Predictive score for clinical complications during intra-hospital transports of infants treated in a neonatal unit.

Abstract

To develop and validate a predictive score for clinical complications during intra-hospital transport of infants treated in neonatal units.
This was a cross-sectional study nested in a prospective cohort of infants transported within a public university hospital from January 2001 to December 2008. Transports during even (n=301) and odd (n = 394) years were compared to develop and validate a predictive score. The points attributed to each score variable were derived from multiple logistic regression analysis. The predictive performance and the score calibration were analyzed by a receiver operating characteristic (ROC) curve and Hosmer-Lemeshow test, respectively.
Infants with a mean gestational age of 35 ± 4 weeks and a birth weight of 2457 ± 841 g were studied. In the derivation cohort, clinical complications occurred in 74 (24.6%) transports. Logistic regression analysis identified five variables associated with these complications and assigned corresponding point values: gestation at birth [<28 weeks (6 pts); 28-34 weeks (3 pts); >34 weeks (2 pts)]; pre-transport temperature [<36.3°Cor >37°C(3pts); 36.3-37.0°C (2 pts)]; underlying pathological condition [CNS malformation (4 pts); other (2 pts)]; transport destination [surgery (5 pts); magnetic resonance or computed tomography imaging (3 pts); other (2 pts)]; and pre-transport respiratory support [mechanical ventilation (8 pts); supplemental oxygen (7 pts); no oxygen (2 pts)]. For the derivation and validation cohorts, the areas under the ROC curve were 0.770 and 0.712, respectively. Expected and observed frequencies of complications were similar between the two cohorts.
The predictive score developed and validated in this study presented adequate discriminative power and calibration. This score can help identify infants at risk of clinical complications during intra-hospital transports.

Full text

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CLINICAL SCIENCE
Predictive score for clinical complications during
intra-hospital transports of infants treated in a
neonatal unit
Anna Luiza Pires Vieira, Ame ´lia Miyashiro Nunes dos Santos, Mariana Kobayashi Okuyama, Milton Harumi
Miyoshi, Maria Fernanda Branco de Almeida, Ruth Guinsburg
Department of Pediatrics - Neonatal Division of Medicine. Federal University of Sa ˜o Paulo, Sa ˜o Paulo/SP, Brazil.
OBJECTIVE: To develop and validate a predictive score for clinical complications during intra-hospital transport of
infants treated in neonatal units.
METHODS: This was a cross-sectional study nested in a prospective cohort of infants transported within a public
university hospital from January 2001 to December 2008. Transports during even (n=301) and odd (n=394) years
were compared to develop and validate a predictive score. The points attributed to each score variable were derived
from multiple logistic regression analysis. The predictive performance and the score calibration were analyzed by a
receiver operating characteristic (ROC) curve and Hosmer-Lemeshow test, respectively.
RESULTS: Infants with a mean gestational age of 35¡4 weeks and a birth weight of 2457¡841 g were studied.
In the derivation cohort, clinical complications occurred in 74 (24.6%) transports. Logistic regression analysis
identified five variables associated with these complications and assigned corresponding point values:
gestation at birth [,28 weeks (6 pts); 28-34 weeks (3 pts); .34 weeks (2 pts)]; pre-transport temperature
[,36.3˚C or .37˚C (3 pts); 36.3-37.0˚C (2 pts)]; underlying pathological condition [CNS malformation (4 pts);
other (2 pts)]; transport destination [surgery (5 pts); magnetic resonance or computed tomography imaging
(3 pts); other (2 pts)]; and pre-transport respiratory support [mechanical ventilation (8 pts); supplemental
oxygen (7 pts); no oxygen (2 pts)]. For the derivation and validation cohorts, the areas under the ROC curve
were 0.770 and 0.712, respectively. Expected and observed frequencies of complications were similar between
the two cohorts.
CONCLUSION: The predictive score developed and validated in this study presented adequate discriminative power
and calibration. This score can help identify infants at risk of clinical complications during intra-hospital transports.
