1. Introduction
Interventional cardiology procedures (ICP) are some of the major medical examination methods applied for the detection of cardiovascular diseases under fluoroscopic X-ray guidance to obtain images of the heart chambers, valves, and surrounding blood vessels [1]. The most frequently reported cardiac procedures by interventional fluoroscopy are coronary angiography (CA), percutaneous coronary intervention (PCI), and combined CA with PCI (CA/PCI) [2]. Due to the use of X-rays in interventional cardiac procedures, it is considered one of the main medical procedures in which patients are exposed to high doses of radiation. X-rays are ionizing radiation and pose a significant risk, with the main radiation-induced side effects being being skin injury (deterministic effect or tissue reaction) and increased cancer risk (stochastic effect) [3]. Therefore, more preventive measures and studies are needed to reduce the radiation dose. The radiation dose of the patient during interventional cardiology is influenced by three types of factors. First, the technical factors affecting the radiation dose (X-ray beam quality, X-ray geometry, X-ray beam limitation devices, and fluoroscopic and acquisition imaging dose rate settings). Second are procedure-related factors, which include the increase in the treatment of complex lesions, such as chronic total occlusions, because of improvements in techniques and PCI equipment. The third is the group of factors that are patient-related (body mass index (BMI), comorbidities, and seriousness of coronary artery disease) [4,5].
In general, for radiation protection and development of quality assurance programs, reference levels (RLs) were introduced by the International Commission on Radiological Protection. Establishing RLs for interventional cardiology is challenging because there are many factors influencing these procedures that lead to a wide dose distribution [6]. In interventional cardiology (IC), several research studies have focused on the dose area product for the establishment of reference levels and dose optimization [7,8]. However, literature data reveal that the most commonly studied parameters for cardiac interventions are BMI, fluoroscopy time (FT), peak skin dose, and dose area product (DAP) for each procedure [2-10].
The aim of this study was to examine how to control the level of radiation exposure, to analyze and study the factors affecting the increase in radiation exposure from the specified level, and to estimate the incidence of high radiation dose procedures using a logistic regression method.
2. Methods and materials
2.1. Logistic Regression
Logistic regression is a reliable method of identifying which variables have an impact on a topic of interest. The process of performing a regression allows one to confidently determine which factors matter most and which can be ignored. Binary logistic regression is used to estimate the association of one or more independent (predictor) variables with a binary dependent (outcome) variable. A binary (or dichotomous) variable is a categorical variable that can only take two different values or levels. The model usually has two types of objectives: predictive or explanatory. In a model with predictive objectives, we aim to establish a parsimonious model, i.e., a model involving the least number of variables that best explains the dependent variable. In the case of a model with explanatory objectives, we aim to study the causal relationship between a ‘cause’ variable and an ‘effect’ variable. Given a set of values of the independent variables, we wish to estimate the probability that the event of interest will occur and evaluate the influence each independent variable has upon the response in the form of an odds ratio (OR). The form for predicted probabilities is expressed as a natural logarithm (ln) of the odds ratio [11].
where,
2.2. Data collection
The data from CA, PCI, and combined CA and PCI (CA/PCI) performed from 1 January to 31 August 2021 were collected from the Erbil Heart Center in the Kurdistan region in Iraq. For each procedure, the following data were collected: patient characteristics (age, sex, weight, and length to calculate BMI), exposure factors kV, mAs, and FT, and dosimetry indicators. Clinical data and technical factors were gathered from 29 coronary angiography (CA), 30 percutaneous transluminal intervention (PCI), and 30 double set-up (CA/PCI) procedures; all performed using the femoral approach. The data were gathered using a stratified random sampling method. This center has 10 cardiologists, 10 nurses, and 10 radiology technicians, with three active to angiography systems angiography rooms. In room 1, a GE Innova 2100 C-arm fluoroscope system 1316440G2283 model is set up, and room 2 is geared with Philips C-arm fluoroscope system 722064 185 and 105935 181 models.
