The existing prediction formulas for in-hospital mortality of very low birth weight (VLBW) infants were mostly developed in the 1990s or 2000s and thus may not reflect the recently improved levels of neonatal care. We conducted this study to build a model for predicting the in-hospital mortality using perinatal factors available soon after birth.
We gathered data on VLBW infants from the Korean Neonatal Network, a nationwide, prospective, Web-based registry that enrolled patients from 2013 to 2017. Perinatal variables that were significantly associated with mortality in univariate logistic regression or those with apparent clinical importance were included in the multivariable logistic regression model. The final formula was constructed by considering the collinearity, parsimony, goodness of fit, and clinical interpretation.
A total of 9248 VLBW infants were analyzed, including 1105 (11.9%) who died during hospitalization. The mean gestational age was 29.0 ± 2.9 weeks and the mean birth weight was 1096 ± 280 g. Significant variables used in the final equation included polyhydramnios, oligohydramnios, gestational age, Apgar score at 1 minute, intubation at birth, birth weight, and base excess. In internal validation, the area under the curve (AUC) for the prediction of in-hospital mortality was 0.870 and the optimism-corrected AUC was 0.867. The prediction equation revealed good discrimination and calibration in the external validation as well (AUC: 0.876).
The newly developed Korean Neonatal Network prediction formula for in-hospital mortality could be a useful tool in counseling by providing a reliable prediction for the in-hospital mortality of VLBW infants.
Prediction of in-hospital mortality of very low birth weight (VLBW) infants is helpful for counseling. However, the existing prediction formulas were mostly developed in the 1990s or 2000s and thus do not reflect the recently improved levels of neonatal care.
We developed a prediction formula based on the data from a prospective registry of VLBW infants born between 2013 and 2017. The newly developed formula accurately predicted in-hospital mortality of VLBW infants using perinatal variables obtained soon after birth.
The survival rate of extremely preterm infants has increased with the advances in neonatal care.1–4 After the birth of extremely preterm infants, parents commonly inquire about the chance of survival during counseling; moreover, parents of infants in the gray zone of gestational ages between 23 and 24 weeks also seek counseling soon after delivery.5,6 To this end, accurate prediction of survival and neurodevelopmental outcomes in such infants is needed. Several scoring systems for predicting neonatal survival outcomes have been developed in the United States, Canada, and the United Kingdom.5,7–10 However, many of them were established several decades ago; thus, a newer predictive system that considers the recent advancements in neonatal care and the improved survival outcomes is needed.
In South Korea, the Korean Neonatal Network (KNN) was established to standardize and improve the quality of treatment at the NICU and management skills by coordinating with multiple international neonatal networks. Furthermore, KNN data could contribute to improving their quality of life by improving their survival rates and reducing major complications in high-risk infants.11 We therefore analyzed the prospectively gathered data from the KNN registry to build the KNN prediction formula that provides reliable prediction of the in-hospital mortality using perinatal factors that are available soon after birth.
Methods
Study Population and Data Selection
We retrospectively analyzed the data in the KNN registry, a national prospective Web-based registry for very low birth weight (VLBW) infants who were born between 2013 and 2017 in South Korea. Among 10 425 patients, we excluded infants who (1) died within 24 hours after birth (n = 277), (2) were transferred to another hospital during hospitalization or hospitalized for >365 days (n = 542), and (3) were born at ≤22+3 weeks’ gestation whose weight percentile based on the 2013 Fenton preterm growth chart12,13 could not be determined (n = 36). We also excluded infants born at hospitals that were not registered for the KNN and then transferred to KNN-registered hospitals (n = 322).
We used multiple perinatal factors analysis obtained within 1 to 2 hours after birth as risk factors in the analysis. Factors used for the analysis included maternal age, parity, amount of amniotic fluid, maternal and/or paternal education and nationality, artificial fertilization, maternal disease (ie, diabetes mellitus [DM], hypertension), preterm premature rupture of membrane (PPROM), and steroid use. Neonatal factors used for the analysis included birthplace (inborn or outborn), gestational age, sex, Apgar score at 1 and 5 minutes, severe congenital anomaly, the need for resuscitation during delivery through oxygen supply, positive pressure ventilation, intubation or cardiac compression, multiple gestation, birth weight, height at birth, head circumference at birth, body temperature at admission, and initial pH and base excess obtained from blood gas analysis.
Definition of Risk Factors
Oligohydramnios and polyhydramnios were defined as the index of amniotic fluid <5 and >24 cm, respectively. DM on maternal history was categorized as gestational DM and overt DM on the basis of the obstetric medical record. Hypertension on maternal history was categorized as pregnancy-induced hypertension, including eclampsia and preeclampsia regardless of proteinuria or edema, and chronic hypertension, including superimposed hypertension. Artificial fertilization only included in vitro fertilization with or without embryo transfer and did not include intrauterine insemination or ovulation induction. Severe congenital anomaly included life-threatening anomalies such as tracheal obstruction. The percentile of birth weight was determined by a Web-based calculator on the basis of the 2013 Fenton growth curve.12,13 Small for gestational age was defined as birth weight less than the 3rd percentile or less than the 10th percentile. The serial process of initial resuscitation was performed on the basis of the neonatal resuscitation program of the International Liaison Committee on Resuscitation guideline.14 The initial pH and base excess were obtained from venous, arterial, or capillary blood within 1 hour after birth. Body temperature measured within 1 hour after admission to the NICU was used.
Data Analysis and Statistics
We evaluated the statistical properties (eg, potential outliers, normality, and missing data) of the baseline and follow-up measurements using summary statistics and graphical tools. Categorical variables are presented as counts and percentages, and continuous variables are presented as means and SDs or medians and interquartile ranges depending on their distribution. The missing data mechanism was speculated to be missing completely at random or at least missing at random; as such, we used the multiple imputation method.
To build the model for predicting the in-hospital mortality, we investigated each potential covariate using graphical tools and the univariate logistic regression model. For continuous predictors, we checked the linearity of the relationship between each predictor and the log odds of the outcomes and considered appropriate transformation if the relationship was not linear. The covariates that were either significant in the univariate model or considered clinically important regardless of statistical significance were entered into the multivariable logistic regression model. The final model was selected by backward elimination after removing variables with collinearity. This model building process was performed on the basis of careful considerations on collinearity, parsimony, goodness of fit, and clinical interpretation to develop an accurate, precise, and useful prediction model.
For internal validation of the model, we performed the receiver operating characteristics (ROCs) analysis and calculated the area under the curve (AUC). A total of 500 bootstrap samples were generated to estimate the optimism of the AUC, and the optimism-corrected AUC was calculated as a measure of the performance of our prediction model.
Ethics Statement
The KNN registry was approved by the institutional review board of each participating hospital. Standard informed consent was obtained from the parents of the infants before registration in the KNN.
Results
Characteristics of the Study Population
A total of 9248 VLBW infants were included in this study, among whom 1105 (11.9%) died during hospitalization. The mean gestational age at birth was 29.0 ± 2.9 weeks and the mean birth weight was 1095.9 ± 280.1 g. Compared with mothers of the survived infants (survival group), mothers of the dead infants (death group) had lower proportions of DM and hypertension and higher proportions of PPROM and amount of amniotic fluid (polyhydramnios or oligohydramnios) (Table 1).
Parental Characteristics of the Infants According to Survival
Variables . | All Infants (N = 9248) . | Survival (n = 8143) . | Death (n = 1105) . | P . |
---|---|---|---|---|
