Evaluation of radiologic evidence of metabolic bone disease in very low birth weight infants at fourth week of life

Analysis of clinical risk factors for metabolic bone disease of prematurity

Objective

To analyze clinical data related to preterm infants and identify risk factors for metabolic bone disease of prematurity (MBDP).

Methods

This study involved 856 newborns with a gestational age of less than 37 weeks or a weight of less than 1,500g at the Second Hospital of Jilin University. Multifactorial analysis was performed using logistic regression models to explore the risk factors for MBDP. Linear regression was used to investigate the factors affecting the time of alkaline phosphatase (ALP) exceedance and the peak value of ALP in the MBDP group.

Results

In the MBDP group, ALP excesses occurred in preterm infants at an average of 39.33 days after birth, and the mean value of peak ALP was 691.41 IU/L. Parenteral nutrition and the application of assisted ventilation were independent risk factors for MBDP, with ORs of 1.02 and 1.03 respectively. Gestational age was found to be a protective factor for earlier time of onset of ALP exceedance (β = 2.24,) and the increase in the peak value of ALP (β = -16.30).

Conclusion

Parenteral nutrition and the application of assisted ventilation are independent risk factors for MBDP. Gestational age is a major factor influencing the time of onset of ALP exceedance and the peak value of ALP in infants with MBDP.

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

Importance

Early 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.

Objectives

To 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 Participants

A 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.

Results

Of 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 Relevance

In 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.

Don't Forget the Bones: Incidence and Risk Factors of Metabolic Bone Disease in a Cohort of Preterm Infants

Metabolic bone disease of prematurity (MBD) is a condition of reduced bone mineral content (BMC) compared to that expected for gestational age (GA). Preterm birth interrupts the physiological process of calcium (Ca) and phosphorus (P) deposition that occurs mostly in the third trimester of pregnancy, leading to an inadequate bone mineralization during intrauterine life (IUL). After birth, an insufficient intake of Ca and P carries on this alteration, resulting in overt disease. If MBD is often a self-limited condition, in some cases it could hesitate the permanent alteration of bone structures with growth faltering and failure to wean off mechanical ventilation due to excessive chest wall compliance. Despite advances in neonatal intensive care, MBD is still frequent in preterm infants, with an incidence of 16−23% in very-low-birth-weight (VLBW, birth weight <1500 g) and 40−60% in extremely low-birth-weight (ELBW, birth weight <1000 g) infants. Several risk factors are associated with MBD (e.g., malabsorption syndrome, parenteral nutrition (PN), pulmonary bronchodysplasia (BPD), necrotizing enterocolitis (NEC), and some chronic medications). The aim of this study was to evaluate the rate of MBD in a cohort of VLBWI and the role of some risk factors. We enrolled 238 VLBWIs (107 male). 52 subjects were classified as increased risk (G1) and 186 as standard risk (G2) according to serum alkaline phosphatase (ALP) and phosphorus (P) levels. G1 subjects have lower GA (p < 0.01) and BW (p < 0.001). Moreover, they need longer PN support (p < 0.05) and invasive ventilation (p < 0.01). G1 presented a higher rate of BPD (p = 0.026). At linear regression analysis, BW and PN resulted as independent predictor of increased risk (p = 0.001, p = 0.040, respectively). Preventive strategies are fundamental to prevent chronic alteration in bone structures and to reduce the risk of short stature. Screening for MBD based on serum ALP could be helpful in clinical practice to identify subjects at increased risk.