Hourly Kinetics of Critical Organ Dysfunction in Extremely Preterm Infants

Critical Organ Dysfunction and Preoperative Mortality in Newborns with Hypoplastic Left Heart Syndrome

Hypoplastic left heart syndrome (HLHS) is fatal without surgical intervention. An important subset of HLHS patients die prior to surgical intervention, but this population is underevaluated. The neonatal sequential organ failure assessment score (nSOFA) is an operational definition of organ dysfunction that can identify those with a high risk of mortality among neonatal intensive care unit (NICU) patients. The utility of the nSOFA to predict preoperative mortality in the unique HLHS population is unknown and could inform care, particularly care provided by neonatology staff. We performed a multicenter retrospective cohort study of HLHS cases across three level IV NICUs from January 1, 2009 to December 3, 2023. Patients were classified as either survived or died prior to surgical intervention. Demographic variables were curated from medical records including the maximum nSOFA (nSOFAmax) before surgical intervention or death. We identified 265 patients with HLHS over the study period. The nSOFAmax was greater in patients who died preoperatively (14/265; 5%) compared with survivors to surgical intervention (median 8 [interquartile range, 6, 12] vs. 2 [0, 4]; p < 0.001). The area under receiver operating characteristics curve for the nSOFAmax to discriminate for mortality was 0.93 (95% confidence interval, 0.88-0.98; p < 0.001). Compared with an nSOFAmax of 0, the likelihood ratio for preoperative death doubled at 2, tripled at 4, and was 10-fold at 9. This is the first demonstration of nSOFA utility in specific to congenital heart disease and HLHS. The nSOFAmax represents a novel, electronic health record-compatible, and generalizable method to identify patient-level organ dysfunction and risk for preoperative mortality in HLHS patients. KEY POINTS: · An important subset of HLHS patients die preoperatively.. · nSOFA can be used to measure preoperative HLHS severity.. · nSOFA predicts preoperative mortality risk in HLHS patients..

The frequency and timing of sepsis-associated coagulopathy in the neonatal intensive care unit

Introduction

Sepsis is a common cause of morbidity and mortality in the neonatal intensive care unit (NICU). The frequency and severity of sepsis-associated coagulopathy as well as its relationship to illness severity are unclear.

Methods

We performed a single-center, retrospective, observational cohort study of all infants admitted to the University of Florida Health (UF Health), level IV NICU between January 1st 2012 to March 1st 2020 to measure the frequency of sepsis-associated coagulopathy as well as its temporal relationship to critical illness in the NICU population. All clinical data in the electronic health record were extracted and deposited into an integrated data repository that was used for this work.

Results

We identified 225 new sepsis episodes in 216 patients. An evaluation for sepsis-associated coagulopathy was performed in 96 (43%) episodes. Gram-negative pathogen, nSOFA score at evaluation, and mortality were greater among episodes that included a coagulopathy evaluation compared with those that did not. Abnormal coagulation results were common (271/339 evaluations; 80%) and were predominantly prothrombin times. Intervention (plasma or cryoprecipitate) followed a minority (84/271; 31%) of abnormal results, occurred in 40/96 (42%) episodes that were often associated with >1 intervention (29/40; 73%), and coincided with thrombocytopenia in 37/40 (93%) and platelet transfusion in 27/40 (68%). Shapley Additive Explanations modeling demonstrated strong predictive performance for the composite outcome of death and/or treatment for coagulopathy in neonates (f1 score 0.8, area under receiver operating characteristic curve 0.83 for those with abnormal coagulation values). The three most important features influencing the composite outcome of death or treatment for coagulopathy included administration of vasoactive medications, hematologic dysfunction assessed by the maximum nSOFA platelet score, and early sepsis (≤72 h after birth).

