Prediction of mortality in very low birth weight neonates in Spain

Prediction of bronchopulmonary dysplasia in very preterm infants: competitive risk model nomogram

Objective

To develop predictive clinical models of bronchopulmonary dysplasia (BPD) through competing risk analysis.

Methods

Retrospective observational cohort study, including preterm newborns ≤32 weeks gestational age, conducted between January 1, 2013 and September 30, 2022 in a third-level Neonatal Intensive Care Unit in Spain. A prediction study was carried out using competing risk models, where the event of interest was BPD and the competing event was death. A multivariate competing risk model was developed separately for each postnatal day (days 1, 3, 7 and 14). Nomograms to predict BPD risk were developed from the coefficients of the final models and internally validated.

Results

A total of 306 patients were included in the study, of which 73 (23.9%) developed BPD and 29 (9.5%) died. On day 1, the model with the greatest predictive capacity was that including birth weight, days since rupture of membranes, and surfactant requirement (area under the receiver operating characteristic (ROC) curve (AUC), 0.896; 95% CI, 0.792-0.999). On day 3, the final predictive model was based on the variables birth weight, surfactant requirement, and Fraction of Inspired Oxygen (FiO2) (AUC, 0.891; 95% CI, 0.792-0.989).

Conclusions

Competing risk analysis allowed accurate prediction of BPD, avoiding the potential bias resulting from the exclusion of deceased newborns or the use of combined outcomes. The resulting models are based on clinical variables measured at bedside during the first 3 days of life, can be easily implemented in clinical practice, and can enable earlier identification of patients at high risk of BPD.

Development and validation of a model to predict mortality risk among extremely preterm infants during the early postnatal period: a multicentre prospective cohort study

BACKGROUND

Recently, with the rapid development of the perinatal medical system and related life-saving techniques, both the short-term and long-term prognoses of extremely preterm infants (EPIs) have improved significantly. In rapidly industrialising countries like China, the survival rates of EPIs have notably increased due to the swift socioeconomic development. However, there is still a reasonably lower positive response towards the treatment of EPIs than we expected, and the current situation of withdrawing care is an urgent task for perinatal medical practitioners.

OBJECTIVE

To develop and validate a model that is practicable for EPIs as soon as possible after birth by regression analysis, to assess the risk of mortality and chance of survival.

METHODS

This multicentre prospective cohort study used datasets from the Sino-Northern Neonatal Network, including 46 neonatal intensive care units (NICUs). Risk factors including maternal and neonatal variables were collected within 1 hour post-childbirth. The training set consisted of data from 41 NICUs located within the Shandong Province of China, while the validation set included data from 5 NICUs outside Shandong Province. A total of 1363 neonates were included in the study.

RESULTS

Gestational age, birth weight, pH and lactic acid in blood gas analysis within the first hour of birth, moderate-to-severe hypothermia on admission and adequate antenatal corticosteroids were influencing factors for EPIs' mortality with important predictive ability. The area under the curve values for internal validation of our prediction model and Clinical Risk Index for Babies-II scores were 0.81 and 0.76, and for external validation, 0.80 and 0.51, respectively. Moreover, the Hosmer-Lemeshow test showed that our model has a constant degree of calibration.

CONCLUSIONS

There was good predictive accuracy for mortality of EPIs based on influencing factors prenatally and within 1 hour after delivery. Predicting the risk of mortality of EPIs as soon as possible after birth can effectively guide parents to be proactive in treating more EPIs with life-saving value.

TRIAL REGISTRATION NUMBER

ChiCTR1900025234.

Risk factors and neonatal outcomes of pulmonary air leak syndrome in extremely preterm infants: A nationwide descriptive cohort study

Most extremely preterm infants (EPIs), who were born before 28 weeks of gestation, with pulmonary air leak syndrome (ALS) are symptomatic, often severe, and require drainage. EPIs with severe air leak syndrome (sALS) that require tube drainage or needle aspiration are at high risk of morbidities and mortality. This study aimed to investigate perinatal characteristics, morbidities, and mortality in EPIs with sALS, and to estimate the risk of mortality according to gestational age (GA). A prospective cohort study conducted from 2013 to 2020 compiled the Korean Neonatal Network database to evaluate the incidence, perinatal characteristics, and outcomes of sALS in EPIs born before 28 weeks of gestation. Among 5666 EPIs, the incidence of sALS was 9.4% and inversely related to GA. From this cohort, we compared 532 EPIs with sALS to 1064 EPIs without sALS as controls, matching the subjects by GA and birth weight. Preterm premature rupture of membranes, oligohydramnios, resuscitation after birth, low Apgar scores, repeated surfactant administration, persistent pulmonary hypertension of the newborn, and pulmonary hemorrhage were associated with the development of pneumothorax. The sALS group required a higher fraction of inspired oxygen and more invasive respiratory support at both 28 days of life and 36 weeks of postmenstrual age. The sALS group had a higher incidence of bronchopulmonary dysplasia and major brain injury. The mortality rate was higher in the sALS group than in the control group (55.3% vs 32.5%, P < .001), and the ALS group had a 1.7 times risk of mortality than the control group. More attention should be paid to sALS in EPIs because the frequency of sALS increased as GA decreased, and the risk of mortality was more significant at lower GA.

