Population-Based Estimates of In-Unit Survival for
Very Preterm Infants
WHAT’S KNOWN ON THIS SUBJECT: Survival estimates for preterm infants are vital for counseling parents, informing care, and planning services. Widely use estimates of in-unit survival derived from a large UK population for infants born at,33 weeks’gestational age have been available since 1999.
WHAT THIS STUDY ADDS: These survival charts have been updated and will be of use to clinicians, parents, and managers. An alternative method for graphical representation of survival probabilities is offered: contour survival plots.
abstract
BACKGROUND:Estimates of the probability of survival of very preterm infants admitted to NICU care are vital for counseling parents, inform-ing care, and planninform-ing services. In 1999, easy-to-use charts of survival according to gestation, birth weight, and gender were published in the United Kingdom. These charts are widely used in clinical care and for benchmarking survival, and they form the core of the Clinical Risk Index for Babies II score. Since their publication, the survival of preterm infants has improved, and the charts therefore need updating.
METHODS: A logistic model was fitted with gestational age, birth weight, and gender. Nonlinear functions were estimated by using frac-tional polynomials. Bootstrap methods were used to assess the inter-nal validity of the final model. The final model was assessed both overall and for subgroups of infants by using Farrington’s statistic, the c-statistic, Cox regression coefficients, and the Brier score.
RESULTS:A total of 2995 white singleton infants born at 23+0to 32+6 weeks’ gestation in 2008 through 2010 were identified; 2751 (91.9%) infants survived to discharge. A prediction model was estimated and good model fit confirmed (area under receiver-operating characteristics curve = 0.86). Survival ranged from 27.7% (23 weeks) to 99.1% (32 weeks) for boys and from 34.5% (23 weeks) to 99.3% (32 weeks) for girls. Updated charts were produced showing estimated survival according to gestation, birth weight and gender, together with contour plots displaying points of equal survival.
CONCLUSIONS:These survival charts have been updated and will be of use to clinicians, parents, and managers.Pediatrics 2013;131:e425– e432
AUTHORS:Bradley N. Manktelow, PhD, Sarah E. Seaton, MSc, David J. Field, DM, and Elizabeth S. Draper, PhD
Department of Health Sciences, University of Leicester, Leicester, United Kingdom
KEY WORDS
mortality, neonatal, prediction, very preterm
ABBREVIATION
TNS—The Neonatal Survey
Dr Manktelow oversaw the analysis of the data, contributed to the drafting of the article, and approved thefinal manuscript as submitted; Ms Seaton undertook the analysis of the data, contributed to the drafting of the article, and approved thefinal manuscript as submitted; and Professor Field and Professor Draper derived the original concept of the article, supervised data collection, contributed to the drafting of the article, and approved thefinal manuscript as submitted.
This report is independent research arising from a Research Methods Fellowship supported by the National Institute for Health Research. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health.
www.pediatrics.org/cgi/doi/10.1542/peds.2012-2189
doi:10.1542/peds.2012-2189
Accepted for publication Oct 1, 2012
Address correspondence to Bradley N. Manktelow, PhD, Department of Health Sciences, University of Leicester, 22-28 Princess Rd West, Leicester LE1 6TP, United Kingdom. E-mail: brad. [email protected]
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE:The authors have indicated they have nofinancial relationships relevant to this article to disclose.
FUNDING:The Neonatal Survey is one of The Infant Mortality and Morbidity Studies, which are funded by the Primary Care Trusts of the East Midlands and Yorkshire of England. Ms Seaton was funded by a Research Methods Fellowship award from the National Institute for Health Research.
and planning services.1,2 There are a large number of prediction models that have been proposed to estimate survival for very preterm or low birth weight deliveries.3,4 Such models are useful because they can potentially provide more accurate estimates of the probability of survival than clinical as-sessment alone.5–7 However, these models are often derived by using data from individual hospitals rather than from a population based on residence. The organization of neonatal intensive care units (NICUs) into providers of-fering different levels of care means that predictions derived from a hospi-tal-based cohort may not accurately reflect survival in the whole population. To obtain a complete picture of in-unit mortality survival, estimates derived from a cohort defined according to residence, rather than place of treat-ment, are required.
In 1999, residence-based estimates of the survival to discharge of very pre-term infants were published in the United Kingdom in the form of easy-to-use charts.8 The charts, sometimes referred to as the“Draper Grid,”show survival according to gestational age, birth weight, and gender. They were derived by using data from The Neo-natal Survey (TNS), an ongoing population-based survey of NICUs in the East Midlands and Yorkshire re-gions of England.9 These charts have been widely used in clinical care and for benchmarking survival, and they have been validated by using data from the Netherlands.10 The charts also form the core of the Clinical Risk Index for Babies II score.11 Although the charts were updated in 2003,12 im-proved survival to discharge has been reported in this population over the last 10 years, particularly for infants born at 24 and 25 weeks’ gestational
quired updating to reflect these changes in survival.
Previously, survival charts for fetuses known to be alive at the onset of labor were also produced. However, because data on stillbirths and infants who died before admission to an NICU are cur-rently unavailable for England, only the charts for infants admitted to an NICU could be updated.
METHODS
Participants
Data were identified from TNS, a population-based audit of inpatient neonatal care based in the East Midlands and Yorkshire regions of England.9 All neonatal services in the regions contribute to TNS, and NICUs in adjacent regions also permit data col-lection on eligible infants. The current survey was established in 1990 and now covers an area that has∼120 000 births each year, with 29 consultant-led and 14 midwife-led delivery units (6 co-located with consultant units). In-formation is collected on all infants admitted to an NICU who are born at
,33 weeks’gestational age to mothers resident in the study area. Seven part-time neonatal nurses prospectively collect the data during regular visits to the 29 NICUs, with audits and validation checks undertaken to ensure data collection is complete.
White singleton infants born at 23+0to 32+6 weeks’ gestational age between January 1, 2008, and December 31, 2010, were selected for this analysis. Infants were excluded if they had lethal congenital anomalies, missing data on gender or indeterminate gender, or implausible birth weight for gesta-tional age (defined as being .3 SDs from the median for their gestation-al age and gender). Gestationgestation-al age was defined according to the following
’
scan (.20 weeks’ gestational age); and postnatal examination (least reli-able). If the difference between ma-ternal dates and early scan was .7 days, the early scan was used to de-termine the period of gestation.
Statistical Methods
A logistic model was developed to predict the probability of survival by using gestational age in days, birth weight, and gender as predictors. Two-way interactions were investigated and were included if significant at the 10% significance level. An indicator variable for infants born at 23+0to 23+6weeks’ gestational age was included to allow for the differences in this group com-pared with other preterm births. Non-linear functions were modeled by using fractional polynomials21 and the final form selected using the change in de-viance. The internal validity of the cho-sen model was investigated through the use of bootstrapping techniques. Five hundred bootstrap samples were se-lected with replacement and the model selection process repeated with the c-statistic (area under the receiver-operating characteristic curve)22 and Cox’s regression coefficients23 moni-tored for each sample. Similarly, boot-strap methods were used to determine whether interactions were required in the model.
discriminatory performance of thefinal model was quantified by using the c-statistic,22 and the calibration was assessed according to Cox’s calibration regression.23 Table 1 provides details regarding the methods used to describe the goodness-of-fit and predictive power of the model. The predictive perfor-mance of the model was assessed both overall and according to subgroups based on gestational age and whether birth weight was above or below the 50th percentile.
Predicted survival percentages were calculated separately for male and fe-male infants according to week of gestational age at birth and birth weight in increments of 250 g and then reproduced on updated Draper Grids.
