Prediction of infected pancreatic necrosis in acute necrotizing pancreatitis by the modified pancreatitis activity scoring system.

acute necrotizing pancreatitis infected pancreatic necrosis modified pancreatitis activity scoring system opioid pancreatitis activity scoring system

Journal

United European gastroenterology journal
ISSN: 2050-6414
Titre abrégé: United European Gastroenterol J
Pays: England
ID NLM: 101606807

Informations de publication

Date de publication:
02 2023
Historique:
received: 21 07 2022
accepted: 12 11 2022
pubmed: 30 12 2022
medline: 4 2 2023
entrez: 29 12 2022
Statut: ppublish

Résumé

Infected pancreatic necrosis (IPN) is a significant complication of acute necrotizing pancreatitis (ANP). Early identification of patients at high risk of IPN would enable appropriate treatment, but there is a lack of valid tools. This study aimed to assess the performance of the Pancreatitis Activity Scoring System (PASS) and its modifications (by removing or reducing the weight of opioid usage) in predicting IPN in a cohort of predicted severe ANP patients. Data was prospectively collected in the TRACE trial (2017-2020) involving 16 sites across China. The predictive performance of PASS, modified PASS (mPASS), and conventional indices were assessed by the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow Ĉ-test, Brier score, and Fagan's nomogram. Multivariate logistic regression analysis (MLRA) was used to define the relationship between the best-performing PASS/mPASS model and IPN. A total of 508 subjects were enrolled (median age, 43 years; 62.8% males) in the original trial, and 122 developed IPN (24%) within 90 days after randomization. Compared with non-IPN patients, the scores of PASS and its modified models were significantly higher in the IPN patients (all p < 0.001). Among the PASS and its modifications, mPASS-4 had the largest AUC, the lowest Brier score, and good calibration. The mPASS-4 model demonstrated an AUC of 0.752 in predicting IPN (the optimal cut-off for the mPASS-4 was 292.5) and outperformed the conventional indices. The MLRA results showed that mPASS-4 >292.5 was an independent risk factor of IPN (OR: 3.6, 95% CI: 2.1-6.3). The PASS and its modifications during the first week of ANP onset predict the development of IPN, with mPASS-4 performing best. The mPASS-4 model simplifies the original PASS, increasing the likelihood of clinical implementation.

Identifiants

pubmed: 36579414
doi: 10.1002/ueg2.12353
pmc: PMC9892470
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

69-78

Informations de copyright

© 2022 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC. on behalf of United European Gastroenterology.

Références

Gut. 2013 Jan;62(1):102-11
pubmed: 23100216
Pancreas. 2018 Aug;47(7):e40-e41
pubmed: 29985848
Clin Gastroenterol Hepatol. 2022 Jun;20(6):1334-1342.e4
pubmed: 34543736
Am J Gastroenterol. 2013 Sep;108(9):1400-15; 1416
pubmed: 23896955
N Engl J Med. 2022 Sep 15;387(11):989-1000
pubmed: 36103415
Pancreatology. 2013 Jul-Aug;13(4):355-9
pubmed: 23890133
N Engl J Med. 1975 Jul 31;293(5):257
pubmed: 1143310
Am J Gastroenterol. 2017 Jul;112(7):1144-1152
pubmed: 28462914
Lipids Health Dis. 2020 Apr 7;19(1):63
pubmed: 32264896
Clin Transl Gastroenterol. 2021 Sep 22;12(9):e00405
pubmed: 34597275
World J Surg. 2022 Apr;46(4):878-890
pubmed: 34994837
Gastroenterology. 2019 Mar;156(4):867-871
pubmed: 30776344
Am J Gastroenterol. 2018 May;113(5):755-764
pubmed: 29545634
Dig Dis Sci. 2015 Feb;60(2):537-42
pubmed: 24623313
N Engl J Med. 2021 Oct 7;385(15):1372-1381
pubmed: 34614330
Nutrients. 2017 Sep 08;9(9):
pubmed: 28885553
United European Gastroenterol J. 2023 Feb;11(1):69-78
pubmed: 36579414
Gut. 2019 Jun;68(6):1044-1051
pubmed: 29950344
J Gastroenterol Hepatol. 2021 Sep;36(9):2416-2423
pubmed: 33604947
Gut. 2020 Mar;69(3):604-605
pubmed: 31233394
N Engl J Med. 2016 Nov 17;375(20):1972-1981
pubmed: 27959604
Pancreas. 2021 Jul 1;50(6):859-866
pubmed: 34347734
Pancreatology. 2004;4(6):551-9; discussion 559-60
pubmed: 15550764
Medicine (Baltimore). 2017 Jul;96(30):e7487
pubmed: 28746189
Intensive Care Med. 2022 Jul;48(7):899-909
pubmed: 35713670
World J Emerg Surg. 2019 Jun 13;14:27
pubmed: 31210778
BMJ Open. 2020 Sep 29;10(9):e037231
pubmed: 32994239
J Gastrointest Surg. 2020 Mar;24(3):590-597
pubmed: 30891659
Crit Care Med. 2016 May;44(5):910-7
pubmed: 26783860
Am J Gastroenterol. 2018 Sep;113(9):1393-1394
pubmed: 29880970
Biometrics. 1988 Sep;44(3):837-45
pubmed: 3203132
Clin Gastroenterol Hepatol. 2005 Feb;3(2):159-66
pubmed: 15704050

Auteurs

Wenjian Mao (W)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

Kang Li (K)

Department of Critical Care Medicine, The Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.

Jing Zhou (J)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

Miao Chen (M)

Department of Critical Care Medicine, The Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.

Bo Ye (B)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

Gang Li (G)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

Vikesh Singh (V)

Pancreatitis Centre, Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.

James Buxbaum (J)

Department of Medicine, Division of Gastroenterology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.

Xiaoyun Fu (X)

Department of Critical Care Medicine, The Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.

Zhihui Tong (Z)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

Yuxiu Liu (Y)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
Department of Medical Statistics, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.

John Windsor (J)

Surgical and Translational Research Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.

Weiqin Li (W)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

Lu Ke (L)

Department of Critical Care Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Critical Care Medicine, Jinling Hospital, Medical College of Nanjing University, Nanjing, Jiangsu, China.
National Institute of Healthcare Data Science, Nanjing University, Nanjing, Jiangsu, China.

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