Literature DB >> 8190582

Estimating neonatal mortality risk: an analysis of clinicians' judgments.

S M Stevens1, D K Richardson, J E Gray, D A Goldmann, M C McCormick.   

Abstract

BACKGROUND: Clinicians' estimates of mortality risk in the neonatal intensive care unit (NICU) have implications for patient triage, transfer, initiation and termination of life support, and allocation of medical resources. The accuracy of these judgments has not been studied, nor the differences between nurses and attending physicians.
OBJECTIVES: 1) evaluate the accuracy of subjective judgments of NICU unit mortality risk, 2) identify the key components of clinician judgments, 3) compare accuracy between attending physicians and nurses, and 4) examine the utility of combining an objectively computed risk and clinician judgments to improve predictions.
METHODS: We obtained estimates of mortality risk on 544 admissions to two NICUs on the day of admission from the attending physician and primary nurse. These were compared with an objective computed mortality risk based on birth weight and the Score for Neonatal Acute Physiology (SNAP) using a linear judgment analysis model, as well as with actual outcomes.
RESULTS: Physicians and nurses had good discriminating power with actual mortality rates ranging from 0% among low risk patients to 67% among those with the highest mortality estimates. Physicians had a tendency to overestimate mortality risk. Clinicians base their estimates on the same factors and similar judgment weights as the empiric mortality risk model (22% birth weight, 62% illness severity (SNAP), 13% low Apgar, and 3% for intrauterine growth restriction). Clinicians place additional emphasis on therapeutic as well as physiologic factors. When the computed risk and physician judgment were combined, both made significant contributions in a logistic mortality risk model.
CONCLUSIONS: Clinician judgments of mortality risk are fairly accurate and similar to an objective illness severity index. This simple method provides insight into clinical decision making and has important applications in improving direct patient care, appropriate allocation of medical resources, and medical training.

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Year:  1994        PMID: 8190582

Source DB:  PubMed          Journal:  Pediatrics        ISSN: 0031-4005            Impact factor:   7.124


  10 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

Review 2.  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

3.  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

4.  Identifying sick children requiring referral to hospital in Bangladesh.

Authors:  H D Kalter; J A Schillinger; M Hossain; G Burnham; S Saha; V de Wit; N Z Khan; B Schwartz; R E Black
Journal:  Bull World Health Organ       Date:  1997       Impact factor: 9.408

5.  Incorporation of physiological trend and interaction effects in neonatal severity of illness scores: an experiment using a variant of the Richardson score.

Authors:  Michael Kuzniewicz; David Draper; Gabriel J Escobar
Journal:  Intensive Care Med       Date:  2007-06-19       Impact factor: 17.440

6.  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

7.  Certainty and mortality prediction in critically ill children.

Authors:  J P Marcin; R K Pretzlaff; M M Pollack; K M Patel; U E Ruttimann
Journal:  J Med Ethics       Date:  2004-06       Impact factor: 2.903

8.  Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients.

Authors:  Ian E R Waudby-Smith; Nam Tran; Joel A Dubin; Joon Lee
Journal:  PLoS One       Date:  2018-06-07       Impact factor: 3.240

9.  Neonatal mortality risk assessment using SNAPPE- II score in a neonatal intensive care unit.

Authors:  Dipak Muktan; Rupa R Singh; Nisha K Bhatta; Dheeraj Shah
Journal:  BMC Pediatr       Date:  2019-08-13       Impact factor: 2.125

10.  Daily mortality of infants born at less than 30weeks' gestation.

Authors:  Christoph P Hornik; Ashley L Sherwood; C Michael Cotten; Matthew M Laughon; Reese H Clark; P Brian Smith
Journal:  Early Hum Dev       Date:  2016-03-25       Impact factor: 2.699

  10 in total

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