Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study
BackgroundNeonatal jaundice affects more than half of neonates. As bilirubin values usually peak few days after hospital discharge, jaundice remains a leading cause of rehospitalization. The recently developed BiliPredics algorithm, integrated in the first CE-approved bilirubin prediction tool, pred...
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Frontiers Media S.A.
2025-02-01
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| author | Britta Steffens Britta Steffens Gilbert Koch Gilbert Koch Corinna Engel Axel R. Franz Marc Pfister Marc Pfister Marc Pfister Sven Wellmann Sven Wellmann |
| author_facet | Britta Steffens Britta Steffens Gilbert Koch Gilbert Koch Corinna Engel Axel R. Franz Marc Pfister Marc Pfister Marc Pfister Sven Wellmann Sven Wellmann |
| author_sort | Britta Steffens |
| collection | DOAJ |
| description | BackgroundNeonatal jaundice affects more than half of neonates. As bilirubin values usually peak few days after hospital discharge, jaundice remains a leading cause of rehospitalization. The recently developed BiliPredics algorithm, integrated in the first CE-approved bilirubin prediction tool, predicts individual bilirubin progression for up to 60 h into the future. Goal of the prospective study was to assess accuracy of this algorithm in predicting individual bilirubin prior to hospital discharge in neonates.MethodsA prospective multi-center study was conducted in 2021 at the University Children's Hospitals in Tübingen and Regensburg, Germany. Various scenarios differing in type and number of bilirubin measurements and in prediction horizon were tested. Primary objective was prediction accuracy of the BiliPredics algorithm based on total serum bilirubin (TSB) measurements or based on transcutaneous bilirubin (TcB) measurements alone. Secondary objective was prediction accuracy based on combinations of TSB and TcB measurements. For assessment of accuracy, two validation metrics, absolute prediction error (aPE) and relative prediction error (rPE), and two clinical acceptance conditions, margin of error of the 95%-confidence interval (95%-CI) and percentage of clinically relevant mis-predictions defined as aPE>85μmol/L, were investigated.ResultsOut of 455 enrolled neonates, 276 neonates met bilirubin inclusion criteria and were included in the analyses. Irrespective from tested prediction horizons, median rPE was small (8.5% to 9.5%) utilizing TSB measurements for up to 30 and 60 h and slightly higher (13.8%) utilizing TcB measurements for up to 48 h. The same applied for median aPE. Both clinical acceptance conditions were fulfilled across tested scenarios. Results for combined TSB-TcB scenarios up to a prediction horizon of 48 h without adjustment for type of measurement were comparable to TSB and TcB scenarios fulfilling both clinical acceptance conditions.ConclusionResults from this prospective study in neonates confirm that the BiliPredics algorithm accurately predicts bilirubin progression up to 60 h with TSB measurements and up to 48 h with TcB or combined TSB-TcB measurements. As such, prediction tools utilizing this algorithm are expected to facilitate and safely optimize jaundice risk assessment at hospital discharge with the potential to reduce jaundice-related rehospitalizations. |
| format | Article |
| id | doaj-art-7e57cdb2c6be413cafad9c5a3c50b2db |
| institution | DOAJ |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-02-01 |
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| series | Frontiers in Digital Health |
| spelling | doaj-art-7e57cdb2c6be413cafad9c5a3c50b2db2025-08-20T02:48:42ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-02-01710.3389/fdgth.2025.14971651497165Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center studyBritta Steffens0Britta Steffens1Gilbert Koch2Gilbert Koch3Corinna Engel4Axel R. Franz5Marc Pfister6Marc Pfister7Marc Pfister8Sven Wellmann9Sven Wellmann10Pediatric Pharmacology and Pharmacometrics, University of Basel Children’s Hospital (UKBB), Basel, SwitzerlandResearch and Development, NeoPredics AG, Basel, SwitzerlandPediatric Pharmacology and Pharmacometrics, University of Basel Children’s Hospital (UKBB), Basel, SwitzerlandResearch and Development, NeoPredics AG, Basel, SwitzerlandCenter for Pediatric Clinical