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Coggins SA, Carr LH, Harris MC, Srinivasan L. Sepsis Huddles in the Neonatal Intensive Care Unit: A Retrospective Cohort Study of Late-onset Infection Recognition and Severity Assessment. J Pediatr 2024; 272:114117. [PMID: 38815749 DOI: 10.1016/j.jpeds.2024.114117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/15/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
OBJECTIVE To analyze relationships between provider-documented signs prompting sepsis evaluations, assessments of illness severity, and late-onset infection (LOI). STUDY DESIGN Retrospective cohort study of all infants receiving a sepsis huddle in conjunction with a LOI evaluation. Participants were ≥3 days old and admitted to a level IV neonatal intensive care unit (NICU) from September 2018 through May 2021. Data were extracted from standardized sepsis huddle notes in the electronic health record, including clinical signs prompting LOI evaluations, illness severity assessments (from least to most severe: green, yellow, and red), and management plans. To analyze relationships of sepsis huddle characteristics with the detection of culture-confirmed LOI (bacteremia, urinary tract infection, or meningitis), we utilized diagnostic test statistics, area under the receiver-operator characteristic analyses, and multivariable logistic regression. RESULTS We identified 1209 eligible sepsis huddles among 604 infants. There were 111 culture-confirmed LOI episodes (9% of all huddles). Twelve clinical signs of infection poorly distinguished infants with and without LOI, with sensitivity for each ranging from 2% to 36% and area under the receiver-operator characteristic ranging 0.49-0.53. Multivariable logistic regression identified increasing odds of infection with higher perceived illness severity at the time of sepsis huddle, adjusted for gestational age and receipt of intensive care supports. CONCLUSIONS Clinical signs prompting sepsis huddles were nonspecific and not predictive of concurrent LOI. Higher perceived illness severity was associated with presence of infection, despite some misclassification based on objective criteria. In level IV NICUs, antimicrobial stewardship through development of criteria for antibiotic noninitiation may be challenging, as presenting signs of LOI are similar among infants with and without confirmed infection.
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Affiliation(s)
- Sarah A Coggins
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA; Clinical Futures, Children's Hospital of Philadelphia, Philadelphia, PA.
| | - Leah H Carr
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Mary Catherine Harris
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA
| | - Lakshmi Srinivasan
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA
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Agakidou E, Chatziioannidis I, Kontou A, Stathopoulou T, Chotas W, Sarafidis K. An Update on Pharmacologic Management of Neonatal Hypotension: When, Why, and Which Medication. CHILDREN (BASEL, SWITZERLAND) 2024; 11:490. [PMID: 38671707 PMCID: PMC11049273 DOI: 10.3390/children11040490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/30/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
Anti-hypotensive treatment, which includes dopamine, dobutamine, epinephrine, norepinephrine, milrinone, vasopressin, terlipressin, levosimendan, and glucocorticoids, is a long-established intervention in neonates with arterial hypotension (AH). However, there are still gaps in knowledge and issues that need clarification. The main questions and challenges that neonatologists face relate to the reference ranges of arterial blood pressure in presumably healthy neonates in relation to gestational and postnatal age; the arterial blood pressure level that potentially affects perfusion of critical organs; the incorporation of targeted echocardiography and near-infrared spectroscopy for assessing heart function and cerebral perfusion in clinical practice; the indication, timing, and choice of medication for each individual patient; the limited randomized clinical trials in neonates with sometimes conflicting results; and the sparse data regarding the potential effect of early hypotension or anti-hypotensive medications on long-term neurodevelopment. In this review, after a short review of AH definitions used in neonates and existing data on pathophysiology of AH, we discuss currently available data on pharmacokinetic and hemodynamic effects, as well as the effectiveness and safety of anti-hypotensive medications in neonates. In addition, data on the comparisons between anti-hypotensive medications and current suggestions for the main indications of each medication are discussed.
