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Cited by 98Sepsis Diagnosis and TreatmentHealthcare Technology and Patient MonitoringMachine Learning in Healthcare

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

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Moss, Travis J., Lake, Douglas E., Calland, J. Forrest, Enfield, Kyle B., Delos, John B., Fairchild, Karen D., Moorman, J. Randall·2016·Critical Care Medicine
DOI 10.1097/ccm.0000000000001738PMC PMC4987175
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Abstract

Objectives: Patients 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. Design: We 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). Setting: University 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. Interventions: None. Measurements and Main Results: We 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. Conclusions: Subacute 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.

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  • None of the studies mentioned their code was released and only one paper reported adhering to a reporting standard.

    “None of the studies mentioned their code was released and only one paper reported adhering to a reporting standard [36].”

    From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [36]

    The Materials and Methods (Study design; page unavailable) explicitly says this paper used the TRIPOD reporting checklist. It does not establish that only one study in the citing paper's set reported following a standard. The passages also do not establish the set-wide claim that none mentioned releasing code. (LLM, conf=0.84)

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Research works printed but not linked: 31

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Authors
Moss, T. J., Lake, D. E., Calland, J. F., Enfield, K. B., Delos, J. B., Fairchild, K. D., & Moorman, J. R.
Affiliations
  • University of Virginia (US)
  • University of Virginia Medical Center (US)
  • William & Mary (US)
ORCID
Venue
Critical Care Medicine
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2016-09
Volume
44
Issue
9
Pages
1639-1648
ISSN
0090-3493 (ISSN-L)
DOI
10.1097/ccm.0000000000001738
PMC
PMC4987175
OpenAlex
W2342293764
Cited by
98
References
3
Citations by year
2026: 8 · 2025: 10 · 2024: 5 · 2023: 3 · 2022: 15 · 2021: 18 · 2020: 5 · 2019: 10 · 2018: 10 · 2017: 10 · 2016: 4
FWCI
10.63
Type
journal article
Language
en
Open access
Locations
  • Critical Care Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • W&M Publish (College of William & Mary) (submittedVersion)
Funders
Eunice Kennedy Shriver National Institute of Child Health and Human Development (RC2HD064488, R01HD072071)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
early warning signs, critical illness prediction, respiratory failure, sepsis, hemorrhage, vital signs, electrocardiogram, physiological monitoring, unplanned intubation, neonatal intensive care
MeSH terms
Adult, Catastrophic Illness, Critical Care, Hemorrhage, Humans, Infant, Newborn, Length of Stay, Middle Aged, Monitoring, Physiologic, Prognosis, Respiratory Insufficiency, Retrospective Studies, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Hospital Mortality, Sepsis, Vital Signs

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