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

Wang, Jie; Zhao, Qian; Chen, Baochang; Sun, Jingfei; Huang, Jiayu; Meng, Jinfeng; Li, Shangbin; Yan, Weichen; Ren, Changjun; Lü, Hao

doi:10.1371/journal.pone.0269180

Cited by 13 publications

(24 citation statements)

References 41 publications

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“…In this study, multivariate logistic regression analysis showed that GA was a risk factor for MBD. Several studies have shown that the risk of developing MBD is increased for neonates born at a GA < 32 weeks, and in preterm infants, the younger the GA and the lower the birth weight, the higher the incidence and severity of MBD[6, 13,14]. The main reasons are related to the following factors: Mineralization begins during the embryonic period, but the accumulation of minerals mainly occurs in the third trimester of pregnancy, especially at 32-36 weeks.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Our findings revealed that ELBW and VLBW had a significant influence on the risk of MBD during infancy. Previous studies 7,19,30,31 identified birth weight along with gestational age as the strongest risk factors for MBD in preterm infants and recommended that infants with birth weights less than 1500 g be screened for MBD. 32 Approximately 80% of fetal bone mineral accretion occurs during the last 3 months of pregnancy, and preterm neonates will miss, partially or fully, this critical period of bone growth.…”

Section: Jama Network Open | Pediatricsmentioning

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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“…To improve linear growth in preterm infants with risk factors for MBD, regular follow-up and monitoring are necessary. The objectives are to maintain blood calcium and blood phosphorus within normal limits and avoid excessive urinary calcium excretion [ 95 ].…”

Section: Screening and Monitoringmentioning

confidence: 99%

“…For early diagnosis of mineral deficiency, measurement of serum biochemical markers is essential. The most commonly used blood biochemical indicators are serum calcium, serum phosphorus, ALP, PTH, and 25(OH)D [ 95 ].…”

Section: Screening and Monitoringmentioning

confidence: 99%

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Prenatal and Neonatal Bone Health: Updated Review on Early Identification of Newborns at High Risk for Osteopenia

Perrone,

Caporilli,

Grassi

et al. 2023

Nutrients

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Bone health starts with maternal health and nutrition, which influences bone mass and density already in utero. The mechanisms underlying the effect of the intrauterine environment on bone health are partly unknown but certainly include the ‘foetal programming’ of oxidative stress and endocrine systems, which influence later skeletal growth and development. With this narrative review, we describe the current evidence for identifying patients with risk factors for developing osteopenia, today’s management of these populations, and screening and prevention programs based on gestational age, weight, and morbidity. Challenges for bone health prevention include the need for new technologies that are specific and applicable to pregnant women, the foetus, and, later, the newborn. Radiofrequency ultrasound spectrometry (REMS) has proven to be a useful tool in the assessment of bone mineral density (BMD) in pregnant women. Few studies have reported that transmission ultrasound can also be used to assess BMD in newborns. The advantages of this technology in the foetus and newborn are the absence of ionising radiation, ease of use, and, above all, the possibility of performing longitudinal studies from intrauterine to extrauterine life. The use of these technologies already in the intrauterine period could help prevent associated diseases, such as osteoporosis and osteopenia, which are characterised by a reduction in bone mass and degeneration of bone structure and lead to an increased risk of fractures in adulthood with considerable social repercussions for the related direct and indirect costs.

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

Cited by 13 publications

References 41 publications

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

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

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

Prenatal and Neonatal Bone Health: Updated Review on Early Identification of Newborns at High Risk for Osteopenia

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