Modified NEOMOD score as a neonatal mortality prediction tool in a medium-income country: A validation diagnostic test study

Background and Aims

Multiple organ dysfunction (MOD) is a potentially reversible physiological disorder that involves two or more systems. Modified NEOMOD (Neonatal Multiple Organ Dysfunction score) scale could be a useful instrument to measure MOD and predict mortality. Our aim was to validate modified NEOMOD in patients from a neonatal intensive care unit (NICU) of a middle-income country.

Methods

Diagnostic test study. Preterm newborns admitted NICU were included. Daily values were collected from birthday to Day 14. MOD was defined as at least one point in two or more systems. The lowest score is 0 and the maximum is 16. The outcome variable was mortality. Secondary outcomes were bronchopulmonary dysplasia, retinopathy of prematurity (ROP), late-onset neonatal sepsis (LONS), intraventricular hemorrhage (IVH) and length of hospital stay. Area under the curve (AUC) and Hosmer-Lemeshow test were calculated to evaluate scale discrimination and calibration. Logistic regression was used to estimate the association between daily modified NEOMOD score and death.

Results

We included 273 patients who met the inclusion criteria. MOD incidence was 74.4%. The median gestational age in patients with MOD was 30 (interquartile range [IQR]: 27-33) and in patients without MOD it was 32 (IQR: 31-33) (p < 0.001). There were 40 deaths (14.6%), 38 (18.7%) from the MOD group and 2 (2.9%) from non-MOD group. On accumulated Day 7, AUC was 0.89 (95% confidence interval [CI]: 0.83-0.95). Modified NEOMOD had good calibration (X 2 = 2.94, p = 0.982). DBP (12.8% vs. 2.9%, p = 0.001), ROP (3.9% vs. 0%, p = 0.090), IVH (33% vs. 12.9%, p < 0.001), and LONS (36.5% vs. 8.6%, p < 0.001) frequency was higher in the MOD group than non-MOD group. Length of hospital stay also was higher in MOD group (median 21 days [IQR 7-44] vs. median 5 days [IQR 4-9], p = 0.004).

Conclusion

Modified NEOMOD scale presents good discrimination and calibration for death in preterm children. This scale could help in clinical decision-making in real-time.

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.

Correction: Prediction of mortality in very low birth weight neonates in Spain

[This corrects the article DOI: 10.1371/journal.pone.0235794.].

Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes

BACKGROUND

Perinatal complications are common burdens for neonates born from mother with pPROM. Physicians and parents sometimes need to make critical decisions about neonatal care with short- and long-term implications on infant's health and families and it is important to predict severe neonatal outcomes with high accuracy.

METHODS

The study was based on our prospective study on 1001 preterm infants born from mother with pPROM from August 1, 2017, to March 31, 2018 in three hospitals in China. Multivariable logistic regression analysis was applied to build a predicting model incorporating obstetric and neonatal characteristics available within the first day of NICU admission. We used enhanced bootstrap resampling for internal validation.

RESULTS

One thousand one-hundred pregnancies with PROM at preterm with a single fetus were included in our study. SNO was diagnosed in 180 (17.98%) neonates. On multivariate analysis of the primary cohort, independent factors for SNO were respiratory support on the first day,, surfactant on day 1, and birth weight, which were selected into the nomogram. The model displayed good discrimination with a C-index of 0.838 (95%CI, 0.802-0.874) and good calibration performance. High C-index value of 0.835 could still be reached in the internal validation and the calibration curve showed good agreement. Decision curve analysis showed if the threshold is > 15%, using our model would achieve higher net benefit than model with birthweight as the only one predictor.

CONCLUSION

Variables available on the first day in NICU including respiratory support on the first day, the use of surfactant on the first day and birthweight could be used to predict the risk of SNO in infants born from mother with pPROM with good discrimination and calibration performance.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.