# A comprehensive prognostic model for older adults discharged to skilled nursing facilities. > **NIH NIH R03** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $161,500 ## Abstract PROJECT SUMMARY/ABSTRACT An estimated 20% of hospitalized older adults were discharged to a skilled nursing facility (SNF) in 2019 with many subsequently experiencing potentially adverse outcomes, including hospital re-admission, entering long- term care (LTC) rather than returning to the community, and death within 6 months. During this critical transition period following a hospitalization, clinicians, patients, and families often have discordant expectations about the trajectory of the SNF stay which can lead to dissatisfaction with care and disagreements over care plans. For SNF clinicians managing these patients, decision making around management of acute and chronic conditions, treatment preferences, and advance care planning is hindered by a lack of accurate prognostic information. Providing individualized risk estimates for a variety of outcomes following SNF admission can help frame these important discussions and facilitate shared decision making. Given that there are no widely used prognostic tools for older adults specifically discharged to a SNF, the objective of this study is to develop an easy-to-use and parsimonious prediction model that jointly models multiple outcomes. A 20% sample of community-dwelling Medicare beneficiaries aged 65 years and older discharged from a hospital to a SNF will be used to investigate two specific aims: (1) develop and internally validate a Day 1 prognostic model to be used on day 1 of SNF admission that provides risk estimates of multiple outcomes including hospital re- admission, discharge home without readmission, prolonged SNF stay >100 days (suggesting transition to LTC), and 6-month mortality and (2) develop an Updated model which provides refined estimates using detailed information from the Minimum Data Set (MDS) assessment that may not be readily available or widely collected by clinicians on day 1 of SNF admission. The result of these aims will be 2 easy-to-use parsimonious models that can provide accurate and well calibrated estimates of outcomes following a SNF admission. Significance and Innovation: Results from the proposed research project will directly inform clinical practice by allowing SNF clinicians to input specific patient characteristics into a web calculator to obtain risk estimates for multiple outcomes that can frame conversations with patients and families around clinical management decisions and future planning. This project is innovative because it will be the first SNF outcome prediction model to predict multiple clinically relevant outcomes simultaneously using a minimal number of variables that can be easily implemented in clinical practice. Future directions of this work will involve investigating more advanced modeling techniques, such as dynamic predictions and multi-state modeling, and ultimately explore how providing this prognostic information can improve satisfaction with care and other patient-centered outcomes in a randomized clinical trial. ## Key facts - **NIH application ID:** 10723510 - **Project number:** 1R03AG082859-01 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO - **Principal Investigator:** William James Deardorff - **Activity code:** R03 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $161,500 - **Award type:** 1 - **Project period:** 2023-09-01 → 2025-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10723510 ## Citation > US National Institutes of Health, RePORTER application 10723510, A comprehensive prognostic model for older adults discharged to skilled nursing facilities. (1R03AG082859-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10723510. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*