n test between Z score and management efficiency Dimension.
Dependent Variable: Z Score
Predictors: (Constant), Market Price
Source researcher from data analysis.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \n(Constant) | \n1.355 | \n.683 | \n\n | 1.985 | \n.049 | \n
Share Market Price | \n1.051 | \n.270 | \n.289 | \n3.888 | \n.000 | \n
Model | \nBeta In | \nt | \nSig. | \nPartial Correlation | \nCollinearity Statistics | \n|
---|---|---|---|---|---|---|
Tolerance | \n||||||
1 | \nFinance Cost /Total Assets | \n-.025\nb\n\n | \n−.338 | \n.736 | \n−.026 | \n.999 | \n
Return on Equities | \n.061\nb\n\n | \n.820 | \n.413 | \n.064 | \n.997 | \n|
Total Cost /Total Income | \n.086\nb\n\n | \n1.122 | \n.264 | \n.087 | \n.941 | \n|
Earnings Per Share | \n-.082\nb\n\n | \n−1.093 | \n.276 | \n−.085 | \n.976 | \n
Excluded Variables\na\n from the model between Z score and management efficiency Dimension.
Dependent Variable: Z Score
Predictors in the Model: (Constant), Market Price
Source researcher from data analysis.
According to Table 16: Z score = 1.36 + 1.05 share market price.
\nThree ratios are examined using step wise method,they are: Quick Ratio,Net loan to Total Assets;Net Loan/total Deposits Table 18 shows model Summary,it indicates that Net Loan to Total Assets ratio interpret 89% of changes in Z score. Tables 19 and 20 show significance of the test at 0.0001 level.), and Table 21 shows excluded variables(ratios) from the model between Z score and Liquidity dimension in camel model those are include Quick ratio,and current ratio. So that, these ratios should excluded from banks evaluation methods in the future.
\nModel | \nR | \nR Square\nb\n\n | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.896\na\n\n | \n.803 | \n.800 | \n3.27566281 | \n
Model summary between Z score and Liquidity Dimension.
Predictors: Net Loan/Total Assets
For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.
Source researcher from data analysis.
Model | \nSum of Squares | \ndf | \nMean Square | \nF | \nSig. | \n|
---|---|---|---|---|---|---|
1 | \nRegression | \n3021.721 | \n1 | \n3021.721 | \n281.615 | \n.000\nc\n\n | \n
Residual | \n740.368 | \n69 | \n10.730 | \n\n | \n | |
Total | \n3762.089\nd\n\n | \n70 | \n\n | \n | \n |
Dependent Variable: Z Score
Linear Regression through the Origin
Predictors: Net Loan/Total Assets
This total sum of squares is not corrected for the constant because the constant is zero for regression through the origin.
Source researcher from data analysis.
\nTable 19 shows significance of the test.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \nNet Loan/Total Assets | \n12.499 | \n.745 | \n.896 | \n16.781 | \n.000 | \n
Three ratios are used return on Equity (ROE), return on Assets (ROA), Earning Per share (EPS). Table 22 shows the variables tested as indicators of Earning Dimension in Camel model, Table 23 shows the model between Earning Dimension and Z score, and Table 24 shows significant of the model between Z score and Earning Dimension ratios, but Table 25 shows significance of individual variable, it indicate that only Earnings Per share (EPS) as Earning indicator affects positively and significantly on Z score. However EPS did not use previously as Earning indicator in reviewed studies. The results of Tables 23 and 25 show that only Earning per share can affect negatively on Z score at 1% level, however there is no significant effect of ROE&ROA on Z score.
\nModel | \nVariables Entered\na\n\n | \nVariables Removed | \nMethod | \n
---|---|---|---|
1 | \nEarnings Per Share in dollar, Return on Assets, Return on Equity\nb\n\n | \n. | \nEnter | \n
Variables entered/Removed.
Dependent Variable: Z Score
All requested variables entered.
Source the researcher from data analysis.
Model | \nR | \nR Square | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.372\na\n\n | \n.138 | \n.099 | \n6.53448 | \n
Model summary between Z score and Earning Dimension ratios.
Predictors: (Constant), Earning Per Share in dollar, Return on Assets, Return on Equity
Source the researcher from data analysis.
Model | \nSum of Squares | \ndf | \nMean Square | \nF | \nSig. | \n|
---|---|---|---|---|---|---|
1 | \nRegression | \n451.655 | \n3 | \n150.552 | \n3.526 | \n.020\nb\n\n | \n
Residual | \n2818.162 | \n66 | \n42.699 | \n\n | \n | |
Total | \n3269.817 | \n69 | \n\n | \n | \n |
ANOVA\na\n test between Z score and Earning Dimension ratios.
Dependent Variable: Z Score
Predictors: (Constant), Earning Per Share in dollar, Return on Assets, Return on Equity
Source the researcher from data analysis.
\nTable 24 shows significant of the model between Z score and Earning Dimension ratios.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \n(Constant) | \n4.340 | \n1.554 | \n\n | 2.793 | \n.007 | \n
Return on Assets | \n121.732 | \n132.351 | \n.181 | \n.920 | \n.361 | \n|
Return on Equity | \n−5.350 | \n22.077 | \n−.052 | \n−.242 | \n.809 | \n|
Earnings Per Share in dollar | \n−2.860 | \n.992 | \n−.384 | \n−2.883 | \n.005 | \n
Only one ratio (provision/gross Loan) is used to measure sensitivity effect on Z score Table 26 shows model summary, between Z score and sensitivity ratio which indicates that provision to Gross loan ratio interpret 34% of changes in Z score. Table 27 shows significance of the test (0.004) (Table 28).
\nModel | \nR | \nR Square\nb\n\n | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.342\na\n\n | \n.117 | \n.104 | \n6.93927225 | \n
Model summary between Z score and Sensitivity Dimension.
