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Hair AB, Sullivan KM, Ahmad I, Zaniletti I, Acker SN, Premkumar MH, Reber K, Huff KA, Nayak SP, DiGeronimo R, Kim J, Roberts J, Markel TA, Brozanski B, Sharma J, Piazza AJ, Yanowitz TD. Initial surgery for spontaneous intestinal perforation in extremely low birth weight infants is not associated with mortality or in-hospital morbidities. J Perinatol 2024:10.1038/s41372-024-02037-8. [PMID: 38992239 DOI: 10.1038/s41372-024-02037-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/11/2024] [Accepted: 06/18/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVE Determine short-term outcomes following peritoneal drain (PD), laparotomy (LAP) after PD (PD-LAP), and LAP in extremely low birth weight (ELBW) infants with spontaneous intestinal perforation (SIP). STUDY DESIGN ELBW infants with SIP were identified using the Children's Hospitals Neonatal Database. Mortality and length of stay (LOS) were compared among groups. RESULTS Of 729 SIP infants from 6/2010-12/2016, 383(53%) received PD, 61(8%) PD-LAP, and 285(39%) LAP. PD infants had lower GA at birth, at SIP diagnosis and upon admission than PD-LAP or LAP; and higher sepsis rates than LAP. Bivariate analysis and Kaplan-Meier survival estimates suggested PD had increased mortality vs. PD-LAP and LAP (27%, 11.5%, and 15.8% respectively, p < 0.001). However, surgical approach was not significantly associated with mortality in multivariable analysis accounting for GA and illness severity. LOS did not differ by surgical approach. CONCLUSIONS In ELBW infants with SIP, mortality, and LOS are independent of the initial surgical approach.
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Affiliation(s)
- Amy B Hair
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA.
| | - Kevin M Sullivan
- Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA
- Nemours Children's Hospital, Wilmington, DE, USA
| | - Irfan Ahmad
- Children's Hospital of Orange County, Orange, CA, USA
| | | | - Shannon N Acker
- University of Colorado, Children's Hospital of Colorado, Aurora, CO, USA
| | | | - Kristina Reber
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Katie A Huff
- Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, IN, USA
| | | | - Robert DiGeronimo
- Seattle Children's Hospital, University of Washington, Seattle, WA, USA
| | - Jae Kim
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Jessica Roberts
- Emory University School of Medicine, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Troy A Markel
- Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, IN, USA
| | - Beverly Brozanski
- Washington University School of Medicine, St. Louis Children's Hospital, St. Louis, MO, USA
| | - Jotishna Sharma
- Missouri University of Missouri Kansas City School of Medicine, Children's Mercy Hospital, Kansas City, MO, USA
| | - Anthony J Piazza
- Emory University School of Medicine, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Toby D Yanowitz
- University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
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Neu J, Singh R, Demetrian M, Flores-Torres J, Hudak M, Zupancic JA, Kronström A, Rastad J, Strömberg S, Thuresson M. Clinical Characteristics of Necrotizing Enterocolitis Diagnosed by Independent Adjudication of Abdominal Radiographs, Laparotomy, or Autopsy in Preterm Infants in the "Connection Trial". Am J Perinatol 2024. [PMID: 38986486 DOI: 10.1055/s-0044-1788275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
OBJECTIVE Necrotizing enterocolitis (NEC) classically is diagnosed by radiographic demonstration of pneumatosis intestinalis/portal venous gas (PI/PVG). This study examines clinical characteristics of NEC confirmed by independent evaluation of abdominal radiographs, taken for clinical signs of NEC, or by pathologic findings at laparotomy or autopsy (confirmed NEC [cNEC]). STUDY DESIGN The investigated cohort included 1,382 extremely low birth weight (BW) infants (BW range: 500-1,000 g) with median 27 weeks (range: 23-32) gestational age (GA) at birth. They were randomized into the placebo-controlled "Connection Trial" of the new