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Affiliation(s)
- Eleni Agakidou
- 1st Department of Neonatology and Neonatal Intensive Care, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Ippokrateion General Hospital, 54642 Thessaloniki, Greece; (I.C.); (A.K.); (T.S.); (K.S.)
| | - Ilias Chatziioannidis
- 1st Department of Neonatology and Neonatal Intensive Care, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Ippokrateion General Hospital, 54642 Thessaloniki, Greece; (I.C.); (A.K.); (T.S.); (K.S.)
| | - Angeliki Kontou
- 1st Department of Neonatology and Neonatal Intensive Care, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Ippokrateion General Hospital, 54642 Thessaloniki, Greece; (I.C.); (A.K.); (T.S.); (K.S.)
| | - Theodora Stathopoulou
- 1st Department of Neonatology and Neonatal Intensive Care, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Ippokrateion General Hospital, 54642 Thessaloniki, Greece; (I.C.); (A.K.); (T.S.); (K.S.)
| | - William Chotas
- Department of Neonatology, University of Vermont, Burlington, VT 05405, USA
| | - Kosmas Sarafidis
- 1st Department of Neonatology and Neonatal Intensive Care, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Ippokrateion General Hospital, 54642 Thessaloniki, Greece; (I.C.); (A.K.); (T.S.); (K.S.)
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3
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Bravo MC, Jiménez R, Parrado-Hernández E, Fernández JJ, Pellicer A. Predicting the effectiveness of drugs used for treating cardiovascular conditions in newborn infants. Pediatr Res 2024; 95:1124-1131. [PMID: 38092963 DOI: 10.1038/s41390-023-02964-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/08/2023] [Accepted: 11/27/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND Cardiovascular support (CVS) treatment failure (TF) is associated with a poor prognosis in preterm infants. METHODS Medical charts of infants with a birth weight <1500 g who received either dopamine (Dp) or dobutamine (Db), were reviewed. Treatment response (TR) occurred if blood pressure increased >3rd centile for gestational age or superior vena cava flow was maintained >55 ml/kg/min, with decreased lactate or less negative base excess, without additional CVS. A predictive model of Dp and Db on TR was designed and the impact of TR on survival was analyzed. RESULTS Sixty-six infants (median gestational age 27.3 weeks, median birth weight 864 g) received Dp (n = 44) or Db (n = 22). TR occurred in 59% of the cases treated with Dp and 31% with Db, p = 0.04. Machine learning identified a model that correctly labeled Db response in 90% of the cases and Dp response in 61.4%. Sixteen infants died (9% of the TR group, 39% of the TF group; p = 0.004). Brain or gut morbidity-free survival was observed in 52% vs 30% in the TR and TF groups, respectively (p = 0.08). CONCLUSIONS New predictive models can anticipate Db but not Dp effectiveness in preterm infants. These algorithms may help the clinicians in the decision-making process. IMPACT Failure of cardiovascular support treatment increases the risk of mortality in very low birth weight infants. A predictive model built with machine learning techniques can help anticipate treatment response to dobutamine with high accuracy. Predictive models based on artificial intelligence may guide the clinicians in the decision-making process.
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Affiliation(s)
- María Carmen Bravo
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain.
| | - Raquel Jiménez
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain
- Department of Signal Theory and Communications, Carlos III University, Madrid, Spain
| | | | | | - Adelina Pellicer
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain
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Beam K, Sharma P, Levy P, Beam AL. Artificial intelligence in the neonatal intensive care unit: the time is now. J Perinatol 2024; 44:131-135. [PMID: 37443271 DOI: 10.1038/s41372-023-01719-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
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.
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Affiliation(s)
- Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Puneet Sharma
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Phil Levy
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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5
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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6
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Kloonen RMJS, Varisco G, de Kort E, Andriessen P, Niemarkt HJ, van Pul C. Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study. Physiol Meas 2023; 44:115005. [PMID: 37939392 DOI: 10.1088/1361-6579/ad0ab6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023]
Abstract
Objective. Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure.Approach. Patients with a gestational age (GA) <32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO2) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated.Main results. Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO2as most important features. Excluding GA decreased performance in both models.Significance. In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.
