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Published on: February 18, 2012
Delineating morbidity patterns in preterm infants at near-term age using a data-driven approach
Octavia-Andreea Ciora1, Tanja Seegmüller2, Johannes S Fischer3
1Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany. octavia.ciora@iks.fraunhofer.de.
Insights
This study reveals complex morbidity profiles in preterm infants, identifying shared patterns and risk factors beyond pairwise associations. Machine learning identified distinct infant subgroups with similar co-occurring conditions, aiding personalized monitoring strategies.
Area of Science:
- Neonatology
- Pediatric Critical Care
- Perinatal Medicine
Background:
- Premature birth survival is linked to cardio-respiratory and central nervous system morbidities.
- Existing research on infant morbidities is limited to pairwise associations, missing holistic co-occurrence patterns.
- Understanding comprehensive morbidity profiles is crucial for long-term outcomes in very preterm infants.
Purpose of the Study:
- To delineate and characterize comprehensive morbidity profiles in preterm infants at near-term age.
- To investigate prevalent morbidities including bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), cardiac defects, brain pathology, and retinopathy of prematurity (ROP).
- To identify shared morbidity patterns and risk profiles using a data-driven approach and machine learning.
Main Methods:
- Analysis of two independent prospective cohorts (AIRR and NEuroSIS) totaling 530 very preterm infants.
- Quantification of pairwise morbidity correlations and assessment of BPD's discriminatory power.
- Application of machine learning to identify infant subgroups with similar morbidity profiles.
Main Results:
- Bronchopulmonary dysplasia (BPD) and retinopathy of prematurity (ROP) showed the highest pairwise correlation, followed by BPD with PH and cardiac defects.
- BPD demonstrated limited capacity in discriminating overall morbidity occurrence.
- Machine learning identified distinct patient clusters with shared morbidity patterns (6 in AIRR, 8 in NEuroSIS).
Conclusions:
- The study provides a comprehensive characterization of preterm infant morbidity profiles at discharge, linked to shared pathophysiology.
- Identified morbidity patterns and patient subgroups can inform the development of personalized monitoring strategies.
- Future research should focus on refining risk profiles for tailored interventions in preterm populations.
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.
Trial Registration:
AIRR: DRKS.de, DRKS00004600, 28/01/2013. NEuroSIS: ClinicalTrials.gov, NCT01035190, 18/12/2009.
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