Estimating the Probability of Neonatal Early-Onset Infection on the Basis of Maternal Risk Factors

Puopolo, Karen M.; Draper, David; Wi, Soora; Newman, Thomas B.; Zupancic, John A.F.; Lieberman, Ellice; Smith, Milton T.; Escobar, Gabriel J.

doi:10.1542/peds.2010-3464

Cited by 331 publications

(293 citation statements)

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“…Maternal data are in our previous report. 2 Table 2 shows that case infants tended to have lower gestational age, birth weight, and Apgar scores. The table also shows that significant clinical signs and major deteriorations tended to occur very early (before 6 hours of age).…”

Section: Analytic Approachmentioning

confidence: 99%

“…It is also important to note that the relative contribution of GBS status to our predictive model is very small (2.3%); most of the predictive power of the maternal model comes from gestational age (16.7%), highest antepartum temperature (58.4%), and length of time since membranes ruptured (12.6%). 2 Nonetheless, our approach would benefit from revalidation using data from centers adhering to the CDC' s recommended practices of 2010. Although it is based on a large cohort, our study may not be representative of all newborns.…”

Section: Figurementioning

confidence: 99%

“…In these infants, current incidence of EOS ranges between 0.5 and 1.2 cases per thousand live births. 1,2 This represents a threefold to fivefold decrease over the past 20 years. [3][4][5][6][7] Decreases in incidence have been most pronounced in group B Streptococcus (GBS; Streptococcus agalactiae) sepsis, with 2010 national surveillance reporting 0.25 cases per 1000 live births 8 in contrast to 1.8 cases per 1000 live births in 1990.…”

mentioning

confidence: 99%

“…Our team recently described an efficient predictive model for EOS in newborns $34 weeks' gestation based on information available at the moment of birth. 2 This multivariate model uses highest maternal antepartum temperature, gestational age, length of time a mother' s membranes were ruptured, GBS carriage status, and type of intrapartum antibiotic therapy received to provide a preliminary risk estimate for EOS; it is currently available for clinical use. 16,17 In this phase of our study, we again use objective data to account for the evolving clinical condition of the infant in the first 12 hours after birth.…”

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confidence: 99%

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

et al. 2014

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WHAT'S KNOWN ON THIS SUBJECT:The management of term and near-term newborns suspected of early-onset sepsis, particularly when they are not clearly symptomatic, remains controversial. Methods for quantifying risk that combine maternal factors with a newborn' s evolving clinical examination have been lacking. WHAT THIS STUDY ADDS:This study provides a method for predicting risk of early-onset sepsis. It combines maternal risk factors with objective measures of a newborn' s clinical examination and places newborns into 3 risk groups (treat empirically, observe and evaluate, and continued observation). abstract 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.

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Section: Analytic Approachmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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

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“…Although risk calculators have been created for various diagnoses, procedures, and outcomes, and are used in a range of settings [2,3,5,6,16,18,22,26,35,[42][43][44][45], risk calculators for total joint arthroplasties have not been developed and fully integrated in large healthcare settings. One risk calculator for periprosthetic joint infection and mortality after THA has been developed however, it was developed only for the Medicare population [5].…”

Section: Introductionmentioning

confidence: 99%

Risk Calculators Predict Failures of Knee and Hip Arthroplasties: Findings from a Large Health Maintenance Organization

Paxton

Inacio

Khatod³

et al. 2015

Clinical Orthopaedics &Amp; Related Research

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Background Considering the cost and risk associated with revision Total knee arthroplasty (TKAs) and Total hip arthroplasty (THAs), steps to prevent these operations will help patients and reduce healthcare costs. Revision risk calculators for patients may reduce revision surgery by supporting clinical decision-making at the point of care. Questions/purposes We sought to develop a TKA and THA revision risk calculator using data from a large health-maintenance organization's arthroplasty registry and determine the best set of predictors for the revision risk calculator. Methods Revision risk calculators for THAs and TKAs were developed using a patient cohort from a total joint replacement registry and data from a large US integrated healthcare system. The cohort included all patients who had primary procedures performed in our healthcare system between April 2001 and July 2008 and were followed until January 2014 (TKAs, n = 41,750; THAs, n = 22,721), During the study period, 9% of patients (TKA = 3066/ 34,686; THA=1898/20,285) were lost to followup and 7% died (TKA= 2350/41,750; THA=1419/20,285). The outcome of interest was revision surgery and was defined as replacement of any component for any reason within 5 years postoperatively. Candidate predictors for the revision risk calculator were limited to preoperative patient demographics, comorbidities, and procedure diagnoses. Logistic regression models were used to identify predictors and the Hosmer-Lemeshow goodness-of-fit test and c-statistic were used to choose final models for the revision risk calculator.

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A Quantitative, Risk-Based Approach to the Management of Neonatal Early-Onset Sepsis

et al. 2017

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IMPORTANCE Current algorithms for management of neonatal early-onset sepsis (EOS) result in medical intervention for large numbers of uninfected infants. We developed multivariable prediction models for estimating the risk of EOS among late preterm and term infants based on objective data available at birth and the newborn's clinical status.OBJECTIVES To examine the effect of neonatal EOS risk prediction models on sepsis evaluations and antibiotic use and assess their safety in a large integrated health care system. DESIGN, SETTING, AND PARTICIPANTS The study cohort includes 204 485 infants born at 35 weeks' gestation or later at a Kaiser Permanente Northern California hospital from January 1, 2010, through December 31, 2015. The study compared 3 periods when EOS management was based on (1) national recommended guidelines (baseline period [January 1, 2010, through November 31, 2012]), (2) multivariable estimates of sepsis risk at birth (learning period [December 1, 2012, through June 30, 2014]), and (3) the multivariable risk estimate combined with the infant's clinical condition in the first 24 hours after birth (EOS calculator period [July 1, 2014, through December 31, 2015]). MAIN OUTCOMES AND MEASURES The primary outcome was antibiotic administration in the first 24 hours. Secondary outcomes included blood culture use, antibiotic administration between 24 and 72 hours, clinical outcomes, and readmissions for EOS. RESULTS The study cohort included 204 485 infants born at 35 weeks' gestation or later: 95 343 in the baseline period (mean [SD] age, 39.4 [1.3] weeks; 46 651 male [51.0%]; 37 007 white, non-Hispanic [38.8%]), 52 881 in the learning period (mean [SD] age, 39.3 [1.3] weeks; 27 067 male [51.2%]; 20 175 white, non-Hispanic [38.2%]), and 56 261 in the EOS calculator period (mean [SD] age, 39.4 [1.3] weeks; 28 575 male [50.8%]; 20 484 white, non-Hispanic [36.4%]). In a comparison of the baseline period with the EOS calculator period, blood culture use decreased from 14.5% to 4.9% (adjusted difference, −7.7%; 95% CI, −13.1% to −2.4%). Empirical antibiotic administration in the first 24 hours decreased from 5.0% to 2.6% (adjusted difference, −1.8; 95% CI, −2.4% to −1.3%). No increase in antibiotic use occurred between 24 and 72 hours after birth; use decreased from 0.5% to 0.4% (adjusted difference, 0.0%; 95% CI, −0.1% to 0.2%). The incidence of culture-confirmed EOS was similar during the 3 periods (0.03% in the baseline period, 0.03% in the learning period, and 0.02% in the EOS calculator period). Readmissions for EOS (within 7 days of birth) were rare in all periods (5.2 per 100 000 births in the baseline period, 1.9 per 100 000 births in the learning period, and 5.3 per 100 000 births in the EOS calculator period) and did not differ statistically (P = .70). Incidence of adverse clinical outcomes, including need for inotropes, mechanical ventilation, meningitis, and death, was unchanged after introduction of the EOS calculator.CONCLUSIONS AND RELEVANCE Clinical care algorithms based on individual infant ...

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Estimating the Probability of Neonatal Early-Onset Infection on the Basis of Maternal Risk Factors

Cited by 331 publications

References 20 publications

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

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

Risk Calculators Predict Failures of Knee and Hip Arthroplasties: Findings from a Large Health Maintenance Organization

A Quantitative, Risk-Based Approach to the Management of Neonatal Early-Onset Sepsis

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