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Diagnosis of bacterial infection in children with relapse of nephrotic syndrome: a personalized decision-analytic nomogram and decision curve analysis

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Abstract

Background

Infections associated with nephrotic relapses (NR) are often managed according to physician preferences. A validated prediction tool will aid clinical decision-making and help in rationalizing antibiotic prescriptions. Our objective was to develop a biomarker-based prediction model and a regression nomogram for the prediction of the probability of infection in children with NR. We also aimed to perform a decision curve analysis (DCA).

Methods

This cross-sectional study included children (1–18 years) with NR. The outcome of interest was the presence of bacterial infection as diagnosed using standard clinical definitions. Total leucocyte count (TLC), absolute neutrophil count (ANC), quantitative C-reactive protein (qCRP), and procalcitonin (PCT) were the biomarker predictors. Logistic regression was used to identify the best biomarker model, followed by discrimination and calibration testing. Subsequently, a probability nomogram was constructed and DCA was done to determine the clinical utility and net benefits.

Results

We included 150 relapse episodes. A bacterial infection was diagnosed in 35%. Multivariate analysis showed the ANC + qCRP model to be the best predictive model. This model displayed excellent discrimination (AUC: 0.83), and calibration (optimism-adjusted intercept: 0.015, slope: 0.926). A prediction nomogram and web-application was developed. The superiority of the model was also confirmed by DCA in the probability threshold range of 15–60%.

Conclusions

An ANC-based and qCRP-based internally validated nomogram can be used for the prediction of probability of infection in non-critically ill children with NR. Decision curves from this study will aid in the decision-making of empirical antibiotic therapy, incorporating threshold probabilities as a surrogate of physician preference.

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Data availability

Data will be made available on request.

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Acknowledgements

The authors wish to thank the study participants and their parents, the resident doctors, nurses, and technicians from the departments of Pediatrics, Biochemistry, and Microbiology.

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Authors and Affiliations

  1. Department of Pediatrics, Kalawati Saran Children’s Hospital & Lady Hardinge Medical College, Bangla Sahib Road, New Delhi, India, 110001

    Y. Venkata Narayana, Viswas Chhapola & Soumya Tiwari

  2. Department of Biochemistry, Lady Hardinge Medical College, Bangla Sahib Road, New Delhi, India, 110001

    Ekta Debnath

  3. Department of Microbiology, Lady Hardinge Medical College, Bangla Sahib Road, New Delhi, India, 110001

    Meenakshi Aggarwal

  4. Post-Doctoral Fellow, The Institute of Mathematical Sciences (IMSc), CIT Campus, 4Th Cross St, Tharamani, Chennai, Tamil Nadu, India, 600113

    Om Prakash

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  1. Y. Venkata Narayana
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Contributions

ST, VC, YVN, ED, and MA were involved in the study design and data acquisition. ST and VC were involved in data analysis and interpretation. ST, VC, YVN, ED, and MA drafted the manuscript, performed critical revisions, and gave final approval for submission.

Corresponding author

Correspondence to Soumya Tiwari.

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Ethics approval

The study was approved by the Institutional ethics committee (LHMC/IEC/2019/102), and conducted in accordance with Good Clinical Practice and the Declaration of Helsinki.

Consent to participate

Written informed consent was obtained from the parents or legal guardians of all children. When possible, the assent from pediatric patients themselves was also obtained.

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Study participants gave a written informed consent for publication of the results of the study before their enrolment in the study.

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The authors declare no competing interests.

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Narayana, Y.V., Chhapola, V., Tiwari, S. et al. Diagnosis of bacterial infection in children with relapse of nephrotic syndrome: a personalized decision-analytic nomogram and decision curve analysis. Pediatr Nephrol 38, 2689–2698 (2023). https://doi.org/10.1007/s00467-023-05915-z

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