SynthesisSystematic reviews2017
A systematic review of neonatal treatment intensity scores and their potential application in low-resource setting hospitals for predicting mortality, morbidity and estimating resource use.
Synthesis in Systematic reviews, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Opportunities and Challenges of Prognostic Models for Extremely Preterm Infants.Children (Basel, Switzerland) · 2023Article
- Observational
- Evaluation of Modified Extended Sick Neonate Score to Predict In-Hospital Mortality among Newborns Admitted to Resource-Poor Settings in Rural India.Indian journal of pediatrics · 2023Article
- Determinants of Mortality in a Neonatal Intensive Care Unit in Athens, Greece: A Case-Control Study.Cureus · 2022Article
- External validation of inpatient neonatal mortality prediction models in high-mortality settings.BMC medicine · 2022Article
- Article
- Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure.Biomedicines · 2021Article
- Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit.Journal of personalized medicine · 2021Article
- Changepoint analysis of gestational age and birth weight: proposing a refinement of Diagnosis Related Groups.Pediatric research · 2020Observational
- Modified Sick Neonatal Score (MSNS): A Novel Neonatal Disease Severity Scoring System for Resource-Limited Settings.Critical care research and practice · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundTreatment intensity scores can predict mortality and estimate resource use. They may therefore be of interest for essential neonatal care in low resource settings where neonatal mortality remains high. We sought to systematically review neonatal treatment intensity scores to (1) assess the level of evidence on predictive performance in predicting clinical outcomes and estimating resource utilisation and (2) assess the applicability of the identified models to decision making for neonatal care in low resource settings.
methodsWe conducted a systematic search of PubMed, EMBASE (OVID), CINAHL, Global Health Library (Global index, WHO) and Google Scholar to identify studies published up until 21 December 2016. Included were all articles that used treatments as predictors in neonatal models. Individual studies were appraised using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). In addition, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used as a guiding framework to assess certainty in the evidence for predicting outcomes across studies.
resultsThree thousand two hundred forty-nine articles were screened, of which ten articles were included in the review. All of the studies were conducted in neonatal intensive care units with sample sizes ranging from 22 to 9978, with a median of 163. Two articles reported model development, while eight reported external application of existing models to new populations. Meta-analysis was not possible due heterogeneity in the conduct and reporting of the identified studies. Discrimination as assessed by area under receiver operating characteristic curve was reported for in-hospital mortality, median 0.84 (range 0.75-0.96, three studies), early adverse outcome and late adverse outcome (0.78 and 0.59, respectively, one study).
conclusionExisting neonatal treatment intensity models show promise in predicting mortality and morbidity. There is however low certainty in the evidence on their performance in essential neonatal care in low resource settings as all studies had methodological limitations and were conducted in intensive care. The approach may however be developed further for low resource settings like Kenya because treatment data may be easier to obtain compared to measures of physiological status. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016034205.
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