Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data

Official title

Early Prediction of Bronchopulmonary Dysplasia Using Clinical Data From the First Three Postnatal Weeks in Preterm Infants: A Retrospective Study With Large Language Models

Overview

Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data from the First Three Postnatal Weeks with Large Language Models: A Retrospective Study This retrospective, observational study aims to evaluate the early prediction of bronchopulmonary dysplasia (BPD) in preterm infants using clinical data from the first, second, and third postnatal weeks. The study includes infants born before 32 weeks of gestation or weighing less than 1,500 grams, followed at the Neonatal Intensive Care Unit of Konya City Hospital. The study will compare the performance of different large language models (LLMs), including ChatGPT, Gemini, and Claude, in predicting BPD development. Clinical variables such as gestational age, birth weight, respiratory support, oxygen requirement, mechanical ventilation duration, and infection status will be used. Primary outcome: Accuracy of BPD risk prediction by each AI model compared to actual clinical outcomes. Secondary outcomes: Sensitivity and specificity of predictions, weekly prediction performance, and comparative performance among AI models. The results will provide insight into the potential clinical utility of AI-based approaches for early BPD risk assessment in preterm infants.

Detailed description

Premature birth remains a major risk factor for neonatal morbidity and mortality, with bronchopulmonary dysplasia (BPD) representing one of the most significant chronic pulmonary complications in very preterm infants. Despite advances in neonatal intensive care, early and accurate prediction of BPD remains challenging due to the multifactorial nature of its pathophysiology, involving respiratory support requirements, oxygen exposure, infection burden, and perinatal factors.

This retrospective study evaluates the feasibility of using large language models (LLMs) for early prediction of BPD based on structured clinical data extracted from neonatal intensive care unit (NICU) records. Clinical variables are organized into weekly datasets corresponding to the first, second, and third postnatal weeks to capture the dynamic evolution of respiratory status and clinical condition over time.

Standardized and anonymized patient-level datasets are formatted into structured prompts and provided to multiple LLMs (ChatGPT, Gemini, and Claude). Each model receives identical input variables to ensure comparability. The models are instructed to generate categorical risk stratification (low, medium, high) along with corresponding probability estimates for BPD development.

To ensure methodological consistency, prompt engineering is standardized across all models and time points. Outputs are recorded for each weekly time window, allowing temporal comparison of predictive performance and assessment of how early postnatal data influences model accuracy.

Model outputs are subsequently compared with confirmed clinical outcomes of BPD development in the study population. Performance evaluation focuses on discriminative ability and calibration of predictions across different time points and models.

This design enables a systematic assessment of the potential role of LLM-based approaches in neonatal risk stratification and provides insight into their applicability as supportive clinical decision-making tools in neonatal intensive care settings.

Interventions

• Other Artificial Intelligence-Based Risk Prediction
  Different large language models (ChatGPT, Gemini, Claude) will analyze retrospective clinical data to predict the risk of bronchopulmonary dysplasia (BPD). This is an observational evaluation; no experimental treatment or therapy is administered.

Primary outcome measures

• Accuracy of bronchopulmonary dysplasia (BPD) risk prediction by artificial intelligence (AI) models in preterm infants. [Time frame: Postnatal weeks 1, 2, and 3]

Secondary outcome measures (3)

• Sensitivity and specificity of AI predictions [Time frame: Postnatal weeks 1, 2, and 3]
• Comparison of prediction accuracy across postnatal weeks [Time frame: Postnatal weeks 1, 2, and 3]
• Comparative performance of different AI models [Time frame: Postnatal weeks 1, 2, and 3]

Eligibility criteria

Inclusion criteria

• Preterm infants born before 32 weeks of gestation or with birth weight <1,500 grams
• Admitted and followed in the Neonatal Intensive Care Unit (NICU) of Konya City Hospital
• Availability of complete clinical data in hospital records
• Documented bronchopulmonary dysplasia (BPD) outcome status

Exclusion criteria

• Presence of major congenital anomalies
• Incomplete or missing clinical data
• Death shortly after birth with insufficient follow-up data to determine BPD status

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Turkey (Türkiye) · 1 center

• Konya City Hospital, İstiklal, Adana Çevre Yolu Cd. No:135/1 — Konya

Publications

• 1. Dai D, Chen H, Dong X, Chen J, Mei M, Lu Y, et al. Bronchopulmonary dysplasia predicted by developing a machine learning model of genetic and clinical information. Front Genet. 2021;12:689071. 2. Choi HJ, Lee G, Shin SH, Lee SM, Lee HC, Sohn JA, et al. Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors. Sci Rep. 2025;15:13

Identifiers

NCT: NCT07525167 · KSH_MB_2026_1

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