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Cuna A, Premkumar MH, Sampath V. Artificial intelligence to classify acquired intestinal injury in preterm neonates-a new perspective. Pediatr Res 2024; 96:30-32. [PMID: 38499626 DOI: 10.1038/s41390-024-03148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/02/2024] [Indexed: 03/20/2024]
Affiliation(s)
- Alain Cuna
- Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO, USA
- School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | - Muralidhar H Premkumar
- Division of Neonatology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Venkatesh Sampath
- Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO, USA.
- School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA.
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Sizemore N, Oliphant K, Zheng R, Martin CR, Claud EC, Chattopadhyay I. A digital twin of the infant microbiome to predict neurodevelopmental deficits. SCIENCE ADVANCES 2024; 10:eadj0400. [PMID: 38598636 PMCID: PMC11006218 DOI: 10.1126/sciadv.adj0400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 03/06/2024] [Indexed: 04/12/2024]
Abstract
Despite the recognized gut-brain axis link, natural variations in microbial profiles between patients hinder definition of normal abundance ranges, confounding the impact of dysbiosis on infant neurodevelopment. We infer a digital twin of the infant microbiome, forecasting ecosystem trajectories from a few initial observations. Using 16S ribosomal RNA profiles from 88 preterm infants (398 fecal samples and 32,942 abundance estimates for 91 microbial classes), the model (Q-net) predicts abundance dynamics with R2 = 0.69. Contrasting the fit to Q-nets of typical versus suboptimal development, we can reliably estimate individual deficit risk (Mδ) and identify infants achieving poor future head circumference growth with ≈76% area under the receiver operator characteristic curve, 95% ± 1.8% positive predictive value at 98% specificity at 30 weeks postmenstrual age. We find that early transplantation might mitigate risk for ≈45.2% of the cohort, with potentially negative effects from incorrect supplementation. Q-nets are generative artificial intelligence models for ecosystem dynamics, with broad potential applications.
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Affiliation(s)
- Nicholas Sizemore
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Kaitlyn Oliphant
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, USA
| | - Ruolin Zheng
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Camilia R. Martin
- Division of Neonatology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Erika C. Claud
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, USA
- Neonatology Research, University of Chicago, Chicago, IL 60637, USA
| | - Ishanu Chattopadhyay
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
- Committee on Quantitative Methods in Social, Behavioral, and Health Sciences, University of Chicago, Chicago, IL 60637, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
- Center for Health Statistics, University of Chicago, Chicago, IL 60637, USA
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Stanikova A, Jouza M, Bohosova J, Slaby O, Jabandziev P. Role of the microbiome in pathophysiology of necrotising enterocolitis in preterm neonates. BMJ Paediatr Open 2023; 7:e002172. [PMID: 37918941 PMCID: PMC10626796 DOI: 10.1136/bmjpo-2023-002172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/26/2023] [Indexed: 11/04/2023] Open
Abstract
Although necrotising enterocolitis (NEC) is a serious, life-threatening disease, improved neonatal care is increasing the number of survivors with NEC among extremely preterm neonates. Therapy is nevertheless mostly symptomatic and the mortality rate remains high, especially among neonates requiring surgery. Therefore, it is important to focus on preventing the disease and modifiable risk factors. NEC's pathophysiology is multifaceted, with key factors being immaturity of the immune and barrier protective mechanisms of the premature gut and exaggerated proinflammatory reaction to insults like gut hypoxia, enteral nutrition or microbial dysbiosis. The role of the intestinal microbiome in the pathophysiology of NEC has been a subject of research for many years, but to date no specific pathogen or type of dysbiosis has been connected with NEC development. This review assesses current knowledge as to the role of the intestinal microbiota in the pathophysiology of NEC and the possibilities for positively influencing it.
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Affiliation(s)
- Andrea Stanikova
- Department of Neonatology, University Hospital Brno, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Martin Jouza
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Pediatrics, University Hospital Brno, Brno, Czech Republic
| | - Julia Bohosova
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Ondrej Slaby
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- Department of Biology, University Hospital Brno, Brno, Czech Republic
| | - Petr Jabandziev
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Pediatrics, University Hospital Brno, Brno, Czech Republic
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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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Pammi M, Aghaeepour N, Neu J. Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatr Res 2023; 93:308-315. [PMID: 35804156 PMCID: PMC9825681 DOI: 10.1038/s41390-022-02181-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/12/2022] [Accepted: 05/30/2022] [Indexed: 01/11/2023]
Abstract
Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.
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Affiliation(s)
- Mohan Pammi
- Section of Neonatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA.
| | - Nima Aghaeepour
- Departments of Anesthesiology, Pediatrics, and Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Josef Neu
- Section of Neonatology, Department of Pediatrics, University of Florida, Gainesville, FL, USA
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McAdams RM, Kaur R, Sun Y, Bindra H, Cho SJ, Singh H. Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review. J Perinatol 2022; 42:1561-1575. [PMID: 35562414 DOI: 10.1038/s41372-022-01392-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit. OBJECTIVE To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes. METHODS The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay. RESULTS A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data. CONCLUSION With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.