Predictors: provision /Gross loan (PGL)
For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.
Source the researcher from data analysis.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \nprovision/ Gross loan (PGL) | \n23.147 | \n7.662 | \n.342 | \n3.021 | \n.004 | \n
The researcher used three economic factors as explanatory variables of Z score in Islamic banks, including gross domestic Product Growth rate (GDP), Inflation rate, and exchange rate in Dollar. Table 29 indicates that exchange rate causes69% of changes in Z score, Table 30 shows significance of the model, The result of Table 31 shows that exchange rate affect negatively on Z score in Islamic banks at significance level .1%, Table 32 shows excluded variables (GDP & inflation rate) from the model. According to Table 32 the study can demonstrate there is no causal relation between that inflation rate, GDP and Z score. This result go in contrast with Zeitun [2] he stated that GDP was found to be positively related to banks performance, while inflation negatively related to the banks performance.
\nModel | \nR | \nR Square | \nAdjusted R Square | \nStd. Error of the Estimate | \n
---|---|---|---|---|
1 | \n.699\na\n\n | \n.489 | \n.481 | \n4.95918 | \n
Model summary between Z score and economic factors.
Predictors: (Constant), exchange rate in Dollar
Source the researcher from data analysis.
Model | \nSum of Squares\na\n\n | \ndf | \nMean Square | \nF | \nSig. | \n|
---|---|---|---|---|---|---|
1 | \nRegression | \n1597.460 | \n1 | \n1597.460 | \n64.955 | \n.000\nb\n\n | \n
Residual | \n1672.356 | \n68 | \n24.593 | \n\n | \n | |
Total | \n3269.817 | \n69 | \n\n | \n | \n |
ANOVAa test between Z score and economic factors.
Dependent Variable: Z Score
Predictors: (Constant), exchange rate in Dollar
Source the researcher from data analysis.
Model | \nUnstandardized Coefficients | \nStandardized Coefficients | \nt | \nSig. | \n||
---|---|---|---|---|---|---|
B | \nStd. Error | \nBeta | \n||||
1 | \n(Constant) | \n8.486 | \n.837 | \n\n | 10.138 | \n.000 | \n
exchange rate in Dollar | \n−3.442 | \n.427 | \n−.699 | \n−8.059 | \n.000 | \n
Coefficients\na\n significance of the model between Z score and exchange rate.
Dependent Variable: Z Score.
Source the researcher from data analysis.
Model | \nBeta In | \nt | \nSig. | \nPartial Correlation | \nCollinearity Statistics | \n|
---|---|---|---|---|---|---|
Tolerance | \n||||||
1 | \nInflation rate (%) | \n.054\nb\n\n | \n.617 | \n.539 | \n.075 | \n1.000 | \n
Gross Domestic Product annual Rate (%) | \n-.050\nb\n\n | \n−.561 | \n.577 | \n−.068 | \n.950 | \n
Excluded variables.
Dependent Variable: Z Score
Predictors in the Model: (Constant), exchange rate in Dollar
\nTable 32 shows excluded external economic variables from Z score model.
Source: the researcher from data analysis.
\n
T. equities to T. Asset ratio affects positively with significance level 0.03 on Z score for Islamic banks,it represent 6.6 of changes in Z score.
The best ratio that can measure Capital Adequacy is debit to assets ratio, because it interprets 8.9 of changes in Z score with significance level 0.0001 as compare to Equities to T. Assets ratio which represents 6.6 of changes in Z score of the Islamic banks. so that it can be used as indicator of Capital adequacy in Camel rating system.
Islamic finance portfolio (T. loan) to Total Assets interprets 88% of changes in Z score with positive causal relation at significance level = 0.00001.
Provision of non-performing loan to net loans ratio does not effect on Z score of the bank.
Non-Performing Loan to Total Loan ratio does not effect on Z score of the bank.
Provision of non-performing loan to net loans and Non-Performing Loan toT. Loan ratios are highly correlated, so these ratios can be used as indicators of credit risk in Islamic banks. But they did not affect significantly on Z score.
There are some ratios commonly used in Camel rating system as indicators of management Quality, but they are not Effect on Z score of Islamic banks, those are include cost to income, Return on Equities, Finance Cost to Total Assets, this result is contradicted to the results of Ahsan Mohammad, 2014, Merchant, [1], Zeitun [2].
Market share price represents 28% of changes in Z score in Islamic banks with significant level = 0.0001 with the model (Z score = 1.36 + 1.05 share market price).
Net Loan to Total Assets represent 89% of changes in Z score of Islamic banks, it affect positively by 12.499 times at significance level = 0.0001.
Liquid Assets to Total Deposit commonly used in camel rating system as indicator of liquidity sufficient, but it does not effect on Z score according to the results of this study see Tables 18 and 21.
Earnings per Share effect positively on Z score with significant level = 0.005.
There are some ratios commonly used in Camel rating system as Earning Quality (EQ) indicators, but they are not effect on Z score according to the results of this study see Tables 23–25, those are include return on assets (ROA) and return on Equity (ROE), this result is contradicted to Merchant [1], Zeitun [2]
Provision for non-performing loan /Gross loan ratio effect positively on Z score with significant level = 0.004, it interpret 34% of changes in Z score.
Gross Demotic product (GDP) does not affect significantly on z score of Islamic banks
Inflation rate does not affect significantly on Z score. The results number 15 and 15 are contradicted to the results of Zeitun [2] he stated that GDP was found to be positively related to banks performance, while inflation negatively related to the banks performance.
Exchange rate in foreign currency effect negatively on Z score, it represents 69% of negative changes in Z score.
