Comparing mortality risk models in VLBW and preterm infants: systematic review and meta-analysis

Evaluating the Clinical Risk Index for Babies (CRIB) II Score for Mortality Prediction in Preterm Newborns: A Prospective Observational Study at a Tertiary Care Hospital

INTRODUCTION

Neonatal mortality is an issue that affects both the developed and developing world. It is very important in the neonatal intensive care unit (NICU) to do the assessment of the severity of neonatal illness, which in turn helps in estimating and preventing mortality in the NICU by improving healthcare control and by rational use of resources. This research was carried out to evaluate how effectively the Clinical Risk Index for Babies (CRIB) II score can predict mortality rates among newborns treated in our NICU.  Methodology: This prospective observational study spanned one year, commencing in October 2021 and concluding in September 2022, within the confines of our NICU. The CRIB II score calculation was performed for included newborns, and the outcomes of the newborns were compared. A receiver operating characteristic (ROC) curve was obtained to ascertain the optimal CRIB II cut-off score for predicting mortality.

RESULTS

Within the designated research timeframe, 292 neonates were admitted to the NICU. Forty-four newborns were enrolled in the study. Preterm neonates who died had higher CRIB II scores than those who survived, and their median (IQR) was 6 (1-12) vs. 9.5 (5-14) (p=0.0003). The estimate for the area under the curve was 0.83 (95% CI 0.68-0.92), and the odds ratio of 2.56 suggests neonates with a higher CRIB II score have higher chances of mortality.

CONCLUSION

The CRIB II score is very good at predicting mortality in preterm newborns.

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models

BACKGROUND

Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care. Improving predictive models can help providers and families navigate these unique challenges.

OBJECTIVE

Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission.

METHODS

Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model.

RESULTS

Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension II (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications.

CONCLUSIONS

Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information. Evaluation of larger, more diverse data sets may provide additional clarity on comparative performance.

Antenatal prediction models for outcomes of extremely and very preterm infants based on machine learning

PURPOSE

Predicting individual risks for adverse outcomes in preterm infants is necessary for perinatal management and antenatal counseling for their parents. To evaluate whether a machine learning approach can improve the prediction of severe infant outcomes beyond the performance of conventional logistic models, and to identify maternal and fetal factors that largely contribute to these outcomes.

METHODS

A population-based retrospective study was performed using clinical data of 31,157 infants born at < 32 weeks of gestation and weighing ≤ 1500 g, registered in the Neonatal Research Network of Japan between 2006 and 2015. We developed a conventional logistic model and 6 types of machine learning models based on 12 maternal and fetal factors. Discriminative ability was evaluated using the area under the receiver operating characteristic curves (AUROCs), and the importance of each factor in terms of its contribution to outcomes was evaluated using the SHAP (SHapley Additive exPlanations) value.

RESULTS

The AUROCs of the most discriminative machine learning models were better than those of the conventional models for all outcomes. The AUROCs for in-hospital death and short-term adverse outcomes in the gradient boosting decision tree were significantly higher than those in the conventional model (p = 0.015 and p = 0.002, respectively). The SHAP value analyses showed that gestational age, birth weight, and antenatal corticosteroid treatment were the three most important factors associated with severe infant outcomes.

CONCLUSION

Machine learning models improve the prediction of severe infant outcomes. Moreover, the machine learning approach provides insight into the potential risk factors for severe infant outcomes.

Comparison of STOPS and SNAPPE-II in Predicting Neonatal Survival at Hospital Discharge: A Prospective, Multicentric, Observational Study

OBJECTIVE

To compare SNAPPE-II and STOPS admission severity scores in neonates admitted to neonatal intensive care unit (NICU) with a gestational age of ≥ 33 wk.

METHODS

In this multicenter, prospective, observational study, the sickness scoring was done on all the neonates at 12 h after admission to the NICUs. The scoring systems were compared by the area under the curve (AUC) on the receiver operating characteristics (ROC) curve.

