Development, evaluation and validation of a screening tool for late onset bacteremia in neonates \u2013 a pilot study

Sandra A N Walker,Asaph Rolnitsky,Melanie Cormier,Julie Choudhury,Marion Elligsen,Carla Findlater,Dolores Iaboni

doi:10.1186/s12887-019-1633-1

Sandra A N Walker, Asaph Rolnitsky + Show 5 more

Open Access

https://doi.org/10.1186/s12887-019-1633-1

Copy DOI

Abstract

BackgroundClinical and laboratory parameters can aid in the early identification of neonates at risk for bacteremia before clinical deterioration occurs. However, current prediction models have poor diagnostic capabilities. The objective of this study was to develop, evaluate and validate a screening tool for late onset (> 72 h post admission) neonatal bacteremia using common laboratory and clinical parameters; and determine its predictive value in the identification of bacteremia.MethodsA retrospective chart review of neonates admitted to a neonatal intensive care unit (NICU) between March 1, 2012 and January 14, 2015 and a prospective evaluation of all neonates admitted between January 15, 2015 and March 30, 2015 were completed. Neonates with late-onset bacteremia (> 72 h after NICU admission) were eligible for inclusion in the bacteremic cohort. Bacteremic patients were matched to non-infected controls on several demographic parameters. A Pearson’s Correlation matrix was completed to identify independent variables significantly associated with infection (p < 0.05, univariate analysis). Significant parameters were analyzed using iterative binary logistic regression to identify the simplest significant model (p < 0.05). The predictive value of the model was assessed and the optimal probability cut-off for bacteremia was determined using a Receiver Operating Characteristic curve.ResultsMaximum blood glucose, heart rate, neutrophils and bands were identified as the best predictors of bacteremia in a significant binary logistic regression model. The model’s sensitivity, specificity and accuracy were 90, 80 and 85%, respectively, with a false positive rate of 20% and a false negative rate of 9.7%. At the study bacteremia prevalence rate of 51%, the positive predictive value, negative predictive value and negative post-test probability were 82, 89 and 11%, respectively.ConclusionThe model developed in the current study is superior to currently published neonatal bacteremia screening tools. Validation of the tool in a historic data set of neonates from our institution will be completed.

Highlights

Clinical and laboratory parameters can aid in the early identification of neonates at risk for bacteremia before clinical deterioration occurs
A total of 2214 neonates were admitted to the neonatal intensive care unit (NICU) between March 1, 2012 – March 31, 2015 and 153 of these neonates (7%) (42 cases, 42 matched controls, 69 prospective unmatched controls, 111 total controls) were included in this study (Fig. 1)
A screening tool that accurately predicts the probability of late-onset bacteremia in neonates using four parameters that are readily available through routine blood work and monitoring in the NICU was developed

Summary

Introduction

Clinical and laboratory parameters can aid in the early identification of neonates at risk for bacteremia before clinical deterioration occurs. The objective of this study was to develop, evaluate and validate a screening tool for late onset (> 72 h post admission) neonatal bacteremia using common laboratory and clinical parameters; and determine its predictive value in the identification of bacteremia. The diagnosis of late-onset neonatal sepsis is reached using various signs and symptoms, and often leads to the initiation of empiric, broad spectrum antimicrobial therapy before laboratory results are available [2]. The lack of specificity of symptoms of bacteremia and the overlap of shared symptoms among various neonatal conditions produces an extensive list of differential diagnoses for clinicians to consider and may lead to the overuse of broad-spectrum antibiotics. Healthcare professionals in the neonatal intensive care unit (NICU) lack a standardized, validated prediction tool for bacteremia. Published screening tools that predict bacteremia have deficiencies in their performance metrics (e.g. sensitivity and specificity) which limit their application in clinical practice [5,6,7,8,9,10,11,12,13,14]

Objectives

Methods

Results

Discussion

Conclusion