The results of this study will imply with two groups as following:
\n\n
If Islamic Banks need to increase their Z score with one unit, they should increase liabilities to Assets ratio by 8.9 times
If Islamic Banks need to increase their Z score with one unit, they should increase loan to Assets ratio by 11.5 times.
If Islamic Banks need to increase their Z score with one unit, they should work to increase their share market price with one unit of the currency which is used in the market exchanging plus absolute value = 1.36.
If local currency of the Islamic bank home decrease in front of foreign currencies,Islamic bank should understand that its z score will decrease by 3.4 times
Amendment of Camel rating system should be applied as following:
The important performance indicator of Capital adequacy is Total liabilities to Total assets Ratio, this results is going in consistence with [33, 34, 35] their results have revealed that capital adequacy (ratio of total equity to total assets) is the important performance indicator in the classification of banks
The best ratio can measure Assets Quality is Total Loan to Total Assets.
The best indicator that can measure management efficiency in Camel rating system is Share Market Price
The best indicators of liquidity availability is Net Loan to Total Assets ratio
The important performance indicator of profitability in Camel rating system is earning per share
Provision for non-performing loan to Gross loan ratio should be used as indicators of sensitivity to market risk for Islamic finance.
Some further studies are recommended to conduct such as:
Impact of external economic factors on Islamic banks financial soundness.
The relation between camel dimensions ratios and bank meter model.
The relation between bank age and Z score model.
More techniques should be employed by further studied in Islamic banks field such as neural networks, decision trees,used logit regression and logit analysis ratio, call-report data,and soft computing subsuming seamless hybridization of all the above-mentioned techniques.
\n
The study does not use probability model like log-linear model, because there is no time.
The study focuses on few samples with homogenized characteristics.
Researcher has worked under pressure, because she has huge tasks and responsibility as a result of Covid 19 condition which effects on professors time.
The age of selected bank Alizz bank is excluded because its age less than 10 years. Then start from the beginning and Al Salam Bank was chosen.
\n\nTable 12 shows that total loan to total assets positively on Z score.
\n\nTable 13 shows excluded variables.
\n\nTable 19 shows significance of the test.
\n\nTable 20 shows significant individual variable (net loan to Total assets ratio) effect on Z score.
\n\nTable 25 shows significance of individual variable.
\n\nTable 27 shows significance of the test.
\n\nTable 28 shows individual variable significance.
\n\nTable 32 shows excluded external economic variables from Z score model.
\n\n\n
\n\n
Source the researcher from the data.
\n\n\n
Source the researcher from the data.
\nAortic root dilation (AoD) is frequently an incidentally discovered, asymptomatic finding in that is seen on various imaging modalities [1]. The anatomy of the aortic root includes the annulus, sinuses of Valsalva, sinotubular junction and ascending aorta [1], with the size being a function of a patient’s biologic variables such as height, age, BSA, and gender [1, 2]. However, while natural variations in the size of the aortic root are well known, the identification of progression from normal to pathologic AoD is a key clinical diagnosis that carries significant cardiovascular risk including aortic dissection, rupture, valvular regurgitation and cardiac tamponade [1, 3, 4, 5]. The etiology of pathological AoD is varied, ranging from congenital, infectious, autoimmune, and idiopathic conditions; and influences the medical and surgical management [1, 5]. Due to the variety of clinical conditions that can result in AoD, and the risks associated with worsening AoD, a thorough understanding of the pathophysiology of AoD, noninvasive imaging modalities and pharmacologic therapies is critical. The aim of this chapter is to review the most common conditions associated with AoD, appropriate imaging modalities, and treatment strategies to manage AoD.
\nMultiple etiologies of AoD exist such as Marfan syndrome, bicuspid aortic valve, Loeys-Dietz and Ehler-Danlos syndromes, idiopathic conditions, hypertension, infections, and inflammatory disorders. In this chapter, we will discuss the various etiologies categorized into two standardized groups—genetically-mediated and nongenetically mediated AoD.
\nGenetically-mediated aortic root dilation or enlargement is the leading cause of thoracic aortic aneurysms. Marfan syndrome (MFS), the prototype condition for AoD, and bicuspid aortic valve has led to a greater understanding of AoD pathophysiology, pharmacologic treatment, timing of surgical intervention and optimal surveillance strategies with noninvasive imaging [6].
\nMFS is one of the most common hereditary disorders of connective tissues and is characterized by manifestations in cardiovascular, skeletal, and ocular systems [7]. MFS is the most common genetic cause of thoracic aortic aneurysms (TAAs). Its inheritance is almost exclusively autosomal dominant and mostly involves a mutation of the fibrillin-1 (FBN1) gene encoding the connective tissue structural protein fibrillin-1 [8]. The widely accepted incidence of Marfan syndrome is ~1 in 5000 individuals [9].
\nAlthough the inheritance pattern is predominantly autosomal dominant, rare cases of autosomal recessive FBN1 gene mutations has been described [10]. While patients with Marfan phenotypes usually have an affected family member, 25% of the cases are sporadic due to
The diagnosis of Marfan syndrome is established by using a combination of clinical manifestations, family history, and the presence of FBN1 mutation. In order to facilitate accurate recognition of the syndrome and improve patient management and counseling, a set of defined clinical criteria, called the Ghent nosology was developed [16] and later revised [17] (Table 1). Apart from the genetic testing for FBN1 mutation, Ghent nosology uses a systemic score calculation using clinical manifestations of Marfan and an aortic root dilatation Z-score (see noninvasive imaging below).
\nPatients with family history of Marfan disease | \n
\n
| \n
\n
| \n
\n
| \n
Patients without family history of Marfan disease | \n
\n
| \n
\n
| \n
\n
| \n
\n
| \n
Revised Ghent nosology.
