Suemoto Index

  • Population: Community dwelling adults aged 60 and older
  • Outcome: All cause 10 year mortality
  • Scroll to the bottom for more detailed information

Risk Calculator
1. How old is your patient?
2. What is the sex of your patient?
3. Does your patient have diabetes?
4. Does your patient have heart disease?
5. Does your patient have lung disease?
6. Does your patient have cancer?
7. What is your patient's smoking status?
8. Does your patient use alcohol?
9. What is your patient's body mas index (BMI)?
10. Is your patient engaging in physical activity once or more per week?
11. Because of health problems, does your patient have any difficulty with bathing or showering?
12. Because of health problems, does your patient have any difficulty walking several blocks?
13. Did your patient report correctly today's date (day/month/year)?
14. What is your patient's self-reported health?
Your best guess of 10 year all-cause mortality risk

  • This 10 year-mortality prediction model was developed and validated using data from 5 longitudinal studies of community-dwelling adults: ELSA (English Longitudinal Study of Aging), HRS (Health and Retirement Study), MHAS (Mexican Health and Aging Study), SABE-Sao Paulo (The Health, Well-being and Aging), and SHARE (Survey on Health, Ageing and Retirement in Europe)
  • The model was developed using an individual participant data meta-analysis in 23,615 participants from 16 countries (mean age 70 years old, 46% male, 51% white, 24% 10-year mortality). Model validation was performed in 11,752 participants.
  • Discrimination: The mortality prediction model sorts participants who died from those who lived correctly 76% if the time (Harrell's C).
  • Calibration: The model had good calibration across all risk levels with less than 7% difference between estimated and observed mortality rates.
  • Citation: Suemoto CK, Ueda P, Beltrán-Sánchez, Lebrão ML, Duarte YA, Wong R, Danaei G. Development and Validation of a 10-Year Mortality Prediction Model: Meta-Analysis of Individual Participant Data From Five Cohorts of Older Adults in Developed and Developing Countries. J Gerontol A Biol Sci Med Sci. 2016 Aug 13. pii: glw166. [Epub ahead of print]

DISCLAIMER

The information provided on ePrognosis is designed to complement, not replace, the relationship between a patient and his/her own medical providers. ePrognosis was created with the support of the Division of Geriatrics at the University of California San Francisco. However, its content is strictly the work of its authors and has no affiliation with any organization or institution. This web site does not accept advertisements. If you reproduce the material on the website please cite appropriately. For feedback and questions regarding the site please email Sei Lee, MD (sei.lee@ucsf.edu), Alex Smith, MD (aksmith@ucsf.edu) or Eric Widera, MD (eric.widera@ucsf.edu).