The predicted survival at the midpoint of each cell (week of gestational age plus 4 days and birth weight midpoint) was displayed together with the lowest and highest predicted survival within that cell. To aid interpretation, the second, 10th, 50th, 90th, and 98th UK birth weight centiles29were added to the plot.
In addition to the familiar grids, survival contour plots were also created; these are sometimes referred to as“isosurv” plots30 and have been suggested as a method for displaying survival prob-abilities.30–32Contours were drawn on these plots to denote the points of equal survival (25%, 50%, 75%, 90%, 95%, and 98% survival) according to birth weight and gestational age. The
second, 10th, 50th, 90th, and 98th birth weight centiles were again added to each plot.
All analyses were conducted by using Stata version 11.2 (Stata Corp, College Station, TX) and SAS version 9.2 (SAS Institute, Inc, Cary, NC). Thefigures were produced by using the SAS/GRAPH software.
RESULTS
In total, 3065 white singleton infants were born at 23+0to 32+6weeks’ ges-tational age, between January 1, 2008, and December 31, 2010, to women resident in the TNS regions and who survived to admission to an NICU. Infants were excluded if they had le-thal congenital anomalies (n = 37 [all chromosomal]), indeterminate or missing gender (n= 5), or implausible birth weight for gestational age (n= 28). There were 8 infants with the number of completed weeks of gesta-tion recorded but missing the number of days; for these infants, 4 days was imputed. Therefore, 2995 (97.7%) infants were included in the analysis.
Overall, 2751 (91.9%) infants survived to discharge: male infants, 1484 (91.2%) of 1627; female infants, 1267 (92.6%) of 1368. Survival according to week of gestational age is given in Table 2 along with the survival percentages for white singleton infants included in the charts published in 1999.8
Model Selection
Birth weight was identified as having a nonlinear relationship on the logit scale with the probability of survival and was therefore modeled by using fractional polynomials. There was no evidence that other fractional poly-nomials were required. All 2-way inter-actions between the variables were investigated, and none were found to be significant at the 10% significance level and were only identified as producing an improvement in thefit of the model at TABLE 1 Methods Used to Describe the Goodness-of-Fit and Predictive Power of the Model
The Brier score (B) is defined as26
:
B¼1 n +
n
i¼1 ðxi2piÞ
2
A low value for the Brier score indicates that there is close agreement between observed and predicted outcomes.
The Farrington statistic is an extension of the Pearsonx2statistic and is given by24,25:
x2 F¼+
N j¼1
ðyj2mjpjÞ2 mjpjð12pjÞþ+
N
j¼1
2ð122pjÞ mjpjð12pjÞð
yj2mjpjÞ
The variance can be calculated and the standardized statistic compared with the standard normal distribution.
For Cox’s calibration regression, the following model is used23
:
xi¼
1þexp
2
aþb:log
pi 12pi
21
If the model is perfectly calibrated thena= 0 andb= 1. To examine the significance of these values, 3 tests are performed:
1.a= 0 andb= 1: overall reliability–perfect prediction 2.a= 0 |b= 1: perfect calibration
3.b= 1 |a:overall refinement–the correct amount of variation in the model
Note: the calibration of the overall model by definition is perfect. The calibration of subgroups, however, can be assessed to ensure adequate calibration within groups.
The c-statistic (area under the ROC curve)22
:
c¼0:5ð½+xi 2
þ+xiÞ2+ðrijxi¼1Þ
+xiðn2+xiÞ þ1
The c-statistic, the value of which ranges from 0 to 1, quantifies how well the model discriminates between those who survived and those who did not. A value of 0.5 indicates the model is no better than chance alone, whereas 1 demonstrates there is perfect discrimination.
