Dataset Information

2010 perinatal GBS prevention guideline and resource utilization.

ABSTRACT:

Objectives

To quantify differences in early-onset sepsis (EOS) evaluations, evaluation-associated resource utilization, and EOS cases detected, when comparing time periods before and after the implementation of an EOS algorithm based on the Centers for Disease Control and Prevention (CDC) 2010 guidelines for prevention of perinatal Group B Streptococcus (GBS) disease.

Methods

Retrospective cohort study of infants born at ?36 weeks' gestation from 2009 to 2012 in a single tertiary care center. One 12-month period during which EOS evaluations were based on the CDC 2002 guideline was compared with a second 12-month period during which EOS evaluations were based on the CDC 2010 guideline. A cost minimization analysis was performed to determine the EOS evaluation-associated costs and resources during each time period.

Results

During the study periods, among well-appearing infants ?36 weeks' gestation, EOS evaluations for inadequate GBS prophylaxis decreased from 32/1000 to <1/1000 live births; EOS evaluation-associated costs decreased by $6994 per 1000 live births; and EOS evaluation-associated work hours decreased by 29 per 1000 live births. We found no increase in EOS evaluations for other indications, total NICU admissions, frequency of infants evaluated for symptoms before hospital discharge, or incidence of EOS during the 2 study periods.

Conclusions

Implementation of an EOS algorithm based on CDC 2010 GBS guidelines resulted in a 25% decrease in EOS evaluations performed among well-appearing infants ?36 weeks' gestation, attributable to decreased evaluation of infants born in the setting of inadequate indicated GBS prophylaxis. This resulted in significant changes in EOS evaluation-associated resource expenditures.

SUBMITTER: Mukhopadhyay S

PROVIDER: S-EPMC3904275 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Publications

2010 perinatal GBS prevention guideline and resource utilization.

Mukhopadhyay Sagori S Dukhovny Dmitry D Mao Wenyang W Eichenwald Eric C EC Puopolo Karen M KM

Pediatrics 20140120 2

<h4>Objectives</h4>To quantify differences in early-onset sepsis (EOS) evaluations, evaluation-associated resource utilization, and EOS cases detected, when comparing time periods before and after the implementation of an EOS algorithm based on the Centers for Disease Control and Prevention (CDC) 2010 guidelines for prevention of perinatal Group B Streptococcus (GBS) disease.<h4>Methods</h4>Retrospective cohort study of infants born at ≥36 weeks' gestation from 2009 to 2012 in a single tertiary ...[more]

PMID: 24446442

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Project description:ImportanceThe overwhelming majority of fetal and neonatal deaths occur in low- and middle-income countries. Fetal and neonatal risk assessment tools may be useful to predict the risk of death.ObjectiveTo develop risk prediction models for intrapartum stillbirth and neonatal death.Design, setting, and participantsThis cohort study used data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Global Network for Women's and Children's Health Research population-based vital registry, including clinical sites in South Asia (India and Pakistan), Africa (Democratic Republic of Congo, Zambia, and Kenya), and Latin America (Guatemala). A total of 502 648 pregnancies were prospectively enrolled in the registry.ExposuresRisk factors were added sequentially into the data set in 4 scenarios: (1) prenatal, (2) predelivery, (3) delivery and day 1, and (4) postdelivery through day 2.Main outcomes and measuresData sets were randomly divided into 10 groups of 3 analysis data sets including training (60%), test (20%), and validation (20%). Conventional and advanced machine learning modeling techniques were applied to assess predictive abilities using area under the curve (AUC) for intrapartum stillbirth and neonatal mortality.ResultsAll prenatal and predelivery models had predictive accuracy for both intrapartum stillbirth and neonatal mortality with AUC values 0.71 or less. Five of 6 models for neonatal mortality based on delivery/day 1 and postdelivery/day 2 had increased predictive accuracy with AUC values greater than 0.80. Birth weight was the most important predictor for neonatal death in both postdelivery scenarios with independent predictive ability with AUC values of 0.78 and 0.76, respectively. The addition of 4 other top predictors increased AUC to 0.83 and 0.87 for the postdelivery scenarios, respectively.Conclusions and relevanceModels based on prenatal or predelivery data had predictive accuracy for intrapartum stillbirths and neonatal mortality of AUC values 0.71 or less. Models that incorporated delivery data had good predictive accuracy for risk of neonatal mortality. Birth weight was the most important predictor for neonatal mortality.

Dataset Information

2010 perinatal GBS prevention guideline and resource utilization.

Objectives

Methods

Results

Conclusions

Publications

2010 perinatal GBS prevention guideline and resource utilization.

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