In vitro fertilization | 2136 (23.1) | 1873 (23.0) | 263 (23.8) | .58 |
Maternal DM | <.001 | |||
None | 8381 (90.6) | 7341 (90.2) | 1040 (94.1) | |
Gestational | 763 (8.3) | 709 (8.7) | 54 (4.9) | |
Overt | 104 (1.1) | 93 (1.1) | 11 (1.0) | |
Maternal hypertension | <.001 | |||
None | 7195 (77.8) | 6268 (77.0) | 927 (83.9) | |
Chronic hypertension | 206 (2.2) | 177 (2.2) | 29 (2.6) | |
Pregnancy-induced | 1847 (20.9) | 1698 (20.9) | 149 (13.5) | |
Use of steroid | 7392 (80.7) | 6522 (80.8) | 870 (79.9) | .52 |
Amount of amniotic fluid | <.001 | |||
Normal | 7190 (83.8) | 6393 (84.4) | 797 (79.9) | |
Polyhydramnios | 119 (1.4) | 94 (1.2) | 25 (2.5) | |
Oligohydramnios | 1266 (14.8) | 1090 (14.4) | 176 (17.6) | |
PPROM | 3202 (34.8) | 2772 (34.2) | 430 (39.2) | .001 |
Maternal age | 33.02 ± 4.25 | 33.03 ± 4.22 | 32.98 ± 4.47 | .73 |
Primipara | 4124 (44.6) | 3643 (44.7) | 481 (43.5) | .47 |
Mother’s education level | .05 | |||
Elementary or middle school | 128 (1.8) | 104 (1.6) | 24 (2.8) | |
High school | 1715 (23.8) | 1507 (23.7) | 208 (24.2) | |
University or higher | 5367 (74.4) | 4738 (74.6) | 629 (73.1) | |
Mother’s nationality (foreigner) | 360 (3.9) | 310 (3.8) | 50 (4.5) | .28 |
Father’s education level | .39 | |||
Elementary or middle school | 48 (0.9) | 45 (1.0) | 3 (0.5) | |
High school | 1130 (21.6) | 994 (21.5) | 136 (23) | |
University or higher | 4045 (77.4) | 3592 (77.6) | 453 (76.5) | |
Father’s nationality (foreigner) | 200 (2.2) | 172 (2.1) | 28 (2.5) | .43 |
Marriage status | .07 | |||
Single | 66 (0.7) | 55 (0.7) | 11 (1.0) | |
Married | 9072 (98.1) | 7999 (98.2) | 1073 (97.1) | |
Divorced | 8 (0.1) | 7 (0.1) | 1 (0.1) | |
Living together | 102 (1.1) | 82 (1.0) | 20 (1.8) |
Variables . | All Infants (N = 9248) . | Survival (n = 8143) . | Death (n = 1105) . | P . |
---|---|---|---|---|
In vitro fertilization | 2136 (23.1) | 1873 (23.0) | 263 (23.8) | .58 |
Maternal DM | <.001 | |||
None | 8381 (90.6) | 7341 (90.2) | 1040 (94.1) | |
Gestational | 763 (8.3) | 709 (8.7) | 54 (4.9) | |
Overt | 104 (1.1) | 93 (1.1) | 11 (1.0) | |
Maternal hypertension | <.001 | |||
None | 7195 (77.8) | 6268 (77.0) | 927 (83.9) | |
Chronic hypertension | 206 (2.2) | 177 (2.2) | 29 (2.6) | |
Pregnancy-induced | 1847 (20.9) | 1698 (20.9) | 149 (13.5) | |
Use of steroid | 7392 (80.7) | 6522 (80.8) | 870 (79.9) | .52 |
Amount of amniotic fluid | <.001 | |||
Normal | 7190 (83.8) | 6393 (84.4) | 797 (79.9) | |
Polyhydramnios | 119 (1.4) | 94 (1.2) | 25 (2.5) | |
Oligohydramnios | 1266 (14.8) | 1090 (14.4) | 176 (17.6) | |
PPROM | 3202 (34.8) | 2772 (34.2) | 430 (39.2) | .001 |
Maternal age | 33.02 ± 4.25 | 33.03 ± 4.22 | 32.98 ± 4.47 | .73 |
Primipara | 4124 (44.6) | 3643 (44.7) | 481 (43.5) | .47 |
Mother’s education level | .05 | |||
Elementary or middle school | 128 (1.8) | 104 (1.6) | 24 (2.8) | |
High school | 1715 (23.8) | 1507 (23.7) | 208 (24.2) | |
University or higher | 5367 (74.4) | 4738 (74.6) | 629 (73.1) | |
Mother’s nationality (foreigner) | 360 (3.9) | 310 (3.8) | 50 (4.5) | .28 |
Father’s education level | .39 | |||
Elementary or middle school | 48 (0.9) | 45 (1.0) | 3 (0.5) | |
High school | 1130 (21.6) | 994 (21.5) | 136 (23) | |
University or higher | 4045 (77.4) | 3592 (77.6) | 453 (76.5) | |
Father’s nationality (foreigner) | 200 (2.2) | 172 (2.1) | 28 (2.5) | .43 |
Marriage status | .07 | |||
Single | 66 (0.7) | 55 (0.7) | 11 (1.0) | |
Married | 9072 (98.1) | 7999 (98.2) | 1073 (97.1) | |
Divorced | 8 (0.1) | 7 (0.1) | 1 (0.1) | |
Living together | 102 (1.1) | 82 (1.0) | 20 (1.8) |
Data are n (%) or mean ± SD.
Compared with the infants in the survival group, those in the death group had a higher proportion of congenital anomaly, lower gestational age at birth, lower birth weight, and lower Apgar score at 1 and 5 minutes; other differences between the groups are described in Table 2. The proportion of infants who were small for gestational age did not significantly differ between the 2 groups when using the 3rd percentile cutoff or the 10th percentile cutoff.
Neonatal Characteristics According to Survival
Variables . | All Infants (N = 9248) . | Survival (n = 8143) . | Death (n = 1105) . | P . |
---|---|---|---|---|
Congenital anomaly | 251 (2.7) | 173 (2.1) | 78 (7.1) | <.001 |
Female sex | 4607 (49.8) | 4088 (50.2) | 519 (47.0) | .047 |
Gestational age, wk | 29.04 ± 2.94 | 29.46 ± 2.75 | 25.97 ± 2.38 | <.001 |
Cesarean delivery | 7321 (79.2) | 6521 (80) | 809 (73.2) | <.001 |
Singleton | 5993 (64.8) | 5262 (64.6) | 731 (66.2) | .19 |
Apgar score 1 min | 5 (3–6) | 5 (4–6) | 3 (2–5) | <.001 |
Apgar score 5 min | 7 (6–8) | 7 (6–8) | 6 (4–7) | <.001 |
Birth weight, g | 1095.9 ± 280.1 | 1140.0 ± 254.3 | 771.3 ± 245.6 | <.001 |
<3rd percentile | 948 (10.3) | 833 (10.2) | 115 (10.4) | .90 |
<10th percentile | 1994 (21.6) | 1780 (21.9) | 214 (19.4) | .06 |
Birth height, cm | 36.64 ± 3.57 | 37.12 ± 3.25 | 32.62 ± 3.60 | <.001 |
Birth head circumference, cm | 26.02 ± 2.43 | 26.34 ± 2.23 | 23.34 ± 2.43 | <.001 |
Need for resuscitation at birth | 8194 (88.7) | 7105 (87.4) | 1089 (98.6) | <.001 |
Oxygen | 7522 (81.5) | 6555 (80.6) | 967 (87.6) | <.001 |
Positive pressure ventilation | 7132 (77.2) | 6069 (74.7) | 1063 (96.3) | <.001 |
Intubation | 5608 (60.7) | 4592 (56.5) | 1016 (92.0) | <.001 |
Cardiac compression | 349 (3.8) | 228 (2.8) | 121 (11.0) | <.001 |
Epinephrine | 233 (2.5) | 144 (1.8) | 89 (8.1) | <.001 |
Body temperature at admission, °C | 36.15 ± 0.59 | 36.19 ± 0.66 | 35.84 ± 0.77 | <.001 |
Blood gas pH | 7.27 ± 0.11 | 7.27 ± 0.11 | 7.23 ± 0.14 | <.001 |
Blood gas BE | −5.11 ± 4.10 | −4.88 ± 3.91 | −7.15 ± 4.97 | <.001 |
Variables . | All Infants (N = 9248) . | Survival (n = 8143) . | Death (n = 1105) . | P . |
---|---|---|---|---|
Congenital anomaly | 251 (2.7) | 173 (2.1) | 78 (7.1) | <.001 |
Female sex | 4607 (49.8) | 4088 (50.2) | 519 (47.0) | .047 |
Gestational age, wk | 29.04 ± 2.94 | 29.46 ± 2.75 | 25.97 ± 2.38 | <.001 |
Cesarean delivery | 7321 (79.2) | 6521 (80) | 809 (73.2) | <.001 |
Singleton | 5993 (64.8) | 5262 (64.6) | 731 (66.2) | .19 |
Apgar score 1 min | 5 (3–6) | 5 (4–6) | 3 (2–5) | <.001 |
Apgar score 5 min | 7 (6–8) | 7 (6–8) | 6 (4–7) | <.001 |
Birth weight, g | 1095.9 ± 280.1 | 1140.0 ± 254.3 | 771.3 ± 245.6 | <.001 |
<3rd percentile | 948 (10.3) | 833 (10.2) | 115 (10.4) | .90 |
<10th percentile | 1994 (21.6) | 1780 (21.9) | 214 (19.4) | .06 |
Birth height, cm | 36.64 ± 3.57 | 37.12 ± 3.25 | 32.62 ± 3.60 | <.001 |
Birth head circumference, cm | 26.02 ± 2.43 | 26.34 ± 2.23 | 23.34 ± 2.43 | <.001 |
Need for resuscitation at birth | 8194 (88.7) | 7105 (87.4) | 1089 (98.6) | <.001 |
Oxygen | 7522 (81.5) | 6555 (80.6) | 967 (87.6) | <.001 |
Positive pressure ventilation | 7132 (77.2) | 6069 (74.7) | 1063 (96.3) | <.001 |
Intubation | 5608 (60.7) | 4592 (56.5) | 1016 (92.0) | <.001 |
Cardiac compression | 349 (3.8) | 228 (2.8) | 121 (11.0) | <.001 |
Epinephrine | 233 (2.5) | 144 (1.8) | 89 (8.1) | <.001 |
Body temperature at admission, °C | 36.15 ± 0.59 | 36.19 ± 0.66 | 35.84 ± 0.77 | <.001 |
Blood gas pH | 7.27 ± 0.11 | 7.27 ± 0.11 | 7.23 ± 0.14 | <.001 |
Blood gas BE | −5.11 ± 4.10 | −4.88 ± 3.91 | −7.15 ± 4.97 | <.001 |
Data are n (%) or mean ± SD. Apgar score is expressed as median (interquartile range). BE, base excess.
Univariate Logistic Regression Analysis
In univariate logistic regression, the neonatal factors that were significantly associated with in-hospital mortality are described in Table 3. In the logistic regression model, the relationships between the log odds of the in-hospital mortality and gestational age at birth, body temperature at admission, and pH and base excess in blood gas analysis within 1 hour after birth reached plateaus after the following threshold values: 196 days, 38.0°C, 7.35, and 0, respectively. These variables revealed linear relationships with mortality at values lower than the aforementioned thresholds; therefore, we used the truncated values at the thresholds for these variables. We eliminated the variables that have no significant association with in-hospital mortality or unreliable association with death.
Unadjusted Odds Ratios for In-Hospital Mortality From Univariate Analysis
Variables . | Odds Ratio . | 95% CI . | P . |
---|---|---|---|
PPROM | 1.244 | 1.092–1.416 | .001 |
Amount of amniotic fluid | <.001 | ||
Polyhydramnios | 2.133 | 1.337–3.282 | .001 |
Oligohydramnios | 1.295 | 1.084–1.540 | .004 |
Female sex | 0.879 | 0.774–0.996 | .044 |
Gestational age at birth, d | 0.886 | 0.880–0.892 | <.001 |
Apgar score at 1 min | 0.649 | 0.626–0.671 | <.001 |
Birth weight, g | 0.995 | 0.994–0.995 | <.001 |
Severe congenital anomaly | 3.499 | 2.645–4.589 | <.001 |
The need of initial resuscitation | 10.463 | 6.503–18.301 | <.001 |
Oxygen use for resuscitation | 1.695 | 1.410–2.052 | <.001 |
PPV for resuscitation | 8.800 | 6.506–12.258 | <.001 |
Intubation for resuscitation | 8.893 | 7.165–11.180 | <.001 |
Cardiac compression | 4.266 | 3.380–5.359 | <.001 |
Epinephrine for resuscitation | 4.862 | 3.693–6.369 | <.001 |
Body temperature at admission | 0.437 | 0.397–0.480 | <.001 |
Blood gas pH | 0.020 | 0.010–0.039 | <.001 |
Blood gas BE | 0.890 | 0.876–0.905 | <.001 |
Variables . | Odds Ratio . | 95% CI . | P . |
---|---|---|---|
PPROM | 1.244 | 1.092–1.416 | .001 |
Amount of amniotic fluid | <.001 | ||
Polyhydramnios | 2.133 | 1.337–3.282 | .001 |
Oligohydramnios | 1.295 | 1.084–1.540 | .004 |
Female sex | 0.879 | 0.774–0.996 | .044 |
Gestational age at birth, d | 0.886 | 0.880–0.892 | <.001 |
Apgar score at 1 min | 0.649 | 0.626–0.671 | <.001 |
Birth weight, g | 0.995 | 0.994–0.995 | <.001 |
Severe congenital anomaly | 3.499 | 2.645–4.589 | <.001 |
The need of initial resuscitation | 10.463 | 6.503–18.301 | <.001 |
Oxygen use for resuscitation | 1.695 | 1.410–2.052 | <.001 |
PPV for resuscitation | 8.800 | 6.506–12.258 | <.001 |
Intubation for resuscitation | 8.893 | 7.165–11.180 | <.001 |
Cardiac compression | 4.266 | 3.380–5.359 | <.001 |
Epinephrine for resuscitation | 4.862 | 3.693–6.369 | <.001 |
Body temperature at admission | 0.437 | 0.397–0.480 | <.001 |
Blood gas pH | 0.020 | 0.010–0.039 | <.001 |
Blood gas BE | 0.890 | 0.876–0.905 | <.001 |
BE, base excess; CI, confidence interval.