Studies (CPCS) Tübingen, University Children’s Hospital Tübingen, Tübingen, GermanyCenter for Pediatric Clinical Studies (CPCS) Tübingen, University Children’s Hospital Tübingen, Tübingen, GermanyPediatric Pharmacology and Pharmacometrics, University of Basel Children’s Hospital (UKBB), Basel, SwitzerlandResearch and Development, NeoPredics AG, Basel, SwitzerlandDepartment of Clinical Research, University of Basel, Basel, SwitzerlandResearch and Development, NeoPredics AG, Basel, SwitzerlandDepartment of Neonatology, Hospital St. Hedwig of the Order of St. John of God, University Children’s Hospital Regensburg (KUNO), University of Regensburg, Regensburg, GermanyBackgroundNeonatal jaundice affects more than half of neonates. As bilirubin values usually peak few days after hospital discharge, jaundice remains a leading cause of rehospitalization. The recently developed BiliPredics algorithm, integrated in the first CE-approved bilirubin prediction tool, predicts individual bilirubin progression for up to 60 h into the future. Goal of the prospective study was to assess accuracy of this algorithm in predicting individual bilirubin prior to hospital discharge in neonates.MethodsA prospective multi-center study was conducted in 2021 at the University Children's Hospitals in Tübingen and Regensburg, Germany. Various scenarios differing in type and number of bilirubin measurements and in prediction horizon were tested. Primary objective was prediction accuracy of the BiliPredics algorithm based on total serum bilirubin (TSB) measurements or based on transcutaneous bilirubin (TcB) measurements alone. Secondary objective was prediction accuracy based on combinations of TSB and TcB measurements. For assessment of accuracy, two validation metrics, absolute prediction error (aPE) and relative prediction error (rPE), and two clinical acceptance conditions, margin of error of the 95%-confidence interval (95%-CI) and percentage of clinically relevant mis-predictions defined as aPE>85μmol/L, were investigated.ResultsOut of 455 enrolled neonates, 276 neonates met bilirubin inclusion criteria and were included in the analyses. Irrespective from tested prediction horizons, median rPE was small (8.5% to 9.5%) utilizing TSB measurements for up to 30 and 60 h and slightly higher (13.8%) utilizing TcB measurements for up to 48 h. The same applied for median aPE. Both clinical acceptance conditions were fulfilled across tested scenarios. Results for combined TSB-TcB scenarios up to a prediction horizon of 48 h without adjustment for type of measurement were comparable to TSB and TcB scenarios fulfilling both clinical acceptance conditions.ConclusionResults from this prospective study in neonates confirm that the BiliPredics algorithm accurately predicts bilirubin progression up to 60 h with TSB measurements and up to 48 h with TcB or combined TSB-TcB measurements. As such, prediction tools utilizing this algorithm are expected to facilitate and safely optimize jaundice risk assessment at hospital discharge with the potential to reduce jaundice-related rehospitalizations.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1497165/fullbilirubinhyperbilirubinemianeonatal jaundicemathematical modelpharmacometrics |
| spellingShingle | Britta Steffens Britta Steffens Gilbert Koch Gilbert Koch Corinna Engel Axel R. Franz Marc Pfister Marc Pfister Marc Pfister Sven Wellmann Sven Wellmann Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study Frontiers in Digital Health bilirubin hyperbilirubinemia neonatal jaundice mathematical model pharmacometrics |
| title | Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study |
| title_full | Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study |
| title_fullStr | Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study |
| title_full_unstemmed | Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study |
| title_short | Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study |
| title_sort | assessing accuracy of bilipredics algorithm in predicting individual bilirubin progression in neonates results from a prospective multi center study |
| topic | bilirubin hyperbilirubinemia neonatal jaundice mathematical model pharmacometrics |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1497165/full |
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