Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

Predicting Clinical Deterioration in Mechanically Ventilated Children using High-Frequency Physiologic Data

Award Number: K23HD113816
ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 08/12/2024
PERIOD OF PERFORMANCE END DATE: 07/31/2029

Group Awards By:

View Award Description

Predicting Clinical Deterioration in Mechanically Ventilated Children using High-Frequency Physiologic Data - PROJECT SUMMARY Significance: Clinical deterioration in the PICU, as defined by new or progressive organ dysfunction, occurs in up to 5% of critically ill children during their first week of admission. Importantly, patients with progressive organ dysfunction have a mortality rate as high as 50%, with over 20% of survivors experiencing moderate to severe disability. Earlier recognition of patients at high risk of clinical deterioration is critically needed to inform treatment decisions and personalize health care delivery, which would lead to improved patient outcomes. However, timely prediction is challenging due to limitations in existing models. The few models that try to predict clinical deterioration do not leverage metrics from high-frequency physiological monitor data, such as heart rate variability (HRV), despite evidence that these data are highly predictive of deterioration. For the proposed K23 award, I will examine clinical deterioration in the context of new or worsening cardiovascular and/or respiratory (cardiorespiratory) dysfunction in mechanically ventilated children. My central hypothesis is that a longitudinal, multivariable prediction model of clinical deterioration that incorporates high-frequency physiological monitor data and clinical variables will accurately identify children at risk of new or worsening cardiorespiratory dysfunction and that this model will be useful and acceptable to clinicians. Aim 1. Derive, validate, and compare longitudinal prediction models of new or worsening cardiorespiratory dysfunction in critically ill children requiring mechanical ventilation. Aim 2. Design for the implementation of a clinical deterioration model to improve use in practice. Innovation: Our innovative approach includes (1) high-frequency physiological data, (2) advanced machine learning applications, and (3) a user-centered design approach to optimize the implementation plan. Approach: Aim 1: I will derive and compare machine learning-based prediction models using physiological and EHR-based clinical data in a single center. I will then perform internal and external validation of the best performing model. Aim 2: Using user-centered design principles with critical care clinicians, I will elicit the ideal packaging and implementation plan for incorporating a clinical deterioration model into practice. Then, I will lead iterative usability testing of the design and implementation plan to assess acceptability, usefulness, and desirability. This approach will identify the ideal packaging and implementation plan for the clinicians who will ultimately use the model. Anticipated Outcomes and Public Heath Relevance: This study will provide the foundation to: (1) develop accurate prediction models of deterioration that leverage continuous physiological measures and clinical variables; (2) implement useful prediction models at the bedside to support clinical decision making; and (3) design intervention trials to test the impact on patient outcomes.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $163,080 )
2026	2026	ANN & ROBERT H LURIE CHILDRENS HOSPITAL OF CHICAGO	225 E CHICAGO AVE	CHICAGO	IL	60611	COOK	USA	Child Health and Human Development Extramural Research	000	3	7/27/2026	NON-COMPETING CONTINUATION	$163,080
														Subtotal = $163,080

Issue Date FY: 2025 ( Subtotal = $163,080 )
2025	2025	ANN & ROBERT H LURIE CHILDRENS HOSPITAL OF CHICAGO	225 E CHICAGO AVE	CHICAGO	IL	60611	COOK	USA	Child Health and Human Development Extramural Research	000	2	7/21/2025	NON-COMPETING CONTINUATION	$163,080
														Subtotal = $163,080

Issue Date FY: 2024 ( Subtotal = $163,080 )
2024	2024	ANN & ROBERT H LURIE CHILDRENS HOSPITAL OF CHICAGO	225 E CHICAGO AVE	CHICAGO	IL	60611	COOK	USA	Child Health and Human Development Extramural Research	000	1	8/12/2024	NEW	$163,080
														Subtotal = $163,080

Grand Total All Awards = $489,240

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Predicting Clinical Deterioration in Mechanically Ventilated Children using High-Frequency Physiologic Data

Award Number: K23HD113816

ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 08/12/2024

PERIOD OF PERFORMANCE END DATE: 07/31/2029

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer