Systematic Review ARTICLE
Computed Tomography Severity Index versus other indices in the prediction of severity and mortality in acute pancreatitis: A predictive accuracy meta-analysis
- 1Institute for Translational Medicine, Medical School, University of Pécs, Hungary
- 2Department of Radiology, Medical School, University of Pécs, Hungary
- 3Department of Central Radiology, Markusovszky University Teaching Hospital, Hungary
- 4Doctoral School of Clinical Medicine, University of Szeged, Hungary
- 5Department of Pathophysiology, Faculty of Medicine, University of Szeged, Hungary
- 6Department of Paediatrics, Medical School, University of Pécs, Hungary
- 7Department of Gastroenterology, First Department of Medicine, Medical School, University of Pecs, Hungary
- 8Division of Cardiology, First Department of Medicine, University of Pécs Medical School, Hungary
- 9University of Szeged, Hungary
Background: The management of the moderate and severe forms of acute pancreatitis (AP) with necrosis and multiorgan failure remains a challenge. To predict the severity and mortality of AP multiple clinical, laboratory-, and imaging-based scoring systems are available.
Aim: To investigate, if the computed tomography severity index (CTSI) can predict the outcomes of AP better than other scoring systems.
Methods: A systematic search was performed in 3 databases: Pubmed, Embase, and the Cochrane Library. Eligible records provided data from consecutive AP cases and used CTSI or modified CTSI (mCTSI) alone or in combination with other prognostic scores [Ranson, bedside index of severity in acute pancreatitis (BISAP), Acute Physiology and Chronic Health Examination II (APACHE II), C-reactive protein (CRP)] for the evaluation of severity or mortality of AP. Area under the curves (AUCs) with 95% confidence intervals (CIs) were calculated and aggregated with STATA 14 software using the metandi module.
Results: Altogether, 30 studies were included in our meta-analysis, which contained the data of 5988 AP cases. The pooled AUC for the prediction of mortality was 0.79 (CI 0.73-0.86) for CTSI; 0.87 (CI 0.83-0.90) for BISAP; 0.80 (CI 0.72-0.89) for mCTSI; 0.73 (CI 0.66-0.81) for CRP level; 0.87 (CI 0.81-0.92) for the Ranson score; and 0.91 (CI 0.88-0.93) for the APACHE II score. The APACHE II scoring system had significantly higher predictive value for mortality than CTSI and CRP (p=0.001 and p<0.001, respectively), while the predictive value of CTSI was not statistically different from that of BISAP, mCTSI, CRP or Ranson criteria. The AUC for the prediction of severity of AP were 0.80 (CI 0.76-0.85) for CTSI; 0.79, (CI 0.72-0.86) for BISAP; 0.83 (CI 0.75-0.91) for mCTSI; 0.73 (CI 0.64-0.83) for CRP level; 0.81 (CI 0.75-0.87) for Ranson score and 0.80 (CI 0.77-0.83) for APACHE II score. Regarding severity, all tools performed equally.
Conclusion: Though APACHE II is the most accurate predictor of mortality, CTSI is a good predictor of both mortality and AP severity. When the CT scan has been performed, CTSI is an easily calculable and informative tool, which should be used more often in routine clinical practice.
Keywords: acute pancreatitis, severity, Mortality, accuracy, computed tomography severity index (CTSI)
Received: 04 Jun 2019;
Accepted: 19 Jul 2019.
Edited by:Richard T. Waldron, Cedars-Sinai Medical Center, United States
Reviewed by:Zilvinas Dambrauskas, Lithuanian University of Health Sciences, Lithuania
Christopher Halloran, University of Liverpool, United Kingdom
Copyright: © 2019 Mikó, Vigh, Mátrai, Soós, Garami, Balaskó, Czakó, Mosdósi, Sarlós, Eross, Tenk, Rostás and Hegyi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Peter Hegyi, Institute for Translational Medicine, Medical School, University of Pécs, Pecs, 7624, Hungary, firstname.lastname@example.org