Artificial intelligence in the neonatal intensive care unit: the time is now.

Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Performance of a Large Language Model on Practice Questions for the Neonatal Board Examination.

This Diagnostic/Prognostic Study evaluates the performance of a large language model in generating answers to practice questions for the neonatal-perinatal board examination.

Health-Related Qualities of Life in School-Aged Children with Bronchopulmonary Dysplasia.

OBJECTIVE: To determine health-related quality of life (HRQOL) of school-aged children with bronchopulmonary dysplasia (BPD) using the standardized Patient-Reported Outcomes Measurement Information System (PROMIS) assessment tools. STUDY DESIGN: The Indoor Air Quality and Respiratory Morbidity in Children with BPD Study is an ongoing observational study of school-aged children with BPD. HRQOL is assessed at enrollment by 3 PROMIS questionnaires, Parent Proxy Scale-Global Health 7, Parent Proxy Psychological Stress Experiences-Short Form, and the Parent Proxy Profile-Profile-25. PROMIS data were tested for significant deviation from the standardized T-Score references for normative populations of children. RESULTS: Eighty-nine subjects enrolled in the AERO-BPD study had complete outcome data for HRQOL. The mean age was 9 (±2) years and 43% were female. Mean days on respiratory support totaled 96 (±40). Across all domains, school-aged children with BPD reported similar or slightly better outcomes than the reference sample. Statistically significant findings of lower depression (P < .0001), fatigue (P < .0001), and pain (P < .0001) scores were found; there was no difference in psychological stress experiences (P = .87), global health (P = .06), anxiety (P = .08), relationships (P = .80), and mobility (P = .59) domains. CONCLUSIONS: This study demonstrated that children with BPD may have less depression, fatigue, and pain HRQL than the general population. Once validated, these findings may offer reassurance to parents and providers caring for children with BPD.

National Needs Assessment of Utilization of Common Newborn Clinical Decision Support Tools.

OBJECTIVE: Clinical decision support tools (CDSTs) are common in neonatology, but utilization is rarely examined. We examined the utilization of four CDSTs in newborn care. STUDY DESIGN: A 72-field needs assessment was developed. It was distributed to listservs encompassing trainees, nurse practitioners, hospitalists, and attendings. At the conclusion of data collection, responses were downloaded and analyzed. RESULTS: We received 339 fully completed questionnaires. BiliTool and the Early-Onset Sepsis (EOS) tool were used by > 90% of respondents, the Bronchopulmonary Dysplasia tool by 39%, and the Extremely Preterm Birth tool by 72%. Common reasons CDSTs did not impact clinical care included lack of electronic health record integration, lack of confidence in prediction accuracy, and unhelpful predictions. CONCLUSION: From a national sample of neonatal care providers, there is frequent but variable use of four CDSTs. Understanding the factors that contribute to tool utility is vital prior to development and implementation. KEY POINTS: · Clinical decision support tools are common in medicine.. · There is a varied use of neonatal CDST.. · Understanding the use of CDST is vital for future development..

Prediction of extubation failure among low birthweight neonates using machine learning.

OBJECTIVE: To develop machine learning models predicting extubation failure in low birthweight neonates using large amounts of clinical data. STUDY DESIGN: Retrospective cohort study using MIMIC-III, a large single-center, open-source clinical dataset. Logistic regression and boosted-tree (XGBoost) models using demographics, medications, and vital sign and ventilatory data were developed to predict extubation failure, defined as reintubation within 7 days. RESULTS: 1348 low birthweight (≤2500 g) neonates who received mechanical ventilation within the first 7 days were included, of which 350 (26%) failed a trial of extubation. The best-performing model was a boosted-tree model incorporating demographics, vital signs, ventilator parameters, and medications (AUROC 0.82). The most important features were birthweight, last FiO2, average mean airway pressure, caffeine use, and gestational age. CONCLUSIONS: Machine learning models identified low birthweight ventilated neonates at risk for extubation failure. These models will need to be validated across multiple centers to determine generalizability of this tool.