KEYWORDS: Risk index; transportation of patients; infant newborn; neonatal intensive care units; risk factors.
Vieira AL, dos Santos AM, Okuyama MK, Miyoshi MH, de Almeida MF, Guinsburg R. Predictive score for clinical complications during intra-hospital
transports of infants treated in a neonatal unit. Clinics. 2011;66(4):573-577.
Received for publication on December 3, 2010; First review completed on January 1, 2011; Accepted for publication on January 1, 2011
E-mail: ameliamiyashiro@yahoo.com.br
Tel.: 55 11 5084-0535
INTRODUCTION
During neonatal care, many diagnostic and treatment
procedures require transporting newborns from the neona-
tal intensive care unit (NICU) to different hospital areas.
Despite the high frequency of such transports, there are few
studies assessing the risks posed to newborns and older
infants.1
Foradultsandolderchildren,clinicalcomplicationsofintra-
hospital transports may occur at a similar frequency to inter-
hospital transports.2
Studies of pediatric patients have
demonstrated thatclinical complications,such ashypothermia
and variations in heart rate and blood pressure, occur in 70%
of intra-hospital transports and are associated with the
severity of underlying disease and transport duration.3-4
Vieira et al. (2007) reported a prevalence of hypothermia in
17% of intra-hospital NICU transports; the factors associated
with developing hypothermia were prolonged transports,
body weight less than 3500 g during transport and presence of
a central nervous system (CNS) malformation.1
A tool to predict the risk of clinical complications during
intra-hospital transport could help toplan specific preventive
measures. It would identify cases when the risk versus
benefit ratio of the transport is deemed unfavorable,
postponing the move until better clinical or technical
conditions are obtained. The predictive indices presently
available in clinical neonatal practice were designed to assess
mortality rates during inter-hospital transport;5-7however,
there are no published indices to assess the risk of clinical
problems during intra-hospital transport.
Copyright ? 2011 CLINICS - This is an Open Access article distributed under
the terms of the Creative Commons Attribution Non-Commercial License (http://
creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-
commercial use, distribution, and reproduction in any medium, provided the
original work is properly cited.
CLINICS 2011;66(4):573-577DOI:10.1590/S1807-59322011000400009
573

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The aim of the present study was to develop and validate
an index that predicts clinical complications during the
intra-hospital transport of infants hospitalized in a neonatal
unit.
SUBJECTS AND METHODS
This cross-sectional study nested in a prospective cohort
was carried out at the NICU of the Federal University of Sa ˜o
Paulo (Brazil). All intra-hospital transports of NICU patients
occurring Monday through Friday 8 AM and 5 PM between
January 2001 and December 2008 were included in the
study. A specific team, consisting of a second-year neonatal
fellow and a neonatal nurse or nurse technician trained in
neonatal intensive care, performed all transports. The fellow
was trained in neonatal resuscitation and advanced life
support procedures and had participated in a neonatal
transport course during the first fellowship year, which
included 20 hours of practical and theoretical activities.
A single patient could be included several times in the
study, provided he/she was transported on different days.
The patients were always moved in double-wall transport
incubators with a pulse oximeter and battery-powered
infusion pumps with respiratory support via an electronic
mechanical ventilator.
We prospectively collected patients’ characteristics (gen-
der, gestational age, birth weight, age and weight at the time
of transport, vital signs, underlying disease and respiratory
support before transport) and transport characteristics
(transport date, destination and duration from leaving the
NICU until return).