Multivariable Logistic Regression Analysis and the KNN Prediction Formula
The variables selected for predicting in-hospital mortality after multivariable analysis and their odds ratios are shown in Table 4, and the final equation derived for prediction of in-hospital mortality (the KNN prediction formula) is as follows:
where xb = 8.994 + 0.964 × (polyhydramnios)* + 0.185 × (oligohydramnios)* – 0.111 × Apgar score at 1 minute† – 0.047 gestational age (day)‡ – 0.003 × birth weight (g)‡ + 0.679 × intubation§ – 0.058 × base excess.‖
Multivariable Regression Analysis for In-Hospital Mortality
Variables . | β Coefficient . | Odds Ratio . | 95% CI . | P . |
---|---|---|---|---|
Amount of amniotic fluid (reference: normal) | .008 | |||
Polyhydramnios | .964 | 2.622 | 1.352–4.845 | .003 |
Oligohydramnios | .185 | 1.203 | 0.944–1.524 | .13 |
Total gestation, d | −.047 | 0.954 | 0.943–0.964 | <.001 |
Apgar score at 1 min | −.111 | 0.895 | 0.846–0.948 | <.001 |
Intubation | .679 | 1.972 | 1.444–2.731 | <.001 |
Birth weight, g | −.003 | 0.997 | 0.997–0.998 | <.001 |
BE of initial blood gas | −.058 | 0.943 | 0.924–0.964 | <.001 |
Variables . | β Coefficient . | Odds Ratio . | 95% CI . | P . |
---|---|---|---|---|
Amount of amniotic fluid (reference: normal) | .008 | |||
Polyhydramnios | .964 | 2.622 | 1.352–4.845 | .003 |
Oligohydramnios | .185 | 1.203 | 0.944–1.524 | .13 |
Total gestation, d | −.047 | 0.954 | 0.943–0.964 | <.001 |
Apgar score at 1 min | −.111 | 0.895 | 0.846–0.948 | <.001 |
Intubation | .679 | 1.972 | 1.444–2.731 | <.001 |
Birth weight, g | −.003 | 0.997 | 0.997–0.998 | <.001 |
BE of initial blood gas | −.058 | 0.943 | 0.924–0.964 | <.001 |
BE, base excess; CI, confidence interval.
The ROC analysis revealed that the apparent AUC for the prediction of in-hospital mortality was 0.870. The optimism-corrected AUC of our prediction model based on 500 bootstrapping samples was 0.867 (Fig 1). The calibration plot, which delineated the agreement between the observed and predicted risks of in-hospital mortality, revealed that the prediction model had a good calibration (Fig 2).
ROC curve for in-hospital mortality by using the KNN prediction formula. Based on the ROC curve after 500 bootstrapping, the KNN prediction formula showed an optimism-corrected AUC of 0.8695 in predicting in-hospital mortality.
ROC curve for in-hospital mortality by using the KNN prediction formula. Based on the ROC curve after 500 bootstrapping, the KNN prediction formula showed an optimism-corrected AUC of 0.8695 in predicting in-hospital mortality.
Calibration plot of the final multivariable logistic model for the KNN prediction formula (5% interval quantile) for in-hospital mortality. The linear association between the observed and predicted in-hospital mortalities is shown as the solid diagonal line. The bars of 95% confidence intervals located around the solid line reveal a close to ideal calibration.
Calibration plot of the final multivariable logistic model for the KNN prediction formula (5% interval quantile) for in-hospital mortality. The linear association between the observed and predicted in-hospital mortalities is shown as the solid diagonal line. The bars of 95% confidence intervals located around the solid line reveal a close to ideal calibration.
External Validation
For external validation of the prediction model, we chose the study groups for infants who were born at a different time period (January 2018–December 2018) at 2 tertiary centers of KNN that have different treatment policies. After applying the same exclusion criteria used for the initial study population, a total of 143 infants were included for external validation. As a result, the KNN prediction formula produced an AUC of the ROC curve of 0.876 (Fig 3). The calibration plot of the external validation group also revealed that the prediction model had a good calibration (Fig 4).
ROC curves in the external validation population for in-hospital mortality by using the KNN prediction formula. On the basis of the ROC curve after external validation, the KNN prediction formula revealed an AUC of 0.876.
ROC curves in the external validation population for in-hospital mortality by using the KNN prediction formula. On the basis of the ROC curve after external validation, the KNN prediction formula revealed an AUC of 0.876.
Calibration plot of the final multivariable logistic model for the KNN prediction formula (10% interval quantile) in the external validation group.
Calibration plot of the final multivariable logistic model for the KNN prediction formula (10% interval quantile) in the external validation group.
