Cost impact of procalcitonin-guided decision making on duration of antibiotic therapy for suspected early-onset sepsis in neonates

Abstract Backgrounds The large, international, randomized controlled NeoPInS trial showed that procalcitonin (PCT)-guided decision making was superior to standard care in reducing the duration of antibiotic therapy and hospitalization in neonates suspected of early-onset sepsis (EOS), without increased adverse events. This study aimed to perform a cost-minimization study of the NeoPInS trial, comparing health care costs of standard care and PCT-guided decision making based on the NeoPInS algorithm, and to analyze subgroups based on country, risk category and gestational age. Methods Data from the NeoPInS trial in neonates born after 34 weeks of gestational age with suspected EOS in the first 72 h of life requiring antibiotic therapy were used. We performed a cost-minimization study of health care costs, comparing standard care to PCT-guided decision making. Results In total, 1489 neonates were included in the study, of which 754 were treated according to PCT-guided decision making and 735 received standard care. Mean health care costs of PCT-guided decision making were not significantly different from costs of standard care (€3649 vs. €3616). Considering subgroups, we found a significant reduction in health care costs of PCT-guided decision making for risk category ‘infection unlikely’ and for gestational age ≥ 37 weeks in the Netherlands, Switzerland and the Czech Republic, and for gestational age < 37 weeks in the Czech Republic. Conclusions Health care costs of PCT-guided decision making of term and late-preterm neonates with suspected EOS are not significantly different from costs of standard care. Significant cost reduction was found for risk category ‘infection unlikely,’ and is affected by both the price of PCT-testing and (prolonged) hospitalization due to SAEs

Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics

C-Reactive Protein, Procalcitonin, and White Blood Count to Rule Out Neonatal Early-onset Sepsis Within 36 Hours: A Secondary Analysis of the Neonatal Procalcitonin Intervention Study.

BACKGROUND: Neonatal early-onset sepsis (EOS) is one of the main causes of global neonatal mortality and morbidity, and initiation of early antibiotic treatment is key. However, antibiotics may be harmful. METHODS: We performed a secondary analysis of results from the Neonatal Procalcitonin Intervention Study, a prospective, multicenter, randomized, controlled intervention study. The primary outcome was the diagnostic accuracy of serial measurements of C-reactive protein (CRP), procalcitonin (PCT), and white blood count (WBC) within different time windows to rule out culture-positive EOS (proven sepsis). RESULTS: We analyzed 1678 neonates with 10 899 biomarker measurements (4654 CRP, 2047 PCT, and 4198 WBC) obtained within the first 48 hours after the start of antibiotic therapy due to suspected EOS. The areas under the curve (AUC) comparing no sepsis vs proven sepsis for maximum values of CRP, PCT, and WBC within 36 hours were 0.986, 0.921, and 0.360, respectively. The AUCs for CRP and PCT increased with extended time frames up to 36 hours, but there was no further difference between start to 36 hours vs start to 48 hours. Cutoff values at 16 mg/L for CRP and 2.8 ng/L for PCT provided a sensitivity of 100% for discriminating no sepsis vs proven sepsis. CONCLUSIONS: Normal serial CRP and PCT measurements within 36 hours after the start of empiric antibiotic therapy can exclude the presence of neonatal EOS with a high probability. The negative predictive values of CRP and PCT do not increase after 36 hours

The effect of inhaled nitric oxide on the course of extracorporeal membrane oxygenation and the occurrence of hemorrhagic complications.

Item does not contain fulltextThis study evaluated the relation between prior inhaled nitric oxide (iNO) and the time to initiation and duration of treatment with veno-arterial extracorporeal membrane oxygenation (ECMO) and the occurrence of hemorrhagic complications. A retrospective study was conducted in 59 human newborns treated for respiratory insufficiency with ECMO over a 5-year period. Patients received iNO before ECMO (iNO group) or not (control group). Both groups were compared for patient characteristics, postnatal age at the initiation of ECMO, duration of ECMO treatment, and hemorrhagic complications. There were no significant differences between the iNO group and the control group for patient characteristics and medication use before the ECMO treatment, except for norepinephrine. There was no significant difference for postnatal age at the initiation of ECMO and mean duration of ECMO treatment. We found no statistically significant difference in hemorrhagic complications between both groups. This persisted after correction for diagnosis and ECMO run-time in linear logistic regression analysis. Inhaled nitric oxide before ECMO did not result in a significant delay in the initiation of ECMO or longer duration of the ECMO. There was no significant relationship between the use of prior iNO and the occurrence of hemorrhagic complications during the ECMO treatment

An infant formula with large, milk phospholipid-coated lipid droplets containing a mixture of dairy and vegetable lipids supports adequate growth and is well tolerated in healthy, term infants.