biological drug candidate IBP-9414 with cNEC as one primary endpoint. RESULTS Total 119 infants (8.6%) had cNEC diagnosed at median 14 days of age by confirming PI/PVG at X-ray adjudication (n = 111) and/or by surgery/autopsy (n = 21). Sixteen percent of cNEC cases died. Adverse events of NEC were reported in 8.5% of infants and 4.1% had NEC diagnosed by radiology and surgery/autopsy at the participating centers. Regression analyses showed that the risk of cNEC decreased by 11 to 30% for every 100-g increment in BW and single-week increment in GA and associated cNEC with odds ratios (ORs) > 2.0 for gastrointestinal (GI) perforation and obstruction, hypotension, hypokalemia, hypophosphatemia, and death. Comparing risks of cNEC in infants below and above 750-g BW showed higher ORs (2.7-4.3) for GI perforation, hypotension, hypokalemia, and renal complications in the smaller infants, whereas the bigger infants had higher ORs (1.9-3.2) for serious non-GI events, late-onset sepsis (LOS), and death. Predictors of cNEC (hazard ratio, HR > 1.5) included serious non-GI events (mainly infections), hyponatremia, and hyperglycemia, whereas the HR was 0.52 for intravenous antibiotics. After cNEC diagnosis, there were higher rates of GI perforation and obstruction, hypotension, hypokalemia, and LOS. CONCLUSION Independent adjudication of abdominal radiographs increased radiological recognition of NEC and proved to be feasible in a multicenter study setting as well as able to diagnose clinically relevant NEC. KEY POINTS · Independent adjudication of abdominal radiographs in ELBW infants increased NEC recognition.. · Risk of NEC decreased by 11 to 30% with every 100-g increment in BW and GA week.. · In infants with BW 750 to 1,000 g, the risk of death from NEC was almost twice that in infants with BW 500 to 749 g. · Infants with NEC received antibiotics during one-third and parenteral nutrition during half of the first 7 postnatal weeks..
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Affiliation(s)
- Josef Neu
- Department of Pediatrics, UF Health Shands Children's Hospital, Gainesville, Florida
| | - Rachana Singh
- Department of Pediatrics, Tuft's Children's Hospital, Tuft's University School of Medicine, Boston, Massachusetts
| | - Mihaela Demetrian
- Department of Neonatology, Spitalul Clinic Filantropia, Bucharest, Romania
| | - Jaime Flores-Torres
- Department of Pediatrics, Morsani College of Medicine, University of South Florida, Tampa, Florida
| | - Mark Hudak
- Department of Pediatrics, University of Florida College of Medicine, Jacksonville, Florida
| | - John A Zupancic
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | | | - Jonas Rastad
- Infant Bacterial Therapeutics, Stockholm, Sweden
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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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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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Dantes G, Keane OA, Do L, Rumbika S, Ellis NH, Dutreuil VL, He Z, Bhatia AM. Clinical Predictors of Spontaneous Intestinal Perforation vs Necrotizing Enterocolitis in Extremely and Very Low Birth Weight Neonates. J Pediatr Surg 2024:S0022-3468(24)00400-7. [PMID: 39033072 DOI: 10.1016/j.jpedsurg.2024.06.017] [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: 03/22/2024] [Revised: 06/07/2024] [Accepted: 06/24/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE Spontaneous intestinal perforation (SIP) and necrotizing enterocolitis (NEC) are distinct disease processes associated with significant morbidity and mortality. Initial treatment, laparotomy (LP) versus peritoneal drainage (PD), is disease specific however it can be difficult to distinguish these diagnoses preoperatively. We investigated clinical characteristics associated with each diagnosis and constructed a scoring algorithm for accurate preoperative diagnosis. METHODS A cohort of extreme and very low birth weight (<1500 g) neonates surgically treated for SIP or NEC between 07/2004-09/2022 were reviewed. Clinical characteristics included gestational age (GA), birth weight (BW), feeding history, physical exam, and laboratory/radiological findings. Intraoperative diagnosis was used to determine SIP vs NEC. Pre-drain diagnosis was used for patients treated with PD only. RESULTS 338 neonates were managed for SIP (n = 269, 79.6%) vs NEC (n = 69, 20.4%). PD was definitive treatment in 146 (43.2%) patients and 75 (22.2%) patients were treated with upfront LP. Characteristics associated with SIP included younger GA, younger age at initial laparotomy or drainage (ALD), and history of trophic or no feeds. Multivariate logistic regression determined pneumatosis, abdominal wall erythema, higher ALD and history of feeds to be highly predictive of NEC. A 0-8-point scale was designed based on these characteristics with the area under the receiver operating characteristic curve of 0.819 (95% CI 0.756-0.882) for the diagnosis of NEC. A threshold score of 1.5 had a 95.2% specificity for NEC. CONCLUSION Utilizing clinical characteristics associated with SIP & NEC we developed a scoring system designed to assist surgeons accurately distinguish SIP vs NEC in neonates. TYPE OF STUDY Retrospective Chart Review. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Goeto Dantes
- Department of Surgery, Emory University, Atlanta, GA, USA.
| | - Olivia A Keane
- Department of Surgery, Emory University, Atlanta, GA, USA
| | - Louis Do
- Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Savanah Rumbika
- Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Nathaniel H Ellis
- Division of Pediatric Surgery, Department of Surgery, Emory University School of Medicine, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Valerie L Dutreuil
- Emory Department of Pediatrics, Emory University, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Zhulin He
- Emory Department of Pediatrics, Emory University, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Amina M Bhatia
- Division of Pediatric Surgery, Department of Surgery, Emory University School of Medicine, Children's Healthcare of Atlanta, Atlanta, GA, USA
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Morimoto D, Washio Y, Fukuda K, Sato T, Okamura T, Watanabe H, Yoshimoto J, Tanioka M, Tsukahara H. Machine Learning to Improve Accuracy of Transcutaneous Bilirubinometry. Neonatology 2024:1-8. [PMID: 38684146 DOI: 10.1159/000535970] [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: 06/20/2023] [Accepted: 12/15/2023] [Indexed: 05/02/2024]
Abstract
INTRODUCTION This study aimed to develop models for predicting total serum bilirubin by correcting errors of transcutaneous bilirubin using machine learning based on neonatal biomarkers that could affect spectrophotometric measurements of tissue bilirubin. METHODS This retrospective study included infants born at our hospital (≥36 weeks old, ≥2,000 g) between January 2020 and December 2022. Infants without a phototherapy history were included. Robust linear regression, gradient boosting tree, and neural networks were used for machine learning models. A neural network, inspired by the structure of the human brain, was designed comprising three layers: input, intermediate, and output. RESULTS Totally, 683 infants were included. The mean (minimum-maximum) gestational age, birth weight, participant age, total serum bilirubin, and transcutaneous bilirubin were 39.0 (36.0-42.0) weeks, 3,004 (2,004-4,484) g, 2.8 (1-6) days of age, 8.50 (2.67-18.12) mg/dL, and 7.8 (1.1-18.1) mg/dL, respectively. The neural network model had a root mean square error of 1.03 mg/dL and a mean absolute error of 0.80 mg/dL in cross-validation data. These values were 0.37 mg/dL and 0.28 mg/dL, smaller compared to transcutaneous bilirubin, respectively. The 95% limit of agreement between the neural network estimation and total serum bilirubin was -2.01 to 2.01 mg/dL. Unnecessary blood draws could be reduced by up to 78%. CONCLUSION Using machine learning with transcutaneous bilirubin, total serum bilirubin estimation error was reduced by 25%. This integration could increase accuracy, lessen infant discomfort, and simplify procedures, offering a smart alternative to blood draws by accurately estimating phototherapy thresholds.