2.3. Statistical analysis
Model construction: A binary logistic regression model (BLRM), a statistical approach to predict the presence of a DAP based on the available variables (Kv, mA, FT, and BMI), has been successfully used to predict the presence of a DAP level. It is known that DAP is related to the risk of exposure to radiation, which is widely used in the establishment of RL. DAP is the binary outcome variable used in the analysis. High DAP levels are assigned the value of 1, and routine DAP levels are assigned the value of 0. The BLRM has the following form:
In Eq. (4), the variable Y is the log (natural) of the odds of the event under consideration. In our case, the event will be the occurrence of a high DAP procedure. The βs are the coefficients of the regression calculated by the model of predictor variables kV, mA, FT, and BMI. The regression method was chosen with a free intercept. The justification for this is that the Automatic exposure control (AEC) compensates by keeping the quantity of radiation. The DAP values for 89 patients were dichotomized into three groups, which are CA, PCI, and CA/PCI, for each group divided into two subgroups; for CA, the DAP ≤ 35 Gy.cm2 and > 35 Gy.cm2, for PCI, the DAP ≤ 85 Gy.cm2, and > 85 Gy.cm2 for CA/PCI, the DAP ≤ 130 Gy.cm2 and > 130 Gy.cm2, respectively. The first subgroup is considered the routine radiation dose procedure, and the second is considered the high radiation dose procedure. The choice of level of DAP was based on the European Society of Vascular Interventional Radiology [7,10].
3. Results
Table I shows a summary of the 89 patients’ data in three groups of CA, PCI, and CA/PCI and their associated radiation dose metric with exposure factors. Figure 1 shows the scatter plots matrix, showing DAP as a function of kV, mA, FT, and BMI, respectively. Figure 2 demonstrates the scatter plots of DAP for CA, PCI, CA/PCI with the level of DRLs of the European Society of Interventional Cardiology. Figure 3 boxplots explain the four predicting variables kV, mA, FT, and BMI distribution for the two dependent variable categories of DAP: DAP > 35 Gy.cm2 and DAP ≤ 35 Gy.cm2 for CA, DAP > 85 Gy.cm2 and DAP ≤ 85 Gy.cm2 for PCI, and DAP > 130 Gy.cm2 and DAP ≤ 130 Gy.cm2 for CA/PCI.
Table I Sample size and mean, SD, Max. and Min. for patient characteristics for three group CA, PCI, and CA/PCI, and their associated radiation dose metric with exposure factors.
Sample size | BMI (kg/m2) | kV | mA | FT | DAP (Gy.cm2) | ||
---|---|---|---|---|---|---|---|
CA | 29 | Mean | 26.741 | 80.482 | 13.975 | 3.500 | 44.274 |
S.D | 3.746 | 17.795 | 3.362 | 2.341 | 38.534 | ||
Max. | 33.306 | 120 | 18.7 | 12 | 148 | ||
Min. | 15.241 | 53 | 5.9 | 1.08 | 4.510 | ||
PCI | 30 | Mean | 28.038 | 78.900 | 15.926 | 10.182 | 69.779 |
S.D | 3.417 | 16.649 | 3.049 | 5.931 | 30.257 | ||
Max. | 34.131 | 120 | 19.5 | 24.800 | 148 | ||
Min. | 15.241 | 45 | 8.6 | 2 | 4.510 | ||
CA/PCI | 30 | Mean | 28.646 | 82.466 | 14.4 | 8.181 | 86.224 |
S.D | 3.589 | 18.574 | 3.268 | 4.815 | 59.837 | ||
Max. | 35.651 | 120 | 18.7 | 19.400 | 241.410 | ||
Min. | 23.437 | 54 | 7 | 1.100 | 28.100 |

Figure 1 Scatter plots matrix showing DAP as function of Kv, mA, FT, and BMI, respectively, for CA, PCI, and CA/PCI.

Figure 2 Scatter plots matrix showing DAP Level, the green is for the procedures with DAP >35Gy cm2 and DAP ≤35 Gy cm2. The blue is for the procedures with DAP >85 Gy cm2 and DAP≤80Gy.cm2. The yellow is for the procedures with DAP >130 Gy cm2 and DAP ≤130 Gy.cm2.

Figure 3 Boxplots showing the four predicting variables kV, mA, FT and BMI distribution for the two dependent variable categories of DAP; DPA >35Gy cm2 and DAP ≤35 Gy cm2 for CA,DPA >85 Gy cm2 and DAP≤85Gy for PCI, and DPA >130 Gy cm2 and DAP ≤35 Gy.cm2.
The binary regression analysis was conducted to investigate whether kV, mA, FT, and BMI factors predict DAP levels. The Hosmer-Lemeshow goodness-of-fit was not significant (P > 0.05), indicating that the model was correctly specified Table II. The model correctly predicted 80.0%, 90.5%, and 95.2% of cases where there were routine DAP levels and 64.3%, 33.3%, and 77.8% of cases where there was a high level of DAP, giving an overall percentage correct prediction rate of 72.45%, 73.35%, and 90.0% for CA, PCI, and CA/PCI, respectively. The model’s results showed that independent variables (kV, mA, FT, and BMI) were found to be significant for all three groups: CA, PCI, and CA/PCI. As shown in Table III, the obtained LRM with four predictors (variables) is given by Eq. (5) below:
for CA
for PCI
for CA/PCI
Table II Classification table for CA, PCI, and CA/PCI.
