Evidence map›Paper›PMID 27452809›Full record

Observational studyCritical care medicine2016

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.

Travis J Moss, Douglas E Lake, J Forrest Calland, Kyle B Enfield, John B Delos, Karen D Fairchild, J Randall Moorman

Open access · greenAbstract readObservational Study
In one paragraph

Observational study in Critical care medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 1 pooled it
8.3field-weighted citation impact, top 2% of its field
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

57 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Travis J Moss1Division of Cardiovascular Medicine, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA.2Department of Statistics, University of Virginia, Charlottesville, VA.3Division of Acute Care and Trauma Surgery, Department of Surgery, University of Virginia School of Medicine, Charlottesville, VA.4Division of Pulmonary and Critical Care, Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA.5Department of Physics, College of William and Mary, Williamsburg, VA.6Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA.7Department of Biomedical Engineering, University of Virginia, Charlottesville, VA.8Department of Molecular Physiology, University of Virginia, Charlottesville, VA.
Douglas E Lake
J Forrest Calland
Kyle B Enfield
John B Delos
Karen D Fairchild
J Randall Moorman
University of Virginia · US

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
Neonatal apnea: Online risk score from new analyses of bedside monitor waveformsRC2HD064488 · NICHD · UNIVERSITY OF VIRGINIA · PI KATTWINKEL, JOHN, MOORMAN, JOSEPH RANDALL · 2009 to 2010
$2.0M
NICHD NIH HHS R01 HD072071NICHD NIH HHS RC2 HD064488
6 · The paper itself

Abstract

objectivesPatients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness.

designWe report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation).

settingUniversity of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. PATIENTS: Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data.

interventionsNone. MEASUREMENTS AND MAIN

resultsWe analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar.

conclusionsSubacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.

Indexed as

Catastrophic IllnessCritical CareAdultHemorrhageHospital MortalityHumansInfant, NewbornLength of StayMiddle AgedModels, StatisticalMonitoring, PhysiologicPrognosisReproducibility of ResultsRespiratory InsufficiencyRetrospective StudiesSensitivity and Specificity

Identifiers

PMID27452809
PMCPMC4987175
OpenAlexW2342293764

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.