Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates.


Journal

Diabetes technology & therapeutics
ISSN: 1557-8593
Titre abrégé: Diabetes Technol Ther
Pays: United States
ID NLM: 100889084

Informations de publication

Date de publication:
03 2019
Historique:
entrez: 6 3 2019
pubmed: 6 3 2019
medline: 31 1 2020
Statut: ppublish

Résumé

To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm neonates based on continuous glucose monitoring (CGM). We retrospectively analyzed CGM traces obtained from 50 very preterm neonates, grouped into two sub-cohorts started on CGM within 12 and 48 h of birth, respectively. A CGM linked to an Artificial Intelligence Risk (CLAIR) index was developed to quantify glucose variability during the first 72 h of life in neonates with and without IVH. Brain-US was performed at least twice a day for the first 5 days of birth. An integrated remote monitoring platform was developed to capture major clinical events in real time and gather data for the risk index. The new score performance was further compared with other measures of glucose variability (coefficient of variation [CV] and standard deviation [SD]) and with a clinical risk index for babies II (CRIB-II) as a predictor of IVH event. The two cohorts were analyzed separately for internal validation of the method. The primary cohort consisted of 26 neonates (gestational age 30 [28, 31] weeks; BW1275 g[1090, 1750]). Controls (n = 23) exhibited higher CLAIR index than cases (P = 0.004). A cut-off of 0.69 for the new CLAIR index allowed a 100% sensitivity and an 83% specificity for IVH prediction. The CLAIR index was the sole significant predictor for IVH (P = 0.003) when compared with clinical variables, CV, SD, and CRIB-II. In a subgroup analysis in very low-birth-weight infants, the CLAIR index was the sole variable significantly associated with IVH (P = 0.009). Analysis on the secondary cohort (five cases and 16 controls) confirmed a higher CLAIR index in the controls (P = 0.008), in the absence of a difference for CV, SD, and CRIB-II between the two groups. CGM, combined with the AI-algorithm, provides a high-sensitivity index for risk detection of IVH that reflects the glycemic impairment preceding IVH.

Identifiants

pubmed: 30835533
doi: 10.1089/dia.2018.0383
doi:

Banques de données

ClinicalTrials.gov
['NCT02583776']

Types de publication

Evaluation Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

146-153

Auteurs

Alfonso Galderisi (A)

1 Department of Pediatrics, Yale University, New Haven, Connecticut.
2 Neonatal Intensive Care Unit, Department of Woman's and Child's Health, University of Padova, Padova, Italy.

Luca Zammataro (L)

3 School of Medicine, Yale University, New Haven, Connecticut.

Eleonora Losiouk (E)

4 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Giordano Lanzola (G)

4 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Kristen Kraemer (K)

1 Department of Pediatrics, Yale University, New Haven, Connecticut.

Andrea Facchinetti (A)

5 Department of Information Engineering, University of Padova, Padova, Italy.

Beatrice Galeazzo (B)

2 Neonatal Intensive Care Unit, Department of Woman's and Child's Health, University of Padova, Padova, Italy.

Valentina Favero (V)

2 Neonatal Intensive Care Unit, Department of Woman's and Child's Health, University of Padova, Padova, Italy.

Eugenio Baraldi (E)

2 Neonatal Intensive Care Unit, Department of Woman's and Child's Health, University of Padova, Padova, Italy.

Claudio Cobelli (C)

5 Department of Information Engineering, University of Padova, Padova, Italy.

Daniele Trevisanuto (D)

2 Neonatal Intensive Care Unit, Department of Woman's and Child's Health, University of Padova, Padova, Italy.

Garry M Steil (GM)

6 Harvard Medical School and Boston Children's Hospital, Division of Medicine Critical Care, Boston, Massachusetts.

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Classifications MeSH