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  4. Delineating morbidity patterns in preterm infants at near-term age using a data-driven approach
 
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2024
Journal Article
Title

Delineating morbidity patterns in preterm infants at near-term age using a data-driven approach

Abstract
Background: Long-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles.
Methods: Our study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP). For analysis, we employed two independent, prospective cohorts, comprising a total of 530 very preterm infants: AIRR ("Attention to Infants at Respiratory Risks") and NEuroSIS ("Neonatal European Study of Inhaled Steroids"). Using a data-driven strategy, we successfully characterized morbidity profiles of preterm infants in a stepwise approach and (1) quantified pairwise morbidity correlations, (2) assessed the discriminatory power of BPD (complemented by imaging-based structural and functional lung phenotyping) in relation to these morbidities, (3) investigated collective co-occurrence patterns, and (4) identified infant subgroups who share similar morbidity profiles using machine learning techniques.
Results: First, we showed that, in line with pathophysiologic understanding, BPD and ROP have the highest pairwise correlation, followed by BPD and PH as well as BPD and mild cardiac defects. Second, we revealed that BPD exhibits only limited capacity in discriminating morbidity occurrence, despite its prevalence and clinical indication as a driver of comorbidities. Further, we demonstrated that structural and functional lung phenotyping did not exhibit higher association with morbidity severity than BPD. Lastly, we identified patient clusters that share similar morbidity patterns using machine learning in AIRR (n=6 clusters) and NEuroSIS (n=8 clusters).
Conclusions: By capturing correlations as well as more complex morbidity relations, we provided a comprehensive characterization of morbidity profiles at discharge, linked to shared disease pathophysiology. Future studies could benefit from identifying risk profiles to thereby develop personalized monitoring strategies.
Author(s)
Ciora, Octavia
Fraunhofer-Institut für Kognitive Systeme IKS  
Seegmüller, Tanja
Ludwig-Maximilians-Universität München
Fischer, Johannes  
Fraunhofer-Institut für Kognitive Systeme IKS  
Wirth, Theresa
Fraunhofer-Institut für Kognitive Systeme IKS  
Häfner, Friederike
Ludwig-Maximilians-Universität München
Stoecklein, Sophia
Ludwig-Maximilians-Universität München
Flemmer, Andreas W.
Ludwig-Maximilians-Universität München
Förster, Kai
Ludwig-Maximilians-Universität München
Kindt, Alida
Leiden University
Bassler, Dirk
Hospital Tübingen
Poets, Christian F.
Tübingen University
Ahmidi, Narges
Fraunhofer-Institut für Kognitive Systeme IKS  
Hilgendorff, Anne
Ludwig-Maximilians-Universität München
Journal
BMC pediatrics  
Project(s)
IKS-Ausbauprojekt  
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Open Access
File(s)
Download (2.31 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1186/s12887-024-04702-5
10.24406/publica-2947
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • preterm birth

  • morbidity co-occurrence

  • bronchopulmonary dysplasia

  • risk profile correlation

  • morbidity correlation

  • clustering

  • machine learning

  • ML

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