Discussion
In this study, we used a nationwide, prospective, Web-based registry to gather data on VLBW infants and their variables obtained within 1 to 2 hours after birth to build a formula for predicting the in-hospital mortality rates of such high-risk infants. After multivariable analysis, neonatal factors such as polyhydramnios, Apgar score, gestational age, birth weight, and the need for intubation were included in the formula. Notably, our KNN prediction formula had high AUC values of 0.867 for the prediction of in-hospital mortality in the study population and 0.876 in an independent external validation population.
The currently available scoring systems for predicting early death in neonates include the clinical risk index for babies (CRIB) II score, Score for Neonatal Acute Physiology Perinatal Extension II (SNAPPE-II) score, and Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) calculator for neonatal condition or outcomes.5,7–10 Out of these, the CRIB II score9 and the SNAPPE-II score10 use variables that are collected at 12 hours after birth to predict mortality. In multiple studies, researchers have tried using perinatal factors and early physiologic factors that are obtained soon after birth (at least in the first 24 hours) to predict the probability of death or severe impairments.5,8–10,15 In this respect, we believe that our KNN formula would be also clinically useful because it uses neonatal measurements that could be obtained within 1 to 2 hours after birth.
Similar to the KNN prediction formula, the NICHD calculator5 and the CRIB II score8 use variables that are available soon after birth. Accordingly, birth weight and gestational age were identified as independent predictors of mortality in all 3 scoring systems. However, whereas the NICHD calculator included the antenatal steroid as a predictor, the KNN prediction formula did not. Boghossian et al16 reported that the effect of antenatal steroid on mortality was significant only in infants born between 24 and 25 weeks’ gestation and infants born between 22 and 25 weeks’ gestation from the NICHD calculator.5 In our study cohort, the mean gestational age of the infants as a whole was 29.04 weeks, which may explain the exclusion of antenatal steroid as a significant factor. Similar to our study, the study on the CRIB II score included infants <32 weeks’ gestation and identified birth weight, gestational age, and base excess soon after birth as significant predictors of mortality.8 Singleton was also a predictive factor in the NICHD equation but not in the KNN prediction formula. Multiple gestations was associated with a greater increased risk of mortality in more immature infants born at 26 weeks’ gestation or earlier17,18 ; however, multiple gestations were not significantly associated with mortality in the study from the Vermont Oxford Network on the basis of infants of birth weights ranging from 500 to 1500 g.19
Abnormal amount of amniotic fluid is observed in ∼1% to 7% of pregnancies and is associated with congenital anomalies or maternal disease and increased risk of adverse perinatal outcomes.20–24 Besides congenital anomalies, oligohydramnios is also associated with preterm birth, intrauterine growth restriction, and PPROM20,23 ; moreover, longer duration of oligohydramnios or anhydramnios accompanied with pulmonary hypoplasia is significantly associated with mortality.25 Polyhydramnios and oligohydramnios are associated with low Apgar scores at 1 and 5 minutes and the need for extensive resuscitation.23,26,27 The perinatal mortality rate in polyhydramnios is reported to be 30% to 41% even in cases of idiopathic polyhydramnios without congenital malformation and up to 76% in cases with oligohydramnios.20,23,27,28 In our current study, both polyhydramnios and oligohydramnios revealed significant associations with in-hospital mortality.
In previous reports,29,30 low Apgar scores were associated with poor neonatal conditions in the delivery room and with early neonatal death. In our study, Apgar scores at 1 and 5 minutes revealed similar relationships for in-hospital mortality, with the Apgar score at 1 minute revealing a more linear relationship to in-hospital mortality than that at 5 minutes, especially in scores of 0 to 3. Despite other authors reporting the association between low Apgar scores at 5 minute and neonatal mortality,29,31–34 we chose the Apgar score at 1 minute for the formula. Shankaran et al18 also reported that low Apgar scores at 1 minute were associated with the risk of mortality in infants of birth weights ranging from 501 to 1000 g.
In this study, the risk of in-hospital mortality decreased with higher gestational age until 28 weeks’ gestation. A review article35 summarized the gestational age-specific survival in extremely preterm infants and suggested that the effect of gestational age on mortality was most obvious in infants born between 22 and 25 weeks’ gestation. Lower gestational age was associated with higher mortality in the early period after birth in infants of birth weights from 501 to 1000 g.18 In the study19 from the Vermont Oxford Network, a 10 g increase in birth weight was associated with a 3% decrease in odds of mortality. Accordingly, in our study, higher birth weight was associated with lower in-hospital mortality. However, birth weight less than the 3rd percentile or less than the 10th percentile were not predictive factors for death unlike the results from the study on the SNAPPE-II score.10
In the study by Shankaran et al,18 intubation in the delivery room was associated with a decreased risk of mortality to discharge among infants with birth weights ≤750 g and increased risk of mortality among infants with birth weights >750 g. In our study, intubation was associated with a higher risk of mortality, which may be because our study population included more infants with birth weights ≥750 g.
