# Identifying patient subgroups and processes of care that cause outcome differences following ICU vs. ward triage among patients with acute respiratory failure and sepsis

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $718,366

## Abstract

PROJECT SUMMARY
Decisions to admit patients with acute respiratory failure (ARF) and sepsis (the most common and lethal cause
of the acute respiratory distress syndrome) to intensive care units (ICUs) are highly variable across the US.
And, yet, these triage decisions have a substantial impact on patient outcomes. In our prior work, we used
detailed electronic health record (EHR) data from 9.2 million hospitalizations and found that decisions to admit
ARF patients to wards were associated with a 3.8% absolute increase in mortality. In contrast, choices to admit
sepsis patients to ICUs resulted in considerably longer length of stay and a 5.1% absolute increase in death.
The nationwide impact of such discretionary triage would be exponentially greater. Our findings highlight
tremendous opportunities to improve ARF and sepsis outcomes by identifying the patient subgroups and
processes of care that most strongly contribute to the benefits and harms of ICU- versus ward-based care.
This application proposes to update our ARF and sepsis cohort such that it includes all admissions from 2013
through 2022 across 29 hospitals in the Kaiser Permanente Northern California and University of Pennsylvania
health systems, and incorporate more than 100 more data fields per patient. This curation of highly granular
EHR data will enable us to identify the: (1) distinct patient subgroups and phenotypes among those meeting
the syndromic criteria of `ARF' and `sepsis;' and the (2) processes of care and (3) inpatient complications that
causally explain the observed associations of ICU vs. ward triage with patient outcomes. Our multidisciplinary
team will apply diverse expertise in instrumental variable regression, mediation analyses, machine learning,
complex EHR data, and probabilistic phenotyping to complete three aims that promote our long-term goal of
improving care, and hence outcomes, for patients with ARF and sepsis regardless of where they are treated.
Several methodological innovations will enable us to achieve these goals, and, in turn, to not only surmount
key limitations of prior studies that sought to determine which acutely ill patients benefit from ICU admission,
but identify the mechanisms underlying such triage effects. These data will also allow us to quantify the impact
of COVID-19 on ICU and ward triage patterns, care processes, and outcomes among ARF and sepsis patients,
thereby modernizing our results and enabling their applicability to pandemic eras.
Completing the aims of this study will improve public health by identifying ways in which emergency
departments, ICUs, and wards can improve outcomes for the more than 4 million Americans hospitalized each
year with ARF and/or sepsis. Such results will enable development and testing of personalized triage
algorithms, and guide optimal care for patients without always requiring ICU admission, thereby improving
patient outcomes, reducing health care costs, and preserving ICU capacity for patients who tr...

## Key facts

- **NIH application ID:** 10918227
- **Project number:** 5R01HL166269-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** George L Anesi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $718,366
- **Award type:** 5
- **Project period:** 2023-09-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10918227

## Citation

> US National Institutes of Health, RePORTER application 10918227, Identifying patient subgroups and processes of care that cause outcome differences following ICU vs. ward triage among patients with acute respiratory failure and sepsis (5R01HL166269-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10918227. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*