One of the major causes of mortality and clinical hallmark of Marfan syndrome is aortic root dilation and related complications such as dissection, rupture and/or aortic valvular regurgitation. Aortic root dilation is typically first identified on echocardiography in 60–80% of Marfan patients [18]. Therefore, surveillance echocardiography has been routinely used to serially monitor aortic dimensions. If the aortic root diameter is above 4.5 cm in adults, aortic dilation rates are above 0.5 cm/year, and/or significant aortic insufficiency is already present, more frequent monitoring is recommended [6] (see Diagnosis and Surveillance of Aortic Root Dilation below for more detailed guidelines).
\nBicuspid aortic valve is one of the most frequent congenital heart anomalies in adults, affecting 0.9–2% of the population [19]. Most cases of bicuspid aortic valve are familial and studies show that heritability of the disease is ~90% making it an autosomal dominant pattern with incomplete penetrance [20]. Bicuspid aortic valve can occur alone or with other congenital cardiovascular disorders such as coarctation of the aorta, supravalvular or subvalvular aortic stenosis, and ventricular septal defect [21].
\nThe diagnosis is often established by transthoracic echocardiogram (TTE), which has high sensitivity (~92%) and specificity (~96%) [22]. TTE is also useful for surveillance of potential complications of bicuspid aorta such as aortic stenosis, aortic dilation, aortic regurgitation, and infective endocarditis [23]. Given the risk of inheritance, first degree relatives are also recommended to be screened with TTE [21].
\nPrevalence of aortic dilation in patients with bicuspid aortic valve disease ranges from 20 to 84% depending on the criteria used in different studies [24]. The risk of aortic dilation increases with age and the risk of dissection increases as the aortic diameter increases [25, 26]. When the aortic root diameter is above 4.5 cm, there is a family history of aortic dissection, or aortic diameter change is rapid it is recommended to perform echocardiogram annually [21]. More frequent surveillance is recommended for patients with aortic root diameters approaching surgical thresholds (see Surgical Interventions section below).
\nLoeys-Dietz syndrome (LDS) is a rare congenital syndrome characterized by hypertelorism (widely spaced eyes), a split uvula or cleft palate, tortuous arteries and aortic aneurysms. LDS shares many features with Marfan syndrome [14]. Most of the LDS cases are sporadic or show an autosomal dominant pattern of inheritance [14].
\nThe incidence and prevalence of the disease is still not well established.
\nLoeys-Dietz syndrome was initially classified into two subtypes based on the severity of the cutaneous and craniofacial features but later was divided into six subtypes stratified by genotypes [27]. These subtypes are labeled 1–6 and associated with mutations in TGFBR1, TGFBR2, SMAD3, TGFB2, TGFB3, SMAD2, respectively [27]. Type 1 and type 2 are the most commonly seen subtypes with frequencies of 20 and 55% among all subtypes, respectively [28].
\nAortic root dilation is a hallmark feature of this disease entity and is frequently seen in patients (~80%) [29]. Another vascular manifestation is aneurysms throughout the arterial tree. This is a concerning clinical manifestations of the disease and can cause aggressive arteriopathy; therefore, early operative intervention at ascending aortic diameters of ≥42 mm is recommended [30].
\nEhlers-Danlos syndromes (EDS) are a heterogeneous and relatively rare group of connective tissue disorders characterized by skin hyperextensibility, joint hypermobility, and tissue fragility [31]. Ehler-Danlos syndrome can present with a variety of clinical manifestations and can be caused by different kinds of genetic mutations. Overall prevalence of EDS is ~1 in 5000 and EDS hypermobile (hEDS) is the most common type [31].
\nVascular complications can be seen with different types of EDS; however, it is most commonly seen in type IV (vascular or arterial ecchymotic type; vESD), characterized by an autosomal dominant mutation in COL3A1 (collagen, type III, α-1 gene) encoding type III procollagen [32]. Up to 80% with vESD patients suffer from vascular complications by the age 40 years [32]. Therefore EDS patients, especially vEDS, patients should be routinely evaluated for aortic root disease. These patients are recommended to undergo elective operation at smaller diameters (4.0–5.0 cm) to avoid acute dissection or rupture. Patients with a growth rate of more than 0.5 cm/year in an aorta that is <5.5 cm in diameter are recommended to be considered for operation [33].
\nAortic root dilation is an established phenomenon that has shown strong correlations to key pathobiological factors such as age, body surface area (BSA), height and gender. The correlation of aortic root size with age and BSA were initially described in the development of screening nomograms using M-mode echocardiograms [34]. Follow up studies with 2D echocardiography further validated these correlations, allowing for the development of nomograms for normal patient populations or adjusted for patients with underlying congenital disorders (i.e., Marfan syndrome) [2, 35]. These studies evaluating AoD by echocardiograms are further supported by reviews of autopsy data that show clear correlations to key pathobiological factors such as increased age and height with AoD [36].
\nDespite the validation of age as being correlated strongly with AoD, the mechanism of age on the development of AoD still remains an area of active research. One of the predominant hypotheses is based on the idea of cyclic stress, and how the aorta degrades through gradual mechanical decline of elastin proteins [37]. Elastic arteries, namely the aorta, are estimated to dilate by 10% with each beat [38]. It is hypothesized that the shear stress over a normal lifetime results in the degradation of elastic lamella, resulting in arterial dilation and stiffening [38]. This is corroborated by histologic data demonstrating damage to medial elastin of the proximal aorta [38]. Furthermore, there is evidence to suggest that in the absence of risk factors such as hypertension or atherosclerosis, the aortic wall undergoes age-associated reprograming that is proinflammatory promotes progression of arterial disease [39]. Wang et al. demonstrated in pathologic samples of aortas that age correlated with increased smooth muscle cell invasion, and increased production of downstream angiotensin II mediators [39].
