Evidence map›Paper›PMID 39300276›Full record

ArticlePediatric research2025

Clinical correlates of a high cardiorespiratory risk score for very low birth weight infants.

Sherry L Kausch, Claire C Slevin, Amanda Duncan, Karen D Fairchild, Douglas E Lake, Jessica Keim-Malpass, Zachary A Vesoulis, Brynne A Sullivan

Abstract read
In one paragraph

Article in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. A machine learning system enables just-in-time risk-stratified sepsis evaluations in the neonatal intensive care unit.Journal of perinatology : official journal of the California Perinatal Association · 2026
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sherry L KauschDepartment of Pediatrics, Division of Neonatology, University of Virginia School of Medicine, Charlottesville, VA, USA. slk7s@uvahealth.org.
Claire C SlevinDepartment of Pediatrics, Johns Hopkins University, Baltimore, MD, USA.
Amanda DuncanDepartment of Pediatrics, Division of Newborn Medicine, Washington University in St. Louis, St. Louis, MO, USA.
Karen D FairchildDepartment of Pediatrics, Division of Neonatology, University of Virginia School of Medicine, Charlottesville, VA, USA.
Douglas E LakeDepartment of Medicine, Division of Cardiology, University of Virginia School of Medicine, Charlottesville, VA, USA.
Jessica Keim-MalpassDepartment of Pediatrics, Division of Hematology, University of Virginia School of Medicine, Charlottesville, VA, USA.
Zachary A VesoulisDepartment of Pediatrics, Division of Newborn Medicine, Washington University in St. Louis, St. Louis, MO, USA.
Brynne A SullivanDepartment of Pediatrics, Division of Neonatology, University of Virginia School of Medicine, Charlottesville, VA, USA.

Funding

Predictive Informatics Monitoring in the Neonatal Intensive Care UnitR01HD072071 · NICHD · UNIVERSITY OF VIRGINIA · PI KAREN D FAIRCHILD, Brynne Archer Sullivan · 2014 to 2026
$7.6M
NICHD NIH HHS R01 HD072071
6 · The paper itself

Abstract

backgroundA pulse oximetry warning system (POWS) to analyze heart rate and oxygen saturation data and predict risk of sepsis was developed for very low birth weight (VLBW) infants.

methodsWe determined the clinical correlates and positive predictive value (PPV) of a high POWS score in VLBW infants. In a two-NICU retrospective study, we identified times when POWS increased above 6 (POWS spike). We selected an equal number of control times, matched for gestational and chronologic age. We reviewed records for infection and non-infection events around POWS spikes and control times. We calculated the frequencies and PPV of a POWS spike for infection or another significant event.

resultsWe reviewed 111 POWS spike times and 111 control times. Days near POWS spikes were more likely to have clinical events than control days (77% vs 50%). A POWS spike had 52% PPV for suspected or confirmed infection and 77% for any clinically significant event. Respiratory deterioration occurred near more POWS spike times than control times (34% vs 18%).

conclusionsIn a retrospective cohort, infection and respiratory deterioration were common clinical correlations of a POWS spike. POWS had a high PPV for significant clinical events with or without infection. IMPACT: There are significant gaps in understanding the best approach to implementing continuous sepsis prediction models so that clinicians can best respond to early signals of deterioration. Infection and respiratory deterioration were common clinical events identified at the time of a high predictive model score. Understanding the clinical correlates of a high-risk early warning score will inform future implementation efforts.

Indexed as

Infant, Very Low Birth WeightOximetrySepsisFemaleGestational AgeHeart RateHumansInfant, NewbornIntensive Care Units, NeonatalMaleOxygen SaturationPredictive Value of TestsRetrospective StudiesRisk AssessmentRisk Factors

Identifiers

PMID39300276
PMCPMC12122351

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Registered trials

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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.