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Aims <p>Invasive respiratory interventions save infant lives, yet have negative consequences for their health and neurodevelopment. Here we present the Prognostic Respiratory Intensity Scoring Metric (PRISM) as an accurate, summative tool of respiratory support needs in hospitalized neonates well suited for predicting an infant’s future neurodevelopmental outcomes.</p> Methods <p>218 (112 male) infants were included in this study. We trained a classification and regression tree model using PRISM scores to classify respiratory diagnoses, and compared performance to the Neonatal Sequential Organ Failure Assessment Respiratory subscore (nSOFA-r).</p> Results <p>PRISM outperforms the nSOFA-r in classifying infants with respiratory distress syndrome (PRISM AUC = 0.71, nSOFA-r AUC = 0.64) and chronic lung disease (PRISM AUC = 0.86, nSOFA-r AUC = 0.76). PRISM had significant associations with an infant’s length of hospital stay (β = 0.03 (95% CI [0.02, 0.03], <i>p</i> &lt; 0.001) and gestational age (β = -0.03 (95% CI [-0.03, -0.02], <i>p</i> &lt; 0.001). PRISM successfully identifies which infants will develop an intraventricular hemorrhage (83% accuracy) and retinopathy of prematurity (95% accuracy).</p> Interpretation <p>PRISM is a useful tool for clinicians and researchers alike to summarize the invasive nature of neonatal respiratory support.</p>

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Development and validation of the Prognostic Respiratory Intensity Scoring Metric (PRISM)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

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  • Madelyn G. Nance,
  • Carrington S. Davis,
  • Zoe G. Kitchings,
  • Chad Aldridge,
  • Santina Zanelli,
  • Jennifer Burnsed,
  • Meghan H. Puglia

摘要

Aims

Invasive respiratory interventions save infant lives, yet have negative consequences for their health and neurodevelopment. Here we present the Prognostic Respiratory Intensity Scoring Metric (PRISM) as an accurate, summative tool of respiratory support needs in hospitalized neonates well suited for predicting an infant’s future neurodevelopmental outcomes.

Methods

218 (112 male) infants were included in this study. We trained a classification and regression tree model using PRISM scores to classify respiratory diagnoses, and compared performance to the Neonatal Sequential Organ Failure Assessment Respiratory subscore (nSOFA-r).

Results

PRISM outperforms the nSOFA-r in classifying infants with respiratory distress syndrome (PRISM AUC = 0.71, nSOFA-r AUC = 0.64) and chronic lung disease (PRISM AUC = 0.86, nSOFA-r AUC = 0.76). PRISM had significant associations with an infant’s length of hospital stay (β = 0.03 (95% CI [0.02, 0.03], p < 0.001) and gestational age (β = -0.03 (95% CI [-0.03, -0.02], p < 0.001). PRISM successfully identifies which infants will develop an intraventricular hemorrhage (83% accuracy) and retinopathy of prematurity (95% accuracy).

Interpretation

PRISM is a useful tool for clinicians and researchers alike to summarize the invasive nature of neonatal respiratory support.