BACKGROUND: Lipid droplets in human milk have a mode diameter of ∼4 μm and are surrounded by a native phospholipid-rich membrane. Current infant milk formulas (IMFs) contain small lipid droplets (mode diameter ∼0.5 μm) primarily coated by proteins. A concept IMF was developed mimicking more closely the structure and composition of human milk lipid droplets. OBJECTIVES: This randomized, controlled, double-blind equivalence trial evaluates the safety and tolerance of a concept IMF with large, milk phospholipid-coated lipid droplets (mode diameter 3-5 μm) containing vegetable and dairy lipids in healthy, term infants. METHODS: Fully formula-fed infants were enrolled up to 35 d of age and randomly assigned to 1 of 2 formulas until 17 wk of age: 1) Control IMF with small lipid droplets containing vegetable oils (n = 108); or 2) Concept IMF with large, milk phospholipid-coated lipid droplets comprised of 48% dairy lipids (n = 115). A group of 88 breastfed infants served as reference. Primary outcome was daily weight gain during intervention. Additionally, number and type of adverse events, growth, and tolerance parameters were monitored. RESULTS: Equivalence of daily weight gain was demonstrated (Concept compared with Control IMF: -1.37 g/d; 90% CI: -2.71, -0.02; equivalence margin ± 3 g/d). No relevant group differences were observed in growth, tolerance and number, severity, or relatedness of adverse events. We did observe a higher prevalence of watery stools in the Concept than in the Control IMF group between 5 and 12 wk of age (P < 0.001), closer to the stool characteristics observed in the breastfed group. CONCLUSIONS: An infant formula with large, milk phospholipid-coated lipid droplets containing dairy lipids is safe, well tolerated, and supports an adequate growth in healthy infants. This trial was registered in the Dutch Trial Register (www.trialregister.nl) as NTR3683

Cost impact of procalcitonin-guided decision making on duration of antibiotic therapy for suspected early-onset sepsis in neonates

Backgrounds The large, international, randomized controlled NeoPInS trial showed that procalcitonin (PCT)-guided decision making was superior to standard care in reducing the duration of antibiotic therapy and hospitalization in neonates suspected of early-onset sepsis (EOS), without increased adverse events. This study aimed to perform a cost-minimization study of the NeoPInS trial, comparing health care costs of standard care and PCT-guided decision making based on the NeoPInS algorithm, and to analyze subgroups based on country, risk category and gestational age. Methods Data from the NeoPInS trial in neonates born after 34 weeks of gestational age with suspected EOS in the first 72 h of life requiring antibiotic therapy were used. We performed a cost-minimization study of health care costs, comparing standard care to PCT-guided decision making. Results In total, 1489 neonates were included in the study, of which 754 were treated according to PCT-guided decision making and 735 received standard care. Mean health care costs of PCT-guided decision making were not significantly different from costs of standard care (€3649 vs. €3616). Considering subgroups, we found a significant reduction in health care costs of PCT-guided decision making for risk category ‘infection unlikely’ and for gestational age ≥ 37 weeks in the Netherlands, Switzerland and the Czech Republic, and for gestational age < 37 weeks in the Czech Republic. Conclusions Health care costs of PCT-guided decision making of term and late-preterm neonates with suspected EOS are not significantly different from costs of standard care. Significant cost reduction was found for risk category ‘infection unlikely,’ and is affected by both the price of PCT-testing and (prolonged) hospitalization due to SAEs

Procalcitonin-guided decision making for duration of antibiotic therapy in neonates with suspected early-onset sepsis: a multicentre, randomised controlled trial (NeoPIns)

Background Up to 7% of term and late-preterm neonates in high-income countries receive antibiotics during the first 3 days of life because of suspected early-onset sepsis. The prevalence of culture-proven early-onset sepsis is 0·1% or less in high-income countries, suggesting substantial overtreatment. We assess whether procalcitonin-guided decision making for suspected early-onset sepsis can safely reduce the duration of antibiotic treatment. Methods We did this randomised controlled intervention trial in Dutch (n=11), Swiss (n=4), Canadian (n=2), and Czech (n=1) hospitals. Neonates of gestational age 34 weeks or older, with suspected early-onset sepsis requiring antibiotic treatment were stratified into four risk categories by their treating physicians and randomly assigned [1:1] using a computer-generated list stratified per centre to procalcitonin-guided decision making or standard care-based antibiotic treatment. Neonates who underwent surgery within the first week of life or had major congenital malformations that would have required hospital admission were excluded. Only principal investigators were masked for group assignment. Co-primary outcomes were non-inferiority for re-infection or death in the first month of life (margin 2·0%) and superiority for duration of antibiotic therapy. Intention-to-treat and per-protocol analyses were done. This trial was registered with ClinicalTrials.gov, number NCT00854932. Findings Between May 21, 2009, and Feb 14, 2015, we screened 2440 neonates with suspected early-onset sepsis. 622 infants were excluded due to lack of parental consent, 93 were ineligible for reasons unknown (68), congenital malformation (22), or surgery in the first week of life (3). 14 neonates were excluded as 100% data monitoring or retrieval was not feasible, and one neonate was excluded because their procalcitonin measurements could not be taken. 1710 neonates were enrolled and randomly assigned to either procalcitonin-guided therapy (n=866) or standard therapy (n=844). 1408 neonates underwent per-protocol analysis (745 in the procalcitonin group and 663 standard group). For the procalcitonin group, the duration of antibiotic therapy was reduced (intention to treat: 55·1 vs 65·0 h, p<0·0001; per protocol: 51·8 vs 64·0 h; p<0·0001). No sepsis-related deaths occurred, and 9 (<1%) of 1710 neonates had possible re-infection. The risk difference for non-inferiority was 0·1% (95% CI −4·6 to 4·8) in the intention-to-treat analysis (5 [0·6%] of 866 neonates in the procalcitonin group vs 4 [0·5%] of 844 neonates in the standard group) and 0·1% (−5·2 to 5·3) in the per-protocol analysis (5 [0·7%] of 745 neonates in the procalcitonin group vs 4 [0·6%] of 663 neonates in the standard group). Interpretation Procalcitonin-guided decision making was superior to standard care in reducing antibiotic therapy in neonates with suspected early-onset sepsis. Non-inferiority for re-infection or death could not be shown due to the low occurrence of re-infections and absence of study-related death. Funding The Thrasher Foundation, the NutsOhra Foundation, the Sophia Foundation for Scientific research

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics