CONTEXT: Kangaroo mother care (KMC) is an intervention aimed at improving outcomes among preterm and low birth weight newborns.OBJECTIVE: Conduct a systematic review and meta-analysis estimating the association between KMC and neonatal outcomes. STUDY SELECTION:We included randomized trials and observational studies through April 2014 examining the relationship between KMC and neonatal outcomes among infants of any birth weight or gestational age. Studies with <10 participants, lack of a comparison group without KMC, and those not reporting a quantitative association were excluded. DATA EXTRACTION:Two reviewers extracted data on study design, risk of bias, KMC intervention, neonatal outcomes, relative risk (RR) or mean difference measures.RESULTS: 1035 studies were screened; 124 met inclusion criteria. Among LBW newborns, KMC compared to conventional care was associated with 36% lower mortality(RR 0.64; 95% [CI] 0.46, 0.89). KMC decreased risk of neonatal sepsis (RR 0.53, 95% CI 0.34, 0.83), hypothermia (RR 0.22; 95% CI 0.12, 0.41), hypoglycemia (RR 0.12; 95% CI 0.05, 0.32), and hospital readmission (RR 0.42; 95% CI 0.23, 0.76) and increased exclusive breastfeeding (RR 1.50; 95% CI 1.26, 1.78). Newborns receiving KMC had lower mean respiratory rate and pain measures, and higher oxygen saturation, temperature, and head circumference growth. LIMITATIONS:Lack of data on KMC limited the ability to assess dose-response. CONCLUSIONS:Interventions to scale up KMC implementation are warranted. Epidemiology, c Biostatistics, and d Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Departments of b Global Health and Population, and f Social and Behavioral Sciences, Harvard School of Public Health, Boston, Massachusetts; e Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital, Boston, Massachusetts; g Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, Massachusetts; h Save the Children, Washington, DC; and i Department of Medicine, Boston Children's Hospital, Boston, Massachusetts Dr Boundy conceptualized and designed the study, conducted the literature review, collected the data, conducted the analyses, created the tables and fi gures, and drafted and revised the manuscript; Dr Dastjerdi conducted the literature review, collected and cleaned the data, assisted with table and fi gure creation, and critically reviewed the manuscript; Dr Spiegelman contributed to the study design, statistical analyses, and data interpretation and critically reviewed the manuscript; Drs Fawzi, Missmer, and Lieberman contributed to the study design and data interpretation and critically reviewed the manuscript; Ms Kajeepeta conducted the literature review, collected the data, assisted with fi gure creation, and critically reviewed the manuscript;Dr Wall contributed to the conceptualization and design of the study and data interpretation and critically reviewed the manuscript; Dr Chan conceptualized and designed the study, designed...

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.

A predictive model based on information available in the immediate perinatal period performs better than algorithms based on risk-factor threshold values. This model establishes a prior probability for newborn sepsis, which could be combined with neonatal physical examination and laboratory values to establish a posterior probability to guide treatment decisions.