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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>We developed a nomogram to predict the probability of MA in CHPS patients, enabling early identification of poor prognosis and improving outcomes.</p> Impact statement <p><UnorderedList Mark="Bullet"> <ItemContent> <p>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.</p> </ItemContent> </UnorderedList></p>

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

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Yongcheng Fu,
  • Jian Cheng,
  • Xing Zhou,
  • Xiaohan Qin,
  • Jingyue Wang,
  • Yuanyuan Wang,
  • Shangkun Li,
  • Juan Ding,
  • Da Zhang

摘要

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.