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Affiliation(s)
- Daisaku Morimoto
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan,
| | - Yosuke Washio
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Kana Fukuda
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Takeshi Sato
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Tomoka Okamura
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Hirokazu Watanabe
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Junko Yoshimoto
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Maki Tanioka
- Clinical AI Human Resources Development Program, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Hirokazu Tsukahara
- Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
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Lemas DJ, Du X, Rouhizadeh M, Lewis B, Frank S, Wright L, Spirache A, Gonzalez L, Cheves R, Magalhães M, Zapata R, Reddy R, Xu K, Parker L, Harle C, Young B, Louis-Jaques A, Zhang B, Thompson L, Hogan WR, Modave F. Classifying early infant feeding status from clinical notes using natural language processing and machine learning. Sci Rep 2024; 14:7831. [PMID: 38570569 PMCID: PMC10991582 DOI: 10.1038/s41598-024-58299-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 03/27/2024] [Indexed: 04/05/2024] Open
Abstract
The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.
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Affiliation(s)
- Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA.
- Department of Obstetrics and Gynecology, University of Florida College of Medicine, Gainesville, FL, 32610, USA.
| | - Xinsong Du
- Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Masoud Rouhizadeh
- Department of Pharmaceutical Outcomes and Policy, University of Florida College of Medicine, Gainesville, FL, 32610, USA
- Biomedical Informatics and Data Science Section, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Braeden Lewis
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Simon Frank
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Lauren Wright
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Alex Spirache
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Lisa Gonzalez
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Ryan Cheves
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Marina Magalhães
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, 94305, USA
| | - Ruben Zapata
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Rahul Reddy
- Department of Computer and Information Science, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Ke Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA
| | - Leslie Parker
- Department of Biobehavioral Nursing Science, University of Florida College of Nursing, Gainesville, FL, 32603, USA
| | - Chris Harle
- Health Policy and Management Department, Richard M. Fairbanks School of Public Health, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA
| | - Bridget Young
- Division of Breastfeeding and Lactation Medicine, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Adetola Louis-Jaques
- Department of Obstetrics and Gynecology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
| | - Bouri Zhang
- Health Science Center Libraries, University of Florida, Gainesville, FL, 32610, USA
| | - Lindsay Thompson
- Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, 27101, USA
| | - William R Hogan
- Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - François Modave
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, 32610, USA
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Sullivan BA, Beam K, Vesoulis ZA, Aziz KB, Husain AN, Knake LA, Moreira AG, Hooven TA, Weiss EM, Carr NR, El-Ferzli GT, Patel RM, Simek KA, Hernandez AJ, Barry JS, McAdams RM. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol 2024; 44:1-11. [PMID: 38097685 PMCID: PMC10872325 DOI: 10.1038/s41372-023-01848-5] [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] [Received: 09/11/2023] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
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.