\nIn addition to age and BSA, gender is another key factor which can increase the risk and progression of AoD [40]. In the Framingham study of 1849 men and 2152 women, not currently diagnosed with cardiac disease or having a cardiac history, aortic root was 2.4 mm smaller in women than men with m-mode echocardiography [40]. A systematic review in 2014 of 10,741 patients with hypertension revealed men had a significantly higher incidence of AoD relative to women [41].
\nIn conclusion, a series of biological variables are correlated with AoD, and it is important to take these into account as they are potential confounders or contributors in the evaluation of patients with pathologic AoD. Even exercise capacity has been correlated with AoD, with a recent meta-analysis showing that athletes defined by participation in National Collegiate Athletic Association (NCAA) or international equivalent had an aortic root diameter that was larger than nonathletic controls [42], and a statistically significant increase by measurement of sinuses of Valsalva and aortic root annulus [42]. It is important to understand the significance of biological variables such as age, height, BSA, or gender to fully evaluate pathologic AoD without the influence of known confounders.
\nHypertension is a well-known risk factor for aortic dissection, and in some studies, it is estimated to factor into roughly half of the overall risk for aortic dissection [43]. A recent prospective cohort study of 30,447 patients, 86% of patients who developed aortic dissection had hypertension [4]. However the relationship between hypertension and AoD is not as clearly established. In a Framingham study of 3195 patients, there was no relationship between the development of AoD with hypertension [44]. A subsequent follow up study of Framingham participants evaluating aortic root diameter was positively correlated with mean arterial pressure, but negatively associated with pulse pressure, indicating that the mechanism behind AoD is more complicated [45]. Moreover, investigations have shown that in patients with other comorbidities for AoD, such as, Turner syndrome, hypertension is significantly associated with increased prevalence of AoD [45]. This has led to interesting insights into the cyclic stress hypothesis of the development of AoD [43]. If AoD develops due to chronic shear stress, then it would be expected that AoD is correlated with higher pulse pressure (PP), which would presumably lead to greater stress and aortic dilation [43]. However, studies have reported an inverse relationship between AoD and PP [43]. Additionally a systematic review in 2014 showed that in a population of 10,791 hypertensive patients, 9.1% had AoD with a significant gender skew toward men [41]. However there was no significant correlation of mean arterial pressure or pulse pressure values and AoD [41]. While hypertensive patients have a higher incidence of AoD, the mechanism remains to be further investigated. Moreover, these unclear correlations between MAP, PP, and AoD suggest that the aorta is not static, but a dynamic structure whose response to stress, such as hypertension, is still being elucidated [43].
\nSince the first mass production of penicillin in 1945, the modern era of antibiotics has resulted in a decrease in the prevalence of mycotic aneurysms due to bacterial infections in developed countries [46, 47]. However they can still be found in developing countries, and are rare but well described causes of mycotic aneurysms [46]. Most common pathogens include
\n
Tuberculosis is a relatively common infection especially in developing countries [53].
There have been case reports proposing an association between aortic aneurysms and HIV [50]. In a variety of these cases the causes are generally multifactorial, as the majority of cases have reported coinfections (Q fever and leishmaniasis) or comorbid autoimmune conditions (giant cell arteritis) [55, 56]. It is still an area of investigation as to whether there is a true association, and there is sparse data showing a relationship with AoD.
\nAnkylosing spondylitis, a seronegative spondyloarthropathy, is a chronic, progressive rheumatologic disorder, and was one of the first found to be associated with aortitis [50, 57]. The proposed mechanism of AoD in ankylosing spondylitis is fibrous growth development along the intima, which leads to subsequent weakening [57]. Prior TEE studies further evaluated the prevalence of AoD in ankylosing spondylitis, and 82% of patients with ankylosing spondylitis had aortic root abnormalities [58]. Specifically, 61% of patients had aortic root thickening and 25% of patients had AoD [58]. AoD in these populations is a relatively common phenomenon and is associated with significant cardiac morbidity [45, 57].
\nRelapsing polychondritis is another autoimmune disorder, which is a multisystem inflammatory disorder that primarily affects the cartilaginous structures of the body [59]. Cardiovascular involvement is common, estimated to be the second most frequent cause of death and can result in aneurysm development in 5% of cases of both thoracic and abdominal aorta [50, 59]. AoD has been known to occur, albeit rare, with cases of requiring surgical revision after the development of aortic regurgitation [60, 61].
\nTakayasu arteritis is a chronic granulomatous large vessel vasculitis, predominantly found in pediatric populations [50, 62]. A rare disorder, the pathogenesis is characterized by granulomatous panarteritis that can affect the entirety of the aorta and major branches, however predominantly affects the common carotid and subclavian artery [62]. While rare, there are reports of AoD from Takayasu arteritis resulting in aortic regurgitation [63, 64].
\nGiant cell arteritis is a large vessel vasculitis that is characterized by chronic granulomatous inflammation [50]. While commonly affecting carotid, temporal and vertebral arteries, it has been known to affect the ascending aorta, at times resulting in dissection or aortic valve insufficiency [50]. The development of AoD from GCA may be influenced by other comorbid conditions such as HIV; however, this association is currently only supported with case reports [55].
\nAdditionally left ventricular hypertrophy is reported to be positively correlated with AoD. Early retrospective reviews of echocardiographic studies have shown a positive relationship between LVH and AoD, and this has been further supported in subsequent systematic reviews [41, 65]. Patients with AoD with concomitant left ventricular hypertrophy are shown to have an increased risk of adjusted cardiovascular events [66]. However as with previous studies, the exact mechanism between LVH and AoD is still being determined.