RESULTS

A total of 669 neonates with gestational age ≥ 33 wk (mortality rate: 2.4%), who were admitted to five participating NICUs within 24 h of birth, were included. Both SNAPPE-II and STOPS had the good discriminatory and predictive ability for mortality with AUCs of 0.965 [95% confidence interval (CI): 0.94-0.98] and 0.92 (95% CI: 0.87-0.99), respectively. The STOPS scoring system with a cutoff score ≥ 4 on the ROC curve had 85% accuracy, whereas the SNAPPE-II cutoff score ≥ 33 on the ROC curve had 94% accuracy in predicting mortality.

CONCLUSION

In infants with the gestational age of ≥ 33 wk, SNAPPE-II and STOPS showed similar predictive ability, but the STOPS score, being a simpler clinical tool, might be more useful in resource-limited settings.

The new frontier in ECLS: Artificial placenta and artificial womb for premature infants

Outcomes for extremely low gestational age newborns (ELGANs), defined as <28 weeks estimated gestational age (EGA), remain disproportionately poor. A radical paradigm shift in the treatment of prematurity is to recreate the fetal environment with extracorporeal support and provide an environment for organ maturation using an extracorporeal VV-ECLS artificial placenta (AP) or an AV-ECLS artificial womb (AW). In this article, we will review clinical indications, current approaches in development, ongoing challenges, remaining milestones and ethical considerations prior to clinical translation.

Association between Endotype of Prematurity and Mortality: A Systematic Review, Meta-Analysis, and Meta-Regression

INTRODUCTION

Preterm birth represents the leading cause of neonatal mortality. Pathophysiological pathways, or endotypes, leading to prematurity can be clustered into infection/inflammation and dysfunctional placentation. We aimed to perform a systematic review and meta-analysis exploring the association between these endotypes and risk of mortality during first hospital admission Methods: PROSPERO ID: CRD42020184843. PubMed and Embase were searched for observational studies examining infants with gestational age (GA) ≤34 weeks. Chorioamnionitis represented the infectious-inflammatory endotype, while dysfunctional placentation proxies were hypertensive disorders of pregnancy (HDP) and small for GA (SGA)/intrauterine growth restriction (IUGR). A random-effects model was used to calculate odds ratios (ORs) and 95% confidence intervals. Heterogeneity was studied using random-effects meta-regression analysis.

RESULTS

Of 4,322 potentially relevant studies, 150 (612,580 infants) were included. Meta-analysis showed positive mortality odds for chorioamnionitis (OR: 1.43, 95% confidence interval: 1.25-1.62) and SGA/IUGR (OR: 1.68, 95% confidence interval: 1.38-2.04) but negative mortality odds for HDP (OR 0.74, 95% confidence interval: 0.64-0.86). Chorioamnionitis was associated with a lower GA, while HDP and SGA/IUGR were associated with a higher GA. Meta-regression showed a significant correlation between these differences in GA and mortality odds.

CONCLUSION

Our data suggest that the infectious/inflammatory endotype of prematurity has a greater overall impact on mortality risk as it is the most frequent endotype in the lower GAs. However, when the endotype of placental dysfunction is severe enough to induce growth restriction, it is strongly associated with higher mortality rates even though newborns are more mature.

On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature

BACKGROUND

Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models.

METHODS

We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source.

RESULTS

From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios.

CONCLUSION

Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.

Updates in Neonatal Extracorporeal Membrane Oxygenation and the Artificial Placenta

Extracorporeal life support, initially performed in neonates, is now commonly used for both pediatric and adult patients requiring pulmonary and/or cardiac support. Data suggests the clinical feasibility of Extracorporeal Membrane Oxygenation for premature infants (29-33 weeks estimated gestational age [EGA]). For extremely premature infants less than 28 weeks EGA, an artificial placenta has been developed to recreate the fetal environment. This approach is investigational but clinical translation is promising. In this article, we discuss the current state and advances in neonatal and "preemie Extracorporeal Membrane Oxygenation" and the development of an artificial placenta and its potential use in extremely premature infants.