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Affiliation(s)
- R M J S Kloonen
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB, The Netherlands
| | - G Varisco
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - E de Kort
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - P Andriessen
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - H J Niemarkt
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - C van Pul
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB, The Netherlands
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7
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Chander S, Kumari R, Sadarat F, Luhana S. The Evolution and Future of Intensive Care Management in the Era of Telecritical Care and Artificial Intelligence. Curr Probl Cardiol 2023; 48:101805. [PMID: 37209793 DOI: 10.1016/j.cpcardiol.2023.101805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
Critical care practice has been embodied in the healthcare system since the institutionalization of intensive care units (ICUs) in the late '50s. Over time, this sector has experienced many changes and improvements in providing immediate and dedicated healthcare as patients requiring intensive care are often frail and critically ill with high mortality and morbidity rates. These changes were aided by innovations in diagnostic, therapeutic, and monitoring technologies, as well as the implementation of evidence-based guidelines and organizational structures within the ICU. In this review, we examine these changes in intensive care management over the past 40 years and their impact on the quality of care available to patients. Moreover, the current state of intensive care management is characterized by a multidisciplinary approach and the use of innovative technologies and research databases. Advancements such as telecritical care and artificial intelligence are being increasingly explored, especially since the COVID-19 pandemic, to reduce the length of hospitalization and ICU mortality. With these advancements in intensive care and ever-changing patient needs, critical care experts, hospital managers, and policymakers must also explore appropriate organizational structures and future enhancements within the ICU.
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Affiliation(s)
- Subhash Chander
- Department of Internal Medicine, Mount Sinai Beth Israel Hospital, New York, NY.
| | - Roopa Kumari
- Department of Internal Medicine, Mount Sinai Morningside and West, New York, NY
| | - Fnu Sadarat
- Department of Internal Medicine, University of Buffalo, NY, USA
| | - Sindhu Luhana
- Department of Internal Medicine, Aga Khan University Hospital, Karachi, Pakistan
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Gandhi B, Hagan J, Patil M. EBNEO commentary: Prediction of extubation failure among low birthweight neonates using machine learning. Acta Paediatr 2023; 112:2016-2017. [PMID: 37177905 DOI: 10.1111/apa.16813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Affiliation(s)
- Bheru Gandhi
- Department of Pediatrics, Baylor College of Medicine/Division of Neonatology, Texas Children's Hospital, Houston, Texas, USA
| | - Joseph Hagan
- Baylor College of Medicine/Division of Neonatology, Texas Children's Hospital, Houston, Texas, USA
| | - Monika Patil
- Department of Pediatrics, Baylor College of Medicine/Division of Neonatology, Texas Children's Hospital, Houston, Texas, USA
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McElroy SJ, Lueschow SR. State of the art review on machine learning and artificial intelligence in the study of neonatal necrotizing enterocolitis. Front Pediatr 2023; 11:1182597. [PMID: 37303753 PMCID: PMC10250644 DOI: 10.3389/fped.2023.1182597] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2023] Open
Abstract
Necrotizing Enterocolitis (NEC) is one of the leading causes of gastrointestinal emergency in preterm infants. Although NEC was formally described in the 1960's, there is still difficulty in diagnosis and ultimately treatment for NEC due in part to the multifactorial nature of the disease. Artificial intelligence (AI) and machine learning (ML) techniques have been applied by healthcare researchers over the past 30 years to better understand various diseases. Specifically, NEC researchers have used AI and ML to predict NEC diagnosis, NEC prognosis, discover biomarkers, and evaluate treatment strategies. In this review, we discuss AI and ML techniques, the current literature that has applied AI and ML to NEC, and some of the limitations in the field.
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Affiliation(s)
- Steven J. McElroy
- Department of Pediatrics, University of California Davis, Sacramento, CA, United States
| | - Shiloh R. Lueschow
- Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, United States
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10
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Leiva T, Lueschow S, Burge K, Devette C, McElroy S, Chaaban H. Biomarkers of necrotizing enterocolitis in the era of machine learning and omics. Semin Perinatol 2023; 47:151693. [PMID: 36604292 PMCID: PMC9975050 DOI: 10.1016/j.semperi.2022.151693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Necrotizing enterocolitis (NEC) continues to be a major cause of morbidity and mortality in preterm infants. Despite decades of research in NEC, no reliable biomarkers can accurately diagnose NEC or predict patient prognosis. The recent emergence of multi-omics could potentially shift NEC biomarker discovery, particularly when evaluated using systems biology techniques. Furthermore, the use of machine learning and artificial intelligence in analyzing this 'big data' could enable novel interpretations of NEC subtypes, disease progression, and potential therapeutic targets, allowing for integration with personalized medicine approaches. In this review, we evaluate studies using omics technologies and machine learning in the diagnosis of NEC. Future implications and challenges inherent to the field are also discussed.