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Affiliation(s)
- Brynne A Sullivan
- Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Khyzer B Aziz
- Division of Neonatology, Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
| | - Ameena N Husain
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Lindsey A Knake
- Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Alvaro G Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Thomas A Hooven
- Division of Newborn Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elliott M Weiss
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Treuman Katz Center for Pediatric Bioethics and Palliative Care, Seattle Children's Research Institute, Seattle, WA, USA
| | - Nicholas R Carr
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - George T El-Ferzli
- Division of Neonatology, Department of Pediatrics, Ohio State University, Nationwide Children's Hospital, Columbus, OH, USA
| | - Ravi M Patel
- Division of Neonatology, Department of Pediatrics, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Kelsey A Simek
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Antonio J Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - James S Barry
- Division of Neonatology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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Bethell GS, Hall NJ. Recent advances in our understanding of NEC diagnosis, prognosis and surgical approach. Front Pediatr 2023; 11:1229850. [PMID: 37583622 PMCID: PMC10424793 DOI: 10.3389/fped.2023.1229850] [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: 05/27/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023] Open
Abstract
Necrotising enterocolitis (NEC) remains a devasting condition that has seen limited improvement in outcomes in recent years. The incidence of the disease is increasing as more extremely premature infants survive. NEC is responsible for 1 in 10 neonatal deaths and up to 61% of survivors have significant neurodevelopmental delay. The aim of this review is to highlight recent advances in diagnosis, prognosis and surgical approach in this condition. Many recent studies have reported novel methods of diagnosis of NEC with the aim of earlier and more accurate identification. These include imaging and machine learning techniques. Prognostication of NEC is particularly important to allow earlier escalation of therapy. Around 25% of infants with NEC will require surgery and recent data has shown that time from disease onset to surgery is greater in infants whose indication for surgery is failed medical management, rather than pneumoperitoneum. This indication was also associated with worse outcomes compared to pneumoperitoneum. Ongoing research has highlighted several new methods of disease prognostication which includes differentiating surgical from medical NEC. Finally, recent randomised controlled trials in surgical technique are discussed along with the implications of these for practice. Further, high quality research utilising multi-centre collaborations and high fidelity data from electronic patient records is needed to address the issues discussed and ultimately improve outcomes in NEC.
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Affiliation(s)
- George S Bethell
- University Surgical Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Nigel J Hall
- University Surgical Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
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10
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Klerk DH, van Varsseveld OC, Offringa M, Modi N, Lacher M, Zani A, Pakarinen MP, Koivusalo A, Jester I, Spruce M, Derikx JPM, Bakx R, Ksia A, Vermeulen MJ, Kooi EMW, Hulscher JBF. Development of an international core outcome set for treatment trials in necrotizing enterocolitis-a study protocol. Trials 2023; 24:367. [PMID: 37259112 DOI: 10.1186/s13063-023-07413-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023] Open
Abstract
AIM Necrotizing enterocolitis (NEC) is the most lethal disease of the gastrointestinal tract of preterm infants. New and existing management strategies need clinical evaluation. Large heterogeneity exists in the selection, measurement, and reporting of outcome measures in NEC intervention studies. This hampers meta-analyses and the development of evidence-based management guidelines. We aim to develop a Core Outcome Set (COS) for NEC that includes the most relevant outcomes for patients and physicians, from moment of diagnosis into adulthood. This COS is designed for use in NEC treatment trials, in infants with confirmed NEC. METHODS This study is designed according to COS-STAD (Core Outcome Set-STAndards for Development) recommendations and the COMET (Core Outcome Measures in Effectiveness Trials) Initiative Handbook. We obtained a waiver from the Ethics Review Board and prospectively registered this study with COMET (Study 1920). We will approach 125 clinicians and/or researchers from low-middle and high-income countries based on their scientific output (using SCIVAL, a bibliometric tool). Patients and parents will be approached through local patient organisations. Participants will be separated into three panels, to assess differences in priorities between former patients and parents (1. lay panel), clinicians and researchers involved in the neonatal period (2. neonatal panel) and after the neonatal period (3. post-neonatal panel). They will be presented with outcomes currently used in NEC research, identified through a systematic review, in a Delphi process. Eligible outcome domains are also identified from the patients and parents' perspectives. Using a consensus process, including three online Delphi rounds and a final face-to-face consensus meeting, the COS will be finalised and include outcomes deemed essential to all stakeholders: health care professionals, parents and patients' representatives. The final COS will be reported in accordance with the COS-Standards for reporting (COS-STAR) statement. CONCLUSIONS Development of an international COS will help to improve homogeneity of outcome measure reporting in NEC, will enable adequate and efficient comparison of treatment strategies, and will help the interpretation and implementation of clinical trial results. This will contribute to high-quality evidence regarding the best treatment strategy for NEC in preterm infants.
