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      Vital signs and their cross-correlation in sepsis and NEC: A study of 1065 very low birth weight infants in two NICUs

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          Abstract

          Background

          Subtle changes in vital signs and their interactions occur in preterm infants prior to overt deterioration from late-onset septicemia (LOS) or necrotizing enterocolitis (NEC). Optimizing predictive algorithms may lead to earlier treatment.

          Methods

          For 1065 very low birth weight (VLBW) infants in two NICUs, mean, SD, and cross-correlation of respiratory rate, heart rate (HR), and oxygen saturation (SpO 2) were analyzed hourly (131 infant-years’ data). Cross-correlation (co-trending) between two vital signs was measured allowing a lag of +/− 30 seconds. Cases of LOS and NEC were identified retrospectively (n=186) and vital sign models were evaluated for ability to predict illness diagnosed in the ensuing 24h.

          Results

          The best single illness predictor within and between institutions was cross-correlation of HR-SpO 2. The best combined model (mean SpO 2, SD HR, and cross correlation of HR-SpO 2,) trained at one site with ROC area 0.695 had external ROC area of 0.754 at the other site, and provided additive value to an established HR characteristics index for illness prediction (Net Reclassification Improvement 0.25, 95% CI 0.113, 0.328).

          Conclusion

          Despite minor inter-institutional differences in vital sign patterns of VLBW infants, cross-correlation of HR-SpO 2 and a 3-variable vital sign model performed well at both centers for preclinical detection of sepsis or NEC.

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          Most cited references 13

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          Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide.

          The net reclassification improvement (NRI) is an increasingly popular measure for evaluating improvements in risk predictions. This article details a review of 67 publications in high-impact general clinical journals that considered the NRI. Incomplete reporting of NRI methods, incorrect calculation, and common misinterpretations were found. To aid improved applications of the NRI, the article elaborates on several aspects of the computation and interpretation in various settings. Limitations and controversies are discussed, including the effect of miscalibration of prediction models, the use of the continuous NRI and “clinical NRI,” and the relation with decision analytic measures. A systematic approach toward presenting NRI analysis is proposed: Detail and motivate the methods used for computation of the NRI, use clinically meaningful risk cutoffs for the category-based NRI, report both NRI components, address issues of calibration, and do not interpret the overall NRI as a percentage of the study population reclassified. Promising NRI findings need to be followed with decision analytic or formal cost-effectiveness evaluations.
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            Integrating the predictiveness of a marker with its performance as a classifier.

            There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993-2003.
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              Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness.

              Late-onset neonatal sepsis is a significant cause of morbidity and mortality, and early detection could prove beneficial. Previously, we found that abnormal heart rate characteristics (HRC) of reduced variability and transient decelerations occurred early in the course of neonatal sepsis and sepsis-like illness in infants in a single neonatal intensive care unit (NICU). We hypothesized that this finding can be generalized to other NICUs. We prospectively collected clinical data and continuously measured RR intervals in all infants in two NICUs who stayed for >7 d. We defined episodes of sepsis and sepsis-like illness as acute clinical deteriorations that prompted physicians to obtain blood cultures and start antibiotics. A predictive statistical model yielding an HRC index was developed on a derivation cohort of 316 neonates in the University of Virginia NICU and then applied to the validation cohort of 317 neonates in the Wake Forest University NICU. In the derivation cohort, there were 155 episodes of sepsis and sepsis-like illness in 101 infants, and in the validation cohort, there were 118 episodes in 93 infants. In the validation cohort, the HRC index 1) showed highly significant association with impending sepsis and sepsis-like illness (receiver operator characteristic area 0.75, p < 0.001) and 2) added significantly to the demographic information of birth weight, gestational age, and days of postnatal age in predicting sepsis and sepsis-like illness (p < 0.001). Continuous HRC monitoring is a generally valid and potentially useful noninvasive tool in the early diagnosis of neonatal sepsis and sepsis-like illness.
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                Author and article information

                Journal
                0100714
                6400
                Pediatr Res
                Pediatr. Res.
                Pediatric research
                0031-3998
                1530-0447
                26 October 2016
                03 November 2016
                February 2017
                03 May 2017
                : 81
                : 2
                : 315-321
                Affiliations
                [1 ]Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
                [2 ]Department of Medicine, University of Virginia, Charlottesville, VA, USA
                [3 ]Department of Pediatrics, Columbia University, New York, NY, USA
                Author notes
                Corresponding author information: Karen Fairchild, Department of Pediatrics, University of Virginia, Hospital Drive Box 800386, Charlottesville, VA 22908, (434)924-5428, kdf2n@ 123456virginia.edu
                Article
                NIHMS824942
                10.1038/pr.2016.215
                5309159
                28001143

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                Pediatrics

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