Risk Factors and Clinical Characteristics of Metabolic Bone Disease of Prematurity

Mutlu, Mehmet; Aktürk-Acar, Filiz; Kader, Şebnem; Aslan, Yakup; Karagüzel, Gülay

doi:10.1055/s-0041-1729559

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…A total of 467 documents were obtained from the preliminary search. According to the inclusion and exclusion criteria, 15 articles were finally included [ 9 – 23 ], including 5 Chinese articles [ 9 – 13 ] and 8 English articles [ 14 – 19 , 22 , 23 ], 2 Spanish article [ 20 , 21 ]. The literature screening process and results are shown in Fig 1 .…”

Section: Resultsmentioning

confidence: 99%

Risk factors for metabolic bone disease of prematurity: A meta-analysis

et al. 2022

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Objective To investigate the risk factors for metabolic bone disease of prematurity (MBDP), and to provide a reference for the prevention of MBDP. Methods The databases including China Biomedical Literature Service System, China National Knowledge Infrastructure, Wanfang Data, and Weipu Periodical Database, PubMed, Web of Science, Embase, Cochrane Library and other databases were searched for studies on the risk factors for MBDP published up to June 18, 2021. RevMan 5.3 and Stata 14.1 software were used to perform a Meta analysis. Results A total of 15 articles were included, including 13 case-control studies, 1 current investigation, and 1 retrospective cohort study. There were 1,435 cases in the case group and 2,057 cases in the control group, with a total sample size of 3,492 cases. Meta analysis showed that risk factors for MBDP include birth weight <1000g (OR = 6.62, 95%CI: 2.28–19.25), gestational age <32 weeks (OR = 2.73, 95%CI: 1.07–6.95), septicemia (OR = 2.53, 95%CI: 1.69–3.79), parenteral nutrition time (OR = 4.04, 95%CI: 1.72–9.49), cholestasis (OR = 3.50, 95%CI: 1.49–8.23), intrauterine growth retardation (OR = 6.89, 95%CI: 3.81–12.44), while the birth weight(OR = 0.44, 95%CI: 0.21–0.90) and gestational age (OR = 0.57, 95%CI: 0.44–0.73)are the protective factors of MBDP. Conclusion Factors like birth weight <1000g, gestational age <32 weeks, septicemia, parenteral nutrition time, cholestasis, and intrauterine growth retardation may increase the risk of metabolic bone disease of prematurity.

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Section: Resultsmentioning

confidence: 99%

Risk factors for metabolic bone disease of prematurity: A meta-analysis

et al. 2022

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“… 8 , 9 In addition, the late onset of clinical symptoms and signs combined with the lack of specific biochemical markers make early recognition of MBD challenging. 3 , 10 Although numerous epidemiologic studies 7 , 11 , 12 , 13 have identified several risk factors for MBD during the neonatal period, little work has evaluated the prediction of their development. In addition, traditional risk prediction models are limited in forecasting the probability of an outcome, 14 especially a multifactorial disease with a context of large, high-dimensional, and imbalanced data set.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling to Predict Neonatal Metabolic Bone Disease in the Prenatal and Postnatal Periods

