Evidence map›Paper›PMID 39263300›Full record

ArticleTranslational pediatrics2024

Development and validation of a prediction model for rebound hyperbilirubinemia: a Chinese neonatal cohort study.

Huiyi Li, Xihua Huang, Zhenyu Liang, Haijian Liang, Si He, Li Tang

Abstract read
In one paragraph

Article in Translational pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Huiyi Li *Department of Neonatology, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China.
Xihua Huang *Department of Neonatology, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China.
Zhenyu LiangDepartment of Neonatology, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China.
Haijian LiangDepartment of Neonatology, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China.
Si HeDepartment of Neonatology, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China.
Li TangDepartment of Neonatology, the Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rebound hyperbilirubinemia (HBB) is still present in as high as 10% of newborn babies. However, the applicability of established prediction models for rebound HBB to Chinese newborns is unclear. This study aimed to establish a model to predict HBB rebound after phototherapy among Chinese neonates. Methods: A retrospective cohort study was conducted on 1,035 HBB infants receiving phototherapy. Rebound HBB was defined as total serum bilirubin (TSB) returning to or above the American Academy of Pediatrics (AAP) phototherapy threshold within 72 hours after the end of phototherapy. The predictive effects of previously published two- and three-variable scores were verified. Neonates were randomly assigned in a 6:4 ratio to the training (n=621) group and the testing (n=414) group. All variables in the training set were used to select predictors by least absolute shrinkage and selection operator (LASSO) regression analysis. The internal validation of the prediction model was performed using the testing set. The model's predictive performance was evaluated by area under the curve (AUC), accuracy, sensitivity, and specificity, each with 95% confidence intervals (CIs). Receiver operating characteristic (ROC) and calibration curves were constructed to evaluate the discrimination ability and fitting effect of the prediction model, respectively. Results: Rebound HBB was observed in 210 patients (20.3%). The AUC for the two- and three-variable scores were 0.498 (95% CI: 0.455-0.540) and 0.498 (95% CI: 0.457-0.540), respectively. Predictive factors for the risk of rebound HBB included formula feeding (>3 times/day), standard phototherapy irradiation time, TSB levels and age at termination of phototherapy, neonatal weight, and differences between TSB levels at the phototherapy termination and phototherapy threshold. The prediction model's AUC was 0.935 (95% CI: 0.911-0.958), the sensitivity was 0.880 (95% CI: 0.809-0.950), the specificity was 0.831 (95% CI: 0.790-0.871), and the accuracy was 0.841 (95% CI: 0.805-0.876). Conclusions: The established model performed well in predicting rebound risk among Chinese infants with HBB, which may be beneficial in treating and managing HBB in infants.

Indexed as

Chinese neonatesHyperbilirubinemia (HBB)prediction modelrebound

Identifiers

PMID39263300
PMCPMC11384426

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.