Vasoactive Drug Therapy and Clinical Nursing of Patients with Cardiovascular Disease.

Cardiovascular disease (CVD) is a common human disease with a large number of patients. Vasoactive drugs have a good effect on the contraction and expansion of blood vessels, which can provide certain help for the management of cardiovascular diseases. However, the clinical care of cardiovascular disease has always been based on the superficial resistance level of body fat, weight, and so on, which is unfavorable for the real recovery of patients with cardiovascular disease. This article aims to quantitatively evaluate the effects of clinical care based on vasoactive drug therapy and intrinsic factors of cardiovascular disease. For the treatment of vasoactive drugs, this paper selects the principle of action of the adrenal hormone to judge and analyze the expansion and contraction of the cardiovascular disease. For the clinical nursing of patients with cardiovascular disease, based on multivariate logistic regression, this paper selects internal factors such as age and blood pressure to model the nursing effect. Experiments have shown that the logistic regression model established in this paper can evaluate well the recovery effect of patients with cardiovascular disease. The AUC value of the model reached around 0.9. This showed that the clinical care of patients with cardiovascular disease can not only rationally judge the recovery effect through the model but also adjust the physical and mental conditions of patients with cardiovascular disease according to the coefficients of the model to achieve the best recovery effect.

1. Introduction

Atherosclerotic cardiovascular and cerebrovascular diseases are one of the most common chronic diseases. Strengthening the research on the pathogenesis and prevention of atherosclerosis is the present focus of medical research. Modern medicine believes that atherosclerosis is the common pathological basis of atherosclerotic cardiovascular and cerebrovascular diseases in different parts of the world. It can be seen that they are diseases of the same origin with different diseases. From the perspective of treatment, it reflects the characteristics of the “same treatment for different diseases” in traditional Chinese medicine. In fact, atherosclerosis can be both a disease and pathogenesis from different perspectives. From a local point of view, atherosclerosis is a process in which various pathogenic factors lead to local damage and antidamage of arteries, and it is a disease for the arteries themselves. On the whole, there is no obvious stenosis of the vascular lumen in patients with early atherosclerosis. When the hemodynamics is not affected, there are often no obvious clinical manifestations. Only when lumen stenosis affects organ perfusion will there be corresponding clinical manifestations. At this time, atherosclerosis is the pathogenesis. Vascular endothelium all over the body is affected by the same risk factors in the blood, and the final morphology, lipid deposition, cytokine expression, and so on are all localized rather than systemic. Therefore, it is still a big problem to study the mechanism of why certain parts are prone to atherosclerosis. Modern medicine now recognizes that plaque-prone parts are related to the hemodynamic differences of blood vessels in different parts and the inherent heterogeneity of blood vessels in different vascular beds. Therefore, research on vasoactive drug therapy and clinical nursing of patients with cardiovascular disease is necessary.

Atherosclerosis, cerebral infarction, myocardial infarction, etc., are common cardiovascular diseases in humans. Due to the high incidence of these diseases and the harm of manuscripts, many scholars have studied them. Estruch et al. reanalyzed data related to cardiovascular disease. His research showed that supplementing the Mediterranean diet with extra virgin olive oil or nuts for the primary prevention of cardiovascular disease has a good effect [1]. Marventano et al. conducted a metaanalysis of the Mediterranean diet in relation to the Mediterranean diet to explore the association between Mediterranean diet adherence and CVD morbidity and mortality in prospective studies and randomized controlled trials (RCTs) [2]. Yang et al. conducted a study of stroke patients in China, and their research found that the stroke population in China accounts for a large proportion of the cardiovascular disease population [3]. Guo et al. investigated the association between egg consumption and cardiovascular disease risk (primary outcome), T2D, and mortality in the Caerphilly Prospective Cohort Study (CAPS) and National Diet and Nutrition Survey (NDNS) [4]. Knuckles and Campen studied the association of air pollution exposure with acute and chronic cardiovascular diseases such as acute myocardial infarction, stroke, arrhythmia, atherosclerosis, and metabolic disease [5]. With the development of society, the etiology and manifestations of cardiovascular disease are also changing. Although there are many studies on cardiovascular disease, the origin of its pathogenesis has not been conclusive.

