# Predicting REadmission after Stroke Study (PRESS)

> **NIH NIH R01** · KAISER FOUNDATION RESEARCH INSTITUTE · 2020 · $595,569

## Abstract

PROJECT SUMMARY
 Stroke has a massive impact on patients, their caregivers, and the health system. Approximately 795,000
stroke cases occur each in the US. It is the fifth leading cause of death and the leading cause of long-term
disability, with nearly 7 million stroke survivors in the US. The total annual costs of stroke are projected to
increase $241 billion by 2030, more than twice the cost in 2012 ($105 billion). Given the devastating effects of
a stroke, it is all the more tragic that a substantial proportion of stroke survivors will be readmitted to the
hospital: current 30-day all-cause readmission rates range from 6.5-24.3% and these rates increase to 30.0-
62.2% within one year. Moreover, mortality following the initial hospitalization is also substantial, 5-7% case
fatality, 13-15% at 30 days, and 25-30% at 1 year.
 Despite stroke’s importance, remarkably little quantitative data are available on what patient- and hospital-
level factors play a determinant role in readmission and post-discharge mortality in stroke patients. For
example, a systematic review of predictors of hospital readmission after stroke yielded no risk-standardized
models for comparing hospital readmission performance or predicting readmission risk after stroke.
 We propose to enhance the care of stroke patients and to provide guidance for clinical, basic science, and
health policy researchers by a careful analysis of the relationship between stroke outcomes and patient- and
hospital-level predictors. Our long term goal is to develop comprehensive risk stratification tools that could
inform the design of randomized trials, individual patient standards of care, and public reporting. To advance
this goal, we have the following Specific Aims.
1. We will characterize patient- and hospital-level predictors for readmission and post-discharge
 mortality among stroke patients from 21 hospitals
2. We will develop and prospectively validate predictive models for 30-day readmission and mortality
 following hospitalization for stroke

## Key facts

- **NIH application ID:** 9922366
- **Project number:** 5R01NS099223-04
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** MAI N NGUYEN-HUYNH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $595,569
- **Award type:** 5
- **Project period:** 2017-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9922366

## Citation

> US National Institutes of Health, RePORTER application 9922366, Predicting REadmission after Stroke Study (PRESS) (5R01NS099223-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9922366. Licensed CC0.

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