The differences among various severities of BPD have been intensively investigated in recent years. Patients with severe BPD have worse prognoses and require more intensive care and follow-up. The prediction of severe BPD is of great importance for patients with BPD. Currently, there are dozens of models, few of which can distinguish BPD severity [8,9,10,11]. In the present study, we developed an early prediction model to distinguish the severity of BPD based on a five-year cohort at a level IV NICU. To our knowledge, this is the first prediction model for BPD severity based on a large sample in a Chinese population. The developed model enables an early prediction of BPD severity at 28 days of oxygen treatment.

In the present ordinal logistic regression analysis, we found that GDM, initial FiO2 value, invasive ventilation, acidosis, hypochloremia, CRP level, PDA and positive respiratory culture were independent risk factors for BPD severity.

The present univariable and multivariable analyses suggest that maternal DM may aggravate the clinical severity of BPD. Meta-analysis showed that hyperglycemia during pregnancy may indicate adverse outcomes in preterm infants, such as preterm birth and respiratory distress syndrome [21]. However, in the previous literature, controversy exists regarding whether GDM is related to BPD. A meta-analysis based on studies conducted in developed countries found no difference in the incidence of BPD between infants with and without GDM, and contrasting results have been reported in China [14, 22]. Given that poor DM management may lead to hormonal disorders and unstable glucose levels in neonates, differences in results between these studies may be related to disparities in pregnancy management. As GDM was a significant risk factor for BPD severity in the present study and there is no relevant previous study on BPD severity, further attention should be given to how maternal diabetes influences the infant’s lung development.

Respiratory support plays an important role in BPD development. Non-invasive methods were recommended by randomized controlled trials and meta-analyses to reduce the risk of BPD [21, 23]. Consistent with these studies, we found that intubation in the DR was related to BPD severity, which is probably because of its ventilator-induced lung injury, volutrauma and barotrauma [24]. We also found an initial high FiO2 to be an independent risk factor. Hyperoxia can cause the release of reactive oxygen species, resulting in both cellular and molecular damage to the lung. Thus, attention is needed to avoid intubation in DR. Strategies, such as less-invasive surfactant administration and CPAP, have already been proven to improve respiratory outcomes [24]. In addition, a recent study showed that better lung recruitment maneuvers may reduce the rate of intubation in DR [25].

Acid-based and electrolyte disturbances have been previously identified as risk factors for the development of BPD [26]. Hypochloremia and hyponatremia were related to BPD severity in the present study, which is consistent with previous studies [27]. Several studies have linked electrolyte disturbances to the use of diuretics; however, in the present study, we collected data within the first 14 days of life, when diuretics are seldom used. Thus, electrolyte disturbances in this cohort had a unique pathogenesis, rather than being secondary to therapy. Electrolytes are of paramount importance to both intra- and extracellular fluid balance as well as the normal function of cells. Some researchers suggest that electrolytes disrupt the normal function of cells, while others suggest that they may elevate arginine vasopressin levels, leading to pulmonary edema and, eventually, BPD [26, 28]. Most of the patients with BPD experienced acidosis to some extent in this study. According to the previous literature, acidosis may reflect immature renal function, complex metabolism, or respiratory failure [29,30,31]. Although the mechanism remains unclear, the present study results suggest the importance of close follow-up and the correction of the pH value and serum electrolyte levels in preterm infants.

Inducing systemic inflammatory responses, sepsis has been shown to increase the BPD incidence rate. In the present study, LOS was related to BPD severity, which is consistent with previous studies [32]. However, in the multivariable analysis, the inflammation indicator CRP replaced LOS as an independent risk factor. This is consistent with findings by Yang et al., which found that early CRP levels may be used as an early diagnostic marker for mild-to-severe BPD with an AUC of 0.867 [13]. Although culture remains the gold standard for sepsis, the positive rate is low, especially in early life. With advantages of convenience and quickness, CRP has proven to be a good biomarker for neonatal sepsis. In recent years, new inflammation indicators, such as presepsin, have been proposed to be more promising biomarker of neonatal sepsis [33].

Pulmonary infection also plays an important role in BPD pathology. Both Gram-positive and Gram-negative pathogenic bacteria were associated with an increased risk of greater BPD severity in the present study, while Gram-negative bacteria were more related to the outcome. In the current decade, researchers have focused on the role of respiratory flora in BPD; previous studies have shown a strong association between Gram-negative bacteria and poor respiratory outcomes in preterm infants [34, 35]. Lipopolysaccharide, a major component of the outer membrane of Gram-negative bacteria, interacts with cell-surface receptors, leading to an increased secretion of inflammatory mediators, prompting an inflammatory cascade and causing diffuse lung tissue damage and alveolar simplification [36]. Thus, investigations on methods to reduce Gram-negative bacteria in the airway of chronically high-risk preterm infants are critical.

In summary, the role of inflammation in the pathogenesis of BPD has been firmly established, and both localized and systemic infections were revealed as independent risk factors in the present study. Thus, clinicians should pay more attention to optimal anti-infection treatments, as well as strict nosocomial infection control.

Despite the role of infection in BPD, antibiotics may be a double-edged sword in clinical management. Ting et al. found that among infants without culture-proven sepsis, more antibiotic exposure was associated with adverse neonatal outcomes [37]. Consistent with this, Fajardo et al. found that antibiotic treatment for more than five days in preterm infants with negative cultures was associated with increased BPD risk [38]. In the present study, we found similar results that prolonged antibiotics increased the risk for BPD severity. According to previous studies, this may be because antibiotic use in preterm infants may lead to disruption of the neonatal microbiome development and an increased risk of pulmonary development failure [39, 40]. However, a recent study using stepwise hierarchical analyses suggested the existence of factors confounding the association between early antibiotic exposure and BPD. In other words, antibiotic treatment in early life is more likely a sign of illness severity than a true contributor to BPD.

Factors impeding alveolar maturation, such as BW and GA, are believed to be of prime importance in the pathology of BPD. Contrary to general impressions, the present multivariable analysis indicated no significant differences in GA among BPD severity groups. Since we restricted the cohort to patients with BPD, this result might be partly due to sample homogeneity. Most patients in this cohort were in the saccular stage [41]. Thus, postnatal factors may have displaced GA as the main risk factor.

In previous studies, male preterm infants were more prone to developing BPD than female preterm infants. Estrogen may promote lung maturation and stimulate the expression of PS [42]. In the present study, male neonates accounted for 61% of BPD cases, verifying the susceptibility in boys; however, sex was not related to the clinical severity of BPD according to our data.

Most existing prediction models were developed using traditional statistical methods. However, with computer science widely utilized in clinics, many centers have also attempted to establish BPD prediction models based on artificial intelligence technology in recent years. In 2021, a center in Denmark proposed an artificial intelligence model based on support vector machine learning to predict the occurrence of BPD; by combining postnatal clinical characteristics and exhaled gas nitrogen content, the accuracy reached approximately 90% [8]. Another center established a machine learning model for predicting BPD and severe BPD using clinical data and genomics, with an AUC of 0.872 [9]. Our model performed as well as the previous ones, and we have some advantages. First, the previous study took metabolomic and genomic indicators into account in the models, and the huge cost might render it difficult to popularize the previous models. In the present study, we selected easily accessed clinical factors and validated the findings by ordinal regression analysis, increasing the interpretability of our models. Second, we used different machine learning modeling methods, further supporting the reliability and generalizability of our models. Moreover, our sample size is larger than those of past machine learning studies, which can avoid overfitting risk and thereby biased results.

This study developed an early prediction model using simple selected clinical and laboratory factors. First, all the factors involved are easy to access, which means that the model can be applied in most hospitals even with variability in medical quality. Second, this study also drew attention to some factors related to BPD severity in our own population, which can help improve our clinical management.

The present study has some limitations. First, as a retrospective study, we missed some factors that might be related to BPD, such as chorioamnionitis, early extrauterine growth retardation and early fluid management. We may add the above factors in future prospective studies. Second, as a single-center study, the model still needs external validation in a new group.

In conclusion, in this single-center study, GDM, initial FiO2 value, invasive ventilation, acidosis, hypochloremia, CRP level, PDA and positive respiratory culture were independent risk factors for BPD severity and we developed machine learning models with good performance. With simple factors involved, these models may become a useful tool in clinical practice for the early stratification of BPD, which can help clinicians choose individualized treatment protocols and follow-up strategies in the Chinese populations.