Vital signs were collected prior to transport, during the
transport and upon return to the NICU. Definitions were
determined as follows: hypothermia (axillary temperature
,36˚C), hyperthermia (temperature .37.5˚C),8bradycardia
(HR,80 bpm), tachycardia (HR.180 bpm),9hypoxemia
(oxygen saturation ,88%), hyperoxia (saturation .95%),
desaturation (persistent 5% reduction in baseline oxygen
saturation),3,10hypertension (mean blood pressure .75 or
95 mmHg in newborns and older infants, respectively),
hypotension (mean blood pressure , the gestational age in
weeks plus 5 in newborns and ,55 mmHg in older
infants),11apnea (respiratory pause .20 seconds, with or
without bradycardia or hypoxemia),12hypercapnia (pCO2
.45 mmHg) and hypocapnia (pCO2
Capillary glucose levels were collected prior to transport,
every 60 minutes during transport and immediately upon
the patient’s return to the NICU; hypoglycemia and
hyperglycemia were defined as capillary glucose ,40 mg/
dL and .150 mg/dL, respectively.14
The fellow prospectively recorded clinical complications
during intra-hospital transport on specifically designed
transport forms. The transport data from even years (2002,
2004, 2006 and 2008) were used to determine the relevant
variables and respective scores to design the tool to assess
the risk of clinical complications during intra-hospital
transport. The score was validated using transport data
from odd years.
Score building was based on patient demographic and
clinical variables, characteristics of the transports and the
outcome of interest (presence of one or more complications -
Table 2). The following variables were considered during the
univariate analysis: gestational age at birth; postnatal age at
transport; weight at transport; physiological status immediately
,35 mmHg).13
before the transport (body temperature, heart rate, mean blood
pressure and capillary glucose); underlying diseases; transport
destination; and need for oxygen therapy/mechanical ventila-
tion prior to the transport. The following continuous variables
were grouped to design the risk score: gestational age
(,28 weeks; 28 to 34 weeks; .34 weeks); birth weight and
weightattransport(,1000g;1000to2499g;.2500g);postnatal
age at transport (,7 days; 7 to 28 days; .28 days); body
temperature(,36.3˚Cor.37˚C;36.3-37˚C);underlyingdiseases
(CNS malformation; other); destination (surgery; magnetic
resonance or computed tomography imaging; other); and
respiratory support (mechanical ventilation; supplemental
oxygen; no oxygen therapy). Clinically significant variables
and variables with a p-value ,0.20 in the univariate analysis
were introduced into the multiple logistic regression model.
Modeling was performed by backward stepwise regression
until a final model with p,0.05 for the likelihood ratio was
attained. The weighted risk index for clinical complications
used the derived multiple logistic regression analysis coeffi-
cients. These coefficients were transformed into points by
multiplying the value by 2 and rounding to the nearest whole
number.
Predictive performance and calibration of the developed
model were determined for the validation score. Power to
discriminate transports that carry risk of clinical complica-
tions was determined by the area under the receiver
operating characteristic (ROC) curve, which was considered
adequate if the area under the curve was .0.7.15-17
Table 1. Patient and transport characteristics in the
derivation and validation cohorts.
Derivation
cohort
Validation
cohort
Number of transports
Number of patients
Clinical complications
Male gender
Gestational age ,28 weeks
Gestational age 28-34 weeks
Birth weight ,1000 g
Birth weight 1000-2499 g
Age at transport ,7 days
Age at transport 7-28 days
Weight at transport ,1000 g
Weight at transport 1000-2499 g
Pre-transport temperature ,36.3 or
.37.0˚C
Pre-transport temperature 36.3-
37.0˚C
Pre-transport heart rate (mean¡SD)
Pre-transport MBP (mean¡SD)
Pre-transport oxygen saturation
(mean¡SD)
Pre-transport glucose (mean¡SD)
CNS malformation
Gastrointestinal malformation
Transport for surgery
Transport for MRI or CT scan
On mechanical ventilation at
transport
On supplemental oxygen at transport
Transport duration .120 minutes
Transport duration 60-120 minutes
301
209
74 (24.6%)
126 (60.3%)
18 (6.0%)
68 (22.6%)
24 (8.0%)
113 (37.5%)
99 (32.9%)
100 (33.2%)
26 (8.6%)
90 (29.9%)
83 (27.6%)
394
172
85 (21.6%)
110 (64.0%)
26 (6.6%)
88 (22.3%)
29 (7.4%)
154 (39.1%)
100 (25.4%)
143 (36.3%)
4 (1.0%)
123 (31.2%)
108 (27.4%)
36 (12.0%) 52 (13.2%)
141¡16
51¡10
96¡2
139¡15
54¡11
95¡3
92¡18
95 (31.6%)
35 (11.6%)
64 (21.3%)
101 (33.6%)
36 (12.0%)
97¡22
119 (30.2%)
46 (11.7%)
88 (22.3%)
154 (39.1%)
84 (21.3%)
74 (24.6%)
98 (32.6%)
94 (31.2%)
67 (17.0%)
123 (31.2%)
133 (33.8%)
Oxygen saturation in %; MBP: mean blood pressure in mmHg; glucose in
mg/dL; CNS: central nervous system; MRI: magnetic resonance imaging;
CT: computed tomography.
