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Volume 14 Supplement 2

Sepsis 2010

A fast and accurate diagnostic test for severe sepsis using model-based insulin sensitivity and clinical data

Introduction

Severe sepsis occurs frequently in the ICU and is a leading cause of admission, mortality, and cost. Management guidelines define treatment objectives within the first 6 hours of clinical syndrome presentation. However, blood culture test confirmation may return in up to 48 hours, with only 30 to 50% of presentations having positive blood cultures. Early treatment compliance has demonstrated a decrease in sepsis mortality. Thus, there remains a serious need for an early and accurate diagnostic test for severe sepsis. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glucose control protocols are able to accurately identify SI in real time, without high rates of hypoglycemia [1]. This research explores the diagnostic test properties of a real-time test for severe sepsis.

Methods

A diagnostic biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having severe sepsis based on a clinically validated sepsis score (ss). Kernel density estimates were used for the development of joint probability density profiles for ss ≥2 and ss <2 data hours (213 and 5,858, respectively, of 6,071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined, as well as AUC, positive and negative likelihood ratios (LHR), predictive values, and diagnostic odds ratios (DOR) for in-sample and out-of-sample estimates, respectively

Results

A biomarker including concurrent insulin sensitivity and clinical data for real-time diagnosis of severe sepsis (ss ≥2) achieves 69 to 94% sensitivity, 75 to 94% specificity, 0.78 to 0.99 AUC, 3 to 17 LHR+, 0.06 to 0.4 LHR-, 9 to 38% PPV, 99 to 100% NPV, and 7 to 260 DOR for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. See Figure 1.

figure1

Figure 1

Conclusions

The clinical biomarker shows good to high accuracy and may provide useful information as an early real-time diagnostic test for severe sepsis.

References

  1. 1.

    Wong XW, Chase JG, Shaw GM, Hann CE, Lotz T, Lin J, Singh-Levett I, Hollingsworth LF, Wong OSW, Andreassen S: Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study. Med Eng Phys 2006, 28: 665-681. 10.1016/j.medengphy.2005.10.015

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Correspondence to JD Parente.

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Parente, J., Lee, D., Lin, J. et al. A fast and accurate diagnostic test for severe sepsis using model-based insulin sensitivity and clinical data. Crit Care 14, P13 (2010). https://doi.org/10.1186/cc9116

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Keywords

  • Insulin Sensitivity
  • Hypoglycemia
  • Severe Sepsis
  • Positive Blood Culture
  • Kernel Density Estimate
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