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Background <p>Despite significant advancements in neonatal care, mid to late preterm infants (32–36 weeks’ gestation) remain at high risk for recurrent respiratory tract infections (RRTIs). Current prevention strategies are limited by the absence of individualized risk assessment tools. This study aimed to identify critical perinatal risk factors and to develop a robust, clinically applicable prediction model for RRTI in this vulnerable population.</p> Methods <p>A retrospective cohort study was conducted at a tertiary care hospital, enrolling 288 preterm infants born between April 2023 and April 2024. Comprehensive maternal, perinatal, and postnatal data were extracted from electronic medical records and supplemented by structured caregiver interviews. A multivariable logistic regression analysis using a stepwise selection method (entry criterion: <i>P</i> &lt; 0.05; exit criterion: <i>P</i> &gt; 0.10) was performed to determine independent predictors of RRTI. The derived model was externally validated in a temporally distinct cohort (<i>n</i> = 100) from the same center. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.</p> Results <p>Seven independent predictors were retained in the final model: small-for-gestational-age (OR = 3.53, 95% CI: 1.41–11.61), intrauterine infection (OR = 4.22, 95% CI: 1.81–9.83), mechanical ventilation &gt; 72&#xa0;h (OR = 3.00, 95% CI: 1.27–7.14), prolonged antibiotic use (&gt; 30 days/year; OR = 2.23, 95% CI: 1.01–5.05), maternal passive smoking (OR = 2.91, 95% CI: 1.19–7.14), history of RSV infection (OR = 5.61, 95% CI: 2.24–14.08), and vaginal delivery as a protective factor (OR = 0.24, 95% CI: 0.08–0.71). The prediction model demonstrated excellent discriminatory performance with an AUC of 0.935 in the training cohort and 0.927 in the validation cohort. Overall accuracy was 75.3% for the training set and 82.0% for the validation set.</p> Conclusions <p>This study presents a novel risk stratification tool that effectively identifies high-risk moderate-to-late preterm infants and facilitates targeted interventions, such as RSV prophylaxis and enhanced immune monitoring. This advancement enables tailored RSV immunoprophylaxis planning in low-resource Asian NICUs. Nonetheless, further multi-center validation studies are warranted to confirm the model’s generalizability and to refine its predictive accuracy for broader clinical application.</p>

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Development and validation of a perinatal risk prediction model for recurrent respiratory tract infections in moderate-to-late preterm infants: a retrospective cohort study

  • 期刊论文

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  • 发表日期：

    2025年8月29日

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  • Hongli Yang,
  • Yuqi Wang,
  • Linlin Fu,
  • Ximeng Zhang,
  • Congcong Zhi

摘要

Background

Despite significant advancements in neonatal care, mid to late preterm infants (32–36 weeks’ gestation) remain at high risk for recurrent respiratory tract infections (RRTIs). Current prevention strategies are limited by the absence of individualized risk assessment tools. This study aimed to identify critical perinatal risk factors and to develop a robust, clinically applicable prediction model for RRTI in this vulnerable population.

Methods

A retrospective cohort study was conducted at a tertiary care hospital, enrolling 288 preterm infants born between April 2023 and April 2024. Comprehensive maternal, perinatal, and postnatal data were extracted from electronic medical records and supplemented by structured caregiver interviews. A multivariable logistic regression analysis using a stepwise selection method (entry criterion: P < 0.05; exit criterion: P > 0.10) was performed to determine independent predictors of RRTI. The derived model was externally validated in a temporally distinct cohort (n = 100) from the same center. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

Results

Seven independent predictors were retained in the final model: small-for-gestational-age (OR = 3.53, 95% CI: 1.41–11.61), intrauterine infection (OR = 4.22, 95% CI: 1.81–9.83), mechanical ventilation > 72 h (OR = 3.00, 95% CI: 1.27–7.14), prolonged antibiotic use (> 30 days/year; OR = 2.23, 95% CI: 1.01–5.05), maternal passive smoking (OR = 2.91, 95% CI: 1.19–7.14), history of RSV infection (OR = 5.61, 95% CI: 2.24–14.08), and vaginal delivery as a protective factor (OR = 0.24, 95% CI: 0.08–0.71). The prediction model demonstrated excellent discriminatory performance with an AUC of 0.935 in the training cohort and 0.927 in the validation cohort. Overall accuracy was 75.3% for the training set and 82.0% for the validation set.

Conclusions

This study presents a novel risk stratification tool that effectively identifies high-risk moderate-to-late preterm infants and facilitates targeted interventions, such as RSV prophylaxis and enhanced immune monitoring. This advancement enables tailored RSV immunoprophylaxis planning in low-resource Asian NICUs. Nonetheless, further multi-center validation studies are warranted to confirm the model’s generalizability and to refine its predictive accuracy for broader clinical application.