Leveraging Predictive Pharmacometrics-Based Algorithms to Enhance Perinatal Care—Application to Neonatal Jaundice

Koch, Gilbert; Wilbaux, Mélanie; Kasser, Severin; Schumacher, Kai; Steffens, Britta; Wellmann, Sven; Pfister, Marc

doi:10.3389/fphar.2022.842548

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…And finally, the EPPT works with just one bilirubin measurement and does not require at least two bilirubin measurements as our previously published pharmacometrics-based mathematical-statistical computer program (PMX-based algorithm) ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

Validating the early phototherapy prediction tool across cohorts

Daunhawer,

Schumacher,

Badura

et al. 2023

Front. Pediatr.

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BackgroundHyperbilirubinemia of the newborn infant is a common disease worldwide. However, recognized early and treated appropriately, it typically remains innocuous. We recently developed an early phototherapy prediction tool (EPPT) by means of machine learning (ML) utilizing just one bilirubin measurement and few clinical variables. The aim of this study is to test applicability and performance of the EPPT on a new patient cohort from a different population.Materials and methodsThis work is a retrospective study of prospectively recorded neonatal data from infants born in 2018 in an academic hospital, Regensburg, Germany, meeting the following inclusion criteria: born with 34 completed weeks of gestation or more, at least two total serum bilirubin (TSB) measurement prior to phototherapy. First, the original EPPT—an ensemble of a logistic regression and a random forest—was used in its freely accessible version and evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Second, a new version of the EPPT model was re-trained on the data from the new cohort. Third, the predictive performance, variable importance, sensitivity and specificity were analyzed and compared across the original and re-trained models.ResultsIn total, 1,109 neonates were included with a median (IQR) gestational age of 38.4 (36.6–39.9) and a total of 3,940 bilirubin measurements prior to any phototherapy treatment, which was required in 154 neonates (13.9%). For the phototherapy treatment prediction, the original EPPT achieved a predictive performance of 84.6% AUROC on the new cohort. After re-training the model on a subset of the new dataset, 88.8% AUROC was achieved as evaluated by cross validation. The same five variables as for the original model were found to be most important for the prediction on the new cohort, namely gestational age at birth, birth weight, bilirubin to weight ratio, hours since birth, bilirubin value.DiscussionThe individual risk for treatment requirement in neonatal hyperbilirubinemia is robustly predictable in different patient cohorts with a previously developed ML tool (EPPT) demanding just one TSB value and only four clinical parameters. Further prospective validation studies are needed to develop an effective and safe clinical decision support system.

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Section: Discussionmentioning

confidence: 99%

Validating the early phototherapy prediction tool across cohorts

Daunhawer,

Schumacher,

Badura

et al. 2023

Front. Pediatr.

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Artificial Intelligence Applications in Neonatal Critical Care: A Scoping Review

Sakore,

Devi,

Mahapure

et al. 2024

Journal of Clinical Neonatology

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Background: The development of artificial intelligence (AI) approaches impacted drug discovery, medical imaging, customized diagnostics, and therapeutics. Medicine will be transformed by AI. One such area of medicine where AI is significantly improving care is neonatology. Objective: The objective of this scoping review is to explore the applications of AI in neonatal critical care and its outcome. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a scoping review was conducted utilizing the Web of Science, MEDLINE (PubMed), and Scopus databases. The search was limited to full-text publications on AI applications in neonatal critical care that were published between January 1, 2019, and December 31, 2023. Articles specifically addressing the application of AI in neonatal care have been considered within the scope of this review. At least three reviewers had independently executed the screening, data abstraction, and exploration. Results: Database searches yielded 631 articles, of which 11 met the inclusion criteria. The research encompassed extensive AI applications in neonatal critical care, employed for prognosis, diagnosis, and therapy strategizing. Artificial neural networks, machine learning, deep learning, and shallow hybrid neural networks were the commonly utilized AI techniques (neonatal critical care). These methods were applied to screen for inborn metabolic abnormalities, predict various outcomes, including death and sepsis, identify diseases such as sepsis, and assess neurodevelopmental outcomes in preterm newborns, helping plan several medical treatments. The included research demonstrated encouraging outcomes when using AI in neonatal critical care. Conclusions: AI-driven electronic arrangements upgrade neonatal basic care by improving risk forecast, promising critical commitments to future health care. Be that as it may, careful appraisal, evidence-based considers, and determination of safety, ethics, and information straightforwardness issues are essential before implementation. Acceptance by administrative bodies and the therapeutic community pivots on tending to these concerns.

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Leveraging Predictive Pharmacometrics-Based Algorithms to Enhance Perinatal Care—Application to Neonatal Jaundice

Cited by 2 publications

References 41 publications

Validating the early phototherapy prediction tool across cohorts

Validating the early phototherapy prediction tool across cohorts

Artificial Intelligence Applications in Neonatal Critical Care: A Scoping Review

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