Development of a model-based clinical sepsis biomarker for critically ill patients

Jessica Lin, Jacquelyn D. Parente, J. Geoffrey Chase, Geoffrey M. Shaw, Amy J. Blakemore, Aaron J. LeCompte, Christopher Pretty, Normy Norfiza Abdul Razak, Dominic S. Lee, Christopher E. Hann, Sheng Hui Wang

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48h. Insulin sensitivity (SI) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity SI values were calculated from glycemic control data of 36 patients with sepsis. The hourly SI is compared to the hourly sepsis score (ss) for these patients (ss=0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss≥2) are created for both SI and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an SI cut-off value of 0.00013L/mU/min. Multivariate clinical biomarker combining SI, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.

Original languageEnglish
Pages (from-to)149-155
Number of pages7
JournalComputer Methods and Programs in Biomedicine
Volume102
Issue number2
DOIs
Publication statusPublished - 01 May 2011

Fingerprint

Biomarkers
Critical Illness
Sepsis
Insulin
Insulin Resistance
Intensive care units
Blood pressure
Statistical Distributions
Blood
Sensitivity and Specificity
Respiratory Rate
Intensive Care Units
Heart Rate
Costs
Guidelines
Blood Pressure
Costs and Cost Analysis
Temperature
Mortality

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this

Lin, J., Parente, J. D., Chase, J. G., Shaw, G. M., Blakemore, A. J., LeCompte, A. J., ... Wang, S. H. (2011). Development of a model-based clinical sepsis biomarker for critically ill patients. Computer Methods and Programs in Biomedicine, 102(2), 149-155. https://doi.org/10.1016/j.cmpb.2010.04.002
Lin, Jessica ; Parente, Jacquelyn D. ; Chase, J. Geoffrey ; Shaw, Geoffrey M. ; Blakemore, Amy J. ; LeCompte, Aaron J. ; Pretty, Christopher ; Abdul Razak, Normy Norfiza ; Lee, Dominic S. ; Hann, Christopher E. ; Wang, Sheng Hui. / Development of a model-based clinical sepsis biomarker for critically ill patients. In: Computer Methods and Programs in Biomedicine. 2011 ; Vol. 102, No. 2. pp. 149-155.
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Lin, J, Parente, JD, Chase, JG, Shaw, GM, Blakemore, AJ, LeCompte, AJ, Pretty, C, Abdul Razak, NN, Lee, DS, Hann, CE & Wang, SH 2011, 'Development of a model-based clinical sepsis biomarker for critically ill patients', Computer Methods and Programs in Biomedicine, vol. 102, no. 2, pp. 149-155. https://doi.org/10.1016/j.cmpb.2010.04.002

Development of a model-based clinical sepsis biomarker for critically ill patients. / Lin, Jessica; Parente, Jacquelyn D.; Chase, J. Geoffrey; Shaw, Geoffrey M.; Blakemore, Amy J.; LeCompte, Aaron J.; Pretty, Christopher; Abdul Razak, Normy Norfiza; Lee, Dominic S.; Hann, Christopher E.; Wang, Sheng Hui.

In: Computer Methods and Programs in Biomedicine, Vol. 102, No. 2, 01.05.2011, p. 149-155.

Research output: Contribution to journalArticle

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AU - Lin, Jessica

AU - Parente, Jacquelyn D.

AU - Chase, J. Geoffrey

AU - Shaw, Geoffrey M.

AU - Blakemore, Amy J.

AU - LeCompte, Aaron J.

AU - Pretty, Christopher

AU - Abdul Razak, Normy Norfiza

AU - Lee, Dominic S.

AU - Hann, Christopher E.

AU - Wang, Sheng Hui

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N2 - Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48h. Insulin sensitivity (SI) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity SI values were calculated from glycemic control data of 36 patients with sepsis. The hourly SI is compared to the hourly sepsis score (ss) for these patients (ss=0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss≥2) are created for both SI and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an SI cut-off value of 0.00013L/mU/min. Multivariate clinical biomarker combining SI, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.

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