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Gipson DR, Chang AL, Lure AC, Mehta SA, Gowen T, Shumans E, Stevenson D, de la Cruz D, Aghaeepour N, Neu J. Reassessing acquired neonatal intestinal diseases using unsupervised machine learning. Pediatr Res 2024; 96:165-171. [PMID: 38413766 DOI: 10.1038/s41390-024-03074-x] [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: 08/07/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning. METHODS Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis. RESULTS Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster. CONCLUSION Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases. IMPACT Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.
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Affiliation(s)
- Daniel R Gipson
- University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA.
| | - Alan L Chang
- Stanford University School of Medicine, Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Science, Stanford, CA, USA
| | - Allison C Lure
- Nationwide Children's Hospital, The Ohio State University College of Medicine, Department of Pediatrics, Division of Neonatology, Columbus, OH, USA
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
| | - Sonia A Mehta
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
- University of California, Irvine Medical Center, Department of Pediatrics, Division of Neonatology, Irvine, CA, USA
| | - Taylor Gowen
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
- University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, USA
| | - Erin Shumans
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
| | - David Stevenson
- Stanford University School of Medicine, Department of Pediatrics, Division of Neonatology, Stanford, CA, USA
| | - Diomel de la Cruz
- University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA
| | - Nima Aghaeepour
- Stanford University School of Medicine, Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Science, Stanford, CA, USA
| | - Josef Neu
- University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA
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Ashoori M, O'Toole JM, O'Halloran KD, Naulaers G, Thewissen L, Miletin J, Cheung PY, El-Khuffash A, Van Laere D, Straňák Z, Dempsey EM, McDonald FB. Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants. CHILDREN (BASEL, SWITZERLAND) 2023; 10:917. [PMID: 37371150 DOI: 10.3390/children10060917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE To test the potential utility of applying machine learning methods to regional cerebral (rcSO2) and peripheral oxygen saturation (SpO2) signals to detect brain injury in extremely preterm infants. STUDY DESIGN A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO2 measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO2 data were available for 32 infants. The rcSO2 and SpO2 signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II-IV) using a leave-one-out cross-validation approach. RESULTS The area under the receiver operating characteristic curve (AUC) for the PRD rcSO2 was 0.846 (95% CI: 0.720-0.948), outperforming the rcSO2 threshold approach (AUC 0.593 95% CI 0.399-0.775). Neither the clinical model nor any of the SpO2 models were significantly associated with brain injury. CONCLUSION There was a significant association between the data-driven definition of PRDs in rcSO2 and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.
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Affiliation(s)
- Minoo Ashoori
- INFANT Research Centre, University College Cork, T12 AK54 Cork, Ireland
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, T12 XF62 Cork, Ireland
| | - John M O'Toole
- INFANT Research Centre, University College Cork, T12 AK54 Cork, Ireland
- Department of Paediatrics and Child Health, School of Medicine, College of Medicine and Health, University College Cork, T12 DC4A Cork, Ireland
| | - Ken D O'Halloran
- INFANT Research Centre, University College Cork, T12 AK54 Cork, Ireland
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, T12 XF62 Cork, Ireland
| | - Gunnar Naulaers
- Department of Development and Regeneration, Katholieke Universiteit Leuven, Herestraat 49, 3000 Leuven, Belgium
- Neonatal Intensive Care, Katholieke Universiteit Hospital Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Liesbeth Thewissen
- Neonatal Intensive Care, Katholieke Universiteit Hospital Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Jan Miletin
- Paediatric and Newborn Medicine, Coombe Women's Hospital, D08 XW7X Dublin, Ireland
| | - Po-Yin Cheung
- Department of Paediatrics, University of Alberta, Edmonton, AB T6G 1C9, Canada
| | - Afif El-Khuffash
- Faculty of Medicine and Health Sciences, Royal College of Surgeons in Ireland, D02 P796 Dublin, Ireland
| | - David Van Laere
- Neonatale Intensive Care Unit, Universitair Ziekenhuis, (UZ) Antwerp, Drie Eikenstraat 655, 2650 Antwerp, Belgium
| | - Zbyněk Straňák
- Institute for the Care of Mother and Child, Third Faculty of Medicine, Charles University, 100 00 Prague, Czech Republic
| | - Eugene M Dempsey
- INFANT Research Centre, University College Cork, T12 AK54 Cork, Ireland
- Department of Paediatrics and Child Health, School of Medicine, College of Medicine and Health, University College Cork, T12 DC4A Cork, Ireland
| | - Fiona B McDonald
- INFANT Research Centre, University College Cork, T12 AK54 Cork, Ireland
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, T12 XF62 Cork, Ireland
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Identifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learning. Eur J Pediatr 2023; 182:2173-2179. [PMID: 36853570 DOI: 10.1007/s00431-023-04882-9] [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: 01/24/2023] [Revised: 02/09/2023] [Accepted: 02/15/2023] [Indexed: 03/01/2023]
Abstract
To use unsupervised machine learning to identify potential subphenotypes of preterm infants with patent ductus arteriosus (PDA). The study was conducted retrospectively at a neonatal intensive care unit in Brazil. Patients with a gestational age < 28 weeks who had undergone at least one echocardiogram within the first two weeks of life and had PDA size > 1.5 or LA/AO ratio > 1.5 were included. Agglomerative hierarchical clustering on principal components was used to divide the data into different clusters based on common characteristics. Two distinct subphenotypes of preterm infants with hemodynamically significant PDA were identified: "inflamed," characterized by high leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio, and "respiratory acidosis," characterized by low pH and high pCO2 levels. Conclusions: This study suggests that there may be two distinct subphenotypes of preterm infants with hemodynamically significant PDA: "inflamed" and "respiratory acidosis." By dividing the population into different subgroups based on common characteristics, it is possible to get a more nuanced understanding of the effectiveness of PDA interventions. What is Known: • Treatment of PDA in preterm infants has been controversial. • Stratification of preterm infants with PDA into subgroups is important in order to determine the best treatment. What is New: • Unsupervised machine learning was used to identify two subphenotypes of preterm infants with hemodynamically significant PDA. • The 'inflamed' cluster was characterized by higher values of leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio. The 'respiratory acidosis' cluster was characterized by lower pH values and higher pCO2 values.
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Matsushita FY, Krebs VLJ, Carvalho WBD. Artificial intelligence and machine learning in pediatrics and neonatology healthcare. Rev Assoc Med Bras (1992) 2022; 68:745-750. [PMID: 35766685 PMCID: PMC9575899 DOI: 10.1590/1806-9282.20220177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Felipe Yu Matsushita
- Universidade de São Paulo, Faculty of Medicine, Department of Pediatrics - São Paulo (SP), Brazil
| | - Vera Lucia Jornada Krebs
- Universidade de São Paulo, Faculty of Medicine, Department of Pediatrics - São Paulo (SP), Brazil
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