Early elevated alkaline phosphatase as a surrogate biomarker of ongoing metabolic bone disease of prematurity

Motte-Signoret, Emmanuelle; Jlassi, Marwa; Lecoq, Ludivine; Wachter, Pierre-Yves; Boileau, Pascal

doi:10.1007/s00431-023-04839-y

Cited by 5 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early models for the prediction purposes of bone diseases in women used the ML technique SVM using the features of the menopausal phase, breastfeeding period, height, weight, age of the person, estrogen level, hypertension, and diabetes mellitus as input characteristics. The study 25 demonstrated that the risk of MBD during infancy was significantly influenced by Extremely Low Birth Rate and Very Low Birth Rate.…”

Section: Resultsmentioning

confidence: 90%

Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

Ullah,

Rehman,

Anwar

et al. 2023

Health Science Reports

View full text Add to dashboard Cite

Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine‐learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010–2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K‐nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self‐Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.

show abstract

Section: Resultsmentioning

confidence: 90%

Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

Ullah,

Rehman,

Anwar

et al. 2023

Health Science Reports

View full text Add to dashboard Cite

show abstract

“…Traditionally, the measurement of serum ALP, serum calcium, and serum phosphate is used for screening [10]. In previous reviews, the cut-off level of serum ALP for diagnosing MBD has remained inconclusive [11,12]. We used radiography for the diagnosis of MBD, which highlighted radiographic signs such as the presence of bone rarefaction associated with metaphyseal alterations or subperiosteal bone formations associated with the presence of spontaneous fractures [1].…”

Section: Definitionsmentioning

confidence: 99%

Neurodevelopment Outcomes in Very-Low-Birth-Weight Infants with Metabolic Bone Disease at 2 Years of Age

Chen,

Chang,

Chen

et al. 2024

Children

View full text Add to dashboard Cite

Metabolic bone disease (MBD) predominantly affects preterm infants, particularly very-low-birth-weight (VLBW) infants weighing <1500 g. However, there are limited reports on MBD and neurodevelopmental outcomes. This study aimed to analyze the risk factors for MBD and understand its impact on neurodevelopmental outcomes at 2 years of corrected age. Overall, 749 VLBW infants weighing <1350 g at birth were enrolled. Exclusion criteria were major congenital abnormalities, chromosomal abnormalities, and loss of follow-up on the Bayley Scales of Infant Development, Third Edition (BSID-III) test at 24 months of corrected age. Infants were retrospectively assessed by a trained case manager using the BSID-III test at 6, 12, and 24 months old. Infants were categorized as with or without MBD according to radiographic signs. Of those enrolled, 97 VLBW infants were diagnosed with MBD, compared to 362 VLBW infants without MBD. The proportion of infants that completed three follow-ups was 86%. At the assessment at 2 years of age, infants with MBD had lower and more significant differences in motor, language, and cognitive composites. MBD is associated with poor neurodevelopmental outcomes in cognitive, motor, and language composites for VLBW infants at 24 months of corrected age.

show abstract

Metabolic bone disease in premature infants receiving parenteral nutrition: Incidence, clinical, laboratory and nutritional profile

dos Santos,

de Cassia dos Santos Silveira,

Pérsico

et al. 2025

Early Human Development

View full text Add to dashboard Cite

Early elevated alkaline phosphatase as a surrogate biomarker of ongoing metabolic bone disease of prematurity

Cited by 5 publications

References 41 publications

Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

Neurodevelopment Outcomes in Very-Low-Birth-Weight Infants with Metabolic Bone Disease at 2 Years of Age

Metabolic bone disease in premature infants receiving parenteral nutrition: Incidence, clinical, laboratory and nutritional profile

Contact Info

Product

Resources

About