Combining Immature and Total Neutrophil Counts to Predict Early Onset Sepsis in Term and Late Preterm Newborns

BackgroundThe absolute neutrophil count and the immature/total neutrophil ratio (I/T) provide information about the risk of early onset sepsis in newborns. However, it is not clear how to combine their potentially overlapping information into a single likelihood ratio.MethodsWe obtained electronic records of blood cultures and of complete blood counts with manual differentials drawn <1 hour apart on 66,846 infants ≥ 34 weeks gestation and <72 hours of age born at Kaiser Permanente Northern California and Brigham and Women's Hospitals. We hypothesized that dividing the immature neutrophil count (I) by the total neutrophil count (T) squared (I/T) would provide a useful summary of the risk of infection. We evaluated the ability of the I/T to discriminate newborns with pathogenic bacteremia from other newborns tested using the area under the receiver operating characteristic curve (c).ResultsDiscrimination of the I/T (c = 0.79; 95% confidence interval: 0.76-0.82) was similar to that of logistic models with indicator variables for each of 24 combinations of the absolute neutrophil count and the proportion of immature neutrophils (c = 0.80, 95% confidence interval: 0.77-0.83). Discrimination of the I/T improved with age, from 0.70 at <1 hour to 0.87 at ≥ 4 hours. However, 60% of I/T had likelihood ratios of 0.44-1.3, thus only minimally altering the pretest odds of disease.ConclusionsCalculating the I/T could enhance prediction of early onset sepsis, but the complete blood counts will remain helpful mainly when done at >4 hours of age and when the pretest probability of infection is close to the treatment threshold

Stratification of Risk of Early-Onset Sepsis in Newborns ≥34 Weeks’ Gestation

ObjectiveTo define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation.MethodsWe conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset.ResultsUsing a base population of 608,014 live births ≥ 34 weeks' gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases <72 hours of age and frequency matched them by hospital and year of birth to 1063 controls. Using maternal and neonatal data, we defined a risk stratification scheme that divided the neonatal population into 3 groups: treat empirically (4.1% of all live births, 60.8% of all EOS cases, sepsis incidence of 8.4/1000 live births), observe and evaluate (11.1% of births, 23.4% of cases, 1.2/1000), and continued observation (84.8% of births, 15.7% of cases, incidence 0.11/1000).ConclusionsIt is possible to combine objective maternal data with evolving objective neonatal clinical findings to define more efficient strategies for the evaluation and treatment of EOS in term and late preterm infants. Judicious application of our scheme could result in decreased antibiotic treatment in 80,000 to 240,000 US newborns each year

Stratification of Risk of Early-Onset Sepsis in Newborns ≥34 Weeks’ Gestation

OBJECTIVE: To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥34 weeks’ gestation. METHODS: We conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset. RESULTS: Using a base population of 608 014 live births ≥34 weeks’ gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases <72 hours of age and frequency matched them by hospital and year of birth to 1063 controls. Using maternal and neonatal data, we defined a risk stratification scheme that divided the neonatal population into 3 groups: treat empirically (4.1% of all live births, 60.8% of all EOS cases, sepsis incidence of 8.4/1000 live births), observe and evaluate (11.1% of births, 23.4% of cases, 1.2/1000), and continued observation (84.8% of births, 15.7% of cases, incidence 0.11/1000). CONCLUSIONS: It is possible to combine objective maternal data with evolving objective neonatal clinical findings to define more efficient strategies for the evaluation and treatment of EOS in term and late preterm infants. Judicious application of our scheme could result in decreased antibiotic treatment in 80 000 to 240 000 US newborns each year