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. 2017 Dec 19;4(12):111.
doi: 10.3390/children4120111.

Performance Comparison of Systemic Inflammatory Response Syndrome with Logistic Regression Models to Predict Sepsis in Neonates

Jyoti Thakur  1 , Sharvan Kumar Pahuja  2 , Roop Pahuja  3
Affiliations

Performance Comparison of Systemic Inflammatory Response Syndrome with Logistic Regression Models to Predict Sepsis in Neonates

Jyoti Thakur et al. Children (Basel). .

Abstract

In 2005, an international pediatric sepsis consensus conference defined systemic inflammatory response syndrome (SIRS) for children <18 years of age, but excluded premature infants. In 2012, Hofer et al. investigated the predictive power of SIRS for term neonates. In this paper, we examined the accuracy of SIRS in predicting sepsis in neonates, irrespective of their gestational age (i.e., pre-term, term, and post-term). We also created two prediction models, named Model A and Model B, using binary logistic regression. Both models performed better than SIRS. We also developed an android application so that physicians can easily use Model A and Model B in real-world scenarios. The sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) in cases of SIRS were 16.15%, 95.53%, 3.61, and 0.88, respectively, whereas they were 29.17%, 97.82%, 13.36, and 0.72, respectively, in the case of Model A, and 31.25%, 97.30%, 11.56, and 0.71, respectively, in the case of Model B. All models were significant with p < 0.001.

Keywords: MIMIC III; SIRS; international pediatric sepsis consensus conference; mobile application; neonates; sepsis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study dataset generation from the Medical Information Mart for Intensive Care III (MIMIC III) database.
Figure 2
Figure 2
Screenshot of mobile application.
Figure 3
Figure 3
Flow chart showing the operation of the mobile application.
Figure 4
Figure 4
Bar chart showing the correct predictions made by the three models.
See this image and copyright information in PMC

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