Predictive score for neonatal intra-hospital transports
Vieira ALP et al.
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Calibration was determined by the Hosmer-Lemeshow good-
ness-of-fit test and considered appropriate if p.0.05.18
Statistical analysis was performed using SPSS for Win/
v.17.0 (USA).
This study was approved by the Ethical Committee of the
Federal University of Sa ˜o Paulo, Brazil.
RESULTS
From January 2001 to December 2008, 381 infants were
transported over 695 intra-hospital transports with a mean
gestational age of 35.3¡3.9 weeks (range: 22 to 42 weeks)
and birth weight of 2457¡841 g (range: 580 to 4400 g); 236
(61.9%) infants were males. At least one clinical complica-
tion occurred during 159 transports (22.9%); hypothermia
(12.7%), hyperoxia (5.6%), desaturation (4.1%) and need for
increase the respiratory support (2.3%) were the most
frequent complications.
No patients developed hypoxemia, hypocapnia or hyper-
capnia and hypotension or hypertension during transport.
the characteristics of the transports carried out in the
derivation and validation cohorts and the distribution of
the clinical complications in the two cohorts are shown in
Tables 1 and 2, respectively.
In the final multiple regression model, the dependent
variable ‘‘presence of at least one clinical complication
during the intra-hospital transport’’ was associated with
gestational age, pre-transport body temperature, underlying
disease, transport destination and type of respiratory
support. A score was attributed to each variable present in
the final multiple regression model based on the odds ratios
(Table 3).
The predictive performance of the developed score
discriminating intra-hospital transport with increased risk
for clinical complications was tested by the ROC curve. Each
transport in the even years was scored based on the
variables derived from the final regression model (Table 3).
These values were used to build a ROC curve, with an area
under the curve of 0.770 (95% CI: 0.710 to 0.830). Another
ROC curve built from the data derived from the validation
cohort (transports in odds years) showed an area under the
curve of 0.712 (95% CI: 0.649 to 0.774).
Because a high proportion of neonates with malforma-
tions were present in the two cohorts, the discriminative
power of the developed score was also tested in a subgroup
of infants of the validation cohort without any malforma-
tions (n=158). The area under the ROC curve from this
validation cohort subgroup was 0.708 (95% CI: 0.600-0.815),
similar to the validation cohort with all transports.
To test the calibration of the developed score, the
frequency of clinical complications in the derivation cohort
was compared to the observed frequency in the validation
cohort according to score intervals. There were similar
frequencies of clinical complications in the derivation and
validation cohorts in transports that, respectively, scored
,13 points (8.0% vs. 9.0%); 13-15 points (24.3% vs. 17.0%);
16-20 points (38.0% vs. 35.0%) and .20 points (57.1% vs.
52.4%). Comparison of the expected and observed fre-
quency of clinical complications by the Hosmer-Lemeshow
goodness-of-fit test revealed a p-value of 0.827, indicating a
good calibration of the predictive score (Table 4).
