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Pyloric index: a novel nomogram predictor of metabolic alkalosis in congenital hypertrophic pyloric stenosis

Pediatric Research (2025)Cite this article

Abstract

Background

Metabolic alkalosis (MA) is a common complication of congenital hypertrophic pyloric stenosis (CHPS). This study develops and validates a nomogram to predict MA probability in CHPS infants.

Methods

A retrospective study was conducted on CHPS patients at the First Affiliated Hospital of Zhengzhou University. Patients were divided into CHPS and MA-CHPS groups. Lasso and logistic regression selected predictive factors, and a nomogram was developed. Discriminative ability was assessed using the C-index with bootstrap validation. Calibration curves evaluated predictive accuracy, while decision curve analysis (DCA) assessed clinical applicability.

Results

A total of 107 cases were included in the final analysis, with 81 in the CHPS group and 26 in the MA-CHPS group. The predictive nomogram included the following factors: weight at diagnosis, symptom duration, and pyloric index (PI). The C-index of the predictive nomogram was determined to be 0.818, with a bootstrap validation (1000 resamples) yielding a corrected C-index of 0.798, indicating good discriminative ability. Calibration curves demonstrated a high degree of consistency between predicted and actual results. DCA confirmed good clinical utility.

Conclusion

We developed a nomogram to predict the probability of MA in CHPS patients, enabling early identification of poor prognosis and improving outcomes.

Impact statement

  • This study presents a novel predictive model for assessing the probability of metabolic alkalosis (MA) in congenital hypertrophic pyloric stenosis (CHPS) infants. It is the first model integrating the pyloric index (PI) as a predictive factor, alongside weight at diagnosis and symptom duration. The nomogram demonstrated strong discriminative ability and clinical utility, aiding early MA identification and improving perioperative management. By providing a practical risk stratification tool, this model enhances clinical decision-making, facilitates timely interventions, and ultimately improves outcomes for CHPS infants.

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Fig. 1: Correlation analysis between parameters.
Fig. 2: Variables selected based on LASSO regression.
Fig. 3: AUC values of key feature variables.
Fig. 4
Fig. 5: Validation of the nomogram.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are grateful to those who contributed to this study. We confirm that no Large Language Models (LLMs), including ChatGPT or similar AI-based technologies, were used in the conceptualization, writing, analysis, or editing of this manuscript. All content was independently developed, reviewed, and approved by the authors, ensuring full accountability and compliance with the journal’s authorship criteria.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The project was funded by the Key Scientific Research Projects of Henan Higher Education Institutions (No. 24A320078), the Science and Technology Research Program of Henan Province (No. 242102310256), and the Nursing Research Special Fund of the First Affiliated Hospital of Zhengzhou University (No. HLKY2023015).

Author information

Authors and Affiliations

  1. Department of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Yongcheng Fu, Jingyue Wang, Yuanyuan Wang, Shangkun Li & Da Zhang

  2. Center for Rare Diseases, State Key Laboratory of Complex, Severe, and Rare Diseases, Peking Union Medical College Hospital, Beijing, China

    Jian Cheng

  3. Department of Hepatobiliary Surgery, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Xing Zhou

  4. Department of Pediatric Surgery, Xi’an Children’s Hospital, Xi’an, China

    Xiaohan Qin

  5. Department of Nursing, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Juan Ding

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  1. Yongcheng Fu
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Contributions

F.Y.C. contributed to data curation, formal analysis, and writing of the original draft. Z.D. and D.J. contributed to supervision. C.J., Z.X., Q.X.H. contributed to methodology. W.Y.Y., W.J.Y., L.S.K., D.J. and Z.D. contributed to conceptualization, reviewing and editing, and resources. All authors approved the final manuscript as submitted.

Corresponding authors

Correspondence to Juan Ding or Da Zhang.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committees of the First Affiliated Hospital of Zhengzhou University (Approval Number No. 2025-KY-0737-001).

Informed consent

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

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Fu, Y., Cheng, J., Zhou, X. et al. Pyloric index: a novel nomogram predictor of metabolic alkalosis in congenital hypertrophic pyloric stenosis. Pediatr Res (2025). https://doi.org/10.1038/s41390-025-04382-6

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