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Published on: January 27, 2023
Risk stratification of hemodynamically significant patent ductus arteriosus by clinical and genetic factors
Yu-Xi Chen1, Tian-Tian Xiao2, Hui-Yao Chen3
1Center for Molecular Medicine of Children's Hospital of Fudan University, Institutes of Biomedical Sciences, Fudan University, 138 Yi Xue Yuan Road, Shanghai, China.
Insights
Early identification of high-risk hemodynamically significant patent ductus arteriosus (hsPDA) in neonates is crucial. Clinical and genetic models accurately predict hsPDA risk within three days of life, improving early intervention strategies.
Area of Science:
- Neonatal Medicine
- Genetics
- Cardiology
Background:
- Hemodynamically significant patent ductus arteriosus (hsPDA) is linked to increased neonatal comorbidities.
- Early risk assessment for hsPDA is vital for timely, individualized interventions.
- This study aimed to identify high-risk hsPDA populations for early treatment.
Purpose of the Study:
- To develop predictive models for early identification of high-risk hsPDA.
- To establish a reference for early treatment decisions in neonates with PDA.
- To explore the utility of genetic factors in hsPDA risk stratification.
Main Methods:
- Retrospective cohort study of 2199 infants with PDA.
- Exome sequencing and collapsing analyses to identify a risk gene set (RGS) for hsPDA.
- Multivariate logistic regression combining clinical and genetic features, evaluated by AUC and DCA.
Main Results:
- A clinical model using six variables (including gestational age and respiratory distress syndrome) achieved an AUC of 0.790 within three days of life.
- A simplified clinical model (gestational age, RDS) had an AUC of 0.753.
- Integrating the RGS significantly improved model performance (AUC 0.817) compared to clinical factors alone.
Conclusions:
- Clinical models effectively stratify hsPDA risk in neonates within the first three days of life.
- Genetic features, identified as RGS, can further enhance the predictive accuracy of these models.
- The developed models are clinically useful for early hsPDA risk assessment and management.
Background:
Hemodynamically significant patent ductus arteriosus (hsPDA) is associated with increased comorbidities in neonates. Early evaluation of hsPDA risk is critical to implement individualized intervention. The aim of the study was to provide a powerful reference for the early identification of high-risk hsPDA population and early treatment decisions.
Methods:
We enrolled infants who were diagnosed with PDA and performed exome sequencing. The collapsing analyses were used to find the risk gene set (RGS) of hsPDA for model construction. The credibility of RGS was proven by RNA sequencing. Multivariate logistic regression was performed to establish models combining clinical and genetic features. The models were evaluated by area under the receiver operating curve (AUC) and decision curve analysis (DCA).
Results:
In this retrospective cohort study of 2199 PDA patients, 549 (25.0%) infants were diagnosed with hsPDA. The model [all clinical characteristics selected by least absolute shrinkage and selection operator regression (all CCs)] based on six clinical variables was acquired within three days of life, including gestational age (GA), respiratory distress syndrome (RDS), the lowest platelet count, invasive mechanical ventilation, and positive inotropic and vasoactive drugs. It has an AUC of 0.790 [95% confidence interval (CI) = 0.749-0.832], while the simplified model (basic clinical characteristic model) including GA and RDS has an AUC of 0.753 (95% CI = 0.706-0.799). There was a certain consistency between RGS and differentially expressed genes of the ductus arteriosus in mice. The AUC of the models was improved by RGS, and the improvement was significant (all CCs vs. all CCs + RGS: 0.790 vs. 0.817, P < 0.001). DCA demonstrated that all models were clinically useful.
Conclusions:
Models based on clinical factors were developed to accurately stratify the risk of hsPDA in the first three days of life. Genetic features might further improve the model performance. Video Abstract (MP4 86834 kb).
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