DISCUSSION
This is the first study that developed and validated a
score to predict the presence of clinical complications
during intra-hospital transports of infants hospitalized in
neonatal units. Previously, predictive scores for neonatal
transports have been designed to assess the risk of mortality
in inter-hospital transported infants. The Transport Risk Index
of Physiologic Stability (TRIP) uses physiological parameters
such as temperature, breathing status, blood pressure and
response to noxious stimuli; it was validated to assess the
Table 2. Clinical complications during intra-hospital
transports in the derivation and validation cohorts.
Derivation
cohort (n=301)
Validation
cohort (n=394)
Hypothermia
Hyperoxia
Desaturation
Need for increasing
respiratory support
Hyperglycemia
Hyperthermia
Apnea
Bradycardia
Tachycardia
Hypoglycemia
Bronchospasm
Total
40 (13.3%)
22 (7.3%)
12 (4.0%)
5 (1.7%)
48 (12.2%)
18 (4.6%)
16 (4.1%)
11 (2.8%)
1 (0.5%)
6 (2.0%)
-
2 (0.7%)
-
1 (0.3%)
-
89 (29.6%)
2 (0.5%)
3 (0.8%)
4 (1.0%)
2 (0.5%)
2 (0.5%)
1 (0.3%)
1 (0.3%)
108 (27.4%)
Table 3. Final model of the multiple logistic regression analysis for clinical complications during intra-hospital transports
and the derived score.
Variables OR95% CIpScore
Gestational age ,28 weeks
Gestational age 28-34 weeks
Gestational age .34 weeks
Pre-transport temperature ,36.3˚C or .37.0˚C
Pre-transport temperature 36.3-37.0˚C
CNS malformation
Other diseases
Transport for surgery
Transport for MRI or CT scan
Other destinations
Mechanical ventilation
Supplemental oxygen therapy
No oxygen therapy
3.18
1.50
1.00
1.53
1.00
1.86
1.00
2.34
1.237
1.000
3.98
3.26
1.00
1.01-10.05
0.75-3.00
Reference
0.82-2.87
Reference
0.93-3.71
Reference
1.04-5.27
0.60-2.56
Reference
1.52-8.93
1.72-6.17
Reference
0.049
0.248
6
3
2
3
2
4
2
5
3
2
8
7
2
0.184
0.078
0.036
0.567
,0.001
0.004
MRI: magnetic resonance imaging; CT: computed tomography. Hosmer-Lemeshow test: p=0.443.
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Vieira ALP et al.
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risk of neonatal mortality within seven days after NICU
admission, intra-hospital mortality and grade III/IV intra-
ventricular hemorrhage in the first 72 hours following
transport.6The Mortality Index for Neonatal Transportation
was designed and validated to evaluate the risk of neonatal
death based on first-minute Apgar score, birth weight,
presence of congenital anomalies, postnatal age, arterial pH,
partial oxygen pressure and heart rate obtained at the
moment of inter-hospital transport request.7
NICU patients often require diagnostic and treatment
procedures in other hospital areas, and complications
during transport may aggravate their clinical status; how-
ever, a large proportion of events are potentially preven-
table, especially hypothermia and cardiac and respiratory
deterioration.19In the present study, hypothermia and
respiratory instability were the most frequent observed
adverse events. Thus, the assessment of risk prior to
transport enables better pre-transport planning, instanta-
neous correction of possible problems and evaluation of
risks versus benefits of the procedure.