Jiang

Guo

et al. 2023

JAMA Netw Open

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ImportanceEarly recognition of metabolic bone disease (MBD) in infants is necessary but difficult; an appropriate tool to screen infants at risk of developing MBD is needed.ObjectivesTo develop a predictive model for neonates at risk for MBD in the prenatal and postnatal periods and detect the pivotal exposed factors in each period.Design, Setting, and ParticipantsA diagnostic study was conducted from January 1, 2012, to December 31, 2021, in Shanghai, China. A total of 10 801 pregnant women (singleton pregnancy, followed up until 1 month after parturition) and their infants (n = 10 801) were included. An artificial neural network (ANN) framework was used to build 5 predictive models with different exposures from prenatal to postnatal periods. The receiver operating characteristic curve was used to evaluate the model performance. The importance of each feature was examined and ranked.ResultsOf the 10 801 Chinese women who participated in the study (mean [SD] age, 29.7 [3.9] years), 7104 (65.8%) were local residents, 1001 (9.3%) had uterine scarring, and 138 (1.3%) gave birth to an infant with MBD. Among the 5 ANN models, model 1 (significant prenatal and postnatal factors) showed the highest AUC of 0.981 (95% CI, 0.970-0.992), followed by model 5 (postnatal factors; AUC, 0.977; 95% CI, 0.966-0.988), model 4 (all prenatal factors; AUC, 0.850; 95% CI, 0.785-0.915), model 3 (gestational complications or comorbidities and medication use; AUC, 0.808; 95% CI, 0.726-0.891), and model 2 (maternal nutritional conditions; AUC, 0.647; 95% CI, 0.571-0.723). Birth weight, maternal age at pregnancy, and neonatal disorders (anemia, respiratory distress syndrome, and septicemia) were the most important model 1 characteristics for predicting infants at risk of MBD; among these characteristics, extremely low birth weight (importance, 50.5%) was the most powerful factor. The use of magnesium sulfate during pregnancy (model 4: importance, 21.2%) was the most significant predictor of MBD risk in the prenatal period.Conclusions and RelevanceIn this diagnostic study, ANN appeared to be a simple and efficient tool for identifying neonates at risk for MBD. Combining prenatal and postnatal factors or using postnatal exposures alone provided the most precise prediction. Extremely low birth weight was the most significant predictive factor, whereas magnesium sulfate use during pregnancy could be an important bellwether for MBD before delivery.

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“…MBD mostly occurs 10-16 weeks after birth [3]. In the short term, bone loss can lead to prolonged hospital stay, ventilator dependence, pathological fractures, which are an important type of fractures in infants within 6 months of birth, and short stature [7][8][9]. In clinical work, insu cient early identi cation of MBD in premature infants may delay treatment and lead to serious consequences.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a nomogram model for predicting metabolic bone disease in preterm infants

Liang

Zhuo

Zhou

et al. 2023

Preprint

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Objective To investigate risk factors for metabolic bone disease (MBD) in preterm infants and establish a nomogram model for predicting MBD risk. Methods A total of 1104 preterm infants were enrolled, among whom 809 were as modelling set and 295 were as validation set. The modelling set was divided into the MBD (n = 185) and non-MBD (n = 624) groups. A multivariate logistic regression analysis was used to investigate the independent risk factors for MBD. R software was used to plot the nomogram model, which was then validated by the data of the validation set. The receiver operating characteristic (ROC) and calibration curves were used to evaluate the nomogram model’s performance, and the clinical decision curve was used to assess the clinical practicability of the model. Results Gestational age, time of trophic feeding initiation, parenteral nutrition duration, necrotizing enterocolitis, bronchopulmonary dysplasia, cholestasis and sepsis were independent risk factors for MBD in preterm infants (P < 0.05). The ROC curve of the modelling set had an area under the curve (AUC) of 0.801; the risk prediction value of 0.196 corresponding to the maximum Youden index was the best value, and the prediction critical value was 125 points. The ROC curve of the validation set had an AUC of 0.844. The calibration curve analysis showed good accuracy and consistency between the model’s predicted and actual values. Conclusions The nomogram model provides an efficient tool for the early assessment of MBD risk. Preterm infants with scores ≥ 125 should receive close attention and interventions in the early stage.

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Risk Factors and Clinical Characteristics of Metabolic Bone Disease of Prematurity

Cited by 7 publications

References 19 publications

Risk factors for metabolic bone disease of prematurity: A meta-analysis

Risk factors for metabolic bone disease of prematurity: A meta-analysis

Artificial Neural Network Modeling to Predict Neonatal Metabolic Bone Disease in the Prenatal and Postnatal Periods

Establishment of a nomogram model for predicting metabolic bone disease in preterm infants

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