Where:
•xiis the observed outcome for a given infanti(xi= 1 if infantisurvived to discharge andxi=
0 otherwise),
•piis the predicted probability of survival for infanti
•nis the total number of infants
•Nis the number of unique combinations of values of the covariates
•mjis the number of infants in covariate combinationj
•pjis the predicted probability of survival for infants in covariate combinationj
•riis the value of rank ofpiwhen all values ordered from lowest to highest
ROC, receiver-operating characteristic.
the 10% significance level in 2.2% of the bootstrap samples (meanPvalue = .79). Therefore, no interactions were in-cluded in thefinal model.
The average c-statistic was 0.86 (range: 0.85 to 0.86) in the bootstrap samples, indicating good overall discrimination. The Cox calibration coefficients had a mean intercept of 0.014 (range:–0.44 to 0.43) and a mean slope of 0.99 (range: 0.84 to 1.21), indicating good calibration.
Details of thefinal model are given in the Appendix.
Model Validation
The modelfit statistics for the whole data set and for subgroups are shown in Table 3. Overall, the final selected
model was well fitted to the data (c-statistic: 0.86; Farrington’s statistic:
P= .44; Brier score = 0.058). The model
fit was generally good for all of the subgroups except that the discrimina-tory power of the model was poor for the subgroups based on gestational age (c-statistic: 0.62 to 0.70).
The predictive performance of thefinal model was investigated for singleton infants of non-white ethnic origin, even though they were not included in the estimation of survival rates: n = 812 (South Asian, n= 438; black,n= 165; other,n= 209); number surviving = 742; predicted number surviving = 731.4; c-statistic = 0.875. The predictive per-formance was also investigated for mul-tiple births of all ethnic groups:n= 1284;
Graphical Representation
The estimated survival percentages are given separately for male and female infants in updated versions of the Draper Grid (Fig 1). The contour plots in Fig 2 provide an alternative method for presenting the probability of survival. Due to a categorical variable being used to identify those born at 23 weeks’ gestational age, the contours have been smoothed by using splines at the boundary between 23 and 24 weeks.
DISCUSSION
The probabilities of survival estimated in this article were derived from a large UK population with .120 000 births per year (∼17% of all births in England and Wales). The 2 regions (East Midlands and Yorkshire) are considered repre-sentative of England and Wales, com-prising a mixture of urban and rural populations. In 2010, the neonatal mor-tality rate in the study area was 3.2 per 1000 live births compared with 3.0 per 1000 live births in the whole of England and Wales.
The use of a cohort based on the mothers’residence avoids the biases that can occur when estimates are derived from hospital-specific data due to UK neonatal care providers being organized into networks of NICUs pro-viding different levels of care,33,34 sim-ilar to the pattern of neonatal care seen in most other countries.35–37 Survival rates for individual units, therefore, reflect local policies on care, admission, and transfer; the observed survival for these data for TNS units ranged from 85% to 100%. The use of a cohort based on residence, such as TNS, is required to obtain a complete understanding of survival. However, although it is likely the case that the survival probabilities reported here Gestational Age
(completed weeks)
2008–2010 1994–1997
Male Female Male Female
23 4 (28.6) 8 (34.8) 3 (18.7) 3 (13.0)
24 25 (48.1) 25 (55.6) 7 (20.0) 5 (17.9)
25 47 (73.4) 39 (67.2) 23 (45.1) 17 (43.6)
26 72 (77.4) 61 (83.6) 39 (55.7) 41 (65.1)
27 115 (83.3) 87 (90.0) 69 (75.8) 51 (82.3)
28 125 (88.7) 121 (93.1) 107 (80.4) 78 (82.1)
29 159 (93.0) 147 (96.1) 137 (89.5) 110 (94.8)
30 221 (95.3) 180 (98.9) 151 (95.0) 150 (95.5)
31 303 (99.7) 259 (98.9) 243 (97.6) 183 (98.4)
32 413 (98.8) 340 (98.6) 316 (97.8) 260 (98.5)
Total 1484 (91.2) 1267 (92.6) 1095 (85.5) 898 (86.9)
Data are given asn(%).
