Red Blood Cell Distribution Width in Holistic Nursing Mode for Patients with Acute Left Heart Failure

Acute left heart failure is a very common acute heart failure in clinic, which must be treated immediately. If the treatment time is delayed, the mortality will be greatly increased. At present, there is not a good index to judge the severity of acute left heart failure. Therefore, it is necessary to analyze the relevant data and find a suitable measurement index. The purpose of this paper is to study the application effect of red blood cell (RBC) distribution width in holistic nursing mode for patients with acute left heart failure, taking patients with acute left heart failure in our hospital as the research object. The RBC distribution width of patients was detected, and the mortality and symptom relief time of patients were counted. The results showed that there was no significant difference in mortality when RBC distribution width was young. When the RBC distribution width is greater than 14.6%, the mortality rate increases rapidly. The symptom relief time of holistic nursing group was much shorter than that of general duty nursing group, and the difference was the biggest when it was over 14.6% in RBC distribution width, reaching 8.5 minutes. In the holistic nursing group, there was no significant difference in symptom relief time. In the group of general duty nursing, when the RBC distribution width is more than 14.6%, the time of symptom relief increases sharply. RBC distribution width can be considered to predict the severity of a patient’s lines.

Caso Clínico: Resolución quirúrgica de coartación de aorta en un paciente pediátrico

BACKGROUND: Coarctation of the aorta is a congenital heart disease with an incidence of 4 per 10 000 live births, it may or may not be associated with patent ductus arteriosus as well as other malformations. It is usually asymptomatic and diagnosed by its classic signs such as; arterial pressure gradient between extremities, reduced pulses in the lower extremities, arterial hypertension in the upper extremities or, in severe cases, left heart failure. Its resolution can be percutaneous or surgical, depending on the patient’s age and the characteristics of the defect. CASE REPORTS: A 6-year-old male patient, asymptomatic, with suspected aortic coarctation, due to a difference between arterial pressures in the upper and lower limbs, lower limbs with reduced pulses, and a systolic murmur in the aortic focus. An echocardiogram was requested, which reported a bicuspid aortic valve with raphe, mild regurgitation, and coarctation of the aorta; CT angiography showed coarctation of the juxtaductal aorta; cardiac catheterization showed almost no passage of contrast through the defect, so surgical treatment was scheduled. EVOLUTION: Surgical correction was performed by coartectomy with end-to-end anastomosis and closure of the ductus arteriosus. After the intervention, a relevant improvement in the pressure gradient between the extremities was observed. In the postoperative period the patient presented hypertension, that we managed to control, the patient progressed favorably and was discharged after 4 days without antihypertensive treatment. CONCLUSION: Life expectancy in patients who underwent surgery to correct a congenital heart disease is higher than in those who don’t, so timely diagnosis is an important tool to improve life quality and life expectancy.

Abstract P505: Rv Sarcomeres From Lv-hfref Patients With Low Papi Have Abnormal Rv Thick Filament Structure

Patients with left heart failure and reduced ejection fraction (HFrEF) have variable RV failure that, if present, drastically worsens outcomes. In a cohort of 21 HFrEF patients from two hospital sites, we have previously shown (Aslam et al, Eur J HF; 2020: volume 23, pages 339-341) that like global function, RV myocyte maximum calcium-activated myocyte tension (T max ) is quite variable (COV 27%). To determine if a relationship between RV myocyte function and indices of RV chamber function exists, we trained a random forest classifier based on 41 clinical variables, including hemodynamic, laboratory, and echocardiographic data, and queried the importance of each. This revealed that the most predictive model for reduced T max was based on the pulmonary artery pulsatility index (PAPi), an established clinical index of RV failure. To gain insight into potential mechanisms for depressed T max in HFrEF patients with a low PAPi, we obtained small angle x-ray diffraction patterns in 5 HFrEF patients with depressed PAPi and T max and compared this to 5 non-failing (NF) controls. The equatorial intensity ratio I(1,1)/I(1,0) was reduced in low T max RV muscle fibers vs. controls (0.250.06 vs. 0.180.02, P<0.0001), suggesting myosin heads are more associated with the thick filament backbone. In meridional reflections, we find a significant decrease in M3 band spacing (14.340.03 nm in NF vs. 14.300.01 nm in HFrEF; P=0.0013) suggesting more myosin heads are in the “OFF” configuration. The latter may underly tension reduction in RV myocytes from failing RV HFrEF patients. Ongoing studies will examine these structural changes in HFrEF patients with a broader range of PAPi and T max to test if this association applies. These findings focus attention on thick filament structural and configuration abnormalities as potential culprits underlying RV disease in HFrEF. Further studies using novel sarcomere enhancers will test if these changes can be remedied, and if so, in which patients.

The Role of Deep Learning-Based Echocardiography in the Diagnosis and Evaluation of the Effects of Routine Anti-Heart-Failure Western Medicines in Elderly Patients with Acute Left Heart Failure

Objective. The role of deep learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines was investigated in elderly patients with acute left heart failure (ALHF). Methods. A total of 80 elderly patients with ALHF admitted to Affiliated Hangzhou First People’s Hospital from August 2017 to February 2019 were selected as the research objects, and they were divided randomly into a control group and an observation group, with 40 cases in each group. Then, a deep convolutional neural network (DCNN) algorithm model was established, and image preprocessing was carried out. The binarized threshold segmentation was used for denoising, and the image was for illumination processing to balance the overall brightness of the image and increase the usable data of the model, so as to reduce the interference of subsequent feature extraction. Finally, the detailed module of deep convolutional layer network algorithm was realized. Besides, the patients from the control group were given routine echocardiography, and the observation group underwent echocardiography based on deep learning algorithm. Moreover, the hospitalization status of patients from the two groups was observed and recorded, including mortality rate, rehospitalization rate, average length of hospitalization, and hospitalization expenses. The diagnostic accuracy of the two examination methods was compared, and the electrocardiogram (ECG) and echocardiographic parameters as well as patients’ quality of life were recorded in both groups at the basic state and 5 months after drug treatment. Results. After comparison, the rehospitalization rate and mortality rate of the observation group were lower than the rates of the control group, but the diagnostic accuracy was higher than that of the control group. However, the difference between the two groups of patients was not statistically marked ( P > 0.05 ). The length and expenses of hospitalization of the observation group were both less than those of the control group. The specificity, sensitivity, and accuracy of the examination methods in the observation group were higher than those of the control group, and the differences were statistically marked ( P < 0.05 ). There was a statistically great difference between the interventricular delay (IVD) of the echocardiographic parameters of patients from the two groups at the basic state and the left ventricular electromechanical delay (LVEMD) parameter values after 5 months of treatment ( P < 0.05 ), but there was no significant difference in the other parameters. After treatment, the quality of life of patients from the two groups was improved, while the observation group was more marked than the control group ( P < 0.05 ). Conclusion. Echocardiography based on deep learning algorithm had high diagnostic accuracy and could reduce the possibility of cardiovascular events in patients with heart failure, so as to decrease the mortality rate and diagnosis and treatment costs. Moreover, it had an obvious diagnostic effect, which was conducive to the timely detection and treatment of clinical diseases.