REVIEW PAPER
Neonatal asphyxia – predicting its occurrence and neurodevelopment of children after hypothermia treatment in the context of machine learning models – literature review
1
Student Research Group at the Department of Neonate and Infant Pathology, Medical University, Lublin, Poland
2
Department of Neonate and Infant Pathology, Medical University, Lublin, Poland
Corresponding author
Natalia Biedroń
Student Research Group at the Department of Neonate and Infant Pathology, Medical University of Lublin, Poland
Student Research Group at the Department of Neonate and Infant Pathology, Medical University of Lublin, Poland
J Pre Clin Clin Res. 2024;1(18):26-32
KEYWORDS
TOPICS
ABSTRACT
Introduction and objective:
Perinatal asphyxia (PA) is a condition of impaired blood gas exchange which, if persistent, leads to progressive hypoxemia and hypercapnia. PA is characterized by high neonatal mortality or neurodevelopmental disorders in the child’s later life. PA is closely related to hypoxic-ischemic encephalopathy (HIE), which is a complication of perinatal hypoxia. In recent years, there has been a growing interest in the use of machine learning (ML) and artificial intelligence (AI) in medicine. The development of machine learning models can improve patient care by applying them to diagnosis, prognosis, decision support, and treatment recommendations.
Review methods:
The data for the article was found using the Web of Science, PubMed, Scopus and Google Scholar websites which were thematically selected for work.
Brief description of the state of knowledge:
Currently, no sufficiently effective prophylactic and therapeutic methods can prevent HIE or death in newborns with PA. Hypothermia is currently the only available method that seems to improve the prognosis in neonates with PA; however, it is not completely effective. In recent years, research has been conducted in the use of machine learning models to predict the occurrence of neonatal asphyxia, based on the occurrence of specific risk factors. Additionally, research has been carried out on the use of neuroimaging in predicting the neurodevelopmental condition of children after neonatal asphyxia.
Summary:
Presently, there are no preventive and therapeutic methods that can prevent HIE or death in newborns with severe perinatal hypoxia. In addition to basic knowledge about asphyxia, the review presents issues that could become the beginning of future research in the use of machine learning in neonatology.
Perinatal asphyxia (PA) is a condition of impaired blood gas exchange which, if persistent, leads to progressive hypoxemia and hypercapnia. PA is characterized by high neonatal mortality or neurodevelopmental disorders in the child’s later life. PA is closely related to hypoxic-ischemic encephalopathy (HIE), which is a complication of perinatal hypoxia. In recent years, there has been a growing interest in the use of machine learning (ML) and artificial intelligence (AI) in medicine. The development of machine learning models can improve patient care by applying them to diagnosis, prognosis, decision support, and treatment recommendations.
Review methods:
The data for the article was found using the Web of Science, PubMed, Scopus and Google Scholar websites which were thematically selected for work.
Brief description of the state of knowledge:
Currently, no sufficiently effective prophylactic and therapeutic methods can prevent HIE or death in newborns with PA. Hypothermia is currently the only available method that seems to improve the prognosis in neonates with PA; however, it is not completely effective. In recent years, research has been conducted in the use of machine learning models to predict the occurrence of neonatal asphyxia, based on the occurrence of specific risk factors. Additionally, research has been carried out on the use of neuroimaging in predicting the neurodevelopmental condition of children after neonatal asphyxia.
Summary:
Presently, there are no preventive and therapeutic methods that can prevent HIE or death in newborns with severe perinatal hypoxia. In addition to basic knowledge about asphyxia, the review presents issues that could become the beginning of future research in the use of machine learning in neonatology.
Biedroń N, Tokarzewska A, Rekowska A, Kwiatkowska A, Urbaś W, Tarkowska A. Neonatal asphyxia – predicting its occurrence and
neurodevelopment of children after hypothermia treatment in the context of machine learning models – literature review. J Pre-Clin Clin Res.
2024; 18(1): 26–32. doi: 10.26444/jpccr/183436
REFERENCES (40)
1.
Gomella TL, Cunningham MD, Eyal FG, et al. Perinatal Asphyxia. Neonatology: Management, Procedures, On-Call Problems, Diseases and Drugs 7th ed. McGraw-Hill Education; 2013.
2.
Kurinczuk J, White-Koning M, Badawi N. Epidemiology of neonatal encephalopathy and hypoxic–ischaemic encephalopathy. Early Hum. 2010;86(6):329–338. doi:10.1016/j.earlhumdev.2010.05.010.
3.
Gulczyńska E, Kesiak M, Kryszczyńska J, et al. The first application of therapeutic hypothermia in Poland—Selective head cooling (Cool-Cap) with whole-body moderate hypothermia in a newborn with features of hypoxic ischemic encephalopathy. Ginekol Pol. 2012;83(5):384–387.
4.
