Objective To analyze the influencing factors of prognosis of patients with traumatic brain injury (TBI), and explore the influence of hemoglobin (Hb) level combined with blood pressure variability (BPV) on the quality of prognosis of patients with TBI. Methods The data of 186 TBI patients who received systemic treatment in the Affiliated Zhangjiagang Hospital of Soochow University between January 2020 and December 2021 were retrospectively analyzed. According to the Glasgow Outcome Scale (GOS) 3 months after treatment, they were divided into group A (GOS 4-5, 159 cases) and group B (GOS 1-3, 27 cases). The general clinical data, BPV indexes and Hb levels of the two groups were analyzed by single factor analysis and multiple logistic regression analysis, and the predictive value of the logistic regression model was evaluated by receiver operating characteristic (ROC) curve, sensitivity, specificity and area under the curve (AUC). Results There was no statistical significance in gender, age, body mass index, blood urea nitrogen, prothrombin time, fasting blood glucose level, or smoking history (P>0.05); the patients’ Glasgow Coma Scale at admission in group A was higher than that in group B (P<0.05), and the constituent ratio with a history of hypertension of group A was significantly lower than that of group B (P<0.05). The between-group differences in systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and Hb at admission, and SBP, DBP, and MAP 72 h after treatment were not statistically significant (P>0.05); the SBP-standard deviation (SD), DBP-SD, SPB-coefficient of variation (CV) and DBP-CV of group B 72 h after treatment were significantly higher than those of group A (P<0.05), and the level of Hb was significantly lower than that of group A (P<0.05). Hb [odds ratio (OR)=0.787, 95% confidence interval (CI) (0.633, 0.978), P=0.031], SBP-CV [OR=1.756, 95%CI (1.073, 2.880), P=0.023] and DBP-CV [OR=1.717, 95%CI (1.107, 2.665), P=0.016] were all independent prognostic factors of TBI patients. The ROC showed that the combined index of BPV and Hb was more valuable than that of single prediction, with an AUC of 0.896 [95%CI (0.825, 0.935), P<0.05]. Conclusions Both BPV and Hb are independent factors affecting the prognosis of TBI patients, and their combined application can more effectively predict the prognosis of TBI patients. Therefore, when treating and evaluating the prognosis of TBI patients, closely monitoring the changes in blood pressure and Hb levels can timely and effectively control the development of the disease, and provide scientific reference for subsequent treatment.
The continuous left ventricle blood pressure prediction based on selected heart sound features was realized in this study. The experiments were carried out on three beagle dogs and the variations of cardiac hemodynamics were induced by various dose of epinephrine. The phonocardiogram, electrocardiogram and blood pressures in left ventricle were synchronously acquired. We obtained 28 valid recordings in this study. An artificial neural network was trained with the selected feature to predict left ventricular blood pressure and this trained network made a good performance. The results showed that the absolute average error was 7.3 mm Hg even though the blood pressures had a large range of fluctuation. The average correlation coefficient between the predicted and the measured blood pressure was 0.92. These results showed that the method in this paper was helpful to monitor left ventricular hemodynamics non-invasively and continuously.
Objective To investigate the characteristics of blood pressure and coronary artery impairment in patients with essential hypertension (EH) combining coronary heart disease (CHD). Methods A total of 358 patients with EH combining CHD and other 144 patients with CHD were measured with ambulatory blood pressure monitoring (ABPM), and the parameters of ambulatory blood pressure were analyzed. All the patients underwent coronary angiography. The severity of coronary artery stenosis was evaluated in accordance with the number of impaired arteries. Results Compared to the patients with simplex CHD, those with EH combining CHD had much heavier artery stenosis and more diffuse lesions, with significant differences (χ2=6.03, P=0.019). The 24h systolic blood pressure (SBP), day SBP, night SBP, 24h pulse pressure (PP), day PP and night PP were higher in the patients with EH combining CHD compared to those of the patients with simplex CHD (The t values were 2.580, 2.045, 2.675, 2.037, 2.601, and 1.995, respectively, while the P values were 0.015, 0.037, 0.009, 0.041, 0.017, and 0.047, respectively). Conclusion Compared to the patients with simplex CHD, the patients with EH combining CHD suffer from much severe coronary artery impairment, so a good controlling of blood pressure is advisable to improve the coronary artery impairment for the patients with EH combining CHD.
