Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review

Peng, Haibo; Zhan, Yuan-Li; Chen, You; Jin, Zhen-Chao; Liu, Fang; Wang, Bo; Yu, Zhangbin

doi:10.3389/fped.2022.856159

Cited by 11 publications

(12 citation statements)

References 57 publications

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“…Only infants with moderate or severe BPD were considered to have BPD; as a result, we anticipated that our findings would not differ remarkably from those reported in the current literature. In a sys-tematic review of BPD prediction models, oxygen dependency at a PMA of 36 weeks was the most frequently reported outcome [16]. Our findings are consistent with most of the studies mentioned in that review.…”

Section: Discussionsupporting

confidence: 90%

“…Despite advances in neonatal and perinatal management, BPD remains a major cause of morbidity and mortality in preterm infants. It is essential to further Prediction Model for Bronchopulmonary Dysplasia develop preventive measures and treatment approaches in the early days of life when airway injuries are still reversible [16]. In addition, earlier prediction of BPD may help individualize neonatal care and optimize precision medicine.…”

Section: Discussionmentioning

confidence: 99%

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An Early Prediction Model for Estimating Bronchopulmonary Dysplasia in Preterm Infants

Kostekci,

Bakırarar,

Okulu

et al. 2023

Neonatology

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Introduction: Accurate assessment of the risk for bronchopulmonary dysplasia (BPD) is critical to determine the prognosis and identify infants who will benefit from preventive therapies. Clinical prediction models can support the identification of high-risk patients. In this study, we investigated the potential risk factors for BPD and compared machine learning models for predicting the outcome of BPD/death on days 1, 7, 14, and 28 in preterm infants. We also developed a local BPD estimator. Methods: This study involved 124 infants. We evaluated the composite outcome of BPD/death at a postmenstrual age of 36 weeks and identified risk factors that would improve BPD/death prediction. SPSS for Windows Version 11.5 and Weka 3.9 software were used for the data analysis. Results: To evaluate the combined effect of all variables, all risk factors were taken into consideration. Gestational age, birth weight, mode of respiratory support, intraventricular hemorrhage, necrotizing enterocolitis, surfactant requirement, and late-onset sepsis were risk factors on postnatal days 7, 14, and 28. In a comparison of four different time points (postnatal days 1, 7, 14, and 28), the day 7 model provided the best prediction. According to this model, when a patient was diagnosed with BPD/death, the accuracy rate was 89.5%. Conclusion: The postnatal day 7 model was the best predictor of BPD or death. Future validation studies will help identify infants who may benefit from preventive therapies and develop individualized care.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

An Early Prediction Model for Estimating Bronchopulmonary Dysplasia in Preterm Infants

Kostekci,

Bakırarar,

Okulu

et al. 2023

Neonatology

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“…It should be noted that the use of a competing risks methodology could provide a more unbiased estimate of the probability of BPD and the HR associated with each of the covariates considered, which may lead to a better predictive ability. On the other hand, all variables finally included in the models have been previously described in the literature as risk factors for the disease and are biologically plausible ( 9 ). In this respect, we would like to highlight the simplicity of our models, which consist of a limited number of objective clinical variables, recorded at bedside during the first days of life.…”

Section: Discussionmentioning

confidence: 99%

“…Good-quality predictive models for BPD aim to establish an individual risk of BPD, helping clinicians to identify high-risk patients and individualize care, and hopefully enabling more effective preventive strategies. While a plethora of predictive models have been developed in recent decades ( 9 ), none are fully incorporated into routine clinical practice ( 5 , 10 ) and all suffer from a high risk of bias ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of bronchopulmonary dysplasia in very preterm infants: competitive risk model nomogram

Sucasas-Alonso,

Pértega-Díaz,

Balboa-Barreiro

et al. 2024

Front. Pediatr.