Vasoactive drugs are important antishock chemical drugs, and there are many types and effects of them. There are also many studies on them by related scholars. Al-Shukri et al. explored the effects of vasoactive drugs on bladder wall ischemia and its role in the pathogenesis of OAB [6]. Gifford et al. proposed a new solution for the treatment of liver cirrhosis. He chose vasoactive drug therapy to supply blood to the liver and improve blood flow [7]. Li and Sun found that the key links of fluid resuscitation and the application of vasoactive drugs are insufficiently understood, and there are habitual prescriptions that use dopamine as the first choice or dopamine and norepinephrine as the first choice for combined therapy [8]. Kanchi and Menon aimed to examine the changes in body temperature in OPCABG patients under GA and the effect of vasoactive infusion on body temperature [9]. Zou et al. analyzed the effect of vasoactive drugs on combination therapy in women with chronic pelvic pain syndrome (CPPS). In order to study the microcirculation of the bladder, he used the original technique of intravesical high-frequency Doppler ultrasound. The intensity of pain syndrome and voiding disturbance were assessed using the PUF questionnaire, and psychoemotional status was assessed using the A.T. Depression Scale [10]. However, their research only focused on the specific efficacy of vasoactive drugs for a certain disease and did not analyze the general mechanism of action for cardiovascular diseases.

In this paper, active learning and regression models are used to model the care of cardiovascular disease. Different from machine learning algorithms, active learning algorithms can proactively discover the most valuable markers from the latest samples for experts to keep the learner model up-to-date with current disease trends. Moreover, the discovery of some new risk factors through active feature sampling has important implications for clinical diagnosis and general medical care. Especially in the clinical nursing effect of patients with cardiovascular disease, the rational and digital nursing effect is the key factor in judging the nursing effect.

2. Vasoactive Drug Therapy and Cardiovascular Disease

2.1. Vasoactive Drugs

Vasoactive drugs are drugs that achieve the purpose of antishock by regulating the vasomotor state, changing vascular function, and improving microcirculation blood perfusion, including vasoconstrictors and vasodilators. Vasoactive drugs are mainly used in the adjuvant phase of treatment, and normal blood flow acts on endothelial cells as lamellar shear stress. On the other hand, in atherosclerotic-prone sites, the shear stress exerted on endothelial cells by disturbed blood flow is reduced. Studies have found that abnormal blood flow shear stress can accelerate the process of atherosclerosis, and experimental evidence has shown that changes in blood flow shear stress can affect the structure and function of endothelial cells and smooth muscles, which leads to the deposition of mononuclear macrophages and LDL under the vascular endothelium. Common vasoactive drugs are shown in Figure 1.

Figure 1

Common vasoactive drugs.

Statins are currently the most widely used vasoactive drugs for the treatment of atherosclerosis. It can inhibit the reduction of hydroxymethylglutaryl-CoA reductase, block the synthesis of cholesterol ester, and finally achieve the effect of reducing blood lipids and preventing AS [11]. In addition, studies have shown that in addition to reducing blood lipids, statins have anti-inflammatory, endothelial function, antiplatelet aggregation, stabilizing, and even reversing plaque functions, thereby reducing cardiovascular events. Moreover, niacin, fibrates, and PCSK9 inhibitors can regulate the metabolism of blood lipids through different pathways, thereby playing a role in the treatment of AS.

Angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) can stimulate the expression of platelet-derived growth factors and promote the proliferation of vascular smooth muscle cells because of angiotensin II, thereby leading to atherosclerosis [12]. Studies have shown that in addition to antagonizing angiotensin, ACEI and ARB can also affect the process of atherosclerosis by inhibiting inflammation, protecting vascular endothelium, inhibiting the proliferation of vascular smooth muscle cells, and stabilizing plaque. Among them, the mechanism of action of vasoactive drugs is shown in Figure 2.

Figure 2

Mechanism of action of vasoactive drugs.

2.2. Current Status of Patients with Cardiovascular Disease (CVD)

CVD is a relatively prevalent chronic disease in China. Especially in recent years, under the influence of factors such as population aging, dietary structure, and pollution, the incidence rate has increased. CVD has great harm to human health, and unhealthy behaviors can easily lead to the occurrence of cardiovascular diseases, as shown in Figure 3 [13, 14]. According to the “2014 China Cardiovascular Disease Report,” CVD has become the leading cause of disease-related death among urban and rural residents in China, with 44.8% in rural areas and 41.9% in urban areas.

Figure 3

cardiovascular diseases.

According to the report, the number of CVD patients in China is about 300 million. One person can die from the disease in about 10 seconds, and the mortality rate is increasing year by year, as shown in Figure 4.

Figure 4

Changes in CVD mortality in rural and urban areas.

The cost of late-stage CVD treatment is high, and both the total cost of medical visits and the average cost of each medical visit have risen sharply in recent years. According to 2013 statistics, the total cost of several CVDs such as intracranial hemorrhage, acute myocardial infarction, and cerebral infarction reached 19.238 billion, 11.47 billion, and 39.808 billion yuan, respectively. The average annual growth rates in recent years have reached 19.86%, 33.46%, and 25.37%, respectively, as shown in Figure 5 [15].

Figure 5

Changes in selected CVD hospitalization costs.

In view of the present situation of CVD in China, it is urgent to carry out research work to delay the progression of the disease and reduce the incidence. As a chronic disease, CVD has a slow onset process, a long incubation period, and many modifiable pathogenic factors, leaving enough time for the early prevention and treatment of CVD [16]. Based on the current advanced Internet technology and medical informatization, through the personalized health management strategy of “Internet + medical treatment,” the risk factors of CVD can be comprehensively managed and intervention methods and clinical decisions such as life, diet, exercise, and psychology can be formulated in a targeted manner, which can effectively reduce the incidence and mortality of CVD [17]. The closed-loop and continuous health management model is shown in Figure 6.

Figure 6

CVD health management.

In the process of health management, the risk prediction of CVD is very important. The main functions include the following aspects: (1) By combining quantitative and qualitative analysis of the risk level of each factor and the probability of future incidence, the triage management of the assessed objects can be realized. Interventions are provided by category. A personal health plan is created by intervention. CVD improvement is monitored as planned. Then, the improvement results are evaluated. Relying on this method, the scarce medical resources can be optimally allocated again, and the use of resources can be made more reasonable; (2) by predicting and quantifying the size of the individual's risk of disease, it is helpful to promote the assessed subjects to improve their self-management awareness and mobilize their enthusiasm to fully participate. It can fundamentally alleviate the current dilemma of “seeking a doctor after illness” in the treatment of chronic diseases and change passive treatment to active health management, which is of great significance for alleviating CVD events.

2.3. CVD Regression Model

Logistic regression explores the mapping relationship between CVD risk factors (variables) and the occurrence of CVD (dependent variables) and solves the regression parameters to establish a probabilistic mathematical formula for quantitative probabilistic prediction of novel homogeneous risk factor combinations [18, 19]. Logistic regression is mainly divided into univariate binary classification and multivariate binary regression. The former is used to examine the situation of a single factor, and the latter is used to examine the situation of multiple factors. The establishment of the CVD prediction model is mainly based on multivariate binary regression. For example, in FRS, 6 risk factors including age, sex, TC, HDL-C, SBP, and smoking are taken as variables, and y=0 (no disease) and y=1 (disease) are used as dependent variables.

