An innovative approach for assessing coronary artery lesions: Fusion of wrist pulse and photoplethysmography using a multi-sensor pulse diagnostic device.

Coronary heart disease (CHD) is a leading cause of mortality globally and poses a significant threat to public health. Coronary angiography (CAG) is a gold standard for the clinical diagnosis of CHD, but its invasiveness restricts its widespread application. In this study, we utilized a pulse diagnostic device equipped with pressure and photoelectric sensors to synchronously and non-invasively capture wrist pressure pulse waves and fingertip photoplethysmography (FPPG) of patients undergoing CAG. The extracted features were utilized in constructing random forest-based models to assessing the severity of coronary artery lesions. Notably, Model 3, incorporating both wrist pulse and FPPG features, surpassed Model 1 (solely utilizing wrist pulse features) and Model 2 (solely utilizing FPPG features). Model3 achieved an Accuracy, Precision, Recall, and F1-score of 78.79%, 78.69%, 78.79%, and 78.70%, respectively. Compared to Model1 and Model2, Model 3 exhibited improvements by 4.55%, 5.25%, 4.55%, and 5.12%, and 6.06%, 6.58%, 6.06%, and 6.54% respectively. This fusion of wrist pulse and FPPG features in Model 3 highlights the advantages of multi-source information fusion for model optimization. Additionally, this research provides invaluable insights into the novel development of diagnostic devices imbued with TCM principles and their potential in managing cardiovascular diseases.

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis.

BACKGROUND: The timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' mortality. OBJECTIVE: To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD. METHODS: A total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were compared. RESULTS: The pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively. CONCLUSIONS: The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

Clinical Features for Severely and Critically Ill Patients with COVID-19 in Shandong: A Retrospective Cohort Study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel pathogen, has caused an outbreak of coronavirus disease 2019 (COVID-19) that has spread rapidly around the world. Determining the risk factors for death and the differences in clinical features between severely ill and critically ill patients with SARS-CoV-2 pneumonia has become increasingly important. AIM: This study was intended to provide insight into the difference between severely ill and critically ill patients with SARS-CoV-2 pneumonia. METHODS: In this retrospective, multicenter cohort study, we enrolled 62 seriously ill patients with SARS-CoV-2 pneumonia who had been diagnosed by March 12, 2020. Clinical data, laboratory indexes, chest images, and treatment strategies collected from routine medical records were compared between severely ill and critically ill patients. Univariate and multivariate logistic regression analyses were also conducted to identify the risk factors associated with the progression of patients with severe COVID-19. RESULTS: Of the 62 patients with severe or critical illness, including 7 who died, 30 (48%) patients had underlying diseases, of which the most common was cardiovascular disease (hypertension, 34%, and coronary heart disease, 5%). Compared to patients with severe disease, those with critical disease had distinctly higher white blood cell counts, procalcitonin levels, and D-dimer levels, and lower hemoglobin levels and lymphocyte counts. Multivariate regression showed that a lymphocyte count less than 109/L (odds ratio 20.92, 95% CI 1.76-248.18; p=0.02) at admission increased the risk of developing a critical illness. CONCLUSION: Based on multivariate regression analysis, a lower lymphocyte count (<109/L) on admission is the most critical independent factor that is closely associated with an increased risk of progression to critical illness. Age, underlying diseases, especially hypertension and coronary heart disease, elevated D-dimer, decreased hemoglobin, and SOFA score, and APACH score also need to be taken into account for predicting disease progression. Blood cell counts and procalcitonin levels for the later secondary bacterial infection have a certain reference values.

Identifying Coronary Artery Lesions by Feature Analysis of Radial Pulse Wave: A Case-Control Study.

BACKGROUND: Cardiovascular diseases have been always the most common cause of morbidity and mortality worldwide. Health monitoring of high-risk and suspected patients is essential. Currently, invasive coronary angiography is still the most direct and accurate method of determining the severity of coronary artery lesions, but it may not be the optimal clinical choice for suspected patients who had clinical symptoms of coronary heart disease (CHD) such as chest pain but no coronary artery lesion. Modern medical research indicates that radial pulse waves contain substantial pathophysiologic information about the cardiovascular and circulation systems; therefore, analysis of these waves could be a noninvasive technique for assessing cardiovascular disease. OBJECTIVE: The objective of this study was to analyze the radial pulse wave to construct models for assessing the extent of coronary artery lesions based on pulse features and investigate the latent value of noninvasive detection technology based on pulse wave in the evaluation of cardiovascular disease, so as to promote the development of wearable devices and mobile medicine. METHOD: This study included 529 patients suspected of CHD who had undergone coronary angiography. Patients were sorted into a control group with no lesions, a 1 or 2 lesion group, and a multiple (3 or more) lesion group as determined by coronary angiography. The linear time-domain features and the nonlinear multiscale entropy features of their radial pulse wave signals were compared, and these features were used to construct models for identifying the range of coronary artery lesions using the k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) machine learning algorithms. The average precision of these algorithms was then compared. RESULTS: (1) Compared with the control group, the group with 1 or 2 lesions had increases in their radial pulse wave time-domain features H2/H1, H3/H1, and W2 (P < 0.05), whereas the group with multiple lesions had decreases in MSE1, MSE2, MSE3, MSE4, and MSE5 (P < 0.05). (2) Compared with the 1 or 2 lesion group, the multiple lesion group had increases in T1/T (P < 0.05) and decreases in T and W1 (P < 0.05). (3) The RF model for identifying numbers of coronary artery lesions had a higher average precision than the models built with KNN or DT. Furthermore, average precision of the model was highest (80.98%) if both time-domain features and multiscale entropy features of radial pulse signals were used to construct the model. CONCLUSION: Pulse wave signal can identify the range of coronary artery lesions with acceptable accuracy; this result is promising valuable for assessing the severity of coronary artery lesions. The technique could be used to development of mobile medical treatments or remote home monitoring systems for patients suspected or those at high risk of coronary atherosclerotic heart disease.