Milestones for clinical translation of the artificial placenta

Despite significant advances in the treatment of prematurity, premature birth results in significant mortality and morbidity. In particular, extremely low gestational age newborns (ELGANs) defined as <28 weeks estimated gestational age (EGA) suffer from disproportionate mortality and morbidity. A radical paradigm shift in the treatment of prematurity is to recreate fetal physiology using an extracorporeal VV-ECLS artificial placenta (AP) or an AV-ECLS artificial womb (AW). Over the past 15 years, tremendous advances have been made in the laboratory confirming long-term support and organ protection and ongoing development. The major milestones to clinical application are miniaturization, anticoagulation, clinical risk stratification, specialized critical care protocols, a regulatory path and a strategy and platform to translate technology to the bedside. Currently, several groups are addressing the remaining milestones for clinical translation.

The clinical burden of extremely preterm birth in a large medical records database in the United States: Mortality and survival associated with selected complications

BACKGROUND

Preterm birth is a leading cause of infant mortality, particularly for those born extremely prematurely (EP; <28 weeks' gestational age [GA]). Survivors are predisposed to complications such as bronchopulmonary dysplasia (BPD), chronic lung disease (CLD), intraventricular hemorrhage (IVH), and retinopathy of prematurity (ROP).

AIMS

To examine the epidemiology, complications, and mortality/survival among EP infants.

STUDY DESIGN

Retrospective analysis of electronic medical records from the Kaiser Permanente Northern California database.

SUBJECTS

EP infants live-born between 22 and <28 weeks' GA from 1997 to 2016.

OUTCOME MEASURES

Cumulative all-cause mortality/survival were analyzed and stratified by GA (22 to <24, 24 to <26, 26 to <28 weeks), complications (BPD/CLD, IVH, ROP), and birth period (1997 to 2003, 2004 to 2009, 2010 to 2016). Cox proportional hazard models were constructed to assess the mortality risk associated with BPD/CLD or IVH.

RESULTS

2154 EP infants were identified; of these, 916 deaths were recorded. Mortality was highest during the first 3 months (41.7 % cumulative mortality), and few were reported after 2 years (42.5 % cumulative mortality). Mortality decreased with higher GA and over more recent birth periods. BPD/CLD and IVH grade 3/4 were associated with increased mortality risk versus no complications (adjusted hazard ratios 1.41 and 1.78, respectively).

CONCLUSIONS

The risk of mortality is high during the first few months of life for EP infants, and is even higher for those with BPD and IVH. Despite an overall trend toward increased survival for EP infants, strategies targeting survival of EP infants with these complications are needed.

Comparison of CRIB-II with SNAPPE-II for predicting survival and morbidities before hospital discharge in neonates with gestation ≤ 32 weeks: a prospective multicentric observational study

Various studies validated and compared Score for Neonatal Acute Physiology with Perinatal extension-II (SNAPPE-II) and Clinical Risk Index for Babies-II (CRIB-II) admission sickness severity scores for predicting survival, but very few studies compared them for predicting the morbidities in preterm infants. In this multicenter prospective observational study, SNAPPE-II and CRIB-II newborn illness severity scores were compared for predicting mortality and morbidities in infants with gestational age of ≤ 32 weeks. Major morbidities were classified as bronchopulmonary dysplasia, abnormal cranial ultrasound (presence of intraventricular hemorrhage grade III or more or periventricular leukomalacia grade II to IV), and retinopathy of prematurity requiring treatment. Combined adverse outcome was defined as death or any major morbidity. Comparison of the scoring systems was done by area under the curve (AUC) on receiver operating characteristics curve (ROC curve) analysis. A total of 419 neonates who were admitted to 5 participating NICUs were studied. The mortality rate in the study population was 8.8%. Both CRIB-II (AUC: 0.795) and SNAPPE-II (AUC: 0.78) had good predictive ability for in-hospital mortality. For predicting any one of the major morbidities and combined adverse outcome, CRIB-II had better predictive ability than SNAPPE-II with AUC of 0.83 vs. 0.70 and 0.85 vs. 0.74, respectively.

