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Cited by 6Sepsis Diagnosis and TreatmentMachine Learning in HealthcareHealthcare Technology and Patient Monitoring

Physiologic signatures within six hours of hospitalization identify acute illness phenotypes

Open access
Ren, Yuanfang, Loftus, Tyler J., Li, Yanjun, Guan, Ziyuan, Ruppert, Matthew M., Datta, Shounak, Upchurch, Gilbert R., Tighe, Patrick J. +4 more·2022·PLOS Digital Health
DOI 10.1371/journal.pdig.0000110PMC PMC9802629
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Abstract

During the early stages of hospital admission, clinicians use limited information to make decisions as patient acuity evolves. We hypothesized that clustering analysis of vital signs measured within six hours of hospital admission would reveal distinct patient phenotypes with unique pathophysiological signatures and clinical outcomes. We created a longitudinal electronic health record dataset for 75,762 adult patient admissions to a tertiary care center in 2014–2016 lasting six hours or longer. Physiotypes were derived via unsupervised machine learning in a training cohort of 41,502 patients applying consensus k-means clustering to six vital signs measured within six hours of admission. Reproducibility and correlation with clinical biomarkers and outcomes were assessed in validation cohort of 17,415 patients and testing cohort of 16,845 patients. Training, validation, and testing cohorts had similar age (54–55 years) and sex (55% female), distributions. There were four distinct clusters. Physiotype A had physiologic signals consistent with early vasoplegia, hypothermia, and low-grade inflammation and favorable short-and long-term clinical outcomes despite early, severe illness. Physiotype B exhibited early tachycardia, tachypnea, and hypoxemia followed by the highest incidence of prolonged respiratory insufficiency, sepsis, acute kidney injury, and short- and long-term mortality. Physiotype C had minimal early physiological derangement and favorable clinical outcomes. Physiotype D had the greatest prevalence of chronic cardiovascular and kidney disease, presented with severely elevated blood pressure, and had good short-term outcomes but suffered increased 3-year mortality. Comparing sequential organ failure assessment (SOFA) scores across physiotypes demonstrated that clustering did not simply recapitulate previously established acuity assessments. In a heterogeneous cohort of hospitalized patients, unsupervised machine learning techniques applied to routine, early vital sign data identified physiotypes with unique disease categories and distinct clinical outcomes. This approach has the potential to augment understanding of pathophysiology by distilling thousands of disease states into a few physiological signatures.

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  • The ICU provides a high level of data granularity due to high resolution monitoring systems, capable of capturing rapid changes in a patient's physiological status.

    “The ICU provides a high level of data granularity due to high resolution monitoring systems, capable of capturing the rapid changes in a patient's physiological status [61].”

    From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [61]

    The Methods describe vital signs from hospital admissions resampled hourly, not high-resolution ICU monitoring. The Discussion notes that waveform data could improve precision but does not say ICU systems capture rapid physiological changes. Searches for high-resolution ICU monitoring and rapid changes found no passage addressing the claim. (LLM, conf=0.77)

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Authors
Ren, Y., Loftus, T. J., Li, Y., Guan, Z., Ruppert, M. M., Datta, S., Upchurch, G. R., Tighe, P. J., Rashidi, P., Shickel, B., Ozrazgat-Baslanti, T., & Bihorac, A.
Affiliations
  • University of Florida (US)
  • University of Florida Health (US)
Corresponding author
Bihorac, Azra
ORCID
Venue
PLOS Digital Health
Publisher
Public Library of Science (PLoS)
Published
2022-10-13
Volume
1
Issue
10
Pages
e0000110
ISSN
2767-3170 (ISSN-L)
DOI
10.1371/journal.pdig.0000110
PMC
PMC9802629
OpenAlex
W4306154654
Cited by
6
References
25
Citations by year
2026: 2 · 2025: 2 · 2024: 2
FWCI
0.72
Type
journal article
Language
en
Open access
Licence
Locations
  • PLOS Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of General Medical Sciences (R01 GM110240)
  • National Institute of Biomedical Imaging and Bioengineering (R01 EB029699 and R21 EB027344, R01 EB029699, 1R01EB029699 and 1R21EB027344)
  • National Institute of Neurological Disorders and Stroke (R01 NS120924, 1R01NS120924)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK121730, K01 DK120784, R01 DK123078, and R01 DK121730)
  • National Center For Advancing Translational Sciences of the National Institutes of Health (UL1TR001427)
  • University of Florida Research (AGR DTD 12-02-20)
  • National Science Foundation (CAREER award 1750192)
  • National Institute of General Medical Science (R01GM-110240)
  • ational Institute of General Medical Sciences (K23 GM140268)
  • NIH/NCATS Clinical and Translational Sciences Award to the University of Florida (UL1 TR000064)
Topics
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
Keywords
vital signs, early hospitalization, clinical phenotype, K-means clustering, SOFA score, sepsis, acute kidney injury, respiratory failure, mortality, cardiovascular disease, chronic kidney disease, hypothermia, vasoplegia, tachycardia, hypoxemia

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