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A Clinical Data-Based Nomogram Prediction Model for ARDS in Patients With Acute Pancreatitis.

Xingyan Liu1, Qiuping Gu1, Hongmei He1

  • 1Department of Gastroenterology, Ganzhou People's Hospital, Ganzhou City, Jiangxi Province, 341000, People's Republic of China.

International Journal of General Medicine
|March 23, 2026
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Summary

A nomogram model predicts acute respiratory distress syndrome (ARDS) in acute pancreatitis (AP) patients. Key predictors include age, alcohol history, lactate, RDW, albumin, glucose, and procalcitonin for early risk stratification.

Keywords:
acute pancreatitisacute respiratory distress syndromeinfluence factorsnomogram

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Area of Science:

  • Gastroenterology and Hepatology
  • Critical Care Medicine
  • Pulmonology

Background:

  • Acute pancreatitis (AP) is a common gastrointestinal emergency with potential for severe progression.
  • Acute respiratory distress syndrome (ARDS) is a critical, often fatal, complication of AP.
  • Limited tools exist for early prediction of ARDS in AP patients, hindering timely intervention.

Purpose of the Study:

  • To develop and validate a predictive nomogram model for ARDS in AP patients.
  • To utilize clinical data for early identification of high-risk individuals.

Main Methods:

  • Retrospective enrollment of 280 AP patients (training set) and 129 (validation set).
  • Data collection included clinical and laboratory parameters.
  • Patients were categorized based on ARDS development within 14 days of admission.

Main Results:

  • 74 patients (26.43%) in the training set developed ARDS.
  • Independent risk factors identified: age, alcohol history, lactate, RDW, FBG, PCT.
  • Albumin (ALB) emerged as a protective factor.
  • The nomogram achieved high predictive accuracy (AUC 0.899 internal, 0.927 external validation) with good calibration.

Conclusions:

  • Age, alcohol history, lactate, RDW, albumin, FBG, and PCT are crucial predictors of ARDS in AP.
  • The developed nomogram shows excellent discrimination, calibration, and clinical utility.
  • This tool can aid clinicians in early risk stratification for AP patients at risk of ARDS.