Evidence map›Paper›PMID 28338520›Full record

ArticlePediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2017

Integration of Single-Center Data-Driven Vital Sign Parameters into a Modified Pediatric Early Warning System.

Catherine E Ross, Iliana J Harrysson, Veena V Goel, Erika J Strandberg, Peiyi Kan, Deborah E Franzon, Natalie M Pageler

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Article in Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Catherine E Ross1Division of Medicine Critical Care, Department of Medicine, Boston Children's Hospital, Boston, MA. 2Department of Pediatrics, Santa Clara Valley Medical Center, San Jose, CA. 3Division of Pediatric Hospital Medicine, Department of Pediatrics, Palo Alto Medical Foundation, Sutter Health, Palo Alto, CA. 4Biomedical Informatics, Stanford University School of Medicine, Stanford, CA. 5Statistical Unit, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. 6Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA. 7Division of Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. 8Department of Clinical Informatics, Stanford Children's Health, Stanford, CA. 9Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.
Iliana J Harrysson
Veena V Goel
Erika J Strandberg
Peiyi Kan
Deborah E Franzon
Natalie M Pageler

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

objectivesPediatric early warning systems using expert-derived vital sign parameters demonstrate limited sensitivity and specificity in identifying deterioration. We hypothesized that modified tools using data-driven vital sign parameters would improve the performance of a validated tool.

designRetrospective case control.

settingQuaternary-care children's hospital. PATIENTS: Hospitalized, noncritically ill patients less than 18 years old. Cases were defined as patients who experienced an emergent transfer to an ICU or out-of-ICU cardiac arrest. Controls were patients who never required intensive care. Cases and controls were split into training and testing groups.

interventionsThe Bedside Pediatric Early Warning System was modified by integrating data-driven heart rate and respiratory rate parameters (modified Bedside Pediatric Early Warning System 1 and 2). Modified Bedside Pediatric Early Warning System 1 used the 10th and 90th percentiles as normal parameters, whereas modified Bedside Pediatric Early Warning System 2 used fifth and 95th percentiles. MEASUREMENTS AND MAIN

resultsThe training set consisted of 358 case events and 1,830 controls; the testing set had 331 case events and 1,215 controls. In the sensitivity analysis, 207 of the 331 testing set cases (62.5%) were predicted by the original tool versus 206 (62.2%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 191 (57.7%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. For specificity, 1,005 of the 1,215 testing set control patients (82.7%) were identified by original Bedside Pediatric Early Warning System versus 1,013 (83.1%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 1,055 (86.8%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. There was no net gain in sensitivity and specificity using either of the modified Bedside Pediatric Early Warning System tools.

conclusionsIntegration of data-driven vital sign parameters into a validated pediatric early warning system did not significantly impact sensitivity or specificity, and all the tools showed lower than desired sensitivity and specificity at a single cutoff point. Future work is needed to develop an objective tool that can more accurately predict pediatric decompensation.

Indexed as

Clinical DeteriorationIntensive Care Units, PediatricPatient TransferVital SignsAdolescentCase-Control StudiesChildChild, PreschoolCritical CareCritical IllnessFemaleHumansInfantInfant, NewbornMalePoint-of-Care Testing

Identifiers

PMID28338520
PMCPMC5419852

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