From vital signs to clinical outcomes for patients with sepsis

A machine learning basis for a clinical decision support system

Eren Gultepe, Jeffrey P. Green, Hien H Nguyen, Jason Yeates Adams, Timothy E Albertson, Ilias Tagkopoulos

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

Objective: To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies. Materials and methods: Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk. Results: An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate. Discussion: This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome. Conclusions: Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.

Original languageEnglish (US)
Pages (from-to)315-325
Number of pages11
JournalJournal of the American Medical Informatics Association
Volume21
Issue number2
DOIs
StatePublished - 2014

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Clinical Decision Support Systems
Vital Signs
Sepsis
Lactic Acid
Leukocyte Count
Mortality
Area Under Curve
Systemic Inflammatory Response Syndrome
Machine Learning
Electronic Health Records
Health
Respiratory Rate
ROC Curve
Arterial Pressure

ASJC Scopus subject areas

  • Health Informatics

Cite this

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abstract = "Objective: To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies. Materials and methods: Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (na{\"i}ve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk. Results: An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate. Discussion: This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome. Conclusions: Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.",
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AU - Albertson, Timothy E

AU - Tagkopoulos, Ilias

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