Evidence map›Paper›PMID 41126272›Full record

ArticleEuropean journal of medical research2025

Decoding dynamic lipase trajectory patterns and in-hospital mortality in acute pancreatitis: insights from machine learning in intensive care units.

Yingyi Li, Xiaoqiang Liu, Xiaodong Zhu, Chanchan Lin, Qilin Yang, Zicheng Huang, Yisen Huang

Abstract read
In one paragraph

Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yingyi Li *Department of Gastroenterology, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
Xiaoqiang Liu *Department of Gastroenterology, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
Xiaodong ZhuDepartment of Gastroenterology, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
Chanchan LinDepartment of Gastroenterology, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
Qilin YangDepartment of Critical Care, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zicheng Huang *Department of Gastroenterology, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
Yisen Huang *Department of Gastroenterology, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China. 9201541090@fjmu.edu.cn.

Funding

Quanzhou City Science & Technology Program in China 2024NY001Startup Fund for scientific studies at Fujian Medical University 2023QH1310
6 · The paper itself

Abstract

backgroundSerum lipase levels are crucial biomarkers in acute pancreatitis (AP), yet their dynamic patterns and prognostic implications remain incompletely understood. This study aimed to identify distinct lipase trajectory phenotypes and evaluate their association with in-hospital mortality in AP patients.

methodsWe conducted a retrospective analysis of 834 AP patients from the MIMIC-IV database using latent class trajectory modeling (LCTM) to identify distinct lipase trajectory phenotypes. Cox regression models, adjusted for demographics, comorbidities, clinical therapies, and critical illness markers, were employed to assess the association between trajectory classes and in-hospital mortality.

resultsThree distinct lipase trajectory phenotypes were identified: Class 1 (n = 543) with consistently low levels, Class 2 (n = 51) with extremely high and variable levels, and Class 3 (n = 240) with moderately elevated levels. Class 2 patients were significantly older (66.8 ± 17.6 years) and had higher comorbidity burden (CCI: 5.6 ± 3.0). In-hospital mortality rates were 12.2%, 17.6%, and 19.2% for Classes 1, 2, and 3, respectively. After comprehensive adjustment, both Class 2 (HR: 2.21, 95% CI 1.04-4.71, p = 0.042) and Class 3 (HR: 1.61, 95% CI 1.08-2.40, p = 0.022) showed significantly higher mortality risk compared to Class 1.

conclusionsDynamic lipase trajectory patterns in AP patients demonstrate distinct phenotypes with significant prognostic value for in-hospital mortality. These findings suggest that monitoring lipase trajectories may enhance risk stratification and guide clinical management in AP patients.

Indexed as

Hospital MortalityLipaseMachine LearningPancreatitisAgedBiomarkersFemaleHumansIntensive Care UnitsMaleMiddle AgedPrognosisRetrospective StudiesBiomarkersLipaseAcute pancreatitisIntensive careLipase trajectoriesMachine learningMortality

Identifiers

PMID41126272
PMCPMC12542429

What Socratic holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.