TABLE 3 Goodness-of-Fit, Calibration, and Discriminatory Statistics for the Final Model: Overall and by Subgroups Based on Gestational Age at Birth and Birth Weight
Variable Overall Birth Weight Gestational Age (Weeks+days)
,Median $Median 23+0–26+6 27+0–29+6 30+0–32+6
No. of infants 2995 1753 1242 422 830 1743
No. (%) surviving 2751 (91.9) 1599 (91.2) 1152 (92.8) 281 (66.6) 754 (90.8) 1716 (98.5) Predicted no. surviving 2751 1596.0 1155.0 276.7 760.4 1713.9
C-statistic 0.86 0.85 0.86 0.70 0.67 0.62
Farrington’s statistic P= .44 P= .14 P= .74 P= .78 P= .22 P= .63
Brier score 0.0583 0.0629 0.0517 0.1962 0.0786 0.0152
Cox regression
Intercept NPa 20.075 0.149 20.049 0.398 20.150
Slope NPa 0.971 1.054 1.005 1.133 0.981
Overall reliability — P= .88 P= .77 P= .89 P= .57 P= .92
Calibration — P= .74 P= .71 P= .64 P= .42 P= .69
Refinement — P= .67 P= .54 P= .97 P= .50 P= .93
will not hold for individual units, even in our study area, the probabilities reported are useful as a benchmark against which variations may indicate important differences in performance or indeed practice.38
Published survival rates require regu-lar updating as survival improves for preterm infants. The survival proba-bilities derived from TNS data were originally published in 19998and sub-sequently updated in 2003.12The 2003 survival estimates from TNS were val-idated by using national data from the
Netherlands from 2000 to 2007.10 However, improvements in neonatal survival have been reported in this population over the past 10 years,14 although some studies have suggested that this improvement may have now slowed or reached a plateau.31,39These improvements mean that the estimates required further updating.
Direct comparison of survival in this article with that reported recently from other countries is difficult because rates are usually given for all births rather than just admissions, for networks of
NICUs as opposed to geographically defined populations, or are not given according to gender of the infant. How-ever, a crude comparison suggests that the rates reported here are similar to those from theEunice Kennedy Shriver
National Institute of Child Health and Human Development Neonatal Research Network: in 2007, survival to discharge for live births was 92% at 28 weeks; 91% at 27 weeks; 84% at 26 weeks; 71% at 25 weeks; 54% at 24 weeks; and 21% at 23 weeks.40
The statistical model developed in this analysis includes an indicator variable for birth at 23 weeks’gestational age because the decision-making process for admission is different from that at
$24 weeks. In the United Kingdom, for births at 23 weeks’ gestational age, health professionals discuss with parents the provision of active inter-vention given the individual circum-stances; at 24 weeks, the broad expectation is that active intervention and intensive care will be started un-less an infant is in very poor condition at birth.41Internationally, there seems to be consensus on the level of in-tervention at$25 weeks but the sit-uation is less clear for those infants born at 23 and 24 weeks.42
Other mortality prediction models have been proposed of varying complexity,3 but the aim of the current article was to update a simple prediction model with a small number of highly pre-dictive variables. We believe that the use of weight and gestational age at birth, together with the infant’s gender, allows the estimation of survival rates that are clinically useful without re-lying on drug administration or physi-ologic or pharmacphysi-ologic tests that may not always be performed. The final model used to estimate the survival probabilities is relatively straightfor-ward and contains no interactions be-tween the predictors. The inclusion of birth weight in predictive models for
FIGURE 1
Grid of predicted survival according to gestational age, birth weight, and gender. The main number in each cell is the predicted survival at the midpoint of the cell (week of gestational age plus 4 days and birth weight midpoint). The numbers in parentheses are the lowest and highest predicted survival within that cell. A, Female. B, Male.
