Background: A simple clinical model that can predict all-cause mortality in the middle-aged and older adults in general population based on demographics and physical measurement indicators. The aim of this study was to develop a simple nomogram prediction model for all-cause mortality in middle-aged and elderly general population based on demographics and physical measurement indicators. Methods: This was a prospective cohort study. We used data from the 1999–2006 National Health and Nutrition Examination Survey (NHANES), which included adults aged ≥40 years with mortality status updated through 31 December 2015. Cox proportional hazards regression, nomogram and least absolute shrinkage and selection operator (LASSO) binomial regression model were performed to evaluate the prediction model in the derivation and validation cohort. Results: A total of 13,026 participants (6,414 men, mean age was 61.59±13.80 years) were included, of which 6,671 (3,263 men) and 6,355 (3,151 men) were included in the derivation cohort and validation cohort, respectively. During an average follow-up period of 129.23±9.62 months, 4,321 died. We developed a 9-item nomogram mode included age, gender, smoking, alcohol intake, diabetes, hypertension, marriage status, education and poverty to income ratio (PIR). The area under the curve (AUC) was 0.842 and had good calibration. Internal validation showed good discrimination of the nomogram model with AUC of 0.849 and good calibration. Application of the LASSO regression model in the validation cohort also revealed good discrimination (AUC =0.854) and good calibration. A time-dependent and optimism-corrected AUC value for the model showed no significant relationship with the change of follow-up time. Conclusions: A simple nomogram model, including age, gender, smoking, alcohol intake, diabetes, hypertension, marriage, education and PIR, could predict all-cause mortality well in middle-aged and elderly general population.
- All-cause mortality
- General population
ASJC Scopus subject areas
- Advanced and Specialised Nursing
- Anesthesiology and Pain Medicine