Literature DB >> 7910282

CRIB and SNAP: assessing the risk of death for preterm neonates.

J Rautonen1, A Mäkelä, H Boyd, M Apajasalo, M Pohjavuori.   

Abstract

The performance of three scoring systems for assessing mortality risk for neonates--clinical risk index for babies (CRIB), score for neonatal acute physiology (SNAP), and SNAP's perinatal extension (SNAP-PE)--were tested in the same set of patients. In 222 neonates weighing less than 1500 g at birth, CRIB scores were significantly better for assessing mortality risk than SNAP (p = 0.017) or SNAP-PE (p < 0.001), areas under receiver operating characteristic curves being 0.89 (SE 0.02), 0.82 (0.03), and 0.79 (0.03), respectively. Male sex was independently associated with poor prognosis after taking the CRIB score into account with a risk ratio of 2.75. We conclude that CRIB is the most useful score for comparing the performance of neonatal intensive-care units. New treatment methods, however, may require modifications to the system.

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Year:  1994        PMID: 7910282     DOI: 10.1016/s0140-6736(94)92158-x

Source DB:  PubMed          Journal:  Lancet        ISSN: 0140-6736            Impact factor:   79.321


  13 in total

1.  Role of score for neonatal acute physiology (SNAP) in predicting neonatal mortality.

Authors:  P P Maiya; S Nagashree; M S Shaik
Journal:  Indian J Pediatr       Date:  2001-09       Impact factor: 1.967

2.  Assessing mortality risk in very low birthweight infants: a comparison of CRIB, CRIB-II, and SNAPPE-II.

Authors:  L Gagliardi; A Cavazza; A Brunelli; M Battaglioli; D Merazzi; F Tandoi; D Cella; G F Perotti; M Pelti; I Stucchi; F Frisone; A Avanzini; R Bellù
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  2004-09       Impact factor: 5.747

3.  An illness severity score and neonatal mortality in retrieved neonates.

Authors:  Simon J Broughton; Andrew Berry; Stephen Jacobe; Paul Cheeseman; William O Tarnow-Mordi; Anne Greenough
Journal:  Eur J Pediatr       Date:  2004-04-16       Impact factor: 3.183

Review 4.  Neonatal disease severity scoring systems.

Authors:  J S Dorling; D J Field; B Manktelow
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  2005-01       Impact factor: 5.747

5.  Artificial neural network for risk assessment in preterm neonates.

Authors:  B Zernikow; K Holtmannspoetter; E Michel; W Pielemeier; F Hornschuh; A Westermann; K H Hennecke
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  1998-09       Impact factor: 5.747

6.  Risk adjusted and population based studies of the outcome for high risk infants in Scotland and Australia. International Neonatal Network, Scottish Neonatal Consultants, Nurses Collaborative Study Group.

Authors: 
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  2000-03       Impact factor: 5.747

Review 7.  Comparing mortality risk models in VLBW and preterm infants: systematic review and meta-analysis.

Authors:  Jennifer S McLeod; Anitha Menon; Niki Matusko; Gary M Weiner; Samir K Gadepalli; John Barks; George B Mychaliska; Erin E Perrone
Journal:  J Perinatol       Date:  2020-03-18       Impact factor: 2.521

8.  Lysine kinetics in preterm infants: the importance of enteral feeding.

Authors:  S R D van der Schoor; P J Reeds; F Stellaard; J D L Wattimena; P J J Sauer; H A Büller; J B van Goudoever
Journal:  Gut       Date:  2004-01       Impact factor: 23.059

9.  Predicting outcome in very low birthweight infants using an objective measure of illness severity and cranial ultrasound scanning.

Authors:  P W Fowlie; W O Tarnow-Mordi; C R Gould; D Strang
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  1998-05       Impact factor: 5.747

Review 10.  Use of risk stratification indices to predict mortality in critically ill children.

Authors:  Maria Grazia Sacco Casamassima; Jose H Salazar; Dominic Papandria; James Fackler; Kristin Chrouser; Emily F Boss; Fizan Abdullah
Journal:  Eur J Pediatr       Date:  2013-03-23       Impact factor: 3.183

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