It is assumed that the occurrence of event A is a continuous dependent variable y with a value of (−∞, +∞). Setting a critical value c (such as c = 0), y represents the actual observed dependent variable results. Then, the following can be obtained:

When y > 0, then y=1, represents event A.

When y < 0, then y=0, represents event A.

It is assumed that the dependent variable y is linearly related to the variable x, as shown as follows:.

Supposing the probability of occurrence of A is P, the following formulas can be obtained:

In formula (1), ε is the error term, which obeys the logistic distribution. According to the pairwise property of logistic distribution, formula (3) can be rewritten as the following formula:

In formula (4), F(.) is the cumulative distribution function of the error term ε. According to its characteristics, the following formula can be obtained:

Formula (5) is the logistic function, which is distributed in an S-shaped curve. The value interval of the function P is (0 and 1), which ensures that the estimated probability of the model is between (0 and 1) [20].

Given n groups of the CVD observation samples (x, x, ⋯, x; y)(i=1,2, ⋯, n) containing k risk factors, the logistic model can be defined according to formula (5), as shown in the following formula:

In formula (6), α is the regression intercept; (β1, ⋯, β) is the parameter of each variable of the model. Then, the probability of CVD not occurring is as shown in the following formula:

The probability ratio of CVD occurrence and nonoccurrence is called the occurrence ratio (odds), which is obtained from formulas (6) and (7), as shown in the following formulas:

Taking the natural logarithm of formula (8), the logit transformation of p can be obtained, as shown in the following formulas:

For the training group CVD data, with known risk factor levels (x, x, ⋯, x) and observed CVD outcomes y, the maximum likelihood method is used to estimate the value of each parameter.

The conditional probability function of 1 is as shown in the following formula:

Formula (12) can be written as in the following formula:.

Since the label values y of n groups of samples are independent of each other, the likelihood function is as shown in the following formula:

Taking the logarithm of both the sides of formula (14), the log-likelihood function can be obtained as in the following formulas:

The partial derivatives of parameters α and β in formula (15) are calculated and set to 0 respectively, and the overall parameter estimate that maximizes ln [L(θ)] is obtained through the following formulas:

Formulas (17) and (18) are likelihood equations. Since there are k independent variables, there are k + 1 equations. The equations are combined and calculated by Newton–Raphson iteration. Finally, the maximum likelihood estimates of α and β are obtained. Then, they are substituted into formula (6) to establish a logistic regression-based CVD prediction model [21, 22]. The obtained prediction model is based on nursing data, guided by the recovery characteristics of cardiovascular disease, which can provide a new solution for clinical nursing.

3. Vasoactive Drug Therapy

3.1. CVD Risk Factors

The risk factors of CVD vary according to the characteristics of population, race, living environment, dietary habits, and so on, so there are differences in the selection of risk factors in traditional CVD prediction models. The determination of CVD risk factors should meet the following criteria: (a) They are universal. That is, the factors that exist in most CVD patients; (b) they have a close contact. They can individually affect the occurrence and development of CVD; (c) they have significant effects. The risk of morbidity can be reduced after interventional treatment and control.

According to the above criteria, with the diversification of research techniques, more risk factors have been discovered and confirmed. Currently recognized CVD risk factors are shown in Table 1, including continuous factors, dichotomous factors, and multiclassified complex factors.

Table 1

Cardiovascular disease risk table.

