**3. Discussion**

The aim of this chapter is to describe whether the extent of compliance with ACR diagnostic and interventional imaging guidelines by US hospitals influences inhospital mortality rates of patients diagnosed with different types of aneurysms. The findings were expected to provide predictors of mortality outcomes under a given set of patient factors and hospital contexts. The need for any change in the guidelines or practices to reduce aneurysm mortality rates could be identified and recommended. Preliminary results had confirmed that out of the six main aortic aneurysm types, abdominal aortic aneurysm (AAA) was the most widespread type. About 75% of all aortic aneurysms was either AAA or rAAA. Another 21% of aneurysms belonged to thoracic aneurysm (TA). Thus, AAA and TA are the two types of aneurysms of specific concern. Although rupturing almost ensures death, only 3.4% of patients reported with ruptured aneurysm of any type. If only these patients die, overall mortality rate should be around 3.4%. Now the question arises:

#### *Challenges for Intelligent Data Analysis Methods in Medical Image Analysis during Surgical… DOI: http://dx.doi.org/10.5772/intechopen.86711*

which imaging methods were more commonly used in the case of ruptured aneurysms? Based on the findings, no specific imaging method was chosen for ruptured aneurysms. However, it is not certain that most of the mortalities occurred in the case of ruptured aneurysms only. It is also not certain that any other imaging method would have reduced mortality of patients with ruptured or intact aneurysms. As is evident from the above results, imaging methods were related to mortality rates: DSA recorded the lowest rate. All other imaging methods recorded higher than 3.4%.

The objectives of the study were specifically verified using various tests appropriate to the specific objective. The objectives were to establish that imaging methods had distinct influence on mortality rates, to compare the two intervention procedures in interaction with the imaging methods, to evaluate the impact of compliance with ACR guidelines on mortality rates, to examine the scope of using any patient factor or hospital context as predictors of mortality rates and to assess which imaging method is associated with mortality rate as affected by any of the significant predictors. The study relied on diagnosis and procedure of only the ICD-9 coding registered in NIS data set. NIS data does not include all the sophisticated diagnostic imaging procedure codes. Differentiation of pre- and post-operative imaging is not available in ICD-9 codes and is not indicated in NIS data also. This study was limited to the study of most common aneurysms and not all.

Only 5 years' data were included in this study. A more detailed study may need to be done for firm conclusions. Compatibility between NIS data and ICD codes need to be tested by using ICD-10 instead of ICD-9 to verify whether compatibility improves by this. Although several works reported increasing use of CT and MRI, this was not reflected in a data set as recent as 2008–2012. Similarly, increasing use of EVAR compared to OAR was also not reflected. This needs further investigation. How far probabilistic estimates of mortality based on predictors will be closer to actual figures is not clear either from published works or from this study. This aspect needs further study by developing such equations and comparing actual with estimates.

There is enough evidence that hospitals are less than fully compliant with ACR appropriateness criteria. However, their number is not known. A survey of US hospitals to evaluate numbers of fully compliant, partially compliant and noncompliant hospitals needs to be done. The latter two need to be persuaded to fully comply with the ACR criteria.

#### **4. Conclusions**

**3. Discussion**

The aim of this chapter is to describe whether the extent of compliance with ACR diagnostic and interventional imaging guidelines by US hospitals influences inhospital mortality rates of patients diagnosed with different types of aneurysms. The findings were expected to provide predictors of mortality outcomes under a given set of patient factors and hospital contexts. The need for any change in the guidelines or practices to reduce aneurysm mortality rates could be identified and recommended. Preliminary results had confirmed that out of the six main aortic aneurysm types, abdominal aortic aneurysm (AAA) was the most widespread type. About 75% of all aortic aneurysms was either AAA or rAAA. Another 21% of aneurysms belonged to thoracic aneurysm (TA). Thus, AAA and TA are the two types of aneurysms of specific concern. Although rupturing almost ensures death, only 3.4% of patients reported with ruptured aneurysm of any type. If only these patients die, overall mortality rate should be around 3.4%. Now the question arises:

**Chi-square tests**

*Chi-square test results on effect of ACR compliance levels of hospitals on frequencies of in-hospital mortality of*

**Symmetric measures**

*Gamma test results on frequencies of in-hospital mortality of aneurysm patients as affected by ACR compliance*