As a result, the score developed in the present study has
several strengths. data for developing the risk index were
collected prospectively and were available for all transports
for the five variables in the model. The score’s design and
validation followed the steps recommended for the con-
struction of predictive models.15,18,20-22Moreover, the score
was validated using a large number of transports and
fulfilled the two basic requirements for a predictive model:
discriminatory power and calibration. The accuracy of the
model was similar to that obtained in the validation sample
(0.77 versus 0.71); predictive models described in the
literature have accuracy values ranging from 0.7 to
0.9.6,7,17,21-23The Transport Risk Index of Physiologic Stability
score developed by Lee et al. showed an area under the ROC
curve of 0.83, 0.76 and 0.74, respectively, for mortality
within 7 days, intra-hospital mortality and grade III/IV
intraventricular hemorrhage in the first 72 hours following
transport and had an accuracy of 0.82.6Similarly, the
Mortality Index for Neonatal Transportation proposed by
Broughton et al. had an accuracy of 0.83 for the prediction
of perinatal and neonatal mortality.7However, these two
predictive scores were not developed for assessing intra-
hospital transport risks.
In the goodness-of-fit test, comparisons between expected
and observed frequencies revealed good calibration of the
proposed score, demonstrating that the value attributed to
each variable in the model was adequate for calculating the
risk of clinical complications at each score range.
In addition, this predictive score is simple, allowing
clinical NICU staff to assess the risk of clinical complications
during intra-hospital transport based on easily obtainable
patient characteristics and transport data. Moreover, the
evaluated parameters are objective and obtainable in any
NICU, even in settings with minimal resources.
However, the present study has several limitations. The
transport population had a high proportion of infants with
congenital malformations. This characteristic may affect the
accuracy of the model when applied to NICUs with a lower
frequency. However, the presence and type of congenital
anomalies were included in the multiple logistic regression
analysis, and the predictive score was adjusted for
malformations. Moreover, the analysis of the area under
the ROC curve was similar in the validation cohort,
independent of the inclusion of infants with malformations.
These data favor the external validity of the developed
score. Finally, because the present study was carried out at a
single center, other characteristics related to patients,
transports or hospital areas that were not assessed in the
present model may have significant influence on risks for
clinical complications during intra-hospital transports.
Multicenter studies are needed to generalize the obtained
results.
In conclusion, the proposed score showed adequate
accuracy and calibration to predict the presence of at least
one clinical complication during intra-hospital transports of
patients in the NICU setting when this procedure was
performed by trained staff with non-sophisticated but
appropriate equipment.
REFERENCES
1. Vieira ALPV, Guinsburg R, Santos AMN, Miyoshi MH, Peres CA, Lora
MI. Intra-hospital transport of neonatal intensive care patients: risk
factors for complications. Rev Paul Pediatr. 2007;25:247-53.
2. Waydhas C. Intrahospital transport of critically ill patients. Crit Care.
1999;3:R83-9, doi: 10.1186/cc362.
3. Wallen E, Venkataraman ST, Grosso MJ, Kiene K, Orr RA. Intrahospital
transport of critically ill pediatric patients. Crit Care Med. 1995;23:1588-
95, doi: 10.1097/00003246-199509000-00020.
4. Ferrer AP, Sucupira AC, Grisi SJ. Causes of hospitalization among
children ages zero to nine years old in the city of Sa ˜o Paulo, Brazil. Clinics
(Sao Paulo). 2010;65:35-44, doi: 10.1590/S1807-59322010000100007.
5. Hermansen MC, Hasan S, Hoppin J, Cunningham MD. A validation of a
scoring system to evaluate the condition of transported very-low-
birthweight neonates. Am J Perinatol. 1988;5:74-8, doi: 10.1055/s-2007-
999659.
6. Lee SK, Zupancic JA, Pendray M, Thiessen P, Schmidt B, Whyte R, et al.
Transport risk index of physiologic stability: A practical system for
assessing infant transport care. J Pediatr. 2001;139:220-6, doi: 10.1067/
mpd.2001.115576.
7. Broughton SJ, Berry A, Jacobe S, Cheeseman P, Tarnow-Mordi WO,
Greenough A. The mortality index for neonatal transportation score: A
new mortality prediction model for retrieved neonates. Pediatrics.
2004;114:e424-e8, doi: 10.1542/peds.2003-0960-L.