Serial	Main danger	Potential danger
1	Gender	Overweight or body mass index
2	Age	Serum triglycerides
3	Family history	Insulin resistance
4	Hypertension	Plasma homocysteine
5	Diabetes	Clotting factor
6	Serum total cholesterol	Chronic inflammation
7	High-density lipoprotein cholesterol	Serum apolipoprotein A level
8	Smoking	Other pathomechanical factors

In Table 1, gender and age are uncontrollable factors, which is the reason why CVD patients are mainly middle-aged and elderly. Primary risk factors are those that are primarily included in traditional CVD prediction models. Potential risk factors are other pathological markers associated with CVD that have been identified with the advent of marker technology. With the complexity of social lifestyle and living environment, the influence of socioeconomic and psychological factors on CVD is more important such as the level of education (categories such as primary school, middle school, high school, university, and others), occupation (categories such as sales, staff, secretaries, and so on), and other classified complex factors. There is a highly nonlinear relationship between the categories, which may contain important information on the effects of CVD. If such factors cannot be included in the CVD prediction model, the prediction results will lose their authenticity and have no practical significance.

3.2. Application of Vasoactive Drugs

3.2.1. Management of Vascular Anesthesia Drugs

During induction of anesthesia, severe choking reactions caused by laryngoscope and endotracheal intubation should be avoided. During recovery from anesthesia, the stress response to the airway in the state of tracheal extubation or tracheal tube retention should be suppressed. After opening the skull, brain relaxation should be maintained to facilitate surgical operations, and patient agitation or circulatory fluctuations caused by intraoperative surgical operations should also be suppressed. Several key issues in the management of anesthesia for induction neurosurgery were shared by both the groups, including the control of intracranial pressure/brain relaxation; fluid infusion; maintenance of blood volume and circulation stability; management of PaCO2; application of osmotic dehydration; and management of depth of anesthesia, body temperature, blood sugar, and others. In order to eliminate the interference of these common problems on the experimental data as much as possible, during the research process, these indexes were controlled at the same level in the two experimental groups. Target-controlled infusion (TCI) was used to maintain the same plasma concentrations of anesthetics during discussion and surgery. The intraoperative depth of anesthesia (BIS) was controlled at the same level. Muscle relaxants were administered at the same induction dose (mg/kg) and maintenance dose (1/3 of the induction dose at 45-minute intervals).

3.2.2. Management of Arterial Blood Pressure

The present clinical view is that the autoregulatory response of the brain tissue to a drop in blood pressure may be impaired after acute central nervous system injury, especially after brain injury and subarachnoid hemorrhage. Blood flow to some of the brain tissues is significantly reduced. Therefore, cerebral perfusion pressure should be maintained at normal or even higher than normal levels during such a surgery. Secondly, the brain tissue is compressed when the distractor is used during the operation, and the local compression of the brain tissue will reduce its effective perfusion pressure. The intraoperative blood pressure fluctuation range in both the study groups remained within 20% of the baseline value. Vasoactive drugs were given according to the situation during the operation, and the heart rate was maintained at 50–120 beats/min.

3.2.3. Intestinal and Lactic Acid Management

A retrospective cohort study of 187 patients with chronic cardiovascular disease showed that although 93% of patients had elevated lactate levels, the mean level was only 4.2 mmol/L, with a wide range (0.2 to 19 mmol/L). There were also studies showing that lactate is not reliable in assessing tissue ischemia in patients on epinephrine because epinephrine itself can cause hyperlactatemia. Only three patients in this study were given epinephrine, but their lactate was not elevated. Compared with the EN tolerance group and the EN intolerance group, the lactate increase ratio in the EN intolerance group accounted for about 86.9%, which was significantly higher than that in the EN tolerance group (49.3%). The dosage of vasoactive drugs was also higher in patients with EN intolerance. It was considered that this may be related to the degree of tissue hypoperfusion and hypoxemia. It can be seen that although it cannot be determined that the increase of lactate is directly related to the EN nutritional tolerance, lactate can reflect intestinal perfusion to a certain extent.