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Affiliation(s)
- Daphne H Klerk
- Division of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Otis C van Varsseveld
- Department of Surgery, Division of Paediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Martin Offringa
- Division of Neonatology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Neena Modi
- Section of Neonatal Medicine, School of Public Health, Chelsea and Westminster Hospital campus, Imperial College London, London, UK
| | - Martin Lacher
- Department of Paediatric Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Augusto Zani
- Department of General and Thoracic Surgery, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Mikko P Pakarinen
- Department of Paediatric Surgery, Children's Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Antti Koivusalo
- Department of Paediatric Surgery, Children's Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Ingo Jester
- Departments of Paediatric Surgery, Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | | | - Joep P M Derikx
- Department of Paediatric Surgery, UMC, Emma Children's Hospital, Amsterdam, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Roel Bakx
- Department of Paediatric Surgery, UMC, Emma Children's Hospital, Amsterdam, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Amine Ksia
- Department of Surgery, Department of Paediatric Surgery, Monastir Medical School, Fattouma Bourguiba Hospital, Monastir University, Monastir, Tunisia
| | - Marijn J Vermeulen
- Care4Neo, Neonatal Patient and Parent Organization, Rotterdam, the Netherlands
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Elisabeth M W Kooi
- Division of Neonatology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Jan B F Hulscher
- Department of Surgery, Division of Paediatric Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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11
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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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12
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Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:204-215. [PMID: 37197647 PMCID: PMC10110825 DOI: 10.1007/s43657-022-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 05/19/2023]
Abstract
Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.
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Affiliation(s)
- Tiantian Xiao
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000 China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Wenhao Zhou
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
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13
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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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14
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Schiller EA, Cohen K, Lin X, El-Khawam R, Hanna N. Extracellular Vesicle-microRNAs as Diagnostic Biomarkers in Preterm Neonates. Int J Mol Sci 2023; 24:2622. [PMID: 36768944 PMCID: PMC9916767 DOI: 10.3390/ijms24032622] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Neonates born prematurely (<37 weeks of gestation) are at a significantly increased risk of developing inflammatory conditions associated with high mortality rates, including necrotizing enterocolitis, bronchopulmonary dysplasia, and hypoxic-ischemic brain damage. Recently, research has focused on characterizing the content of extracellular vesicles (EVs), particularly microRNAs (miRNAs), for diagnostic use. Here, we describe the most recent work on EVs-miRNAs biomarkers discovery for conditions that commonly affect premature neonates.
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Affiliation(s)
- Emily A. Schiller
- Department of Foundational Medicine, New York University Long Island School of Medicine, Mineola, NY 11501, USA
| | - Koral Cohen
- Department of Foundational Medicine, New York University Long Island School of Medicine, Mineola, NY 11501, USA
| | - Xinhua Lin
- Department of Foundational Medicine, New York University Long Island School of Medicine, Mineola, NY 11501, USA
| | - Rania El-Khawam
- Department of Pediatrics, Division of Neonatology, New York University Langone Long Island Hospital, Mineola, NY 11501, USA
| | - Nazeeh Hanna
- Department of Foundational Medicine, New York University Long Island School of Medicine, Mineola, NY 11501, USA
- Department of Pediatrics, Division of Neonatology, New York University Langone Long Island Hospital, Mineola, NY 11501, USA
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15
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Necrotizing Enterocolitis: The Role of Hypoxia, Gut Microbiome, and Microbial Metabolites. Int J Mol Sci 2023; 24:ijms24032471. [PMID: 36768793 PMCID: PMC9917134 DOI: 10.3390/ijms24032471] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/15/2023] [Accepted: 01/17/2023] [Indexed: 02/01/2023] Open
Abstract
Necrotizing enterocolitis (NEC) is a life-threatening disease that predominantly affects very low birth weight preterm infants. Development of NEC in preterm infants is accompanied by high mortality. Surgical treatment of NEC can be complicated by short bowel syndrome, intestinal failure, parenteral nutrition-associated liver disease, and neurodevelopmental delay. Issues surrounding pathogenesis, prevention, and treatment of NEC remain unclear. This review summarizes data on prenatal risk factors for NEC, the role of pre-eclampsia, and intrauterine growth retardation in the pathogenesis of NEC. The role of hypoxia in NEC is discussed. Recent data on the role of the intestinal microbiome in the development of NEC, and features of the metabolome that can serve as potential biomarkers, are presented. The Pseudomonadota phylum is known to be associated with NEC in preterm neonates, and the role of other bacteria and their metabolites in NEC pathogenesis is also discussed. The most promising approaches for preventing and treating NEC are summarized.
