Clinical parameters predicting failure of empirical antibacterial therapy in early onset neonatal sepsis, identified by classification and regression tree analysis

Abstract

Background: About 10-20% of neonates with suspected or proven early onset sepsis (EOS) fail on the empiric antibiotic regimen of ampicillin or penicillin and gentamicin. We aimed to identify clinical and laboratory markers associated with empiric antibiotic treatment failure in neonates with suspected EOS.

Methods: Maternal and early neonatal characteristics predicting failure of empiric antibiotic treatment were identified by univariate logistic regression analysis from a prospective database of 283 neonates admitted to neonatal intensive care unit within 72 hours of life and requiring antibiotic therapy with penicillin or ampicillin and gentamicin. Variables, identified as significant by univariate analysis, were entered into stepwise multiple logistic regression (MLR) analysis and classification and regression tree (CRT) analysis to develop a decision algorithm for clinical application. In order to ensure the earliest possible timing separate analysis for 24 and 72 hours of age was performed.

Results: At 24 hours of age neonates with hypoglycaemia < or = 2.55 mmol/L together with CRP values > 1.35 mg/L or those with BW < or = 678 g had more than 30% likelihood of treatment failure. In normoglycaemic neonates with higher BW the best predictors of treatment failure at 24 hours were GA < or = 27 weeks and among those, with higher GA, WBC < or = 8.25 x 10(9) L(-1) together with platelet count < or = 143 x 10(9) L(-1). The algorithm allowed capture of 75% of treatment failure cases with a specificity of 89%. By 72 hours of age minimum platelet count < or = 94.5 x 10(9) L(-1) with need for vasoactive treatment or leukopaenia < or = 3.5 x 10(9) L(-1) or leukocytosis > 39.8 x 10(9) L(-1) or blood glucose < or = 1.65 mmol/L allowed capture of 81% of treatment failure cases with the specificity of 88%. The performance of MLR and CRT models was similar, except for higher specificity of the CRT at 72 h, compared to MLR analysis.

Conclusion: There is an identifiable group of neonates with high risk of EOS, likely to fail on conventional antibiotic therapy.

Figure 1

Decision tree based on data available by 24 h from birth with patient distribution. P(TF) - probability of treatment failure. For laboratory values the decision algorithm includes the highest and/or lowest values registered within the first 24 h of life. Patient groups with P(TF) ≥ 0.3, identified as treatment failures, are surrounded by black box. WBC - white blood cell count.

Figure 2

Decision tree based on data available by 72 h from birth with patient distribution. P(TF) - probability of treatment failure. For laboratory values the decision algorithm includes the highest and/or lowest values registered within the first 72 h of life. Patient groups with P(TF) > 0.27, identified as treatment failures, are surrounded by black box. WBC - white blood cell count.