CONCLUSION

In infants with gestational age of ≤ 32 weeks, both CRIB-II and SNAPPE-II are good scoring systems for predicting mortality. CRIB-II, being a simpler scoring system and having better predictive ability for major morbidities and combined adverse outcome, is preferable over SNAPPE-II.

WHAT IS KNOWN

• SNAPPE-II and CRIB-II scores have good predictive ability on in-hospital mortality in preterm neonates.

WHAT IS NEW

• SNAPPE-II and CRIB-II both have good predictive ability for mortality, but CRIB-II has better ability for short-term morbidities related to the prematurity.

A data-driven health index for neonatal morbidities

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

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Traditional definitions of prematurity based on gestational age need to be updated

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Deep learning of maternal clinical data improves predictions of neonatal morbidity

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Data-driven model leverages birthweight, type of delivery and maternal race

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Accurate risk prediction can inform clinical decisions

Perinatal risk factors for mortality in very preterm infants-A nationwide, population-based discriminant analysis

Aim

To assess the strength of associations between interrelated perinatal risk factors and mortality in very preterm infants.

Methods

Information on all live‐born infants delivered in Sweden at 22–31 weeks of gestational age (GA) from 2011 to 2019 was gathered from the Swedish Neonatal Quality Register, excluding infants with major malformations or not resuscitated because of anticipated poor prognosis. Twenty‐seven perinatal risk factors available at birth were exposures and in‐hospital mortality outcome. Orthogonal partial least squares discriminant analysis was applied to assess proximity between individual risk factors and mortality, and receiver operating characteristic (ROC) curves were used to estimate discriminant ability.

Results

In total, 638 of 8,396 (7.6%) infants died. Thirteen risk factors discriminated reduced mortality; the most important were higher Apgar scores at 5 and 10 min, GA and birthweight. Restricting the analysis to preterm infants <28 weeks’ GA (n = 2939, 16.9% mortality) added antenatal corticosteroid therapy as significantly associated with lower mortality. The area under the ROC curve (the C‐statistic) using all risk factors was 0.86, as determined after both internal and external validation.

Conclusion

Apgar scores, gestational age and birthweight show stronger associations with mortality in very preterm infants than several other perinatal risk factors available at birth.

Comparison of different neonatal illness severity scores in predicting mortality risk of extremely low birth weight infants

To compare different illness severity scores in predicting mortality risk of extremely low birth weight infants (ELBWI). From January 1st, 2019 to January 1st, 2020, all ELBWI admitted in the Children's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital and the First Affiliated Hospital of Nanjing Medical University were included in the study. ELBWI with admission age ≥1 h, gestational age ≥37 weeks and incomplete data required for scoring were excluded. The clinical data were collected, neonatal critical illness score (NCIS), score for neonatal acute physiology version Ⅱ (SNAP-Ⅱ), simplified version of the score for neonatal acute physiology perinatal extension (SNAPPE-Ⅱ), clinical risk index for babies (CRIB) and CRIB-Ⅱ were calculated. The scores of the fatal group and the survival group were compared, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the above illness severity scores for the mortality risk of ELBWI. Pearson correlation analysis was used to analyze the correlation between illness scores and birth weight, illness scores and gestational age. A total of 192 ELBWI were finally included, of whom 114 cases survived (survival group) and 78 cases died (fatal group). There were significant differences in birth weight, gestational age and Apgar scores between fatal group and survival group (all <0.01). There were significant differences in NCIS, SNAP-Ⅱ, SNAPPE-Ⅱ, CRIB and CRIB-Ⅱ between fatal group and survival group (all <0.01). The CRIB had a relatively higher predictive value for the mortality risk. Its area under the ROC curve (AUC) was 0.787, the sensitivity was 0.678, the specificity was 0.804, and the Youden index was 0.482. The scores of NCIS, SNAP-Ⅱ, SNAPPE-Ⅱ, CRIB and CRIB-Ⅱ were significantly correlated with birth weight and gestational age (all <0.05). The correlation coefficients of CRIB-Ⅱ and CRIB with birth weight and gestational age were relatively large, and the correlations coefficients of NCIS with birth weight and gestational age were the smallest (0.191 and 0.244, respectively). Among these five illness severity scores, CRIB has better predictive value for the mortality risk in ELBWI. NCIS, which is widely used in China, has relatively lower sensitivity and specificity, and needs to be further revised.