Albrecht M, Zitta K, Groenendaal F, et al. Neuroprotective strategies as follows perinatal hypoxia-ischemia: Taking aim at NOS. Free Radic Biol Med. 2019;142:123–131. doi:10.1016/j.freeradbiomed.2019.02.025.
5.
Tarkowska A. Hypoxic-Ischemic Brain Injury after Perinatal Asphyxia as a Possible Factor in the Pathology of Alzheimer’s Disease. In: Pluta R, editor. Cerebral Ischemia. Brisbane: Exon Publications; 2021. Chapter 4. http://www.ncbi.nlm.nih.gov/bo....
6.
Tarkowska A, Furmaga-Jabłońska W, Bogucki J, et al. Hypothermia after Perinatal Asphyxia Does Not Affect Genes Responsible for Amyloid Production in Neonatal Peripheral Lymphocytes. J Clin Med. 2022;11(12):3263. doi:10.3390/jcm11123263.
7.
Tunç Ş, Oglak SC, Gedik Özköse Z, et al. The evaluation of the antepartum and intrapartum risk factors in predicting the risk of birth asphyxia. J Obstet Gynaecol Res. 2022;48(6):1370–1378. doi:10.1111/jog.15214.
8.
WHO, UNICEF, UN Population Fund, World Bank Group, and the UN PopulationDivision. Levels & Trends in Child Mortality Report 2019 Estimates developed by the UN Inter-agency Group for Child Mortality Estimation. Geneva: World Health Organization; 2019.
9.
Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380(14):1347–1358. doi:10.1056/NEJMra1814259.
10.
Zhou G, Zhang XB, Qu XD, et al. Differential diagnosis of autism spectrum disorder and global developmental delay based on machine learning and Children Neuropsychological and Behavioral Scale. Zhongguo Dang Dai Er Ke Za Zhi. 2023;25(10):1028–1033. Chinese. doi:10.7499/j.issn.1008-8830.2306024.
11.
Vassar R, Schadl K, Cahill-Rowley K, et al. Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born Very Preterm. Pediatr Neurol. 2020 Jul;108:86–92. doi:10.1016/j.pediatrneurol.2020.02.007.
12.
Gschwandtner L, Hartmann M, Oberdorfer L, et al. Deep learning for estimation of functional brain maturation from EEG of premature neonates. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:104–107. doi:10.1109/EMBC44109.2020.9175380.
13.
Gillam-Krakauer M, Gowen Jr C. Birth Asphyxia. In: StatPearls. StatPearls Publishing, Treasure Island; 2023. http://www.ncbi.nlm.nih.gov/bo....
14.
Dworkin PH. Pediatrics. Philadelphia: Lippincott Williams & Wilkins; 2000. p.371.
15.
Wu F, Ding T, et al. Incidence of neonatal asphyxia and contributing factors for the develpment of severe asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: A multicenter study. Chin J Contemp Pediatr. 2019;21(1):6–10. https://doi.org/10.7499/j.issn....
16.
Locatelli A, Lambicchi L, Incerti M, et al. Is perinatal asphyxia predictable? BMC Pregnancy Childbirth. 2020;20:186. https://doi.org/10.1186/s12884....
17.
Mulugeta T, Sebsibe G, Fenta FA, et al. Risk Factors of Perinatal Asphyxia Among Newborns Delivered at Public Hospitals in Addis Ababa, Ethiopia: Case-Control Study. J Pediatr Pharmacol Ther. 2020;11:297–306. doi:10.2147/PHMT.S260788.
18.
Torres-Munoz J, Fonseca-Perez JE, Laurent K. Biological and Psychosocial Factors, Risk Behaviors, and Perinatal Asphyxia in a University Hospital: Matched Case–Control Study, Cali, Colombia (2012–2014). Front Public Health. 2021;9:535737. doi:10.3389/fpubh.2021.535737.
19.
Ogunkunle TO, Odiachi H, Chuma JR, et al. Postnatal Outcomes and Risk Factors for In-Hospital Mortality among Asphyxiated Newborns in a Low-Resource Hospital Setting: Experience from North-Central Nigeria. Ann Glob Health 2020;86(1):63. doi:10.5334/aogh.2884.
20.
Perinatal hypoxia—A journal for pediatricians | Forum Pediatrii Praktycznej. Retrieved 25 April 2023, from https://forumpediatrii. pl/artykul/niedotlenienie-okoloporodowe.
21.
Tesfa D, Tiruneh SA, Azanaw MM, et al. Prognostic risk score development to predict birth asphyxia using maternal and fetal characteristics in South Gondar zone hospitals, north West Ethiopia. BMC Pediatr. 2022;22:537. https://doi.org/10.1186/s12887....
22.
Middleton P, Shepherd E, Flenady V, et al. Planned early birth versus expectant management (waiting) for prelabour rupture of membranes at term (37 weeks or more). Cochrane Database Syst Rev. 2017;1(1):CD005302. doi:10.1002/14651858.
23.