In order to improve the accuracy of blood pressure measurement in wearable devices, this paper presents a method for detecting blood pressure based on multiple parameters of pulse wave. Based on regression analysis between blood pressure and the characteristic parameters of pulse wave, such as the pulse wave transit time (PWTT), cardiac output, coefficient of pulse wave, the average slope of the ascending branch, heart rate, etc. we established a model to calculate blood pressure. For overcoming the application deficiencies caused by measuring ECG in wearable device, such as replacing electrodes and ECG lead sets which are not convenient, we calculated the PWTT with heart sound as reference (PWTTPCG). We experimentally verified the detection of blood pressure based on PWTTPCG and based on multiple parameters of pulse wave. The experiment results showed that it was feasible to calculate the PWTT from PWTTPCG. The mean measurement error of the systolic and diastolic blood pressure calculated by the model based on multiple parameters of pulse wave is 1.62 mm Hg and 1.12 mm Hg, increased by 57% and 53% compared to those of the model based on simple parameter. This method has more measurement accuracy.
摘要:目的：研究成都地区中老年人群体重指数(BMI)与高血压患病率及血压水平的关系。方法:按照随机抽样的方法抽取样本,对711人(平均年龄为63.28±6.25岁;男性占57.8%)进行了相关调查,调查内容中包括身高、体重、血压及脉搏等。结果:成都地区中老年人群的超重及肥胖所占比重较大(约45%),按BMI分组(lt;18.5 kg/m2,18.5~23.9 kg/m2,24~27.9 kg/m2,≥28.0 kg/m2)的高血压患病率分别是31.6%,54.8%,64.4%,82.8%,差异有统计学意义。采用logistic回归分析发现在调整年龄、性别、腰围及尿酸等后,BMI对高血压的患病率有独立影响。在整个人群及女性病人中,血压随着BMI的升高而有升高的趋势,差异有统计学意义。结论:成都地区中老年人群超重及肥胖所占比重较大。BMI可以影响高血压的患病率及影响女性病人的血压水平,是高血压的独立危险因素。Abstract: Objective: To investigate the effects of body mass index on prevalence of hypertension and blood pressure in the elderly. MethodsA survey, including height, weight, blood pressure and pulse, was carried out in a general population of Chengdu. A total of 711 subjects (average age: 63.28±6.25 years; male: 57.8%) were recruited by random sampling method. Results：The proportion of overweight and obesity was about 45%. The hypertension prevalence rate was significantly positively correlated with BMI (Plt;0.01), and that was also seen in the level of SBP and DBP for the female (Plt;0.05). In logistic regression analysis adjusting for age, gender, waist, uric acid, the standardized OR for higher BMI (≥28.0 kg/m2) as a risk factor of hypertension was 5.140. Conclusion：The proportion of overweight and obesity was great in Chengdu area. BMI can affect the prevalence rate of hypertension and the level of blood pressure.
摘要:目的:研究老年患者动脉弹性功能与围术期血压变化的关系。方法:随机选择68例ASA分级Ⅰ－Ⅱ级行全麻手术的老年患者,根据检查所得动脉弹性的结果分为四组,分别是A组(C1、C2均正常),B组(C1异常,C2正常),C组(C1正常,C2异常),D组(C1、C2均异常)。测量其术前血压及全麻诱导8分钟后的血压水平。结果:〓动脉弹性功能不良的患者其术前MAP较高,且全麻诱导以后血压波动的比例较大。结论:高血压病的老年患者动脉弹性功能普遍降低;动脉弹性下降的老年病人全麻诱导后血压波动较大。Abstract: Objective:To investigate the relationship between the function of arterial elasticity and BP changes during perioperation in senile patients.Methods: 68 senile patients ASA class Ⅰor Ⅱ undergoing elective surgery under general anesthestia, were divided into four groups by evaluation of arterial elasticity (C1 was for large arterial elastic index and C2 for small. C1 and C2 were normal in group A, only C2 normal in group B, only C1 normal in group C, neither was normal in group D). Arterial blood pressure (BP) before operation and 8 min after induction were monitored and recorded. Results: Patients with dysfunction of arterial elasticity presented higher MAP during preoperation and significant BP changes after induction. Conclusion: Hypertension plays a key role in arterial elasticity.Arterial Blood Pressure of the senile patients with decreased arterial elasticity changes significantly after general anesthesia induction.
