# Predicting Patient Instability Noninvasively for Nursing Care – Three (PPINNC-3)

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $798,419

## Abstract

PREDICTING PATIENT INSTABILITY NONINVASIVELY FOR NURSING CARE (PPINNC-3)
Project Summary
Timely recognition and forecasting of cardiorespiratory instability (CRI) in hospitalized patients in step-down
units (SDU) has clear implications for strategies to reduce preventable morbidity and mortality. Continuous
noninvasive vital signs (VS) monitoring is widely used to facilitate nurse detection of actionable events
requiring a diagnostic or therapeutic response, yet failure-to-rescue rates in US hospitals, defined as death due
to complications, remain high. Traditional VS monitoring alarms are largely based on numeric threshold
exceedance, translating to very low true positive rates and adversely leading to alarm fatigue and reactive
nursing care. We and others have demonstrated that decompensation evolves over time and featurization,
trending, and phenotyping of multi-channel VS time series for building relevant models for forecasting CRI is
feasible. Over the past two funding periods, we have built the largest multi-site database of EHR-linked, high
fidelity VS monitoring data from SDU patients known to us. Using this multi-expert, multi-tier ground truth
annotated database we have begun to build clinically relevant Machine Learning models to discriminate
artifactual anomaly from real CRI with high accuracy, as well as to classify mild vs. serious CRI and forecast
cases that require escalation of care or up transfer to intensive care. We now aim to move these extensive
efforts to fruition and refine and build these models in the workflow for prospective validation and clinical
deployment. The specific aims of this renewal application are: 1) build and deploy a real-time data streaming
architecture at bedside for an intelligent alerting system, including the iterative design and usability testing of a
clinician-facing graphical user interface; 2) build and externally validate a multi-layered alerting system
forecasting CRI; and 3) perform a prospective validation of the intelligent alerting system, including silent
deployment and evaluation at SDUs at UPITT and UCSF followed by prospective field testing at a 16-bed SDU
at UPITT. The final deliverable is an intelligent alerting system for detection and mitigation of CRI in SDU
patients of sufficient readiness to be deployed in a multicenter human effectiveness trial as a next step.
Building such a clinically relevant system in clinical workflow to predict patient instability has important
implications for reducing preventable morbidity and mortality, eliminating alarm fatigue, improving patient
safety, nursing care logistics (monitoring frequency, case load and mixture, staff allocation) and care delivery
systems (triage, bed allocation, prevention of adverse events).

## Key facts

- **NIH application ID:** 10388671
- **Project number:** 2R01NR013912-08A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Salah S Al-Zaiti
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $798,419
- **Award type:** 2
- **Project period:** 2012-09-27 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10388671

## Citation

> US National Institutes of Health, RePORTER application 10388671, Predicting Patient Instability Noninvasively for Nursing Care – Three (PPINNC-3) (2R01NR013912-08A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10388671. Licensed CC0.

---

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