Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease.

BACKGROUND: To develop a reliable model to predict rapid kidney function decline (RKFD) among population at risk of cardiovascular disease. METHODS: In this retrospective study, key monitoring residents including the elderly, and patients with hypertension or diabetes of China National Basic Public Health Service who underwent community annual physical examinations from January 2015 to December 2020 were included. Healthy records were extracted from regional chronic disease management platform. RKFD was defined as the reduction of estimated glomerular filtration rate (eGFR) ≥ 40% during follow-up period. The entire cohort were randomly assigned to a development cohort and a validation cohort in a 2:1 ratio. Cox regression analysis was used to identify the independent predictors. A nomogram was established based on the development cohort. The concordance index (C-index) and calibration plots were calculated. Decision curve analysis was applied to evaluate the clinical utility. RESULTS: A total of 8455 subjects were included. During the median follow-up period of 3.72 years, the incidence of RKFD was 11.96% (n = 1011), 11.98% (n = 676) and 11.92% (n = 335) in the entire cohort, development cohort and validation cohort, respectively. Age, eGFR, hemoglobin, systolic blood pressure, and diabetes were identified as predictors for RKFD. Good discriminating performance was observed in both the development (C-index, 0.73) and the validation (C-index, 0.71) cohorts, and the AUCs for predicting 5-years RKFD was 0.763 and 0.740 in the development and the validation cohort, respectively. Decision curve analysis further confirmed the clinical utility of the nomogram. CONCLUSIONS: Our nomogram based on five readily accessible variables (age, eGFR, hemoglobin, systolic blood pressure, and diabetes) is a useful tool to identify high risk patients for RKFD among population at risk of cardiovascular disease in primary care. Whereas, further external validations are needed before clinical generalization.

The Association Between the Frequency of Annual Health Checks Participation and the Control of Cardiovascular Risk Factors.

Background: General health checks can help in controlling cardiovascular risk factors. However, few studies have investigated whether regular participation in annual health checks could further improve the control of cardiovascular risk factors compared with intermittent participation. Therefore, our study aimed to explore the association between the frequency of annual health check participation and the control of cardiovascular risk factors. Methods: Residents aged ≥ 65 years or having chronic diseases (hypertension or diabetes) from 37 communities of Guangzhou, Guangdong, who participated in the Basic Public Health Service project between January 2015 and December 2019, were enrolled and divided into 3 groups ("Sometimes," "Usually," and "Always") according to their frequencies of annual health check participation. Multivariable linear regression models were performed to assess the association between the frequency of annual health check participation and the control of cardiovascular risk factors. A subgroup analysis stratified by gender was also conducted. Results: In total, 9,102 participants were finally included. Significant differences were identified between groups in systolic blood pressure (SBP), diastolic blood pressure (DBP), weight, fasting glucose, total cholesterol, high-density lipoprotein cholesterol, and serum creatinine. After fully adjusting for confounding factors, residents who always participated in the annual health check tended to have lower SBP (ß = -4.36, 95% CI: -5.46; -3.26, p < 0.001), fasting glucose (ß = -0.27, 95% CI: -0.38; -0.15, p < 0.001), and total cholesterol (ß = -0.19, 95% CI: -0.26; -0.13, p < 0.001), compared with those who attended sometimes. Furthermore, gender did not alter these associations. Conclusion: A higher frequency of annual health check participation was associated with lower SBP, fasting glucose, and total cholesterol.

Impact of the caFFR-Guided Functional SYNTAX Score on Ventricular Tachycardia/Fibrillation Development in Patients With Acute Myocardial Infarction.

Aims: To explore the relationship between the severity of coronary artery disease (CAD) and the occurrence of ventricular tachycardia/ventricular fibrillation (VT/VF) in patients with acute myocardial infarction (AMI). Methods: We retrospectively enrolled 705 patients with AMI, who were hospitalized and underwent percutaneous coronary intervention (PCI), in Nanfang Hospital from July 2017 to July 2020. Logistic regression analysis and backward stepwise approach were taken to select the correlation factors. The left and the receiver operating characteristic curves (ROC) analysis were plotted to observe the discriminative power of the SYNTAX score (SS)/caFFR-guided functional SS (FSScaFFR) on the incident VT/VF. Results: About 58 (8.2%) patients experienced life-threatening VT/VF. The FSScaFFR (OR: 1.155; 95% CI: 1.047 to 1.273; p = 0.004) was an independent predictor of VT/VF after AMI. The ROC analysis showed that the discriminative power of FSScaFFR on the incident VT/VF was significantly better than SS (0.759 vs.0.695, p < 0.0001). Patients with VT/VF were categorized into 2 groups according to the interval between the onset of AMI and the VT/VF. The logistic regression analysis revealed that FSScaFFR was a significant independent correlation of early- and late-VT/VF. Conclusion: The incident VT/VF in patients with AMI is closely associated with the severity of CAD evaluated by SS and FSScaFFR. Compared to SS, FSScaFFR has a higher correlation with VT/VF, and FSScaFFR was demonstrated to be an independent correlation factor of incident VT/VF after AMI.

Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study.

AIMS: To develop a nomogram for incident chronic kidney disease (CKD) risk evaluation among community residents with high cardiovascular disease (CVD) risk. METHODS: In this retrospective cohort study, 5730 non-CKD residents with high CVD risk participating the National Basic Public Health Service between January 2015 and December 2020 in Guangzhou were included. Endpoint was incident CKD defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2 during the follow-up period. The entire cohorts were randomly (2:1) assigned to a development cohort and a validation cohort. Predictors of incident CKD were selected by multivariable Cox regression and stepwise approach. A nomogram based on these predictors was developed and evaluated with concordance index (C-index) and area under curve (AUC). RESULTS: During the median follow-up period of 4.22 years, the incidence of CKD was 19.09% (n=1094) in the entire cohort, 19.03% (727 patients) in the development cohort and 19.21% (367 patients) in the validation cohort. Age, body mass index, eGFR 60-89 mL/min/1.73 m2, diabetes and hypertension were selected as predictors. The nomogram demonstrated a good discriminative power with C-index of 0.778 and 0.785 in the development and validation cohort. The 3-year, 4-year and 5-year AUCs were 0.817, 0.814 and 0.834 in the development cohort, and 0.830, 0.847 and 0.839 in the validation cohort. CONCLUSION: Our nomogram based on five readily available predictors is a reliable tool to identify high-CVD risk patients at risk of incident CKD. This prediction model may help improving the healthcare strategies in primary care.

[Identification methods of crop and weeds based on Vis/NIR spectroscopy and RBF-NN model].

The automated recognition of crop and weed by using Vis/NIR spectral in field is one of hottest research branches of agriculture engineering. If the recognition is efficient and effective, then the variate operations of herbicide or fertilizer spraying in field could be realized. Many researches have pointed out that the reflectance rate of green plant leaves could be used to identify the varieties. As the colors and surface textures of crop and weed were change in different living phases, these changes may exert great influence on the reflectance spectral of plant leaves. Vis/NIR spectra of three weeds and one crop in two different terms were recorded by spectral meter ASD FieldSpec Pro FR. Its wave band is from 325 to 1 075 nm. The scan time was 270 ms. The scanning times of per sample was set to 30 times. Firstly, 23 days after the planting of soybean, some soybean leaves and weeds leave were picked from the field, and brought to lab to record spectral. The lighting condition was controlled by an artificial halogen bulb. Secondly, on the 45th day, the same experiment was done. The three weeds were goose grass, alligator alternanthera and emarginate amaranth. The crop was soybean seedling. Totally 378 samples were drawn for two terms. As one original reflectance spectrum contains 651 float numbers, the total data volume was huge. Using wavelet transform to compress data volume and extract characteristic spectral data was tried. The result was 114 float numbers per sample. Among them, 250 samples from two terms were used as input data to build artificial neural network model, and those left were used to check the validation. Radial basis function neural network model is widely used in pattern recognition problems. It is a nonlinear and self adaptive parallel. By assigning a 1 by 4 vector to each spectral samples, the source data could be used to build an RBF-NN model. All the samples were assigned these standard output data. Then, the left 128 samples were used to check the performance of the model. The result is that only 3 samples from the second term of goose grass were wrongly classified as alligator alternanthera, which showed that RBF neural network have strong ability to differentiate spectra of species of plant, and that there was no massive difference of NIR spectra of one plant in different life periods. This demonstrated that the NIR spectra could be used to identify crop from weed with no need to care about the living stages of these plants.

[Research on discrimination method of orange juice variety based on spectroscopy technology].

In order to quickly analyze varieties of orange juice with near infrared spectra, firstly, principal component analysis (PCA) was used to analysze the clustering of orange juice samples, and the characteristic differentia of four orange juice varieties was obtained through qualitative analysis. Then plentiful spectral data were compressed by wavelet transform (WT) and the model was built with radial basis function neural network (RBF-NN), which offered a quantitative analysis of orange juice varieties discrimination. The model regarded the compressed data as the input of RBF-NN input vectors and built a RBF-NN model. Two hundred forty samples from four varieties were selected randomly to build the training model, which in turn was used to predict the varieties of 60 unknown samples. The discrimination rate of 100% was achieved by WT-RBFNN method. It was indicated that wavelet transform combined with RBF-NN is an available method for variety discrimination based on the near infrared reflectance spectroscopy technology. It offered a new approach to the fast discrimination of varieties of orange juice.