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Affiliation(s)
- Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ravneet Kaur
- Child Health Imprints (CHIL) USA Inc, Madison, WI, USA
| | - Yao Sun
- Division of Neonatology, University of California San Francisco, San Francisco, CA, USA
| | | | - Su Jin Cho
- College of Medicine, Ewha Womans University Seoul, Seoul, Korea
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Gagné D, Shajari E, Thibault MP, Noël JF, Boisvert FM, Babakissa C, Levy E, Gagnon H, Brunet MA, Grynspan D, Ferretti E, Bertelle V, Beaulieu JF. Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis. Int J Mol Sci 2022; 23:ijms231911601. [PMID: 36232903 PMCID: PMC9569884 DOI: 10.3390/ijms231911601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/05/2022] Open
Abstract
Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.
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Affiliation(s)
- David Gagné
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Elmira Shajari
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Marie-Pier Thibault
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Noël
- PhenoSwitch Bioscience Inc., 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - François-Michel Boisvert
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Corentin Babakissa
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Emile Levy
- Research Center, Centre Hospitalier Universitaire Ste-Justine, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Hugo Gagnon
- PhenoSwitch Bioscience Inc., 975 Rue Léon-Trépanier, Sherbrooke, QC J1G 5J6, Canada
| | - Marie A. Brunet
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - David Grynspan
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Colombia, Vancouver, BC V6T 2B5, Canada
| | - Emanuela Ferretti
- Division of Neonatology, Department of Pediatrics, Children’s Hospital of Eastern Ontario (CHEO) and CHEO Research Institute, Ottawa, ON K1H 8L1, Canada
| | - Valérie Bertelle
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Division of Neonatology, Department of Pediatrics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Jean-François Beaulieu
- Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
- Correspondence:
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Lin YC, Salleb-Aouissi A, Hooven TA. Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota. BMC Bioinformatics 2022; 23:104. [PMID: 35337258 PMCID: PMC8953333 DOI: 10.1186/s12859-022-04618-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/23/2022] [Indexed: 12/18/2022] Open
Abstract
Background Necrotizing enterocolitis (NEC) is a common, potentially catastrophic intestinal disease among very low birthweight premature infants. Affecting up to 15% of neonates born weighing less than 1500 g, NEC causes sudden-onset, progressive intestinal inflammation and necrosis, which can lead to significant bowel loss, multi-organ injury, or death. No unifying cause of NEC has been identified, nor is there any reliable biomarker that indicates an individual patient’s risk of the disease. Without a way to predict NEC in advance, the current medical strategy involves close clinical monitoring in an effort to treat babies with NEC as quickly as possible before irrecoverable intestinal damage occurs. In this report, we describe a novel machine learning application for generating dynamic, individualized NEC risk scores based on intestinal microbiota data, which can be determined from sequencing bacterial DNA from otherwise discarded infant stool. A central insight that differentiates our work from past efforts was the recognition that disease prediction from stool microbiota represents a specific subtype of machine learning problem known as multiple instance learning (MIL). Results We used a neural network-based MIL architecture, which we tested on independent datasets from two cohorts encompassing 3595 stool samples from 261 at-risk infants. Our report also introduces a new concept called the “growing bag” analysis, which applies MIL over time, allowing incorporation of past data into each new risk calculation. This approach allowed early, accurate NEC prediction, with a mean sensitivity of 86% and specificity of 90%. True-positive NEC predictions occurred an average of 8 days before disease onset. We also demonstrate that an attention-gated mechanism incorporated into our MIL algorithm permits interpretation of NEC risk, identifying several bacterial taxa that past work has associated with NEC, and potentially pointing the way toward new hypotheses about NEC pathogenesis. Our system is flexible, accepting microbiota data generated from targeted 16S or “shotgun” whole-genome DNA sequencing. It performs well in the setting of common, potentially confounding preterm neonatal clinical events such as perinatal cardiopulmonary depression, antibiotic administration, feeding disruptions, or transitions between breast feeding and formula. Conclusions We have developed and validated a robust MIL-based system for NEC prediction from harmlessly collected premature infant stool. While this system was developed for NEC prediction, our MIL approach may also be applicable to other diseases characterized by changes in the human microbiota. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04618-w.
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Affiliation(s)
- Yun Chao Lin
- Department of Computer Science, Columbia University, 1214 Amsterdam Ave., Mailcode 0401, New York, 10027, USA
| | - Ansaf Salleb-Aouissi
- Department of Computer Science, Columbia University, 1214 Amsterdam Ave., Mailcode 0401, New York, 10027, USA.
| | - Thomas A Hooven
- Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, USA.,Richard King Mellon Institute for Pediatric Research, UPMC Children's Hospital of Pittsburgh, Pittsburgh, USA
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