Observed | Predicted | ||||
---|---|---|---|---|---|
DAP.Classes | Percentage Correct | ||||
≤ 35 | > 35 | ||||
CA | DAP.Classes | ≤ 35 | 14 | 2 | 87.5 |
> 35 | 8 | 5 | 38.5 | ||
Overall Percentage | 65.5 | ||||
DAP.Classes | |||||
≤ 85 | > 85 | ||||
PCI | DAP.Classes | ≤ 85 | 20 | 2 | 90.9 |
> 85 | 5 | 3 | 37.5 | ||
Overall Percentage | 76.7 | ||||
DAP.Classes | |||||
≤ 130 | > 130 | ||||
CA/PCI | DAP Classes | ≤ 130 | 20 | 1 | 95.2 |
> 130 | 2 | 7 | 77.8 | ||
Overall Percentage | 90.0 |
Table III Results of the logistic regression analysis for all the variables that may be related to the occurrence of high dose area product for CA, PCI, and CA/PCI procedures.
β | S.E. | Wald | Exp(β) | 95% C.I. for EXP(β) | |||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
CA | |||||||
Step 1 a | Kv | 0.020 | 0.024 | 0.501 | 1.021 | 0.962 | 1.061 |
FT | 0.500 | 0.296 | 2.859 | 1.649 | 0.966 | 4.303 | |
mA | -0.125 | 0.110 | 1.297 | 0.882 | 0.686 | 1.077 | |
BMI (kg/m2) | -0.011 | 0.098 | 0.012 | 0.989 | 0.789 | 1.167 | |
PCI | |||||||
Step 1 a | Kv | 0.016 | 0.026 | 0.358 | 1.016 | 0.963 | 1.060 |
FT | 0.084 | 0.073 | 1.328 | 1.088 | 0.935 | 1.227 | |
mA | -0.211 | 0.132 | 2.533 | 0.810 | 0.672 | 1.078 | |
BMI (kg/m2) | 0.007 | 0.106 | 0.004 | 1.007 | 0.832 | 1.219 | |
CA/PCI | |||||||
Step 1 a | Kv | 0.0109 | 0.081 | 0.638 | 1.011 | 0.799 | 1.099 |
FT | 0.030 | 0.028 | 0.511 | 1.030 | 0.593 | 1.791 | |
mA | -0.021 | 0.087 | 0.327 | 0.979 | 1.035 | 6.978 | |
BMI (kg/m2) | -0.188 | 0.217 | 0.751 | 0.829 | 0.542 | 1.268 |
a. Variable(s) entered on step 1: Kv, mA, FT, BMI (kg/m2).
When a logistic regression is calculated, as in Eq. (5) and Table III for CA, the regression coefficients (
In other words, the exponential function of the regression coefficient (
4. Discussion
The scatter plots matrix in Fig. 1; showing DAP as a function of kV, mA, FT, and BMI, respectively. We note that the scatter plot for kV and FT are strong, positive association because as kV and FT increases, so the DAP increased, but we note that the scatter plot for mA and BMI are strong, negative association, because in general, as mA and BMI increase, their DAP decreases. According to the results of the binary regression model, all the factors studied in this research, namely kV, mA, FT, and BMI, have a significant relationship with the DAP levels and confidently determine this result, which agrees with those published by others [2,4,5,11]. However, when reviewing the various studies on patient dosimetry, there appears to be a considerable variation in the radiation doses received by patients, as shown in Table IV. As there are a number of possible explanations, it is necessary to establish such a model to explain the radiation doses received by patients during interventional cardiology procedures and to detect the reason why the patient received a high dose and higher than the permissible dose level. When coefficient β of the variable is positive, we obtain OR > 1, and it therefore corresponds to a risk factor. If the value β is negative, OR will be < 1, and the variable therefore corresponds to a protective factor [12,13]. According to this, we concluded that for CA, the odds of
5. Conclusion
We can conclude that according to the model, increased kV and FT are the two factors that increase the risk, while decreased mA is a factor that helps in radiation protection. From this, we can identify variables that have an effect on the DAP level in interventional cardiology. Also, we state that regression analysis is a reliable method for evaluating user protocols in a center or hospital. We can also identify the most critical factors and the factors that can be disregarded. Thus, we conclude that the regression analysis method can be used in quality assurance and driving diagnostic reference levels and dose optimization.