\nAortic root dilation is typically a silent disease, with most cases being diagnosed incidentally on imaging. AoD can become symptomatic as the aneurysm enlarges. Aortic root aneurysms grow at an average of 1–4 mm/year [5], with a faster rate of growth noted in patients with bicuspid aortic valves, Marfan syndrome, ESRD, male gender, and smokers [5, 67]. When the aneurysm enlarges to the point of compressing surrounding structures the patient may begin to observe symptoms—the most common of which is chest pain, seen in up to 75% of patients [5, 68]. Other nonspecific symptoms can include back pain, abdominal pain and fatigue (though only present in 5% of patients).
\nAdditionally, patients may present with symptoms secondary to complications of a dilated aortic root such as aortic insufficiency and congestive heart failure. Thus, patients can develop dyspnea as the presenting symptom of aortic root dilation up to 40% of the time [68]. Other presenting symptoms may be related to the complications noted in Table 2 [69, 70, 71, 72, 73, 74].
\nComplication of aortic root aneurysm | \nPresenting symptom | \n
---|---|
Aortic insufficiency, aortic regurgitation | \nDyspnea, diastolic murmur, congestive heart failure symptoms | \n
Aortic dissection | \nSharp chest pain, may radiate to the back | \n
Thromboembolism | \nSymptoms of stroke | \n
Compression of tracheal or bronchus | \nHemoptysis, cough, recurrent pneumonitis | \n
Compression of left recurrent laryngeal nerve | \nHoarseness | \n
Compression of superior vena cava | \nSigns of superior vena cava syndrome | \n
Compression of esophagus | \nDysphagia | \n
Complications and presenting symptoms of aortic root dilation.
Acute aortic emergencies that occur secondary to aortic root dilation include dissection, rupture, and aortic insufficiency. As the aortic root diameter increases, the risk for aortic dissection and rupture rises [75]. Aortic dissections are the most common acute aortic emergencies [76], and can be classified depending on the segment of the aorta affected: type A dissections involve the ascending aorta (seen in aortic root dilation), while type B dissections are those that occur distal to the left subclavian artery.
\nAortic dissection most commonly presents with acute onset chest pain that may radiate to the back. The character of the pain has traditionally been described as ripping or tearing in nature, however over half of patients may instead complain of sharp pain [77]. In addition, geriatric populations are less likely to have an acute onset of pain [78]. Physical exam findings that may be present include unequal blood pressures in the upper extremities, a new diastolic murmur indicative of acute aortic regurgitation, or muffled heart sounds secondary to tamponade (with proximal extension of the dissection). Imaging may be notable for widening of the mediastinum on CXR [77]. In order to aid in the diagnosis of a dissection, an aortic dissection detection risk score (ADD-RS) has been developed. The score is comprised of three categories: the presence of high risk conditions such as Marfan syndrome, the presence of typical symptoms (such as abrupt onset chest pain), and the presence of physical exam findings such as unequal blood pressure readings in the upper extremities. Each group is given a score of 1 if a feature is present, and the total score helps pave the next steps of workup—a score of 0 can be followed by diagnostic workup of other pathologies, while scores of 2–3 should be followed by expedited workup and immediate surgical consultation for possible aortic dissection [79].
\nAortic rupture is also an acute and life-threatening complication of aortic root dilation. It can present similarly to aortic dissection with regards to chest pain, however rupture can lead to severe and abrupt hypotension. Moreover, contingent with the site of rupture the patient may have symptoms such as hemoptysis [80] (if there is rupture into the lung), hematemesis [81] (if there is rupture into the esophagus), or cardiogenic shock [82] (if there is rupture into the pericardial cavity with resultant tamponade physiology).
\nAortic root dilation may also lead to aortic insufficiency. Roughly 30% of aortic insufficiency is now recognized as being caused by aortic root dilation, surpassing the incidence of any valvular cause [83]. The pathophysiology is related to stretching of the aortic valve annulus secondary to aortic root dilation, which results in incomplete closure of the aortic leaflets during diastole. Unfortunately, at the onset of aortic regurgitation, patients may be asymptomatic; therefore, congestive heart failure can develop when the regurgitant valve goes unnoticed.
\nWhile aortic root aneurysms are known to grow at an average of 1–4 mm/year [5], it is difficult to ascertain how fast an individual’s aortic root aneurysm will grow, therefore necessitating surveillance imaging. The frequency of surveillance imaging recommended is dependent on the etiology of the aortic root dilatation as well as its size, with genetically mediated aortic disease having a lower threshold for more frequent (biannual) imaging [84]. At the very least however patients are recommended to have annual imaging for aortic root dilation to closely monitor the aortic diameter. The impact that frequent imaging (CT, MR angiography or echocardiography) has on public health is likely significant, with cumulative costs. In addition, any patient with a bicuspid aortic valve should be screened for a thoracic aortic aneurysm, as well as screening all first-degree family members of a patient with genetic conditions such as Marfan syndrome [85].
\nThe aortic root is the most proximal segment of the aorta. It extends from the annulus of the aortic valve to the sinotubular junction (STJ). It is composed of the left, right, and non coronary sinuses of Valsalva. The diameter of the aorta decreases as it moves distally. The aortic root is assessed using multimodality imaging techniques. These include transthoracic echocardiogram (TTE), cardiac magnetic resonance imaging (cMRI), and cardiac computed tomography angiography (cCTA).
\nTTE is widely used for the detection and monitoring of aortic root pathology. Early studies established age- and sex-specific nomograms for aortic root measurements [86]. These studies used the motion mode (M-mode) of TTE, in which the amplitude of the ultrasound pulses amplitudes is converted to corresponding level on gray-scale imaging [86]. Using the M-mode, the American Society of Echocardiography (ASE) has recommended using the leading-edge to leading-edge approach for measuring the aortic root [87]. Later studies used 2D TTE and obtained reference measurements of the aortic root. This is now preferred over M-mode images, which may be off-axis and are subject to aortic motion that may produce erroneous measurements.