**Variables in the equation**

Step 1a ACR compliance rating .676 0.215 9.909 1 0.002 0.508 0.334 0.775 Constant 1.472 0.417 12.472 1 0.000 0.229

Ordinal by ordinal Gamma .328 .032 7.617 .000

Pearson chi-square 92.255<sup>a</sup> 2 .000 Likelihood ratio 78.896 2 .000 Linear-by-linear association 92.252 1 .000

*Two cells (33.3%) have expected count less than 5. The minimum expected count is 0.12*

N of valid cases 38,104

*Aortic Aneurysm and Aortic Dissection*

N of valid cases 38,104

*Variable(s) entered on step 1: ACR compliance rating*

*ACR compliance rating and various constants values.*

*Using the asymptotic standard error assuming the null hypothesis*

*a*

*a*

*b*

*a*

**38**

**Table 13.**

**Table 12.**

**Table 11.**

*aneurysm patients in the USA.*

*Not assuming the null hypothesis*

*levels of hospitals in the USA.*

**Value df Asymp. sig. (two-sided)**

**Value Asymp. Std. Errora Approx. Tb Approx. Sig.**

**B S.E. Wald df Sig. Exp(B) 95% C.I. for EXP(B)**

**Lower Upper**

Recognizing the high mortality rates in certain aneurysm conditions, factors related to this were examined. Imaging methods have an important role in diagnosis and treatment interventions. ACR has published appropriateness criteria for diagnostic imaging. It was contended that if hospitals followed ACR guidelines, it would improve diagnosis and in turn intervention procedure also. The research was aimed at this aspect to develop predictors for mortality due to imaging methods and intervention procedures. Patient characteristics like age, gender, race, comorbidities and insurance type for medical reimbursement and hospital contexts like size, location, geographical region, type and admission types were included as variables for the study. The basic variables were four imaging methods and their combinations with EVAR and OAR upon which the patient characteristics and hospital contexts were superimposed. NIS data for the period of 2008–2012 from more than 4300 US hospitals were used. After prescribed data cleaning procedures, net sample size of 38,263 patients was obtained for detailed study. Apart from descriptive

statistics, ANOVA, chi-square, logistic multiple regression, McNemar's and gamma tests were used for dealing with different objectives of the study. AAA and TA were most frequent aneurysm types. DSA and US were the most frequent imaging methods. OAR was much more frequently used than EVAR. Age group, male, and comorbidities had distinct effects on aneurysm frequency. More patients went to urban teaching and urban non-teaching, large-volume hospitals for emergency and elective admissions and were supported by medical reimbursement schemes. However, none of these patient characteristics or hospital contexts had any effect on frequency-based ranking of imaging methods or intervention procedures.

Results supported the view that imaging methods have a distinct effect on mortality. DSA recorded lowest and CT recorded highest mortality. Out of the intervention procedures, EVAR had lower mortality than OAR. However, in combination, OAR with DSA as the imaging method recorded lowest mortality. There was a distinct effect of hospital stay on these mortalities due to imaging methods with longer than 10 days for any imaging method increasing mortality risk. Definite effect of ACR compliance was observed. With increasing compliance, mortality rate reduced and became zero with full compliance. Thus, improving ACR compliance and patients selecting only compliant hospitals will reduce aneurysm mortality significantly. Results of logistic multiple regression were used for the development of probability equations for mortality with imaging methods alone and in combination with intervention procedures. From a detailed analysis of patient characteristics and hospital contexts, age group and comorbidities emerged as the most important predictors of mortality probability. Other factors were less important as they provided inconsistent results.

Overall, imaging methods affect mortality, and increasing compliance with ACR appropriateness criteria reduces mortality considerably. Probability of in-hospital mortality can be predicted using models with imaging methods with or without intervention procedures and adding age and comorbidity as predictors.

**Author details**

Abdullah Al Amoudi<sup>1</sup>

Department, NJ, USA

**41**

, Shankar Srinivasan<sup>2</sup> and Mohamed Yacin Sikkandar<sup>1</sup>

1 College of Applied Medical Sciences, Majmaah University, Majmaah, Saudi Arabia

*Challenges for Intelligent Data Analysis Methods in Medical Image Analysis during Surgical…*

*DOI: http://dx.doi.org/10.5772/intechopen.86711*

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

2 Biomedical Informatics Program in Rutgers-SHP's Health Informatics

\*Address all correspondence to: m.sikkandar@mu.edu.sa

provided the original work is properly cited.

\*
