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Children 2017, 4(12), 111; https://doi.org/10.3390/children4120111

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

Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India
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Received: 2 November 2017 / Revised: 25 November 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
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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. View Full-Text
Keywords: neonates; sepsis; MIMIC III; SIRS; mobile application; international pediatric sepsis consensus conference neonates; sepsis; MIMIC III; SIRS; mobile application; international pediatric sepsis consensus conference
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Thakur, J.; Pahuja, S.K.; Pahuja, R. Performance Comparison of Systemic Inflammatory Response Syndrome with Logistic Regression Models to Predict Sepsis in Neonates. Children 2017, 4, 111.

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