Specificity of International Classification of Diseases codes for bronchopulmonary dysplasia: an investigation using electronic health record data and a large insurance database.

OBJECTIVE: International Classification of Diseases (ICD) codes in electronic health records (EHRs) are increasingly used for health services research, in spite of unknown diagnostic accuracy. The accuracy of ICD codes to identify bronchopulmonary dysplasia (BPD) is unknown. STUDY DESIGN: Retrospective cohort study in a single-center NICU (n = 166) to evaluate sensitivity and specificity of ICD-10 codes for the diagnosis of BPD. Analysis of large insurance claims database (n = 7887) to determine date of assignment of the code. RESULTS: The sensitivity of any BPD-related ICD codes ranged from 0.82 to 0.95, while the specificity ranged from 0.25 to 0.36. In a large national insurance database, the most common date of ICD-9 or ICD-10 code assignment was the day of birth, which is inconsistent with the clinical definition. CONCLUSIONS: ICD codes registered for BPD are unlikely to accurately reflect the current clinical definition and should be interpreted with caution.

COVID-19: neonatal-perinatal perspectives.

The coronavirus disease 2019 (COVID-19) pandemic, resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused severe and widespread illness in adults, including pregnant women, while rarely infecting neonates. An incomplete understanding of disease pathogenesis and viral spread has resulted in evolving guidelines to reduce transmission from infected mothers to neonates. Fortunately, the risk of neonatal infection via perinatal/postnatal transmission is low when recommended precautions are followed. However, the psychosocial implications of these practices and racial/ethnic disparities highlighted by this pandemic must also be addressed when caring for mothers and their newborns. This review provides a comprehensive overview of neonatal-perinatal perspectives of COVID-19, ranging from the basic science of infection and recommendations for care of pregnant women and neonates to important psychosocial, ethical, and racial/ethnic topics emerging as a result of both the pandemic and the response of the healthcare community to the care of infected individuals.

Antecedents and outcomes of hypothermia at admission to the neonatal intensive care unit.

Objective: To evaluate the association between morbidities preceding and following neonatal intensive care unit (NICU) admission with hypothermia.Study design: NICU admission temperatures for 1271 infants admitted to the NICU at Tufts Medical Center (TMC) between 2012 and 2015 were compared to all Vermont Oxford Network (VON) centers in 2014. We analyzed demographic data, prevalence of hypothermia, and associations with prenatal and neonatal morbidities.Result: Prevalence of hypothermia at TMC was 19% compared to 25% in the VON. We found a significant association between hypothermia and maternal race, birth weight, gestational age, antenatal steroids, chorioamnionitis, mode of delivery, and Apgar scores.Conclusion: Continued emphasis should be placed on avoiding neonatal hypothermia during the first hours of postnatal life.

Computerized Physician Order Entry in the Neonatal Intensive Care Unit: A Narrative Review.

BACKGROUND: Computerized physician order entry (CPOE) has grown since the early 1990s. While many systems serve adult patients, systems for pediatric and neonatal populations have lagged. Adapting adult CPOE systems for pediatric use may require significant modifications to address complexities associated with pediatric care such as daily weight changes and small medication doses. OBJECTIVE: This article aims to review the neonatal intensive care unit (NICU) CPOE literature to characterize trends in the introduction of this technology and to identify potential areas for further research. METHODS: Articles pertaining to NICU CPOE were identified in MEDLINE using MeSH terms "medical order entry systems," "drug therapy," "intensive care unit, neonatal," "infant, newborn," etc. Two physician reviewers evaluated each article for inclusion and exclusion criteria. Consensus judgments were used to classify the articles into five categories: medication safety, usability/alerts, clinical practice, clinical decision Support (CDS), and implementation. Articles addressing pediatric (nonneonatal) CPOE were included if they were applicable to the NICU setting. RESULTS: Sixty-nine articles were identified using MeSH search criteria. Twenty-two additional articles were identified by hand-searching bibliographies and 6 articles were added after the review process. Fifty-five articles met exclusion criteria, for a final set of 42 articles. Medication safety was the focus of 22 articles, followed by clinical practice (10), CDS (10), implementation (11), and usability/alerts (4). Several addressed more than one category. No study showed a decrease in medication safety post-CPOE implementation. Within clinical practice articles, CPOE implementation showed no effect on blood glucose levels or time to antibiotic administration but showed conflicting results on mortality rates. Implementation studies were largely descriptive of single-hospital experiences. CONCLUSION: CPOE implementation within the NICU has demonstrated improvement in medication safety, with the most consistent benefit involving a reduction in medication errors and wrong-time administration errors. Additional research is needed to understand the potential limitations of CPOE systems in neonatal intensive care and how CPOE affects mortality.