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Affiliation(s)
- Tyler Leiva
- Department of Surgery, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Shiloh Lueschow
- Department of Microbiology and Immunology, Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Kathryn Burge
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, 1200 N. Everett Dr., ETNP 7504, Oklahoma City, OK 73104, USA
| | - Christa Devette
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, 1200 N. Everett Dr., ETNP 7504, Oklahoma City, OK 73104, USA
| | - Steven McElroy
- Department of Pediatrics, University of California Davis, Sacramento, CA, USA
| | - Hala Chaaban
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, 1200 N. Everett Dr., ETNP 7504, Oklahoma City, OK 73104, USA.
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11
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Sharma P, Beam K, Levy P, Beam AL. PD(AI): the role of artificial intelligence in the management of patent ductus arteriosus. J Perinatol 2023; 43:257-258. [PMID: 36646822 DOI: 10.1038/s41372-023-01606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 01/18/2023]
Affiliation(s)
- Puneet Sharma
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA.
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Philip Levy
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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12
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Jeong H, Kamaleswaran R. Pivotal challenges in artificial intelligence and machine learning applications for neonatal care. Semin Fetal Neonatal Med 2022; 27:101393. [PMID: 36266181 DOI: 10.1016/j.siny.2022.101393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Clinical decision support systems (CDSS) that are developed based on artificial intelligence and machine learning (AI/ML) approaches carry transformative potentials in improving the way neonatal care is practiced. From the use of the data available from electronic health records to physiological sensors and imaging modalities, CDSS can be used to predict clinical outcomes (such as mortality rate, hospital length of state, or surgical outcome) or early warning signs of diseases in neonates. However, only a limited number of clinical decision support systems for neonatal care are currently deployed in healthcare facilities or even implemented during pilot trials (or prospective studies). This is mostly due to the unresolved challenges in developing a real-time supported clinical decision support system, which mainly consists of three phases: model development, model evaluation, and real-time deployment. In this review, we introduce some of the pivotal challenges and factors we must consider during the implementation of real-time supported CDSS.
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Affiliation(s)
- Hayoung Jeong
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA.
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Zhu S, Zhou L, Feng Y, Zhu J, Shu Q, Li H. Understanding the risk factors for adverse events during exchange transfusion in neonatal hyperbilirubinemia using explainable artificial intelligence. BMC Pediatr 2022; 22:567. [PMID: 36180854 PMCID: PMC9523933 DOI: 10.1186/s12887-022-03615-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 09/14/2022] [Indexed: 11/20/2022] Open
Abstract
Objective To understand the risk factors associated with adverse events during exchange transfusion (ET) in severe neonatal hyperbilirubinemia. Study design We conducted a retrospective study of infants with hyperbilirubinemia who underwent ET within 30 days of birth from 2015 to 2020 in a children’s hospital. Both traditional statistical analysis and state-of-the-art explainable artificial intelligence (XAI) were used to identify the risk factors. Results A total of 188 ET cases were included; 7 major adverse events, including hyperglycemia (86.2%), top-up transfusion after ET (50.5%), hypocalcemia (42.6%), hyponatremia (42.6%), thrombocytopenia (38.3%), metabolic acidosis (25.5%), and hypokalemia (25.5%), and their risk factors were identified. Some novel and interesting findings were identified by XAI. Conclusions XAI not only achieved better performance in predicting adverse events during ET but also helped clinicians to more deeply understand nonlinear relationships and generate actionable knowledge for practice.
Supplementary Information The online version contains supplementary material available at 10.1186/s12887-022-03615-5.
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Affiliation(s)
- Shuzhen Zhu
- The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Lianjuan Zhou
- The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yuqing Feng
- The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Jihua Zhu
- The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Qiang Shu
- The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Haomin Li
- The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
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