8. Mayfield SR, Bhatia J, Nakamura K, Rios G, Bell E. Temperature
measurement in term and preterm neonates. J Pediatr. 1984;104:271-5,
doi: 10.1016/S0022-3476(84)81011-2.
9. Richards JM, Alexander JR, Shinebourne EA, de Swiet M, Wilson AJ,
Southall DP. Sequential 22-hour profiles of breathing patterns and heart
Table 4. Expected and observed frequency of clinical complications according to the predictive score intervals.
Expected frequencyObserved frequencyTotal
Score n/total% n/total%n/total%
,13
13-15
16-20
.20
TOTAL
10/125
17/70
27/71
20/35
74/301
8.0
24.3
38.0
57.1
24.6
13/144
18/106
43/123
11/21
85/394
9.0
17.0
35.0
52.4
21.6
23/269
35/176
70/194
31/56
159/695
8.6
19.9
36.1
55.4
22.9
Hosmer-Lemeshow test x2: p=0.827.
Predictive score for neonatal intra-hospital transports
Vieira ALP et al.
CLINICS 2011;66(4):573-577
576

Page 5

rate in 110 full-term infants during their first 6 months of life. Pediatrics.
1984;74:763-77.
10. Duddell G, Cornish J, Bartlett R. What constitutes adequate oxygenation?
Pediatrics. 1990;85:39-41.
11. Park MK, Menard SM. Normative, oscillometric blood pressure values in
the first 5 years in an office setting. Am J Dis Child. 1989;143:860-4.
12. Miller MJ, Martin RJ. Apnea of prematurity. Clin Perinatol. 1992;19:789-
808.
13. Mariani G, Cifuentes J, Carlo WA. Randomized trial of permissive
hypercapnia in preterm infants. Pediatrics. 1999;104:1082-1088, doi: 10.
1542/peds.104.5.1082.
14. Nicholl R. What is the normal range of blood glucose concentrations in
healthy term newborn? Arch Dis Child. 2003;88:238-9, doi: 10.1136/adc.
88.3.238
15. Hosmer D W, Lemeshow S. Assessing the fit of the model. In: Applied
Logistic Regression. NY: John Willey & sons, 2000:160-4.
16. Lee SK, McMillan DD, Ohlsson A, Pendray M, Synnes A, Whyte R,
et al. Variations in practice and outcomes of the Canadian NICU
Network 1996-77. Pediatrics. 2000;106:1070-9, doi: 10.1542/peds.106.5.
1070.
17. Richardson DK, Corcoran JD, Escobar GJ, Lee SK. SNAP-II and SNAPPE-
II: simplified newborn illness severity scores. J Pediatr. 2001;138:92-100,
doi: 10.1067/mpd.2001.109608.
18. Hosmer DW, Lemeshow S. A review of goodness of fit statistics for use
in the development of logistic regression models. Am J Epidemiol.
1982;115:92-106.
19. Moss SJ, Embleton ND, Fenton AC. Towards safer neonatal transfer: the
importance of critical incident review. Arch Dis Child. 2005;90:729-32,
doi: 10.1136/adc.2004.066639.
20. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver
operating characteristic (ROC) curve. Radiology. 1982;143:29-36.
21. Kollef MH, Schuster DP. Predicting intensive care unit outcome with
scoring systems. Underlying concepts and principles. Critical Care Clin.
1994;10:1-18.
22. Ledoux D, Canivet JL, Preiser JC, Lefrancq J, Damas P. SAPS 3 admission
score: an external validation in a general intensive care population.
Intensive Care Med. 2008;34:1873-7, doi: 10.1007/s00134-008-1187-4.
23. [no authors listed] The CRIB (clinical risk index for babies) score: a tool
for assessing initial neonatal risk and comparing performance of
neonatal intensive care units. The International Neonatal Network
Lancet. 1993;342:193-8, doi: 10.1016/0140-6736(93)92296-6.
CLINICS 2011;66(4):573-577Predictive score for neonatal intra-hospital transports
Vieira ALP et al.
577