Abstract
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. The participants were divided into an sPDA group and an asymptomatic PDA or spontaneously close PDA (nPDA) group. The sPDA group was further divided into treated and untreated subgroups. A total of 47 perinatal risk factors were collected and analyzed. Multiple logistic regression was used as a standard analytic tool, and five AI algorithms were used to identify the factors associated with sPDA. Combining a large database of risk factors from nationwide registries and AI techniques achieved higher accuracy and better performance of the PDA prediction tasks, and the ensemble methods showed the best performances.
Highlights
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations
This study aimed to investigate the perinatal risk factors leading to symptomatic PDA (sPDA) and sPDA treatments for very low birth weight (VLBW) infants in a nationwide cohort registry and to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis
light gradient boosting machine (L-GBM) achieved the highest performance at predicting sPDA/nPDA in terms of accuracy (0.77 [95% confidence intervals (CIs), 0.75–0.79]), area under the receiver operating curve (AUC) (0.82 [95% CI, 0.80–0.84]) and specificity (0.84 [95% CI, 0.81–0.86]), and multiple logistic regression (MLR) performed best with sensitivity (0.85 [95% CI, 0.83–0.87])
Summary
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample populations. We aimed to compare the performance of artificial intelligence (AI) analysis with that of conventional analysis to identify risk factors associated with symptomatic PDA (sPDA) in very low birth weight infants. This nationwide cohort study included 8369 very low birth weight (VLBW) infants. This study aimed to investigate the perinatal risk factors leading to sPDA and sPDA treatments for very low birth weight (VLBW) infants in a nationwide cohort registry and to compare the performance of AI analysis with that of conventional analysis. This study may support the idea that an integrated combination of Al and conventional analysis can synergistically aid clinical risk prediction and therapy selection in medicine
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