Development and validation of a depression risk prediction nomogram for US Adults with hypertension, based on NHANES 2007–2018

Abstract

Depression is of increasing concern as its prevalence increases. Our study’s objective was to create and evaluate a nomogram to predict the likelihood that hypertension patients may experience depression. 13293 people with hypertension who were under 20 years old were chosen from the National Health and Nutrition Examination Survey (NHANES) database between 2007 and 2018 for this study. The training and validation sets were split up into the dataset at random in a 7:3 ratio. To find independent predictors, univariate and multivariate logistic regression were employed on the training set. Using information from the validation set, nomogram was subsequently created and internally validated. The effectiveness of the nomogram is assessed using calibration curve and receiver operator characteristic (ROC) curve. Combining univariate logistic regression analysis and multifactor logistic regression analysis, the results showed that age, sex, race, marital, education level, sleep time on workdays, poverty to income ratio, smoking, alcohol consumption, sedentary time and heart failure status were risk factors for hypertensive patients suffering from depression and were included in the nomogram model, and ROC analysis showed that the AUC of the training set was 0.757 (0.797–0.586), with a sensitivity of 0.586; the AUC of the test set was 0.724 (0.712–0.626), with a sensitivity of 0.626, which was a good fit. Decision curve analysis further confirms the value of nomogram for clinical application. In the civilian non-institutionalized population of the United States, our study suggests a nomogram that can aid in predicting the likelihood of depression in hypertension patients and aiding in the selection of the most effective treatments.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

“…Studies have demonstrated that nomogram enable accurate compared to traditional scoring systems (Wang et al, 2021). It is now widely used for risk Frontiers in Pharmacology frontiersin.org prediction of many diseases (Li et al, 2021;Tan et al, 2022;Wang et al, 2023). Therefore, the creation of mortality risk nomogram based on the information about ATN patients can inform existing critical care assessment programs.…”

Research on the psychological status of medical staff during the COVID-19 epidemic in China: A longitudinal study

“…Previous research has highlighted the modifiable association between the severity of depression and triglyceride levels, indicating a positive relationship with the high Framingham Risk Score. [ 26 , 27 ]…”

Association between red cell distribution width and hypertension: Results from NHANES 1999–2018

“…In our study, several potential factors that could influence the association between RDW and hypertension were carefully selected. These covariates include: Age (years), Gender (male/female),Race (Mexican American/ Other Race /Non-Hispanic White/Non-Hispanic Black),Body Mass Index (BMI, kg/m²),Waist circumference, Education level(≤11th Grade, High School, College, College above), Family poverty income ratio (PIR),Smoking status(Yes/No), Alcohol use; Presence of chronic conditions: Chronic congestive heart failure, Coronary heart disease(Angina pectoris, Heart attack), Stroke, Weak/failing kidneys, Diabetes, Chronic bronchitis, Emphysema, Thyroid disease; laboratory parameters: White blood cell count (1000 cells/uL), Lymphocyte number (1000 cells/uL), Red blood cell count (million cells/uL), Hemoglobin (g/dL), Hematocrit (%), Red cell distribution width (%), Platelet count (1000 cells/uL), Mean platelet volume (fL), Blood Urea Nitrogen (mmol/L), Total Calcium (mmol/L), Serum Cholesterol (mmol/L), Creatinine (umol/L), Glucose (mmol/L), Triglycerides (mmol/L), Uric acid (umol/L), Sodium (mmol/L), Potassium (mmol/L), LDL-cholesterol (mg/dL), HDL-cholesterol (mg/dL) [ 18 , 19 ]. These covariates were chosen to comprehensively account for potential confounding factors and ensure a robust analysis of the association between RDW and hypertension in our study.…”