Examining Perceptions of Computerized Physician Order Entry in a Neonatal Intensive Care Unit.

BACKGROUND: Computerized provider order entry (CPOE) is a technology with potential to transform care delivery. While CPOE systems have been studied in adult populations, less is known about the implementation of CPOE in the neonatal intensive care unit (NICU) and perceptions of nurses and physicians using the system. OBJECTIVE: To examine perceptions of clinicians before and after CPOE implementation in the NICU of a pediatric hospital. METHODS: A cross-sectional survey of clinicians working in a Level III NICU was conducted. The survey was distributed before and after CPOE implementation. Participants were asked about their perception of CPOE on patient care delivery, implementation of the system, and effect on job satisfaction. A qualitative section inquired about additional concerns surrounding implementation. Responses were tabulated and analyzed using the Chi-square test. RESULTS: The survey was distributed to 158 clinicians with a 47% response rate for pre-implementation and 45% for post-implementation. Clinicians understood why CPOE was implemented, but felt there was incomplete technical training. The expectation for increased job satisfaction and ability to recruit high-quality staff was high. However, there was concern about the ability to deliver appropriate treatments before and after implementation. Physicians were more optimistic about CPOE implementation than nurses who remained concerned that workflow may be altered. CONCLUSIONS: Introducing CPOE is a potentially risky endeavor and must be done carefully to mitigate harm. Although high expectations of the system can be met, it is important to attend to differing expectations among clinicians with varied levels of comfort with technology. Interdisciplinary collaboration is critical in planning a functioning CPOE to ensure that efficient workflow is maintained and appropriate supports for individuals with a lower degree of technical literacy is available.

Predictors of positive cerebrospinal fluid cultures in infants with bacteremia.

BACKGROUND: Meningitis causes substantial morbidity and mortality in hospitalized infants. There is no consensus on the ability of blood cultures to predict results from cerebrospinal fluid (CSF) cultures in hospitalized infants. METHODS: We used the Pediatrix Medical Group database of infants discharged from 333 neonatal intensive care units between 1997 and 2011. We identified all infants with a positive blood culture and a CSF culture obtained within 3 days. We evaluated the odds of a concordant blood-CSF culture pair, controlling for severity of illness, organism type, gestational age, day of blood culture and blood-CSF culture pairing, exposure to CSF-penetrating antibiotics and the presence of a ventriculo-peritoneal shunt. RESULTS: We identified 8839 infants with 9408 blood-CSF culture pairs. Serratia marcescens (24/227, 11%) and Streptococcus pneumoniae (7/64, 11%) had the highest proportion of concordant blood-CSF culture pairs. The presence of a ventriculo-peritoneal shunt, as well as timing of the CSF culture on the same day as the blood culture, were associated with increased odds of blood-CSF culture pair concordance-odds ratio = 3.87 (95% confidence interval; 2.59-5.78) and 6.11 (2.81-13.24), respectively. CONCLUSION: The frequency of blood-CSF culture pair concordance is related to organism type and to the timing of the CSF culture in relation to the blood culture.