Development of an artificial placenta for support of premature infants: narrative review of the history, recent milestones, and future innovation

Over 50 years ago, visionary researchers began work on an extracorporeal artificial placenta to support premature infants. Despite rudimentary technology and incomplete understanding of fetal physiology, these pioneering scientists laid the foundation for future work. The research was episodic, as medical advances improved outcomes of premature infants and extracorporeal life support (ECLS) was introduced for the treatment of term and near-term infants with respiratory or cardiac failure. Despite ongoing medical advances, extremely premature infants continue to suffer a disproportionate burden of mortality and morbidity due to organ immaturity and unintended iatrogenic consequences of medical treatment. With advancing technology and innovative approaches, there has been a resurgence of interest in developing an artificial placenta to further diminish the mortality and morbidity of prematurity. Two related but distinct platforms have emerged to support premature infants by recreating fetal physiology: a system based on arteriovenous (AV) ECLS and one based on veno-venous (VV) ECLS. The AV-ECLS approach utilizes only the umbilical vessels for cannulation. It requires immediate transition of the infant at the time of birth to a fluid-filled artificial womb to prevent umbilical vessel spasm and avoid gas ventilation. In contradistinction, the VV-ECLS approach utilizes the umbilical vein and the internal jugular vein. It would be applied after birth to infants failing maximal medical therapy or preemptively if risk stratified for high mortality and morbidity. Animal studies are promising, demonstrating prolonged support and ongoing organ development in both systems. The milestones for clinical translation are currently being evaluated.

Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis

CONTEXT

Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants.

OBJECTIVE

Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality.

DATA SOURCES

Medline was searched for all articles (up to June 2020).

STUDY SELECTION

All developed or externally validated prognostic models for mortality prediction in liveborn infants born <32 weeks' gestation and/or <1500 g birth weight were included.

DATA EXTRACTION

Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool.

RESULTS

One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score.

LIMITATIONS

Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded.

CONCLUSIONS

Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.

Maternal sleep duration and neonatal birth weight: the Japan Environment and Children's Study

BACKGROUND

The adequate maternal sleep duration required for favorable obstetric outcomes is unknown. We evaluated the association between maternal sleep duration and low birth weight infants, small for gestational age infants, and macrosomia.

METHODS

Participants enrolled in the Japan Environment and Children's Study, a nationwide birth cohort study, with singleton pregnancies after 22 weeks, who gave birth between 2011 and 2014 were enrolled and categorized into five groups according to maternal sleep duration during pregnancy: < 6.0 h, 6.0-7.9 h, 8.0-8.9 h, 9.0-9.9 h, and 10.0-12.0 h. We evaluated the association between maternal sleep duration and the incidence of low birth weight infants (< 2500 g), very low birth weight infants (< 1500 g), small for gestational age infants, and macrosomia (> 4000 g), with women with maternal sleep duration of 6.0-7.9 h as the reference, using a multiple logistic regression model.

RESULTS

In total, 82,171 participants were analyzed. The adjusted odds ratios (95% confidence intervals) for low birth weight infants in women with maternal sleep duration of 9.0-9.9 h and 10.0-12.0 h and for small for gestational age infants in women with maternal sleep duration of 9.0-9.9 h were 0.90 (0.83-0.99), 0.86 (0.76-0.99), and 0.91 (0.82-0.99), respectively, before adjusting for excessive gestational weight gain. No significant association was observed between maternal sleep duration and these outcomes after adjusting for excessive gestational weight gain. Among women with appropriate gestational weight gain, the adjusted odds ratios (95% confidence intervals) for low birth weight infants and for small for gestational age infants with sleep duration of 9.0-9.9 h were 0.88 (0.80-0.97) and 0.87 (0.78-0.97), respectively.

