A comparative study of antihypertensive drugs prediction models for the elderly based on machine learning algorithms.

Background: Globally, blood pressure management strategies were ineffective, and a low percentage of patients receiving hypertension treatment had their blood pressure controlled. In this study, we aimed to build a medication prediction model by correlating patient attributes with medications to help physicians quickly and rationally match appropriate medications. Methods: We collected clinical data from elderly hypertensive patients during hospitalization and combined statistical methods and machine learning (ML) algorithms to filter out typical indicators. We constructed five ML models to evaluate all datasets using 5-fold cross-validation. Include random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models. And the performance of the models was evaluated using the micro-F1 score. Results: Our experiments showed that by statistical methods and ML algorithms for feature selection, we finally selected Age, SBP, DBP, Lymph, RBC, HCT, MCHC, PLT, AST, TBIL, Cr, UA, Urea, K, Na, Ga, TP, GLU, TC, TG, γ-GT, Gender, HTN CAD, and RI as feature metrics of the models. LightGBM had the best prediction performance with the micro-F1 of 78.45%, which was higher than the other four models. Conclusion: LightGBM model has good results in predicting antihypertensive medication regimens, and the model can be beneficial in improving the personalization of hypertension treatment.

A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study.

Background: Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population. Materials and methods: We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (n = 880) and an external validation set (n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively. Results: The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722-0.803) and 0.679 (95% CI = 0.572-0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/). Conclusion: Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.

Establishment and Validation of a Non-invasive Diagnostic Nomogram to Identify Heart Failure in Patients With Coronary Heart Disease.

Background: Heart failure (HF) is an end-stage manifestation of and cause of death in coronary heart disease (CHD). The objective of this study was to establish and validate a non-invasive diagnostic nomogram to identify HF in patients with CHD. Methods: We retrospectively analyzed the clinical data of 44,772 CHD patients from five tertiary hospitals. Univariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify independent factors. A nomogram based on the multivariate logistic regression model was constructed using these independent factors. The concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram. Results: The predictive factors in the multivariate model included hypertension, age, and the total bilirubin, uric acid, urea nitrogen, triglyceride, and total cholesterol levels. The area under the curve (AUC) values of the nomogram in the training set, internal validation set, external validation set1, and external validation set2 were 0.720 (95% CI: 0.712-0.727), 0.723 (95% CI: 0.712-0.735), 0.692 (95% CI: 0.674-0.710), and 0.655 (95% CI: 0.634-0.677), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. Conclusion: The developed predictive model combines the clinical and laboratory factors of patients with CHD and is useful in individualized prediction of HF probability for clinical decision-making during treatment and management.

Middle-aged and elderly users' continuous usage intention of health maintenance-oriented WeChat official accounts: empirical study based on a hybrid model in China.

BACKGROUND: Although middle-aged and elderly users are the main group targeted by health maintenance-oriented WeChat official accounts (HM-WOAs), few studies have explored the relationship of these accounts and their users. Exploring the factors that influence the continuous adoption of WOAs is helpful to strengthen the health education of middle-aged and elderly individuals. OBJECTIVE: We developed a new theoretical model and explored the factors that influence middle-aged and elderly individuals' continuous usage intention for HM-WOA. Performance expectancy mediated the effects of the model in explaining continuous usage intention and introduced health literacy into the model. METHODS: We established a hybrid theoretical model on the basis of the unified theory of acceptance and use of technology 2 model (UTAUT2), the health belief model (BHM), protection motivation theory (PMT), and health literacy. We collected valid responses from 396 middle-aged and elderly users aged ≥ 45 years in China. To verify our hypotheses, we analyzed the data using structural equation modeling. RESULTS: Performance expectancy (ß = 0.383, P < 0.001), hedonic motivation (ß = 0.502, P < 0.001), social influence (ß = 0.134, P = 0.049), and threat appraisal (ß = 0.136, P < 0.001) positively influenced middle-aged and elderly users' continuous usage intention. Perceived health threat (ß = - 0.065, P = 0.053) did not have a significant effect on continuous usage intention. Both threat appraisal (ß = 0.579, P < 0.001) and health literacy (ß = 0.579, P < 0.001) positively affected performance expectancy. Threat appraisal indirectly affected continuous usage intention through performance expectancy mediation. CONCLUSIONS: Our new theoretical model is useful for understanding middle-aged and elderly users' continuous usage intention for HM-WOA. Performance expectancy plays a mediation role between threat appraisal and continuous usage intention, and health literacy positively affects performance expectancy.

Establishment and Validation of a Non-Invasive Diagnostic Nomogram to Identify Spontaneous Bacterial Peritonitis in Patients With Decompensated Cirrhosis.

BACKGROUND: Spontaneous bacterial peritonitis (SBP) is a common and life-threatening infection in patients with decompensated cirrhosis (DC), and it is accompanied with high mortality and morbidity. However, early diagnosis of spontaneous bacterial peritonitis (SBP) is not possible because of the lack of typical symptoms or the low patient compliance and positivity rate of the ascites puncture test. We aimed to establish and validate a non-invasive diagnostic nomogram to identify SBP in patients with DC. METHOD: Data were collected from 4,607 patients with DC from July 2015 to December 2019 in two tertiary hospitals in Chongqing, China (A and B). Patients with DC were divided into the SBP group (995 cases) and the non-SBP group (3,612 cases) depending on whether the patients had SBP during hospitalization. About 70% (2,685 cases) of patients in hospital A were randomly selected as the traindata, and the remaining 30% (1,152 cases) were used as the internal validation set. Patients in hospital B (770 cases) were used as the external validation set. The univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to screen variables, and logistic regression was used to determine independent predictors to construct a nomogram to identify patients with SBP. Area under curve (AUC), calibration curve, and dynamic component analysis (DCA) were carried out to determine the effectiveness of the nomogram. RESULT: The nomogram was composed of seven variables, namely, mean red blood cell hemoglobin concentration (odds ratio [OR] = 1.010, 95% CI: 1.004-1.016), prothrombin time (OR = 1.038, 95% CI: 1.015-1.063), lymphocyte percentage (OR = 0.955, 95% CI: 0.943-0.967), prealbumin (OR = 0.990, 95% CI: 0.987-0.993), total bilirubin (OR = 1.003 95% CI: 1.002-1.004), abnormal C-reactive protein (CRP) level (OR = 1.395, 95% CI: 1.107-1.755), and abnormal procalcitonin levels (OR = 1.975 95% CI: 1.522-2.556). Good discrimination of the model was observed in the internal and external validation sets (AUC = 0.800 and 0.745, respectively). The calibration curve result indicated that the nomogram was well-calibrated. The DCA curve of the nomogram presented good clinical application ability. CONCLUSION: This study identified the independent risk factors of SBP in patients with DC and used them to construct a nomogram, which may provide clinical reference information for the diagnosis of SBP in patients with DC.