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  • Clinical Research Article
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A predictive model for prognosis in very low birth weight infants with late-onset sepsis

Pediatric Research volume 94, pages 643–652 (2023)Cite this article

Abstract

Objectives

This study aims to develop a predictive model to assess the probability of poor prognosis in very low birth weight infants (VLBWI) with late-onset sepsis (LOS).

Methods

A total of 309 eligible VLBWI with LOS were included in the study. Logistic regression was used to determine prognostic factors for VLBWI with LOS. A nomogram incorporating these factors was created to predict the probability of poor prognosis. Poor prognosis includes death and survival with severe complications.

Results

In the developmental cohort, the incidence of poor prognosis was 59.5% (147/247). Forward stepwise logistic regression analysis showed that HCO3, albumin (ALB), ionized calcium (iCa), blood urea nitrogen (BUN), gestational age (GA), and birth weight (BW) were independent predictors of poor prognosis in VLBWI with LOS. The predictive model showed good discrimination and calibration. In the developmental cohort, the prediction model had a sensitivity of 83.7%, a specificity of 74.0%, and a C-index of 0.845 (95% confidence interval: 0.795–0.894).

Conclusion

Our study identified independent predictors of poor prognosis in VLBWI with LOS and used them to construct a predictive model. This model can help clinicians to identify high-risk groups with poor prognosis early and provide important clinical reference information.

Impact

This article highlights the development of a predictive model to assess the probability of poor prognosis in very low birth weight infants with late-onset sepsis (LOS). The model constructed in this manuscript was the first model to predict the poor prognosis of VLBWI with LOS. We mean a poor prognosis that includes death and some severe complications that may lead to long-term disability. Clinicians can use the model’s scoring results to assess a patient’s condition and accurately identify the occurrence of poor prognosis.

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Fig. 1
Fig. 2: The nomogram predicts the probability of poor prognosis in very low birth weight infants with late-onset Sepsis.
Fig. 3: ROC curves of the six independent predictors (BW, GA, ALB, HCO3, iCa, BUN) in the development cohort.
Fig. 4: ROC curves in development and validation cohorts.
Fig. 5: Calibration plots in the development cohort and the validation cohort.

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Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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Author information

Authors and Affiliations

  1. Department of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, People’s Republic of China

    Xuejie Zheng, Jinyu Chen, Qiyuan Cheng & Lili Wang

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  1. Xuejie Zheng
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  2. Jinyu Chen
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  3. Qiyuan Cheng
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  4. Lili Wang
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Contributions

All authors contributed to the study conception and design. LLW conceived and supervised the study; XJZ, JYC, QYC were involved in the data collection and analysis; The first draft of the manuscript was written by XJZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Lili Wang.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Our study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University. All manipulations involving humans in this study were under the principles in the Declaration of Helsinki. We confirm that all data are anonymous and confidential. Therefore, due to the retrospective nature of the current study, the requirement for informed consent has been waived.

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This was a retrospective study, so the requirement for informed consent was waived.

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Zheng, X., Chen, J., Cheng, Q. et al. A predictive model for prognosis in very low birth weight infants with late-onset sepsis. Pediatr Res 94, 643–652 (2023). https://doi.org/10.1038/s41390-023-02480-x

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