Evidence map›Paper›PMID 41807700›Full record

ArticlePediatric research2026

Modeling heart rate patterns to quantify neonatal opioid withdrawal syndrome.

Sherry L Kausch, Sara Manetta, Angela Gummadi, Katy N Krahn, Amanda Duncan, Rachel Benz, William E King, Hayley Friedman, Colm P Travers, Namasivayam Ambalavanan and 2 more

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In one paragraph

Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Sherry L KauschDepartment of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA. slk7s@uvahealth.org.
Sara ManettaDepartment of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA.
Angela GummadiDepartment of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA.
Katy N KrahnDepartment of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA.
Amanda DuncanDepartment of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Rachel BenzDepartment of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA.
William E KingMedical Predictive Science Corporation, Charlottesville, VA, USA.
Hayley FriedmanDepartment of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Colm P TraversDepartment of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA.
Namasivayam AmbalavananDepartment of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA.
Zachary A VesoulisDepartment of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Brynne A SullivanDepartment of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA.

Funding

POWS for NOWS: Using physiomarkers as an objective tool for assessing the withdrawing infantR18EB035019 · NIBIB · UNIVERSITY OF VIRGINIA · PI SULLIVAN, BRYNNE ARCHER, VESOULIS, ZACHARY ANDREW · 2023 to 2023
$3.1M
Non-invasive oscillometry to measure lung mechanics, response to treatments, and predict longer-term pulmonary outcomes among preterm infants: a prospective cohort studyK23HL157618 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Colm Peter Travers · 2022 to 2026
$862k
NHLBI NIH HHS K23 HL157618NIBIB NIH HHS R18 EB035019
6 · The paper itself

Abstract

backgroundNeonatal Opioid Withdrawal Syndrome (NOWS) is managed using intermittent, observation-based assessments. Opioid withdrawal causes autonomic dysfunction, altering control of heart rate and breathing. We hypothesized that heart rate (HR) and oxygenation (SpO

objectiveTo characterize differences in HR and SpO

methodsWe included term infants with tNOWS and controls admitted to one of three academic NICUs. We calculated HR and SpO

resultsWe studied 64 infants with tNOWS and 96 control infants. Higher HR and increased HR variability were associated with tNOWS. A logistic regression model using HR-based metrics identified infants with tNOWS with an AUC of 0.758.

conclusionsHR patterns detected tNOWS in term infants. A predictive model using continuous HR data provides a noninvasive measure associated with withdrawal severity in infants requiring opioid replacement. IMPACT: Heart rate patterns identified NOWS in term infants. A predictive model using continuous HR data provides a noninvasive measure associated with withdrawal severity in infants requiring opioid replacement. Clinicians may be able to use the risk estimates produced by this model for targeted interventions for patients where treatment is indicated.

Identifiers

PMID41807700
PMCPMC13581346

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.