Liu J, Cao HY, Fu W. Lung ultrasonography to diagnose meconium aspiration syndrome of the newborn. J Int Med Res. 2016;44(6):1534–1542. doi:10.1177/0300060516663954.
24.
Altman M, Sandström A, Petersson G, Frisell T, Cnattingius S, Stephansson O. Prolonged second stage of labor is associated with low Apgar score. Eur J Epidemiol. 2015;30(11):1209–15. doi:10.1007/s10654-015-0043-4.
25.
Darsareh F, Ranjbar A, Farashah MV, Mehrnoush V, Shekari M, Jahromi MS. Application of machine learning to identify risk factors of birth asphyxia. BMC Pregnancy Childbirth. 2023 Mar 8;23(1):156. doi:10.1186/s12884-023-05486-9. PMID: 36890453; PMCID: PMC9993370.
26.
Walas W, Wilińska M, Bekiesińska-Figatowska M, et al. Methods for assessing the severity of perinatal asphyxia and early prognostic tools in neonates with hypoxic–ischemic encephalopathy treated with therapeutic hypothermia. Adv Clin Exp Med. 2020;29(8):1011–1016. doi:10.17219/acem/124437.
27.
Gerner GJ, Burton VJ, Poretti A, et al. Transfontanellar duplex brain ultrasonography resistive indices as a prognostic tool in neonatal hypoxic-ischemic encephalopathy before and after treatment with therapeutic hypothermia. J Perinatol. 2016;36(3):202–206. doi:10.1038/jp.2015.169.
28.
Zhang Y, Zhang JL, Li Y. Computed tomography diagnosis of neonatal hypoxic ischemic encephalopathy combined with intracranial hemorrhage and clinical nursing treatment. J Biol Regul Homeost Agents. 2016;30(2):511–515.
29.
Lakatos A, Kolossváry M, Szabó M, et al. Neurodevelopmental effect of intracranial hemorrhage observed in hypoxic ischemic brain injury in hypothermia-treated asphyxiated neonates—An MRI study. BMC Pediatr. 2019;19(1):430. doi:10.1186/s12887-019-1777-z.
30.
Pluta R, Furmaga-Jabłońska W, Januszewski S, et al. Melatonin: A Potential Candidate for the Treatment of Experimental and Clinical Perinatal Asphyxia. Molecules. 2023;28(3):1105. doi:10.3390/molecules28031105.
32.
Edwards AD, Brocklehurst P, Gunn AJ, et al. Neurological outcomes at 18 months of age after moderate hypothermia for perinatal hypoxic ischaemic encephalopathy: Synthesis and meta-analysis of trial data. BMJ. 2010;340:c363. doi:10.1136/bmj.c363.
33.
Królak-Olejnik B. Standardy opieki medycznej nad noworodkiem w Polsce: zalecenia Polskiego Towarzystwa Neonatologicznego. Warszawa: Media-Press Sp. z oo; 2021.
34.
Gonzalez-Ibarra F, Varon J, Lopez-Meza E. Therapeutic Hypothermia: Critical Review of the Molecular Mechanisms of Action. Front Neurol. 2011;2:1–8. doi:10.3389/fneur.2011.00004.
35.
Sweetman D, Kelly L, Hurley T, et al. Troponin T correlates with MRI results in neonatal encephalopathy. Acta Paediatr. 2020;00:1–5. doi:10.1111/apa.15255.
36.
Apaydin U, Erol E, Yildiz A, et al. The use of neuroimaging, Prechtl’s general movement assessment and the Hammersmith infant neurological examination in determining the prognosis in 2-year-old infants with hypoxic ischemic encephalopathy who were treated with hypothermia. Early Human Development. 2021;163:105487. doi:10.1016/j.earlhumdev.2021.105487.
37.
Lally PJ, Montaldo P, Oliveira V, et al. Magnetic resonance spectroscopy assessment of brain injury after moderate hypothermia in neonatal encephalopathy: a prospective multicentre cohort study. Lancet Neurol. 2019;18(1):35–45. doi:10.1016/S1474-4422(18)30325-9.
38.
Alderliesten T, de Vries LS, Staats L, et al. MRI and spectroscopy in (near) term neonates with perinatal asphyxia and therapeutic hypothermia. Arch Dis Child Fetal Neonatal Ed. 2017;102(2):F147-F152. doi:10.1136/archdischild-2016-310514.
39.
Parmentier CEJ, Lequin MH, et al. Additional Value of 3-Month Cranial Magnetic Resonance Imaging in Infants with Neonatal Encephalopathy following Perinatal Asphyxia. J Pediatr. 2023;258:113402. doi:10.1016/j.jpeds.2023.113402.
40.
Parmentier CEJ, de Vries LS, Groenendaal F. Magnetic Resonance Imaging in (Near-)Term Infants with Hypoxic-Ischemic Encephalopathy. Diagnostics. 2022;12(3):645. https://doi.org/10.3390/diagno....
| eISSN: | 1898-7516 |
| ISSN: | 1898-2395 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