Conclusions

A coagulopathy evaluation was performed in a minority of NICU patients with sepsis and was associated with greater illness severity and mortality. Abnormal results were common but infrequently associated with intervention, and intervention was contemporaneous with thrombocytopenia. The most important feature that influenced the composite outcome of death or treatment for coagulopathy was the administration of vasoactive-inotropic medications. These data help to identify NICU patients at risk of sepsis-associated coagulopathy.

Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics

Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

Interaction of hydrocortisone and illness severity on head growth in cohort of ELBW infants

BACKGROUND

Extremely low birth weight (ELBW) infants comprise a fragile population at risk for neurodevelopmental disabilities (NDD). Systemic steroids were previously associated with NDD, but more recent studies suggest hydrocortisone (HCT) may improve survival without increasing NDD. However, the effects of HCT on head growth adjusted for illness severity during NICU hospitalization are unknown. Thus, we hypothesize that HCT will protect head growth, accounting for illness severity using a modified neonatal Sequential Organ Failure Assessment (M-nSOFA) score.

METHODS

We conducted a retrospective study that included infants born at 23-29 weeks gestational age (GA) and < 1000 g. Our study included 73 infants, 41% of whom received HCT.

RESULTS

We found negative correlations between growth parameters and age, similar between HCT and control patients. HCT-exposed infants had lower GA but similar normalized birth weights; HCT-exposed infants also had higher illness severity and longer lengths of hospital stay. We found an interaction between HCT exposure and illness severity on head growth, such that infants exposed to HCT had better head growth compared to those not exposed to HCT when adjusted for illness severity.

CONCLUSION

These findings emphasize the importance of considering patient illness severity and suggest that HCT use may offer additional benefits not previously considered.

IMPACT

This is the first study to assess the relationship between head growth and illness severity in extremely preterm infants with extremely low birth weights during their initial NICU hospitalization. Infants exposed to hydrocortisone (HCT) were overall more ill than those not exposed, yet HCT exposed infants had better preserved head growth relative to illness severity. Better understanding of the effects of HCT exposure on this vulnerable population will help guide more informed decisions on the relative risks and benefits for HCT use.

Evaluation of the Neonatal Sequential Organ Failure Assessment and Mortality Risk in Neonates with Early-Onset Infection

BACKGROUND

The discriminative utility of the neonatal sequential organ failure assessment (nSOFA) for early-onset sepsis (EOS) mortality in the neonatal intensive care unit (NICU) is unknown.

OBJECTIVES

The objective of the study was to determine the utility of nSOFA for EOS mortality.

METHODS

Multicenter, retrospective cohort study of NICU patients with EOS between 2012 and 2023. nSOFA scores of survivors and non-survivors were compared, and area under the receiver operating characteristics curve (AUROC) for mortality was calculated.

RESULTS

104 subjects were identified (88 lived, 16 died). AUROC at blood culture collection (T0), 6 h after collection (T6), and the maximum nSOFA at T0 or T6 (T0-6max) were 0.76 (95% CI: 0.62, 0.91), 0.89 (0.80, 0.99), and 0.87 (0.77, 0.97), respectively. Analyses restricted to birthweight (<1.5, <1 kg) or gestational age (<32, <29 week) cutoffs revealed AUROC ranges of 0.86-0.92 for T6 and 0.82-0.84 for T0-6max.

CONCLUSIONS

The nSOFA showed good-to-excellent discrimination of mortality among infants with EOS in the NICU.

Vital sign-based detection of sepsis in neonates using machine learning

AIM

Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis.

METHODS

Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time-domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion.

RESULTS

Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150-fold.

CONCLUSION

The present algorithm using non-invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning-assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Frequency of Acute Kidney Injury and Association With Mortality Among Extremely Preterm Infants

IMPORTANCE

Neonatal acute kidney injury (AKI) is common and associated with morbidity and mortality. The temporal relationship between AKI and critical illness, as well as the frequency of AKI definition components (urine output and serum creatinine [sCr] concentration change), are unknown in extremely low-birth-weight (ELBW) (<1000 g), extremely preterm (<29 weeks' completed gestational age [GA]) infants.

