Evidence map›Paper›PMID 40772595›Full record

ArticleANZ journal of surgery2025

The Predictive Effect of "Real Amylase Value": A More Accurate Predictor for Postoperative Pancreatic Fistula.

Ahmet Cihangir Emral, Gökay Çetinkaya, Kürşat Dikmen, Mustafa Kerem

Abstract read
In one paragraph

Article in ANZ journal of surgery, 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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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ahmet Cihangir EmralDepartment of General Surgery, Atılım University Faculty of Medicine, Ankara, Turkey.ORCID 0000-0003-3976-1387
Gökay ÇetinkayaDepartment of General Surgery, Sağlık Bilimleri University Gülhane Eğitim Ve Araştırma Hastanesi, Ankara, Turkey.ORCID 0000-0002-1333-5059
Kürşat DikmenDepartment of General Surgery, Gazi University Faculty of Medicine, Ankara, Turkey.ORCID 0000-0002-9150-9499
Mustafa KeremDepartment of General Surgery, Gazi University Faculty of Medicine, Ankara, Turkey.ORCID 0000-0002-1797-6291

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostoperative pancreatic fistula (POPF) is a common and serious complication following pancreatic surgery. While several studies have attempted to predict the development of POPF using drain amylase concentration, predictive values vary widely due to factors like abdominal irrigation and chylous drainage, which can dilute the amylase levels. This study aims to evaluate whether the "Real Amylase Value" (RAV), calculated as the product of drain amylase concentration and drainage volume, provides a more reliable prediction of POPF compared to conventional amylase concentration. Better prediction of pancreatic fistula development could lead to closer clinical monitoring of these patients, reassessment of hospital stay duration, and more careful management of drains over an extended period. Additionally, carefully managing the timing of drain removal may improve patient recovery and discharge process. METHODOLOGY: Data from 198 patients who underwent pancreaticoduodenectomy (PD) and distal pancreatectomy (DP) were retrospectively analyzed. Drain amylase concentrations and drainage volumes were measured on postoperative days (POD) 1 and 3, and the RAV (U) was calculated. Real Amylase Value (RAV) (U) was calculated using the formula: RAV (U) = Drain Amylase Concentration (U/L) × Drainage Amount (L). Predictive values for POPF were evaluated using receiver operating characteristic (ROC) curve analysis, comparing conventional amylase concentration (U/L) and RAV (U).

resultsOn POD1, the RAV (U) demonstrated greater predictive value for POPF compared to the conventional drain amylase concentration (U/L) with an area under the ROC curve (AUROC) of 0.85 versus 0.79, respectively. Similarly, on POD3, RAV showed superior predictive accuracy (AUROC 0.89) compared to amylase concentration (AUROC 0.79).

conclusionThe RAV (U) offers a more accurate and reliable prediction of POPF than traditional drain amylase concentration (U/L), with improved sensitivity and specificity. This method could refine clinical management, particularly in the timing of drain removal and early intervention strategies for patients at high risk of developing pancreatic fistulas.

Indexed as

AmylasesPancreatectomyPancreatic FistulaPancreaticoduodenectomyPostoperative ComplicationsAdultAgedDrainageFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesROC CurveAmylasesamount of drain amylasedrain amylase concentrationpancreatic fistulareal amylase value

Identifiers

PMID40772595
PMCPMC12484427

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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 OpenQuestion graph.