In addition, critically ill patients receiving vasoactive drugs should be monitored daily for bowel sounds and bowel movements, as decreased bowel sounds or bowel movements may be early indicators of bowel ischemia. In this paper, there was no statistical difference between the tolerance group and the intolerance group in defecation, but clinicians should be vigilant about the symptoms of gastrointestinal intolerance in patients. When vasoactive drugs are used for EN complications, the most serious complication is NOBN. Further aggravation of intestinal microcirculation ischemia, mucosal damage, or local inflammatory response increases the risk of developing NOBN. The reason may be that the metabolic process of enteral feeding increases the energy demand of intestinal epithelial cells, and vasoactive drugs cause splanchnic hypoperfusion through the vasoconstriction effect caused by sympathetic nerve excitation. In this case, the mucosal layer preferentially receives blood perfusion, while the submucosa, muscularis, and serosa are relatively underperfused. Hypoperfusion is a precursor to intestinal necrosis and may be associated with mucosal inflammation and injury, which can be exacerbated by reperfusion. Studies have shown that NOBN associated with EN has a higher mortality rate than other causes of mesenteric ischemia.

3.3. Use of Vasoactive Drugs and Coronary Angiography

Vasoactive drug use and CAG were performed by experienced cardiologists. Two nonparticipating cardiovascular intervention personnel read independently, and the results were taken as the mean of both. According to the CTFC results, if the CTFC value of any one or (and) several coronary vessels is greater than or equal to 27 frames, the phenomenon of coronary slow blood flow can be considered. In patients with coronary slow blood flow confirmed by CAG, real-time vital signs of ECG monitoring equipment were recorded after the first coronary angiography. The patients were then given musk Tongxin-dropping pills to be sublingually administered. For patients with slow absorption, the drug can be gently crushed with teeth and mixed with a little normal saline to accelerate the absorption. Studies have shown that under normal circumstances, the dropping pills can be completely melted and absorbed under the tongue for 3–8 minutes. This paper took a median of 5 min. 5 min later, the coronary angiography was performed again, and the blood pressure and heart rate after taking the drug were recorded. The CTFC values after taking the drug were collected in the same way. Whether the patient had discomfort after taking the medicine during the operation was observed. Blood was drawn within 24 hours after coronary angiography for retesting of the liver and kidney function. Coronary slow blood flow is one of the more common phenomena in coronary angiography, which is characterized by no obvious vascular structural lesions in coronary angiography but slow blood flows in distal blood vessels and delayed myocardial perfusion. Since the phenomenon was discovered and reported in 1972, it has become a research hotspot. Due to its particularity, it can be directly identified by the naked eye in angiography, so direct coronary or intravenous administration can directly observe the improvement of blood flow in angiography. In coronary angiography, the mean CTFC value of slow blood vessels was reduced from 43 frames (29–73 frames) to 32 frames (20–60 frames) after injection of isosorbide dinitrate. After further intracoronary injection of a 1 mg nicorandil needle, the CTFC value of coronary blood flow further decreased, reaching 25 frames (12–34 frames). Finally, the CTFC value of coronary angiography returned to the original level.

4. CVD Clinical Nursing Based on Regression Technology

4.1. Preparation of Experiment

In order to prove the problems existing in the regression model in the face of complex factors and the effectiveness of the solutions, two parts of the experiment were designed by taking logistic regression as an example: (a) Direct logistic regression experiments. The data of the training group (80% of the instances in each dataset) were used to solve the value of the regression parameter (α, β1, ⋯, β) and establish an expression model. The independent variable data of the test group (the remaining instances) were substituted, and the obtained prediction results were compared with the real labels to find the correct rate; (b) dummy variables were set for multicategory independent variables, and logistic regression (denoted as Dv-Logistic) was performed on the processed data according to the experimental procedure. The model accuracy obtained from the two experiments was compared.

Experiments were conducted using data containing complex risk factors, and two datasets, Statlog(Heart) and Heart Disease Database, in the UCI machine learning library were selected. Statlog(Heart) contained 270 groups of instances. The Heart Disease Database contained 820 instances. Each instance had 76 attributes. The same attributes and labels as Statlog (Heart) were extracted, and the missing data were preprocessed by the mean method. The attributes of the two datasets included continuous, binary, ordered multicategory, and unordered multicategory variables, as shown in Table 2. 80% of the instances were taken as training samples and 20% as test samples.

