{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:50:49Z","timestamp":1787068249288,"version":"build-2736575974"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a clinical prediction model for identifying intra-abdominal infection (IAI) in patients with severe acute pancreatitis (SAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a retrospective cohort study of patients diagnosed with SAP at our institution between January 2020 and December 2023. A total of 415 eligible patients were enrolled and randomly allocated into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f291) and a validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f124) in a 7:3 ratio for model development and internal validation. In the training cohort, candidate predictors were selected using least absolute shrinkage and selection operator (LASSO) regression to mitigate overfitting and retain the most clinically relevant variables. A multivariable logistic regression model was subsequently constructed, and a nomogram was developed to facilitate individualized risk assessment. Model performance was evaluated based on discrimination, calibration, and clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) in both cohorts. Calibration was examined via calibration plots with bootstrapping (1,000 resamples) to correct for optimism. Decision curve analysis (DCA) was performed to determine the net clinical benefit across different risk thresholds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The final cohort comprised 415 patients, with 291 in the training set and 124 in the validation set. LASSO regression identified four independent predictors with non-zero coefficients: hematocrit (HCT), procalcitonin (PCT), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and neutrophil-to-lymphocyte ratio (NLR). The prediction model demonstrated robust discrimination, with an AUC of 0.853 (95% CI: 0.804\u20130.901) in the training set and 0.858 (95% CI: 0.786\u20130.930) in the validation set. Calibration plots indicated excellent agreement between predicted and observed probabilities. DCA confirmed significant clinical utility across a wide range of risk thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed prediction model, incorporating HCT, PCT, APACHE II, and NLR, accurately stratifies the risk of IAI in SAP patients. This tool may facilitate early risk identification, guide timely antibiotic therapy, and optimize clinical decision-making to improve patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1636733","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T05:29:28Z","timestamp":1754544568000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of a clinical prediction model for intra-abdominal infection in severe acute pancreatitis using logistic regression and 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