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ObjectiveTo develop predictive clinical models of bronchopulmonary dysplasia (BPD) through competing risk analysis.MethodsRetrospective observational cohort study, including preterm newborns ≤32 weeks gestational age, conducted between January 1, 2013 and September 30, 2022 in a third-level Neonatal Intensive Care Unit in Spain. A prediction study was carried out using competing risk models, where the event of interest was BPD and the competing event was death. A multivariate competing risk model was developed separately for each postnatal day (days 1, 3, 7 and 14). Nomograms to predict BPD risk were developed from the coefficients of the final models and internally validated.ResultsA total of 306 patients were included in the study, of which 73 (23.9%) developed BPD and 29 (9.5%) died. On day 1, the model with the greatest predictive capacity was that including birth weight, days since rupture of membranes, and surfactant requirement (area under the receiver operating characteristic (ROC) curve (AUC), 0.896; 95% CI, 0.792–0.999). On day 3, the final predictive model was based on the variables birth weight, surfactant requirement, and Fraction of Inspired Oxygen (FiO2) (AUC, 0.891; 95% CI, 0.792–0.989).ConclusionsCompeting risk analysis allowed accurate prediction of BPD, avoiding the potential bias resulting from the exclusion of deceased newborns or the use of combined outcomes. The resulting models are based on clinical variables measured at bedside during the first 3 days of life, can be easily implemented in clinical practice, and can enable earlier identification of patients at high risk of BPD.

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“…In their analysis of 26 studies, Onland et al ( 10 ) identified that predictive models for BPD lacked calibration. Peng et al ( 18 ) reported on 18 models for predicting BPD or death in preterm born at ≤32 weeks' gestation, most of which had many methodological shortcomings and lacked calibration assessment. To address these issues, our study included validation cohorts and calibration to enhance the predictive value of our model.…”

Section: Discussionmentioning

confidence: 99%

A prediction nomogram for moderate-to-severe bronchopulmonary dysplasia in preterm infants < 32 weeks of gestation: A multicenter retrospective study

Zhang

Wei

et al. 2023

Front. Pediatr.

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BackgroundModerate-to-severe bronchopulmonary dysplasia (msBPD) is a serious complication in preterm infants. We aimed to develop a dynamic nomogram for early prediction of msBPD using perinatal factors in preterm infants born at <32 weeks' gestation.MethodsThis multicenter retrospective study conducted at three hospitals in China between January 2017 and December 2021 included data on preterm infants with gestational age (GA) < 32 weeks. All infants were randomly divided into training and validation cohorts (3:1 ratio). Variables were selected by Lasso regression. Multivariate logistic regression was used to build a dynamic nomogram to predict msBPD. The discrimination was verified by receiver operating characteristic curves. Hosmer-Lemeshow test and decision curve analysis (DCA) were used for evaluating calibration and clinical applicability.ResultsA total of 2,067 preterm infants. GA, Apgar 5-min score, small for gestational age (SGA), early onset sepsis, and duration of invasive ventilation were predictors for msBPD by Lasso regression. The area under the curve was 0.894 (95% CI 0.869–0.919) and 0.893 (95% CI 0.855–0.931) in training and validation cohorts. The Hosmer−Lemeshow test calculated P value of 0.059 showing a good fit of the nomogram. The DCA demonstrated significantly clinical benefit of the model in both cohorts. A dynamic nomogram predicting msBPD by perinatal days within postnatal day 7 is available at https://sdxxbxzz.shinyapps.io/BPDpredict/.ConclusionWe assessed the perinatal predictors of msBPD in preterm infants with GA < 32 weeks and built a dynamic nomogram for early risk prediction, providing clinicians a visual tool for early identification of msBPD.

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Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review

Cited by 11 publications

References 57 publications

An Early Prediction Model for Estimating Bronchopulmonary Dysplasia in Preterm Infants

An Early Prediction Model for Estimating Bronchopulmonary Dysplasia in Preterm Infants

Prediction of bronchopulmonary dysplasia in very preterm infants: competitive risk model nomogram

A prediction nomogram for moderate-to-severe bronchopulmonary dysplasia in preterm infants < 32 weeks of gestation: A multicenter retrospective study

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