\nOn echocardiogram, the aortic root diameter is typically measured in the parasternal long-axis view from the right coronary sinus to the opposite sinus of Valsalva. When unable to obtain the long axis view, the parasternal short axis view may provide more accurate measurements. However, universal landmarks to measure the root in this view have not been established. Some suggest measuring the diameter from the right coronary sinus to the opposite commissure. These measurements are typically performed at end diastole, as this represents the resting aortic diameter [88]. In adults, these measurements correlate with age and body size. In addition, the aorta is about 2 mm larger in men compared to women due to differences in body size [89]. Normal values stratified by body surface area and age have been published by the ASE [87].
\nImportantly, TTE is limited by its two-dimensional images and thus does not give a complete depiction of the aortic root. It is also limited by patient factors that limit the visualization windows and thus aortic root measurement. Since the aorta is not a straight tube, it can be imaged obliquely leading to over-estimation of its true diameter. Newer modalities, such cMRI and cCTA, can provide three-dimensional images.
\nDespite ECG-gated CT being the most accurate modality for evaluating the thoracic aorta, it is limited by the radiation and contrast exposure. This is particularly important in younger patients with connective tissue disorders that require serial follow up imaging. Cardiac MRI provides an alternative approach for imaging the thoracic aorta including the aortic root and is considered the preferred modality in select groups. It can be performed with ECG gating to provide motion-free evaluation of the aorta. In addition, young patients, in whom this is more commonly used, can hold their breath for longer periods, allowing acquisition of images with high spatial resolution.
\nCardiac MRI evaluation of the aorta does not require contrast use. MRI sequences used include balanced steady-state free precession (SSFP) sequences, fast imaging employing steady-state acquisition (FIESTA), true fast imaging with steady-state precession (FISP), and balanced fast-field echo (FFE) sequences. These sequences provide a high signal-to-noise ratio and adequate contrast between vessel wall and blood pool [90]. When used with ECG gating and contrast enhanced MRA, images tend to have less artifact, higher resolution, and overall short imaging time. Another approach is to use ECG gating 2D balanced SSFP sequence that is oriented perpendicular to the aortic root in two planes to assess the aortic valve and root throughout the cardiac cycle. In addition, prospective ECG gating and respiratory navigation with three-dimensional balanced SSFP sequences can provide 3D aortic imaging without contrast administration [91, 92].
\nIt is important to note that different methods of aortic measurement have been described and guidelines are less well defined. Aortic root measurements can be challenging given different approaches. Burman et al. found in the Framingham Heart Study that cusp-commissure dimensions better corresponded with reference echocardiographic aortic root measurements [89, 93]. This best correlated with study measurements after averaging the three end-diastolic cusp-commissure measurements [93]. In addition, there is a lack of consensus with regard to measurements used (inner lumen only versus lumen and wall) and whether measurements should be adjusted to body surface area, sex, and age.
\nAlthough TTE is widely used for the imaging and surveillance of aortic root, cardiac computed tomography angiography (cCTA) is currently the most commonly used technique for the study of the thoracic aorta. Main advantages of cCTA are fast scanning times, low artifact sensibility, and wide availability including emergency rooms operating 24 h [94].
\nThe new generation CT scanners acquire high-resolution 3D datasets of the thoracic aorta, showing sensitivities up to 100% and specificities of 98–99% [95]. ECG synchronization is vital for detailed assessment of the aortic root anatomy since it allows suppression of pulsation artifacts [96]. ECG gating also allows viewing images in a particular phase of the cardiac cycle. Unfortunately, the ECG-gated technique can increase the acquisition time and required breath-hold time. In order to minimize the increased acquisition times, employment of a 64 or wider ECG-gated row detector system is suggested [95, 97].
\nModern CT scanners can be used to employ several different cardiac synchronization methods such as prospective ECG triggering where images are only acquired during a specified portion of the cardiac cycle, starting at a predetermined delay from the R wave; retrospective ECG gating where the desired cardiac phase is selected retrospectively from the raw data [95, 97]. The details of each technique will not be discussed in this chapter; however, it is important to determine the advantages and disadvantages of different techniques. The main limitations of CT are related to the radiation exposure and the use of iodinated contrast media and different techniques come at a higher cost of each limitation.
\nFor the surveillance of aortic root, any technique can be used and be useful; therefore, the technique with the least amount of radiation exposure should be selected such as prospective sequential triggering without padding, retrospective gating with tube-current modulation optimized for diastolic-phase datasets only, or a prospectively triggered high-pitch helical acquisition [95, 97]. Retrospective ECG gating acquires redundant helical CT data which allows the reconstruction of images at different cardiac phases and providing detailed images which can be useful in complicated cases and pre-/post-operative imaging since pseudoaneurysm or small leaks which are some of the most common complications of aortic root surgery can only be detected during a specific phase of the cardiac cycle. Iodinated contrast-media is another risk related to CT imaging given the risk of contrast induced nephropathy and allergic reactions of various severity. Surveillance CT data for the dimensions of aortic root can be acquired without contrast injection; however, a complete endoluminal evaluation can only be achieved by the injection of contrast-medium [97].
\nIt is standard of care to monitor the size of aortic aneurysms that are below surgical threshold, <5.5 cm for nongenetic aneurysms and <5.0 cm for genetically-mediated aneurysms [98]. In general, physicians should be conscientious about patient cumulative radiation exposure as there is evidence that it can increase cancer incidence and cancer mortality [99]. One study estimated that ionizing radiation exposure results in 0.7% of total expected baseline cancer incidence and 1% of total cancer mortality. These rates increase with greater cumulative exposure [99]. Therefore, physicians should opt to perform serial CT imaging with longer intervals in the most appropriate patients. A study investigating patients with moderate-risk thoracic aortic aneurysms (defined as size <5.0 cm) showed that patients with aneurysms below 4.3 cm had overall lower risk of significant aneurysm growth or size reaching surgical threshold. Thus, the authors suggested that these subset of patients undergo surveillance CT scans less frequently.
