A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia

Gürsoy, Tuğba; Hayran, Mutlu; Derin, Hatice; Ovalı, Fahri

doi:10.1055/s-0034-1393935

Cited by 21 publications

(7 citation statements)

References 26 publications

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“…Despite these efforts, no reliable and reproducible risk stratification model has been found that would allow an early diagnosis and ease of application within NICUs. A single predictive factor cannot accurately predict the BPD outcome because BPD is a multifactorial disease, and therefore current multivariate modeling demonstrates that the greater the number of clinical predictor variables, the better the modeling results tend to be (Laughon et al, 2011;Gursoy et al, 2015). However, this also increases complexity and impairs practicality in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…(AUROC) ranged from 0.50 to 0.76 for BPD in an external validation study of a systematic review (Onland et al, 2013). Additionally, the ratio of tidal expiratory flow at 50% of expired volume to peak tidal expiratory flow (as one of the mechanical ventilation parameters) gave an AUROC for the development of moderate/severe BPD (sBPD) of 0.774 (Bentsen et al, 2018); mechanical ventilation at 1 week provided an AUROC for the development of BPD and sBPD of 0.77 and 0.83 (Hunt et al, 2018), respectively; and a multifactorial model that included BW, GA, sex, presence of a hemodynamically significant patent ductus arteriosus (as diagnosed by an echocardiogram), respiratory distress syndrome, hypotension within the first 72 h of life, and intraventricular hemorrhage (IVH) delivered an AUROC for the development of BPD of 0.930-which showed a noticeably improved discriminatory performance, but without favorable clinical maneuverability (Gursoy et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information

et al. 2021

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BackgroundAn early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. The risk prediction models nowadays for BPD risk that included only clinical factors but without genetic factors are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately.MethodsExome sequencing was performed in a cohort of 245 premature infants (gestational age <32 weeks), with 131 BPD infants and 114 infants without BPD as controls. A gene burden test was performed to find risk genes with loss-of-function mutations or missense mutations over-represented in BPD and severe BPD (sBPD) patients, with risk gene sets (RGS) defined as BPD–RGS and sBPD–RGS, respectively. We then developed two predictive models for the risk of BPD and sBPD by integrating patient clinical and genetic features. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC).ResultsThirty and 21 genes were included in BPD–RGS and sBPD–RGS, respectively. The predictive model for BPD, which combined the BPD–RGS and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUROC, 0.915 vs. AUROC, 0.814, P = 0.013, respectively) in the independent testing dataset. The same was observed in the predictive model for sBPD (AUROC, 0.907 vs. AUROC, 0.826; P = 0.016).ConclusionThis study suggests that genetic information contributes to susceptibility to BPD. The predictive model in this study, which combined BPD–RGS with basic clinical risk factors, can thus accurately stratify BPD risk in premature infants.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information

et al. 2021

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“…Unfortunately, no single biomarker or combination of biomarkers with a good predictive value has been found. A great number of predictive models using clinical variables have been developed, but none has been able to determine which high-risk patients will eventually develop BPD [13, 14, 24]. Most predictive models agree on a number of BPD risk factors, such as birth weight, GA, chorioamnionitis, preeclampsia, respiratory parameters, etc.…”

Section: Discussionmentioning

confidence: 99%

Exploring clinical, echocardiographic and molecular biomarkers to predict bronchopulmonary dysplasia

et al. 2019

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Introduction Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease in childhood, related to prematurity, and the most common cause of pulmonary hypertension (PH) secondary to pulmonary disease in children. Moderate and severe BPD have a worse outcome and relate more frequently with PH. The prediction of moderate or severe BPD development in extremely premature newborns is vital to implement preventive strategies. Starting with the hypothesis that molecular biomarkers were better than clinical and echocardiographic factors, this study aims to explore the ability of clinical, echocardiographic and analytical variables to predict moderate or severe BPD in a cohort of extremely preterm infants. Patients and methods We designed a prospective longitudinal study, in which we followed a cohort of preterm newborns (gestational age <28 weeks and weight ≤ 1250 grams). In these newborns we recorded weekly clinical and echocardiographic variables as well as blood and tracheal aspirate samples, to analyze molecular biomarkers (IL-6, IL-1, IP10, uric acid, HGF, endothelin-1, VEGF, CCL5). Variables and samples were collected since birth up to week 36 (postmenstrual age), time-point at which the diagnosis of BPD is established. Results We included 50 patients with a median gestational age of 26 weeks (IQR 25–27) and weight of 871 g (SD 161,0) (range 590-1200g). Three patients were excluded due to an early death. Thirty-five patients (74.5%) developed BPD (mild n = 14, moderate n = 15, severe n = 6). We performed a logistic regression in order to identify risk factors for moderate or severe BPD. We compared two predictive models, one with two variables (mechanical ventilation and inter-ventricular septum flattening), and another-one with an additional molecular biomarker (ET-1). Conclusions The combination of clinical and echocardiographic variables is a valuable tool for determining the risk of BPD. We find the two variable model (mechanical ventilation and echocardiographic signs of PH) more practical for clinical and research purposes. Future research on BPD prediction should be oriented to explore the potential role of ET-1.

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“…Some researchers came to the same conclusion (16). Respiratory distress syndrome (RDS) is a common cause of morbidity and mortality related to premature birth and most infants who develop BPD initially suffer from acute RDS (17). Infants with RDS may easily have acidosis due to adverse pulmonary ventilation.…”

Section: Postnatal Characteristicsmentioning

confidence: 99%

A risk factor analysis on disease severity in 47 premature infants with bronchopulmonary dysplasia

Cui

Wang

et al. 2015

IRDR

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Bronchopulmonary Dysplasia (BPD) is a rare chronic lung disease and one of the most difficult complications to treat in premature infants. With the progress at the medical treatment level, an increasing number of BPD premature infants are born, meanwhile, they would be at an increasing risk for numerous complications and rehospitalization because BPD affects many vital organ systems. The pathogenesis of BPD is clearly multifactorial. As the prognosis is closely connected with the severity of BPD, early diagnosis and treatment are of great help to control the development of BPD. This article focuses on risk factors that could influence the severity of BPD in order to provide a reliable basis for early diagnosis, treatment, and better patient assessment.

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A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia

Abstract: With this easy to use scoring system, one can predict the neonate at risk for BPD at 72 hours of life and direct preventive measures toward these infants.

Cited by 21 publications

References 26 publications

Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information

Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information

Exploring clinical, echocardiographic and molecular biomarkers to predict bronchopulmonary dysplasia

A risk factor analysis on disease severity in 47 premature infants with bronchopulmonary dysplasia

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