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16
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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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17
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Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns. Pediatr Res 2023; 93:376-381. [PMID: 36195629 DOI: 10.1038/s41390-022-02322-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/03/2022] [Indexed: 11/09/2022]
Abstract
Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area. IMPACT: Only a few publications on the use of AI in NEC diagnosis are available although they offer some evidence that AI may be helpful in NEC diagnosis. AI requires large, multicentre, and multimodal datasets of high quality for model training and testing. Published results in the literature are based on data from single institutions and, as such, have limited generalisability. Large multicentre studies evaluating broad datasets are needed to evaluate the true potential of AI in diagnosing NEC in a clinical setting.
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18
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Song J, Li Z, Yao G, Wei S, Li L, Wu H. Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis. PLoS One 2022; 17:e0273383. [PMID: 35984833 PMCID: PMC9390903 DOI: 10.1371/journal.pone.0273383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/08/2022] [Indexed: 11/18/2022] Open
Abstract
Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.
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Affiliation(s)
- Jianfei Song
- College of Communication Engineering, Jilin University, Changchun, Jilin, PR China
| | - Zhenyu Li
- Department of Neonatology, Jilin University First Hospital, Changchun, Jilin, PR China
| | - Guijin Yao
- College of Communication Engineering, Jilin University, Changchun, Jilin, PR China
| | - Songping Wei
- College of Communication Engineering, Jilin University, Changchun, Jilin, PR China
| | - Ling Li
- College of Communication Engineering, Jilin University, Changchun, Jilin, PR China
- * E-mail: (LL); (HW)
| | - Hui Wu
- Department of Neonatology, Jilin University First Hospital, Changchun, Jilin, PR China
- * E-mail: (LL); (HW)
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19
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Development of artificial neural networks for early prediction of intestinal perforation in preterm infants. Sci Rep 2022; 12:12112. [PMID: 35840701 PMCID: PMC9287325 DOI: 10.1038/s41598-022-16273-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/07/2022] [Indexed: 11/09/2022] Open
Abstract
Intestinal perforation (IP) in preterm infants is a life-threatening condition that may result in serious complications and increased mortality. Early Prediction of IP in infants is important, but challenging due to its multifactorial and complex nature of the disease. Thus, there are no reliable tools to predict IP in infants. In this study, we developed new machine learning (ML) models for predicting IP in very low birth weight (VLBW) infants and compared their performance to that of classic ML methods. We developed artificial neural networks (ANNs) using VLBW infant data from a nationwide cohort and prospective web-based registry. The new ANN models, which outperformed all other classic ML methods, showed an area under the receiver operating characteristic curve (AUROC) of 0.8832 for predicting IP associated with necrotizing enterocolitis (NEC-IP) and 0.8797 for spontaneous IP (SIP). We tested these algorithms using patient data from our institution, which were not included in the training dataset, and obtained an AUROC of 1.0000 for NEC-IP and 0.9364 for SIP. NEC-IP and SIP in VLBW infants can be predicted at an excellent performance level with these newly developed ML models. https://github.com/kdhRick2222/Early-Prediction-of-Intestinal-Perforation-in-Preterm-Infants.