CONCLUSIONS

Maternal sleep duration of 9.0-9.9 h was significantly associated with the decreased incidence of low birth weight infants and small for gestational age infants in pregnant women with appropriate gestational weight gain, compared with that of 6.0-7.9 h. Care providers should provide proper counseling regarding the association between maternal sleep duration and neonatal birth weight and suggest comprehensive maternal lifestyle modifications to prevent low birth weight and small for gestational age infants.

Antenatal prediction models for short- and medium-term outcomes in preterm infants

INTRODUCTION

In extremely and very preterm infants, predicting individual risks for adverse outcomes antenatally is challenging but necessary for risk-stratified perinatal management and parents' participation in decision-making about treatment. Our aim was to develop and validate prediction models for short-term (neonatal period) and medium-term (3 years of age) outcomes based on antenatal maternal and fetal factors alone.

MATERIAL AND METHODS

A population-based study was conducted on 31 157 neonates weighing ≤1500 g and born between 22 and 31 weeks of gestation registered in the Neonatal Research Network of Japan during 2006-2015. Short-term outcomes were assessed in 31 157 infants and medium-term outcomes were assessed in 13 751 infants among the 31 157 infants. The clinical data were randomly divided into training and validation data sets in a ratio of 2:1. The prediction models were developed by factors selected using stepwise logistic regression from 12 antenatal maternal and fetal factors with the training data set. The number of factors incorporated into the model varied from 3 to 10, on the basis of each outcome. To evaluate predictive performance, the area under the receiver operating characteristics curve (AUROC) was calculated for each outcome with the validation data set.

RESULTS

Among short-term outcomes, AUROCs for in-hospital death, chronic lung disease, intraventricular hemorrhage (grade III or IV) and periventricular leukomalacia were 0.85 (95% CI 0.83-0.86), 0.80 (95% CI 0.79-0.81), 0.78 (95% CI 0.75-0.80), and 0.58 (95% CI 0.55-0.61), respectively. Among medium-term outcomes, AUROCs for cerebral palsy and developmental quotient of <70 at 3 years of age were 0.66 (95% CI 0.63-0.69) and 0.72 (95% CI 0.70-0.74), respectively.

CONCLUSIONS

Although the predictive performance of these models varied for each outcome, their discriminative ability for in-hospital death, chronic lung disease, and intraventricular hemorrhage (grade III or IV) was relatively good. We provided a bedside prediction tool for calculating the likelihood of various infant complications for clinical use. To develop these prediction models would be valuable in each country, and these risk assessment tools could facilitate risk-stratified perinatal management and parents' shared understanding of their infants' subsequent risks.

The CRIB II (Clinical Risk Index for Babies II) Score in Prediction of Neonatal Mortality

Predicting the outcome of neonatal critical patients remains elusive. The multiple factors of maternal state of health (infections, diabetes, gestosis), the placental situation (premature rupture of membranes) as well as multiple factors from the baby (small for gestational age, low Apgar score, low birth infections, mechanical ventilation, hypoglycaemia hyperglycamiea) render the approach to treatment of each patient individual and the outcome uncertain. Several approaches and scales are developed in order to assess the mortality risk in those rather complicated situations.We used the CRIB-II scale to assess the mortality risk in 80 patients delivered in a large tertiary level hospital with more than 4,000 deliveries yearly. The patients were stratified according to all the neonatal risk factors and comorbidities. The CRIB-II scale identified well the mortality rates, but not the outcomes. A large and well-balanced cohort of patients followed for a longer period is required to discern in detail the importance of CRIB-II scale in predicting outcomes in high-risk new-borns. This could serve as an assistance to personalized approach to severely sick children. In addition, it is a valuable method in comparing outcomes in different NICUs and outcomes in different times in the same NICU, thus rendering possible improvements in the same unit and among several NICU departments.