\nManagement focuses on slowing the rate of growth and the complications of aortic root dilation. The line of management that is chosen for a patient depends on symptoms and size of the aneurysm. For patients who are asymptomatic and have root dilation <55 mm, medical management is advised. In patients with Marfan syndrome or a bicuspid aortic valve, the cut off of ≤50 mm is used for medical management [1, 100].
\nThe use of beta blockers has shown a survival benefit in patients with aortic root dilation secondary to Marfan syndrome [101]. While data on survival benefits for patients with bicuspid aortic valves is sparse, the common practice is to also prescribe beta blockers given that both conditions share a similar pathology and therefore both are likely to benefit from beta blockade. The mechanism by which beta blockers slow the progression of aortic root dilation is through their negative inotropic and chronotropic effects, reducing the peak left ventricular ejection rate and therefore decreasing shear stress and the rate of aortic dilation [102].
\nThe goal blood pressure for patients with thoracic aortic aneurysms is <130/80 mmHg. In patients who cannot tolerate beta blockers, calcium channel blockers (CCB) are an alternative group of medications available. While less studied as compared to beta blockers, CCB have also been found to reduce the rate of aortic root dilation [103]. Other agents that can be used for additional blood pressure control include ACE-inhibitors and ARBs.
\nIn order to reduce the risk for complications such as aortic dissection, patients should be counseled on smoking cessation, and cessation of drugs that increase aortic wall stress such as cocaine or other stimulants. In addition patients should have dyslipidemia well controlled, which can be achieved through the use of atorvastatin 40–80 mg daily in individuals with aortic root aneurysms [104, 105].
\nPatients should be counseled on avoiding high intensity and collision sports, such as boxing and cycling. Instead patients should take part in low dynamic sports, such as, golf [5, 106]. Pregnancy should be avoided in patients with Marfan syndrome with an aortic diameter >40 mm, if a patient does chose to become pregnant however there must be close follow up with surveillance imaging of the aortic diameter [5, 101].
\nEmergent surgical interventions are indicated for management of an aortic dissection or rupture, or a symptomatic aneurysm. In addition, surgical repair can be performed electively in high risk patients to prevent propagation of an aneurysm (Table 3). Indications for elective surgical intervention include the absolute size of the aneurysm—if the diameter is over 55 mm, or over 50 mm in patients with Marfan syndrome or bicuspid valves. Other indications for elective surgery include if the rate of growth of an aneurysm surpasses 10 mm/year, and if there is concurrent aortic insufficiency [1, 100]. In addition, patients who undergo aortic insufficiency repair who have concurrent aortic root dilation should be considered for aortic replacement at the time of their surgery—that is since 25% of patients with aortic root diameters >40 mm will eventually also require intervention for their aortic aneurysm [107].
\nEmergent surgical repair | \nElective surgical repair | \n
---|---|
\n
| \n\n
| \n
Indications for emergent and elective surgical repair of aortic root dilation.
As opposed to supravalvular aortic aneurysms, aortic root aneurysms involve the coronary ostia as well as the aortic valve, which have implications on the type of surgical procedure available for patients. There are two approaches for a surgical intervention: radical and conservative. In a radial surgical intervention the patient’s aortic valve and root are replaced (commonly referred to as the Bentall procedure), whereas in conservative interventions only the aortic root is replaced [108].
\nThe Bentall procedure involves replacing the aortic valve with a prosthetic valve, and thus has the caveat of requiring indefinite anticoagulation [5]. If patients have a high bleeding risk it may therefore be worthwhile investigating replacement of the aortic root while preserving the valve. In addition, it is important to note that a large number of patients with aortic root dilation are young (secondary to its association with Marfan syndrome), and therefore lifelong anticoagulation in cases such as these confers a cumulative bleeding risk. Preserving the aortic valve while surgically treating the aortic root dilatation is made possible by the development of two surgical procedures: the first is removing the aortic root while keeping the valve intact. The second method is through re-implantation of the aortic valve [5]. Both the Bentall procedure as well as aortic valve-preserving procedures have been shown to have comparable short and long-term outcomes with regards to the risk of death and valve associated complications. The main difference however is that patients undergoing valve sparing operations were significantly more likely to develop moderate to severe aortic regurgitation later [108].
\nIn patients with both severe aortic stenosis and ascending aortic aneurysm, undergoing surgical aortic valve replacement (sAVR) and concomitant surgical intervention for aortic aneurysms above 4.5 cm is recommended by the American College of Cardiology (ACC) foundation guidelines [84]. However, in high-risk surgical patients, undergoing a transcatheter aortic valve replacement (TAVR) has become an alternative approach that obviates the need for parallel surgical aortic aneurysm intervention. This raises the concern for the safety of TAVR catheter-based delivery system in patients with aortic aneurysms since intraoperative rupture or dissection risk potentially increases. However, a clinical study showed that TAVR does not increase intraoperative aortic dissection/rupture risk or mortality with a median follow-up of 14 months [109]. Therefore, there are no recommendations against performing TAVR in patients with ascending aortic aneurysms.
\nNone.
"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality. Throughout the world, we are seeing progress in attracting, retaining, and promoting women in STEMM. IntechOpen are certainly supporting this work globally by empowering all scientists and ensuring that women are encouraged and enabled to publish and take leading roles within the scientific community." Dr. Catrin Rutland, University of Nottingham, UK
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