Table 2

Experimental data types.

Serial	Property name	Type
1	Age	Continuous
2	Sex	Two-class
3	Cp	Disordered four categories
4	Trestbps	Continuous
5	Chol	Continuous
6	Fbs	Two-class
7	Restecg	Disordered three classifications
8	Thslach	Continuous
9	Exang	Two-class
10	Oldpeak	Continuous
11	Slope	Ordered three-category
12	Ca	Continuous
13	Thal	Ordered three-category

The experiments of logistic regression and setting dummy variables were implemented on the SPSS21.0 software, and the software running environment was Windows 7 32-bit operating system. The configured CPU was Intel(R) Core (TM) i5M460@2.53 GHz. According to the analysis method of logistic regression in SPSS, firstly the original data were used to carry out a single factor regression experiment in SPSS to eliminate unnecessary factors. Then, a direct regression experiment was performed on the culled raw data. Finally, dummy variables were set for multicategory factors into binary factors, and regression experiments were performed in SPSS. Using the intuitive result presentation interface in SPSS, the two experiments were recorded separately.

4.2. Univariate Logistic Regression

First, in order to reduce the computational complexity, a single-factor unconditional logistic regression analysis was performed on the 13 attributes in the original dataset. The results are shown in Figures 7 and 8.

Figure 7

Univariate logistic regression coefficients. (a) Statlog (heart). (b) Heart disease database.

Figure 8

Univariate logistic regression model situation. (a) Statlog (heart). (b) Heart disease database.

As shown in Figure 7, beta is the regression coefficient of univariate regression analysis. S.E. is the standard error. In the two experiments a and b, the univariate logistic regression had similar results, and there was a little difference in the regression coefficients of each factor.

As shown in Figure 8, the P value refers to the degree of correlation between the factor and the outcome of the event, usually with P < 0.05 indicates that the attribute is statistically significant. Therefore, the two factors of Trestbps and Fbs in the two datasets were not meaningful and were eliminated. The OR value is the odds ratio, which is an accurate estimate of the relative risk. If OR > 1, the factor is a risk factor. If OR < 1, the factor is a protective factor.

4.3. Multivariate Logistic Regression

As shown in Figure 9, the multivariate logistic regression after two variables was proposed and was smoother. The standard error of each factor was less than 1. For the constant term, the proportion was larger, which had a greater impact on the establishment of the model. Overall, the simulation results for both datasets tended to be the same, indicating that there was no multicollinearity between the factors. According to formula (18), the nursing model can be established by substituting the regression coefficient obtained by regression analysis.

Figure 9

Multivariate logistic regression results. (a) Statlog (heart). (b) Heart disease database.

Through the analysis of the problem of the logistic regression model oriented to complex factors, according to the conditions and methods of dummy variable setting. Dummy variables were set for multicategory factors in the dataset, and they were turned into binary variables. Among the attributes, Cp, Restecg, Slope, and Thal were the four-category disorder, three-category disorder, and three-category order variables, respectively. The number of dummy variables was 3, 2, 2, and 2 respectively, and the control coding method was selected, as shown in Table 3.

Table 3

Dummy variable settings.

Variable		Dummy variable
		A	B	C
Cp	1	1	0	0
	2	0	1	0
	3	0	0	1
	4	0	0	0
Restecg	0	1	0
	1	0	1
	2	0	0
Slope	0	1	0
	1	0	1
	2	0	0
Thal	0	1	0
	1	0	1
	2	0	0

By setting dummy variables, the number of variables in the model was increased to 17. Experiments were conducted on the altered dataset. The Dv-Logistic model was established and substituted into the test data. The experimental results were compared. The experimental results of the logistic and Dv-Logistic models on the two datasets were compared and analyzed.

