The mortality index for neonatal transportation score: a new mortality prediction model for retrieved neonates.
OBJECTIVE
To develop a mortality prediction score for retrieved neonates based on the information given at the first telephone contact with a retrieval service.


METHODS
Data from the New South Wales Newborn and Pediatric Emergency Transport Service database were examined. Analysis was performed with the results for 2504 infants (median gestational age: 36 weeks; range: 24-43 weeks) who were <72 hours of age at the time of referral and whose outcome (neonatal death or survival) was known. The study population was divided randomly into 2 halves, the derivation and validation cohorts. Univariate analysis was performed to identify variables in the derivation cohort related to neonatal death. The variables were entered into a multivariate logistic regression analysis with neonatal death as the outcome. Receiver operator characteristic (ROC) curves were constructed with the regression model and data from the derivation cohort and then the validation cohort. The results were used to generate an integer-based score, the Mortality Index for Neonatal Transportation (MINT) score. ROC curves were constructed to assess the ability of the MINT score to predict perinatal and neonatal death.


RESULTS
A 7-variable (Apgar score at 1 minute, birth weight, presence of a congenital anomaly, and infant's age, pH, arterial partial pressure of oxygen, and heart rate at the time of the call) model was constructed that generated areas under ROC curves of 0.82 and 0.83 for the derivation and validation cohorts, respectively. The 7 variables were then used to generate the MINT score, which gave areas under ROC curves of 0.80 for both neonatal and perinatal death.


CONCLUSIONS
Data collected at the first telephone contact by the referring hospital with a regionalized transport service can identify neonates at the greatest risk of dying.
Broughton
Simon J
SJ
Department of Child Health, Guy's, King's, and St. Thomas' School of Medicine, King's College, London, United Kingdom.
Berry
Andrew
A
Jacobe
Stephen
S
Cheeseman
Paul
P
Tarnow-Mordi
William O
WO
Greenough
Anne
A
D000704:Analysis of Variance; D016208:Databases, Factual; D006801:Humans; D007226:Infant Mortality; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D016015:Logistic Models; D009517:New South Wales; D011379:Prognosis; D012372:ROC Curve; D012017:Referral and Consultation; D014187:Transportation of Patients / Q000706:statistics & numerical data