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20
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Lobe TE, Bianco FM. Adolescent inguinal hernia repair: a review of the literature and recommendations for selective management. Hernia 2022; 26:831-837. [PMID: 35028731 DOI: 10.1007/s10029-021-02551-0] [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] [Received: 10/19/2021] [Accepted: 12/19/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND The choice of how to repair inguinal hernias in adolescents has historically been a matter of experience and differed between pediatric surgeons who traditionally performed a high ligation of the sac and general surgeons who typically perform a repair using mesh. This up-to-date review thoroughly examines the subject and discusses the suitability of both types of repairs in this unique age group. METHODS A 20-year Pub Med search was performed for the following terms: adolescent hernia repair including reports of mesh hernia repair in adolescents and postoperative complications including chronic inguinal pain and recurrences. RESULTS The evidence in the literature suggests that while there appears to be no difference between the two types of repairs with regards to recurrence and complications, changes in the pelvic floor physiology in adolescents suggest that a selective, individualized approach can be recommended depending on the size and nature of the presenting pathology. CONCLUSIONS A selective approach to the inguinal hernia in adolescent patients based on the size of the defect appears justified.
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Affiliation(s)
- T E Lobe
- Division of Pediatric Surgery, Department of Surgery, The University of Illinois, 840 S Wood Street, Ste 416, Chicago, IL, 60612, USA.
| | - F M Bianco
- Department of Surgery, The University of Illinois, Chicago, IL, USA
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21
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Abstract
Necrotizing enterocolitis (NEC) is considered to be one of the most devastating intestinal diseases seen in neonatal intensive care. Measures to treat NEC are often too late, and we need effective preventative measures to alleviate the burden of this disease. The purpose of this review is to summarize currently used measures, and those showing future promise for prevention.
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Affiliation(s)
- Josef Neu
- University of Florida, Gainesville, FL, USA.
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22
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A critical evaluation of current definitions of necrotizing enterocolitis. Pediatr Res 2022; 91:590-597. [PMID: 34021272 DOI: 10.1038/s41390-021-01570-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/19/2021] [Accepted: 04/26/2021] [Indexed: 01/24/2023]
Abstract
BACKGROUND Necrotizing enterocolitis (NEC) is a devastating intestinal disease of premature infants, with significant mortality and long-term morbidity among survivors. Multiple NEC definitions exist, but no formal head-to-head evaluation has been performed. We hypothesized that contemporary definitions would perform better in evaluation metrics than Bell's and range features would be more frequently identified as important than yes/no features. METHODS Two hundred and nineteen patients from the University of Iowa hospital with NEC, intestinal perforation, or NEC concern were identified from a 10-year retrospective cohort. NEC presence was confirmed by a blinded investigator. Evaluation metrics were calculated using statistics and six supervised machine learning classifiers for current NEC definitions. Feature importance evaluation was performed on each decision tree classifier. RESULTS Newer definitions outperformed Bell's staging using both standard statistics and most machine learning classifiers. The decision tree classifier had the highest overall machine learning scores, which resulted in Non-Bell definitions having high sensitivity (0.826, INC) and specificity (0.969, ST), while Modified Bell (IIA+) had reasonable sensitivity (0.783), but poor specificity (0.531). Feature importance evaluation identified nine criteria as important for diagnosis. CONCLUSIONS This preliminary study suggests that Non-Bell NEC definitions may be better at diagnosing NEC and calls for further examination of definitions and important criteria. IMPACT This article is the first formal head-to-head evaluation of current available definitions of NEC. Non-Bell NEC definitions may be more effective in identifying NEC based on findings from traditional measures of diagnostic performance and machine learning techniques. Nine features were identified as important for diagnosis from the definitions evaluated within the decision tree when performing supervised classification machine learning. This article serves as a preliminary study to formally evaluate the definitions of NEC utilized and should be expounded upon with a larger and more diverse patient cohort.
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