摘要:目的: 观察腰硬联合麻醉在前列腺电切术患者中的临床应用效果。 方法 : 76例经尿道前列腺电切术患者(78±7岁)随机均分为腰硬联合麻醉组(C组)及硬膜外组(E组)。C组以腰硬联合穿刺针于L34穿刺至蛛网膜下腔后,注入05%布比卡因2 mL,通过硬膜外穿刺针置入硬膜外导管;E组行L34间隙硬膜外穿刺置管。记录麻醉起效时间、麻醉效果、麻醉前及麻醉后5、15、30分钟时血压、心率。 结果 : 所有患者均穿刺顺利,麻醉起效时间C组为36±13 min, E组68±15 min;C组麻醉效果完善率为100%,E组为95%;麻醉后两组血压均下降(〖WTBX〗P lt;005),但降幅均未超过基础值的20%;两组麻醉前及麻醉后血压、心率均无显著性差异。 结论 :腰硬联合麻醉用于前列腺电切术具有起效快、麻醉效果佳的优点。Abstract: Objective: To investigate and compare the clinical efficacy and safety of combined spinalepidural(CSEA) and epidural(EA) anesthesia on elderly patients undergoing transurethral resection of the prostate(TURP). Methods : 76 patients(78±7 years) suffering TURP were divided into two group: group CSEA(38cases) and groupEA(38 cases). The dose of bupivacaine in spinal anesthesia is 10 mg. Blood pressure(BP), heart rate(HR) and anesthesia efficacy were observed before anesthesia, 5, 15 and 30min after anesthesia. Results : BP decreased after anesthesia in two groups than before anesthesia(〖WTBX〗P lt;005). The decreases of BP were less than 20% of basises. There were no significant differents of BP and HR between two groups before and after anesthesia. Conclusion :CSEA with bupivacaine 10 mg is safe and efficient in elderly undergoing TURP.
Clinical studies had demonstrated that slow breathing could lower blood pressure significantly. Based on this knowledge, a portable blood pressure depressor was designed in this study. The device used a miniature variable distance capacitive sensor to collect respiratory signal, an STM32 as the main control chip, a WT588D voice chip to generate voice and music and guide slow breathing, and a 3.5-inch color screen to display breathing state and provide guidance. For patients with difficulty in adapting themselves to the slow breathing training, an intelligent guiding breathing algorithm based on feedback regulation mechanism was proposed to train patients to breathe slowly. Ten volunteers with hypertension were recruited and then trained to breathe slowly, accumulating up to 100 times using this device. The results showed that breath rate of the volunteers decreased from 15.16±0.92 times per minute to 9.40±0.29 times per minute, and meanwhile, time length of breath rate less than 8 times per minute in the proportion of total treatment time increased from 0.079±0.017 to 0.392±0.019 as the training times increased. In a conclusion, the proposed blood pressure depressor worked effectively in guiding slow breathing training.
The prevalence of cardiovascular disease in our country is increasing, and it has been a big problem affecting the social and economic development. It has been demonstrated that early intervention of cardiovascular risk factors can effectively reduce cardiovascular disease-caused mortality. Therefore, extensive implementation of cardiovascular testing and risk factor screening in the general population is the key to the prevention and treatment of cardiovascular disease. However, the categories of devices available for quick cardiovascular testing are limited, and in particular, many existing devices suffer from various technical problems, such as complex operation, unclear working principle, or large inter-individual variability in measurement accuracy, which lead to an overall low popularity and reliability of cardiovascular testing. In this study, we introduce the non-invasive measurement mechanisms and relevant technical progresses for several typical cardiovascular indices (e.g., peripheral/central arterial blood pressure, and arterial stiffness), with emphasis on describing the applications of biomechanical modeling and simulation in mechanism verification, analysis of influential factors, and technical improvement/innovation.
Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.