# Clinical Decision Support for Early Detection of Deterioration in Hospitalized Children

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $396,872

## Abstract

PROJECT SUMMARY
Hospitalized children who experience cardiopulmonary deterioration are at increased risk for mortality and long-
term morbidity. Because timely intervention increases survival in children, it is critically important to identify
cardiopulmonary deterioration events as early as possible. However, the current paradigm for detecting these
events in advance has several gaps. First, existing risk prediction methods can lead to fragmented care, as each
unit employs different tools for predicting specific outcomes. For example, risk prediction within the emergency
department (ED) is targeted toward triage; ward-based tools predict the risk of being transferred to the intensive
care unit (ICU), while the ICU focuses on determining the likelihood of death or cardiac arrests. Transitioning
from multiple, siloed risk assessment tools to a single, hospital-wide cardiopulmonary deterioration prediction
model could significantly improve outcomes for children. A second critical gap is that current prediction model
outputs are not accompanied by helpful explanations, a need unmet by standard machine learning (ML)
explainers due to inherent limitations. Developing new algorithms that provide real-time interpretations of model
outputs may increase situational awareness, decrease diagnostic delay, and enable better treatment selection.
Third, to ensure high usage and effective decision-making, any new model should be accompanied by a user
interface explicitly designed using human factors engineering principles.
 The long-term goal is to improve outcomes among children experiencing cardiopulmonary deterioration
by enabling better quality of care. The overall objective of this project is to develop a new clinical decision support
(CDS) tool that is accurate, interpretable, and actionable for early detection of cardiopulmonary deterioration
events in children. In Aim 1, we will use electronic health record (EHR) data from pediatric admissions to four
academic hospitals and ML to derive and externally validate a new hospital-wide cardiopulmonary deterioration
prediction model and compare performances to our preliminary model. In Aim 2, we will develop novel algorithms
that provide physiological explanations and clinical context for model predictions for a given patient. Finally, in
Aim 3, we will create a new CDS tool that embeds the best-performing prediction tool and explainer algorithm
outputs within a graphical user interface purposefully designed to facilitate increased user interaction. The
proposed research is innovative because it incorporates deep learning-based pediatric risk prediction, real-time
explainable algorithms with highlighted clinical context, and human factors engineering for developing the CDS
tool. In addition, the proposed work is significant because it will result in a new, accurate, interpretable, efficient,
and user-friendly CDS tool for risk assessment throughout a pediatric hospital. Ultimately, this powerful tool will
enabl...

## Key facts

- **NIH application ID:** 10855809
- **Project number:** 1R01HL173037-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Anoop Mayampurath
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $396,872
- **Award type:** 1
- **Project period:** 2024-05-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10855809

## Citation

> US National Institutes of Health, RePORTER application 10855809, Clinical Decision Support for Early Detection of Deterioration in Hospitalized Children (1R01HL173037-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10855809. Licensed CC0.

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