2023-12-27 カロリンスカ研究所(KI)
◆これにより、早期に最もリスクのある子供たちに対する予防措置の調整が可能となります。研究では、非スカンジナビアの母国語、長時間の人工呼吸器治療、授乳不足などが、NICU退院後2年での遅れた認知発達の重要なリスク要因として明らかにされました。
<関連情報>
- https://news.ki.se/cognitive-impairment-in-preterm-infants-can-be-predicted-at-the-time-of-discharge-from-the-nicu
- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2813140
機械学習法を用いた超早産児の2年間の認知アウトカムの予測 Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods
Andrea K. Bowe, Gordon Lightbody, Anthony Staines, Deirdre M. Murray, Mikael Norman
JAMA Network Open Published:December 26, 2023
DOI:10.1001/jamanetworkopen.2023.49111
Key Points
Question Can easily available neonatal data identify very preterm infants who will exhibit cognitive delay later in life?
Findings In this prognostic study of cognitive outcomes at 2-year follow-up among 1062 infants born very preterm, a logistic regression model containing 26 neonatal features identified 93% of very preterm infants who screened positive for cognitive delay at 2-year follow up, with a specificity of 46%.
Meaning Use of this model could target those very preterm infants at the highest risk of cognitive delay to receive early and effective intervention.
Abstract
Importance Early intervention can improve cognitive outcomes for very preterm infants but is resource intensive. Identifying those who need early intervention most is important.
Objective To evaluate a model for use in very preterm infants to predict cognitive delay at 2 years of age using routinely available clinical and sociodemographic data.
Design, Setting, and Participants This prognostic study was based on the Swedish Neonatal Quality Register. Nationwide coverage of neonatal data was reached in 2011, and registration of follow-up data opened on January 1, 2015, with inclusion ending on September 31, 2022. A variety of machine learning models were trained and tested to predict cognitive delay. Surviving infants from neonatal units in Sweden with a gestational age younger than 32 weeks and complete data for the Bayley Scales of Infant and Toddler Development, Third Edition cognitive index or cognitive scale scores at 2 years of corrected age were assessed. Infants with major congenital anomalies were excluded.
Exposures A total of 90 variables (containing sociodemographic and clinical information on conditions, investigations, and treatments initiated during pregnancy, delivery, and neonatal unit admission) were examined for predictability.
Main Outcomes and Measures The main outcome was cognitive function at 2 years, categorized as screening positive for cognitive delay (cognitive index score <90) or exhibiting typical cognitive development (score ≥90).
Results A total of 1062 children (median [IQR] birth weight, 880 [720-1100] g; 566 [53.3%] male) were included in the modeling process, of whom 231 (21.8%) had cognitive delay. A logistic regression model containing 26 predictive features achieved an area under the receiver operating curve of 0.77 (95% CI, 0.71-0.83). The 5 most important features for cognitive delay were non-Scandinavian family language, prolonged duration of hospitalization, low birth weight, discharge to other destination than home, and the infant not receiving breastmilk on discharge. At discharge from the neonatal unit, the full model could correctly identify 605 of 650 infants who would have cognitive delay at 24 months (sensitivity, 0.93) and 1081 of 2350 who would not (specificity, 0.46).
Conclusions and Relevance The findings of this study suggest that predictive modeling in neonatal care could enable early and targeted intervention for very preterm infants most at risk for developing cognitive impairment.
