ABSTRACT
BACKGROUND: Long-term conservative approaches are effective management options for asymptomatic uterine fibroids, but not for uterine myomas with excessive growth. In this investigation, a regression model was constructed to evaluate the clinical characteristics related to uterine fibroids' growth. METHODS: In this retrospective study, 19,840 patients with ultrasound-diagnosed uterine fibroids were identified from six centers between 2013 and 2019. In total, 739 patients were followed up for more than 1 year with B-ultrasound test results and clinical test results and had no acute events or surgical treatments. The endpoint was changed in the size of the uterine fibroids. Multivariate stepwise logistic regression analysis was used to identify predictors of uterine fibroid growth, and these were used to build a prediction model. The prediction model's discrimination, calibration, and clinical efficacy were assessed using the area under the curve (AUC)/index of concordance (C-index), calibration plot, decision curve analysis, and clinical impact curve. Internal validation was performed using bootstrapping validation. A linear regression model was constructed to predict uterine fibroids' growth rate without the occurrence of acute events. RESULTS: A total of 513 patients presented with significant growth of uterine fibroids, with an average follow-up time of 927 days, and 267 patients showed negative growth, with an average follow-up time of 960 days. Age, follicle-stimulating hormone (FSH), low-density lipoprotein (LDL), luteinizing hormone (LH), total cholesterol (TCHO), and neutrophil to lymphocyte ratio (NLR) were the main influential factors that predicted the uterine fibroid growth state, and these were used to develop a nomogram with predictive accuracy (AUC: 0.825). A linear regression prediction model was built based on the following factors: FSH, high-density lipoprotein (HDL), LH, triglyceride (TRIG), TCHO, and lymphocyte to monocyte ratio (LMR). The mean square error (MSE) was 0.32. CONCLUSIONS: This study directly measured the growth rate of uterine fibroids. A prediction model assessing the growth rate of asymptomatic uterine fibroids was established. This model is useful for the early detection of potentially rapidly growing uterine fibroids in patients.
ABSTRACT
With increasingly fierce competition for jobs, the pressures on people have risen in recent years, leading to lifestyle and diet disorders that result in significantly higher risks of cardiovascular disease. Hypertension is one of the common chronic cardiovascular diseases; however, mainstream blood pressure measurement devices are relatively heavy. When multiple measurements are required, the user experience and the measurement results may be unsatisfactory. In this paper, we describe the design of a signal collection module that collects pulse waves and electrocardiograph (ECG) signals. The collected signals are input into a signal processing module to filter the noise and amplify the useful physiological signals. Then, we use a wavelet transform to eliminate baseline drift noise and detect the feature points of the pulse waves and ECG signals. We propose the concept of detecting the wave shape associated with an instance, an approach that minimizes the impact of atypical pulse waves on blood pressure measurements. Finally, we propose an improved method for measuring blood pressure based on pulse wave velocity that improves the accuracy of blood pressure measurements by 58%. Moreover, the results meet the american medical instrument promotion association standards, which demonstrate the feasibility of our measurement system.