The receiver operating characteristic curve (ROC) was drawn according to the classification prediction results. By taking the sensitivity and 1-specificity indexes as the ordinate and abscissa, respectively, the sensitivity and specificity were integrated into one index, which avoided the influence of morbidity in CVD prediction and was a commonly used model performance evaluation index in CVD prediction. AUC is an indicator to measure the area under the ROC curve. The larger the value is, the closer the ROC curve is to the upper left corner, and the more reliable the model is. If the AUC is lower than 0.5, it is of no practical significance. If the AUC is between 0.5 and 0.7, the model has lower performance. If the AUC is between 0.7 and 0.9, the model has good performance. If the AUC is greater than 0.9, it proves that the model has high performance.

The statistical logistic model and Dv-Logistic model were used to compare the prediction results of the two datasets with the corresponding real label values to draw ROC curves, as shown in Figure 10.

Figure 10

Comparison of regression models and their improved ROC curves. (a) Statlog (heart). (b) Heart disease database.

For the evaluation of the prediction ability of the model, in addition to using the ROC curve to display it visually, it is also necessary to compare the degree of fit between the category of the prediction result and the corresponding real label value, that is, through comprehensive evaluation such as sensitivity, specificity, and accuracy. In CVD prediction, it is explained as follows: sensitivity (Se): the ability to correctly predict the disease; specificity (Sp): the ability to correctly predict the absence of disease; positive predictive rate (Pp): the proportion of cases that are predicted to have the disease; negative predictive rate (Np): the proportion of instances that are predicted to be not diseased; and accuracy rate (Ac): the proportion of correctly predicted instance categories in all instances.

Statistics and calculation of the test results of the logistic and Dv-Logistic models on the two datasets were carried out for comparative analysis, as shown in Table 4.

Table 4

Comparison of prediction results.

Datasets	Model	Test result
		Se (%)	Sp (%)	Ac (%)	AUC
Statlog (heart)	Logistic	52.28	91.32	69.57	0.76
	Dv-logistic	72.8	97.77	83.86	0.88
Heart disease database	Logistic	54.57	78.55	67.80	0.67
	Dv-logistic	73.32	87.23	81.12	0.81

By analyzing the comparison results in Table 4, the following conclusions can be drawn: (1) For CVD data containing complex factors, the logistic regression model was directly used to predict CVD. The AUC values of the model were 0.7634 and 0.6700, and the prediction accuracy was not high; (2) the Dv-Logistic prediction model of dummy variables was set for complex factors. The AUC value of the model was increased to 0.8784 and 0.7999, and the prediction accuracy was greatly improved; and (3) the above comparison results showed that the regression model has defects in the face of CVD data containing complex factors, using dummy variables to convert multicategory complex factors into binary categories. That is, converting nonlinear data into binary data suitable for the model can solve the problem of incorporating complex factors.

5. Conclusions

Using complex factors for the prediction of CVD can make the model-fitting results close to the real nursing situation, which is of great significance for formulating targeted interventions and improving the efficiency of CVD management. The main line of this paper is to study CVD prediction oriented to complex factors so that the prediction model can incorporate complex factors with high prediction accuracy and stability. For the establishment of the model, the importance of complex factors in CVD prediction is first analyzed. Focusing on the complex factor-oriented CVD prediction problem, the calculation process of the regression prediction model is first studied. The specific reasons why complex factors cannot be used in traditional CVD prediction models were analyzed from the model mechanism, and then, dummy variables were used to change the data format of complex factors to adapt to the regression model. The experimental results showed that complex factors cannot be directly used in traditional CVD regression prediction models. Restricted by various conditions, the prediction model established at each stage can only be tested by the data in the machine learning database, and the amount of data are relatively small. The next step is to collect more real CVD data containing complex factors to test and correct and improve the model to further explore its practical value, comprehensively considering the parallel computing problem when the model is applied in the system.