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Usefulness of Dynamic Assessment of Clinical and Laboratory Factors in Severe Acute Pancreatitis.

Authors :
Librero-Jiménez, Marta
Valverde-López, Francisco
Abellán-Alfocea, Patricia
Fernández-Cano, María Carmen
Fernández-Fernández, Eleazar
Martínez-Cara, Juan Gabriel
López-González, Elisabet
Jiménez-Rosales, Rita
Redondo-Cerezo, Eduardo
Source :
Journal of Clinical Medicine. Aug2024, Vol. 13 Issue 15, p4412. 10p.
Publication Year :
2024

Abstract

Background/Objectives: Early identification of patients at risk of developing severe acute pancreatitis (SAP) is still an issue. Dynamic assessment of clinical and laboratory parameters within the first 48 h of admission may offer valuable insights into the prediction of unfavorable outcomes such as SAP and death. Methods: A prospective observational study was conducted on a cohort of patients admitted for AP at a tertiary referral hospital. Clinical and laboratory data were collected on admission and at 48 h. Patients were classified based on the Revised Atlanta classification. Logistic regression analysis was performed to identify independent risk factors for SAP. Likelihood ratios and post-test probabilities were calculated to assess the clinical usefulness of predictive markers. Results: 227 patients were included, with biliary etiology being the most common and a prevalence of SAP and death of 10.7% and 5.7%, respectively. BISAP ≥ 2 on admission, presence of SIRS after 48 h, rise in heart rate over 20 bpm, and any increase in BUN after 48 h were independent risk factors for SAP. The combination of these factors increased the post-test probability of SAP and death, with BISAP ≥ 2 combined with the presence of SIRS after 48 h showing the highest probability (82% and 73%, respectively). Conclusions: Dynamic assessment of BUN, heart rate, and SIRS within the first 48 h of admission can aid in predicting the development of SAP and death in patients with AP. These findings underscore the importance of continuous monitoring, although multicenter studies are warranted to refine predictive models for SAP. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20770383
Volume :
13
Issue :
15
Database :
Academic Search Index
Journal :
Journal of Clinical Medicine
Publication Type :
Academic Journal
Accession number :
178947897
Full Text :
https://doi.org/10.3390/jcm13154412