preterm infants has been undertaken previously in various ways; for exam-ple, observed birth weight,43 ratio to gestation/gestation-specific mean birth weight,44 difference from gestation/ gestation-specific mean birth weight,8
zscore,30and percentile.45In practice, there is little difference in predicted values obtained by using these various approaches; indeed, they are all ap-proximations of the same underlying model. In this analysis, observed birth weight was included because this is the most straightforward variable to in-terpret and does not require the choice
or estimation of gestation/gender-specific mean birth weight. These data showed no evidence for increased mortality at higher gestation-specific birth weights. Previous evidence for this relationship has been equivocal, with some articles reporting an in-creased risk of mortality for infants with large birth weights for gestational age8,12,30,32whereas others have not.46–48 Although some of this inconsistency may be explained by variations in the type of infants included in the studies, it may also be due to the statistical methods used.49 Fractional polynomials allow
and mortality than conventional poly-nomials.
Thefit of the model to the observed data was investigated for the whole data set and also for clinical subgroups defined a priori. Although any model may seem to predict well overall, this may be the result of underestimating the outcome probability for some groups and over-estimating it for others. It is important, therefore, that subgroups of the data also be examined. The model derived here demonstrated good
goodness-of-fit and calibration both overall and for all subgroups. The discriminatory performance of the models was less clear. Although the discriminatory performance was good overall and for the subgroups defined according to birth weight, it was poor for the subgroups defined according to ges-tational age. This finding is not un-expected because gestational age is a strong predictor of survival, and its effect will not be seen for subgroups defined over a narrow range of gesta-tional ages. However, the important characteristic of this model is its cali-bration, which was shown to be good.
In this article, the survival estimates were derived for singletons of white ethnic origin because the data set was too small to be able to confidently obtain estimates of sufficient precision for infants from multiple births or of non-white ethnic origin. However, thefinal model showed good overall predictive performance for singletons of non-white ethnic origin and for all mul-tiple births, although there was insufficient data to confirm whether this held for subgroups of infants.
Previous estimates of survival obtained by using TNS data have also included survival estimates for all fetuses known to be alive at the onset of labor8,12 by including data from the national
Con-fidential Inquiry into Maternal and
FIGURE 2
Child Health.50Currently, data on still-births and infants who died before admission to an NICU are not available for England, and it was only possible therefore to produce these charts for infants admitted to neonatal intensive care. Because the charts in this article report survival for infants who sur-vived to admission, they will over-estimate survival rates for all births. When the additional data become available, the charts for all births will be updated.
CONCLUSIONS
These survival charts have been updated and will be of use to clinicians, parents, and managers.
APPENDIX: FINAL MODEL DETAILS Thefinal logistic model to predict sur-vival is given by:
LogitðSurvivalÞ ¼b0þ b1:gestationþb2:birthweight1 2þ
b3:genderþb4:gest23
Where:
gestationis the infant’s gestational age in completed weeks and days as a decimal (ie, 28+3 weeks takes the value 28.429);
birth weight is the infant’s birth weight in kilograms;
genderis the infant’s gender: for males,
gender= 0, for females,gender= 1;
gest23 is an indicator for birth at
,24+0 weeks’ gestational age:
gest23 = 1 if gestation at birth
,24+0weeks;gest23= 0 otherwise. The estimates for the model parame-ters are:
b0–10.28 (SE 1.36)
b10.46 (SE 0.05)
b2–0.47 (SE 0.13)
b30.45 (SE 0.16)
b4–0.28 (SE 0.39)
ACKNOWLEDGMENTS
We acknowledge the continuing help and collaboration of the hospitals deliv-ering neonatal care in the study area. We also thank Dr Oliver Kuss for the use of the SAS macro %GOFLOGIT to cal-culate Farrington’s test.
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DOI: 10.1542/peds.2012-2189 originally published online January 14, 2013;
2013;131;e425
Pediatrics
Bradley N. Manktelow, Sarah E. Seaton, David J. Field and Elizabeth S. Draper
Population-Based Estimates of In-Unit Survival for Very Preterm Infants
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