We used the value of pH and base excess obtained from any arterial or venous or capillary blood samples because of good correlation.36–40 In previous report,41,42 lower pH and base excess was associated with adverse outcomes, but pH was not chosen in the final KNN prediction formula. Base excess was associated with mortality after controlling for birth weight, gestational age,43 and acidosis.44
KNN formula could predict the mortality of VLBW infants within 1 to 2 hours after birth using only 6 factors that reflect a wide range of perinatal conditions from prenatal to postnatal, including 1 maternal factor (amount of amniotic fluid), 2 neonatal biological factors (gestational age and birth weight), and 3 postnatal conditions (Apgar score at 1 minute, intubation at birth, and base excess). Because the KNN database was recently established compared with the currently available scoring systems, the KNN prediction formula more accurately reflects the benefits of recent improvements in neonatal care. Although we built the KNN formula with retrospectively analyzed perinatal data, the data themselves were prospectively collected and revealed a high AUC value for the prediction of in-hospital mortality in both external and internal validation. Therefore, we believe that the KNN formula, which was derived through a prospective study, can be used to reliably predict the mortality in the clinic and be readily applied across a wide range of clinical situations because only 6 variables are needed for its calculation.
Despite its strengths, the KNN prediction formula, similar to the CRIB II score, has limited applications in infants born at outside hospitals. Although, the KNN prediction formula was based on data solely obtained from Koreans, because Tyson et al5 showed that race and ethnic groups did not have significant associations with neonatal outcomes, the KNN prediction formula may be investigated for potential usage in predicting the in-hospital mortality of preterm infants of other ethnicities. In consideration of potential future studies on predictive ability of KNN prediction formula and comparison with other scoring systems, we excluded the infants who died within 24 hours after birth, which is similar to the SNAPPE-II score developed for the infants who survived at least 24 hours after birth. Although the exclusion of infants who died within 24 hours may be considered as a limitation of this study, the mortality rate within 24 hours after birth was only 2.9% in the population including the deaths within 24 hours, which was lower than we expected. Moreover, KNN prediction formula revealed a good discrimination (AUC: 0.870; H.W.P., S.Y.P., E.A.K., unpublished observations) even with the infants with 24-hour mortality included.
Conclusions
In this study, we derived a formula for predicting the in-hospital mortality in VLBW infants using perinatal factors available soon after birth. The formula revealed high AUC values in both the internal validation cohort (0.867) and an independent external validation population (0.876). The KNN prediction formula could be a useful tool for counseling parents by providing predictions for the in-hospital mortality rates of their VLBW infants.
Dr Park conceptualized and designed the study, coordinated data collection, designed and executed data collection, drafted the initial manuscript, and reviewed and revised the manuscript for important intellectual content; Prof Park analyzed the data, interpreted the data, and reviewed the manuscript for statistical content; Dr Kim conceptualized and designed the study, supervised data collection, interpreted the data, and critically reviewed and revised the manuscript for important intellectual content; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
In the presence of polyhydramnios or oligohydramnios, 1 is inserted into the equation; otherwise, 0 is inserted into the equation.
Values between 0 and 10 are inserted for the Apgar score at 1 minute.
Gestational age is inserted as total gestational day and birth weight as gram. If the gestational age is >196, then 196 is inserted into the equation.
Intubation refers to the need for intubation for initial resuscitation. If intubation was needed as initial resuscitation, 1 is inserted into the equation; otherwise, 0 is inserted into the equation.
Base excess is the arterial blood gas analysis within 1 hour after birth. If the value of the base excess is <0, then the value itself is inserted into the equation. If the value of the base excess is ≥ 0, then 0 is inserted into the equation.
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: Supported by a research fund from the Korea Disease Control and Prevention Agency (2019-ER7103-01).
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
- AUC
area under the curve
- CRIB
clinical risk index for babies
- DM
diabetes mellitus
- KNN
Korean Neonatal Network
- NICHD
Eunice Kennedy Shriver National Institute of Child Health and Human Development
- PPROM
preterm premature rupture of membrane
- ROC
receiver operating characteristic
- SNAPPE-II
Score for Neonatal Acute Physiology Perinatal Extension II
- VLBW
very low birth weight
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