Assessing the depression risk in the U.S. adults using nomogram

Abstract

Background Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults. Methods This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017–2012). In particular, NHANES (2007–2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011–2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms. Results The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69–0.73) and 0.85 (95% CI, 0.83–0.86), and an externally validated C index of 0.71 (95% CI, 0.68–0.74) and 0.83 (95% CI, 0.81–0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10–17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions. Conclusions This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.

Nomogram reliability for predicting potential risk in postgraduate medical students with anxiety symptoms

“…When it came to forecasting the possible risk of anxiety symptoms among postgraduate medical students, the Nomogram showed a strong Concordance index of 0.787. It can be seen that the model has a good discrimination coefficient and accuracy ( Zhang et al., 2022 ). The Correction curve was close to the standard curve, and the predictive value was basically consistent with the measured value, as shown in Figure 2 .…”

“…This forethought may not seem impressive. But it helps individuals relieve stress and make the optimal treatment decisions when they need to, which also have positive impact on preventing other types of mental illness ( Zhang et al., 2022 ). For medical school administrators, the Nomogram offers new ideas on how to prevent anxiety symptoms in postgraduate medical students.…”

A nomogram and online calculator for predicting depression risk in obese Americans

“…NHANES is a national database that assesses the health and nutritional status of communities living in the United States [ 5 ]. However, previous models primarily targeted patients with specific chronic conditions, such as myocardial infarction and chronic obstructive pulmonary disease (COPD), or focused on healthy adults [ [6] , [7] , [8] ]. This suggests that these models may not be applied to obese patients.…”

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

“…Patients with other cardiovascular diseases have higher levels of depression than the general population [ 11 ]. Sociodemographic characteristics such as gender, race, income, education, age and marital status have been reported to be independently associated with depression [ 12 , 13 ]. Some studies have also found a strong relationship between sleep problems and depression [ 14 ].…”