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The Mortality Index for Neonatal Transportation Score: A New Mortality Prediction Model for Retrieved Neonates

Simon J. Broughton, MRCP^*, Andrew Berry, FRACP, Stephen Jacobe, FRACP, Paul Cheeseman, PhD^*, William O. Tarnow-Mordi, MRCP, DCH, FRCPCH and Neonatal Intensive Care Unit Study Group, Anne Greenough, MD, FRCP^*

^* Department of Child Health, Guy’s, King’s, and St. Thomas’ School of Medicine, King’s College, London, United Kingdom
New South Wales Newborn and Pediatric Emergency Transport Service, Wentworthville, Australia
Westmead Hospital Perinatal Centre, University of Sydney, Sydney, Australia

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.

Conclusion. 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.

Key Words: neonatal mortality • retrieval • neonatal transport

Abbreviations: CRIB, Clinical Risk Index for Babies • SNAP, Score for Neonatal Acute Physiology • NETS, Newborn and Pediatric Emergency Transport Service • NICUS, Neonatal Intensive Care Unit Study • ROC, receiver operator characteristic • PaO₂, arterial partial pressure of oxygen • PaCO₂, arterial partial pressure of carbon dioxide • MINT, Mortality Index for Neonatal Transportation • FIO₂, fraction of inspired oxygen • TRIPS, Transport Risk Index of Physiologic Stability • VLBW, very low birth weight • CI, confidence interval

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