OBJECTIVE

To measure the frequency of AKI from birth to death or discharge with attention to the definition components as well as the temporal relationship of AKI to critical illness and death.

DESIGN, SETTING, AND PARTICIPANTS

A single-center, multiyear, retrospective cohort study was conducted at an academic level IV neonatal intensive care unit between January 1, 2012, and January 1, 2020. Participants included inborn ELBW and infants at 22 to 28 weeks' completed GA with confirmed congenital anomalies who survived 12 hours or more.

EXPOSURES

Extremely preterm birth and ELBW.

MAIN OUTCOMES AND MEASURES

The primary outcome was AKI frequency. The timing, severity, and criteria for AKI were measured. The temporal relationship between AKI, organ dysfunction, and outcomes were quantified using odds ratios (ORs), logistic regression, and Shapley Additive Explanations. Acute kidney injury recognition, imaging, pediatric nephrology consultation, and follow-up were determined.

RESULTS

A total of 436 infants (52% male; 44% Black) met the inclusion criteria (median BW, 725 g; median GA, 25.7 wk). Acute kidney injury was common in the first week of life (44%), primarily based on the change in the sCr concentration criterion (88%), and negatively associated with GA (OR, 0.69; 95% CI, 0.60-0.78), but positively associated with antecedent critical illness (OR, 1.17; 95% CI, 1.12-1.23), severe intraventricular hemorrhage (OR, 1.86; 95% CI, 1.12-3.08), late-onset sepsis (OR, 1.03; 95% CI, 1.02-1.03), and mortality (OR, 2.77; 95% CI, 1.63-4.72). Acute kidney injury had negligible clinical contribution to death within the model (Shapley Additive Explanation, <0.5% change to outcome) relative to antecedent patient-concentration organ dysfunction (6%-15% change). Among infants with severe AKI, recognition (32%), nephrology inpatient consultation (16%), and outpatient follow-up (9%) were not common.

CONCLUSIONS AND RELEVANCE

In this cohort study of ELBW infants, AKI was common in the first week of life, inversely associated with GA, and followed organ (primarily cardiovascular) dysfunction. Acute kidney injury considered as the primary pathway to mortality was rare, and amelioration of AKI to modify death was not well supported.

Evaluation of the Neonatal Sequential Organ Failure Assessment and Mortality Risk in Preterm Infants with Necrotizing Enterocolitis

INTRODUCTION

The neonatal sequential organ failure assessment (nSOFA) score is a tool for calculating mortality risk of infants in the neonatal intensive care unit. The utility of the nSOFA in determining the risk of mortality or the association with surgical intervention among infants with necrotizing enterocolitis (NEC) has not been investigated.

METHODS

We performed a retrospective, cohort study of preterm (<37 weeks) infants with NEC Bell's stage ≥ IIA at six hospitals from 2008 to 2020. An nSOFA score (range 0-15) was assigned to each patient at nine time points from 48 h before or after clinical illness was suspected.

RESULTS

Of the 259 infants, nSOFA scores for infants who died (n = 39) or had the composite outcome of surgery or death (n = 114) were significantly higher (p < 0.05) early in the NEC course compared to nSOFA scores for infants who survived medical NEC. Twelve hours after evaluation, the area under the receiver operating characteristic curve was 0.87 (95% confidence interval [CI], 0.80-0.93) to discriminate for mortality and 0.84 (95% CI, 0.79-0.90) for surgery or death (p < 0.001). A maximum nSOFA score of ≥4 at -6, 0, 6, or 12 h following evaluation was associated with a 20-fold increase in mortality and 19-fold increase in surgery or death compared with a score of <4 (p < 0.001).

CONCLUSION

In this multicenter cohort, the nSOFA score was able to discriminate well for death as well as surgery or death among infants with NEC. The nSOFA is a clinical research tool that may be used in infants with NEC to improve classification by objective quantification of organ dysfunction.