SYSTEMATIC REVIEW article

Front. Physiol., 27 August 2019

Sec. Gastrointestinal Sciences

Volume 10 - 2019 | https://doi.org/10.3389/fphys.2019.01002

Computed Tomography Severity Index vs. Other Indices in the Prediction of Severity and Mortality in Acute Pancreatitis: A Predictive Accuracy Meta-analysis

  • 1. Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary

  • 2. Department of Radiology, Medical School, University of Pécs, Pécs, Hungary

  • 3. Department of Central Radiology, Markusovszky University Teaching Hospital, Szombathely, Hungary

  • 4. Clinical Medicine Doctoral School, University of Szeged, Szeged, Hungary

  • 5. Institute of Bioanalysis, Medical School, University of Pécs, Pécs, Hungary

  • 6. Department of Pathophysiology, University of Szeged, Szeged, Hungary

  • 7. Department of Paediatrics, Medical School, University of Pécs, Pécs, Hungary

  • 8. Division of Gastroenterology, First Department of Medicine, Medical School, University of Pécs, Pécs, Hungary

  • 9. Division of Translational Medicine, First Department of Medicine, University of Pécs, Pécs, Hungary

  • 10. Hungarian Academy of Sciences, University of Szeged Momentum Gastroenterology Multidisciplinary Research Group, Szeged, Hungary

Abstract

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 three 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 5,988 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.

Introduction

Rationale

Acute pancreatitis (AP) is an inflammatory disease of the pancreas, one of the most common causes of hospitalization among gastrointestinal diseases (Lankisch et al., 2015). Based on the revised Atlanta classification, the severity of AP may be mild, moderate, or severe (Banks et al., 2013). Most cases of AP are mild, but the management of the moderate and severe forms of the disease with necrosis and multiorgan failure remains a challenge. The prognosis of the severe form is poor, it occurs in 8.8% of AP (Parniczky et al., 2016) and the mortality of severe AP (SAP) may reach 28% (Parniczky et al., 2016). Therefore, it is necessary to predict the severity of the disease because the early escalation of care and aggressive therapy may prevent complications and adverse outcomes of AP in high-risk patients. Unfortunately, research on pancreatitis is in danger, therefore, attempts to obtain clinically relevant data has very high importance (Szentesi et al., 2016).

Objectives

Currently, there are various scoring systems used for the early prediction of SAP. First, the Ranson score was used (Ranson and Pasternack, 1977), but later the acute physiology and chronic health examination II (APACHE II) scoring system seemed to be more accurate (Yeung et al., 2006). Moreover, several inflammatory parameters such as C-reactive protein (CRP) and interleukin-6 were documented to be clinically relevant in the differentiation of mild and non-mild AP (Sternby et al., 2017). In 2008, a new, easy-to-implement bedside index of severity in acute pancreatitis (BISAP) was proposed for use within 24 h of hospitalization to predict in-hospital mortality (Wu et al., 2008). However, with the improvements of the imaging techniques, contrast-enhanced computed tomography (CT) gets an important place in the diagnosis of AP and its complications. In the early ‘90s, Balthazar and his coworkers developed a numerical scoring system, the CT severity index (CTSI), for the estimation of the severity of AP (Balthazar et al., 1990). It combines the quantification of pancreatic and peripancreatic inflammation with the extent of pancreatic parenchymal necrosis. In 2004, Mortele et al., formulated the modified CTSI (mCTSI) including a simplified evaluation of peripancreatic inflammation and extent of pancreatic parenchymal necrosis and incorporated the extrapancreatic complications (vascular, gastrointestinal, and extrapancreatic parenchymal complications as well as the presence of pleural effusion and/or ascites) in the assessment. This modified index correlated more with the outcome of AP (Mortele et al., 2004). Tables 1A,B show the components of CTSI and mCTSI.

Table 1

(A) Components of the CTSI
Pancreatic inflammation
Normal pancreas0
Focal or diffuse enlargement of the pancreas1
Intrinsic pancreatic abnormalities with inflammatory changes in peripancreatic fat2
Single, ill-defined fluid collection or phlegmon3
Two or more poorly defined collections or presence of gas in or adjacent to the pancreas4
Pancreatic necrosis
None0
≤30%2
>30% and ≤ 50%4
>50%6
(B) Components of the mCTSI
Pancreatic inflammation
Normal pancreas0
Intrinsic pancreatic abnormalities with peripancreatic inflammatory changes2
Pancreatic or peripancreatic fluid collection or peripancreatic fat necrosis4
Pancreatic necrosis
None0
Less 30%2
>30%4
Extrapancreatic complications
Pleural effusion, ascites, vascular complication (venous thrombosis, arterial hemorrhage, pseudoaneurysm), parenchymal complication (infarction, hemorrhage, subcapsular fluid collection), GI involvement (inflammation, perforation, intraluminal fluid collection)2

Components of the CTSI and mCTSI.

Research Question

It is still not clear, which scoring system has the highest predictive accuracy for severity and mortality of AP. The aim of this meta-analysis was to investigate, how accurate CT-based severity indices are in the prediction of the severity and mortality of AP in comparison with other widely accepted and used scoring systems.

Methods

Study Design, Participants, Interventions, Comparators

The systematic search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement (Moher et al., 2009) (Supplementary Document 1). We included studies on AP that used CTSI or mCTSI (index test) and compared them to any of the other scoring systems (Ranson, BISAP, APACHE II) or CRP (reference standards) in terms of their predictive value.

Search Strategy

A systematic search was performed in Pubmed, Embase, and the Cochrane Library (CENTRAL), using the following search query: “acute pancreatitis” AND (“computed tomography severity index” OR CTSI OR “modified computed tomography severity index” OR MCTSI). English language filter was used. Search key in Embase were: (‘acute pancreatitis’/exp OR ‘acute pancreatitis’) AND (ctsi OR mctsi OR ‘computed tomography severity index’/exp OR ‘computed tomography severity index’ OR ‘modified computed tomography severity index’) AND [english]/lim. The search was conducted on 11th March 2018. Duplicates were removed using the (EndNote X7.4, Clarivate Analytics, Philadelphia, PA, US) reference manager software.

Study Selection and Data Extraction

Two independent investigators (EV, AM) selected the studies, and disagreements were resolved by a third reviewer (PH). The records were selected for meta-analysis if (1) AP patients of any severity were enrolled consecutively; (2) if CTSI or mCTSI were used for the prediction of the severity or mortality of AP; and (3) if sensitivity and specificity values, the absolute numbers of true positive (TP), false negative (FN), false positive (FP) and true negative (TN), and/or area under the curve (AUC) were reported (for CTSI/mCTSI regarding AP severity and/or mortality). If other prognostic scores (Ranson, BISAP, APACHE II) or CRP values were also assessed in the selected articles, those results were extracted as well. Only full-text articles were included.

Studies, which met the inclusion criteria were assessed for full-text evaluation. The following data were extracted from the articles: first author; year of the publication; study period; study design; the AP scoring systems used; evaluation time of the scores; the used definition for SAP; sample size based on severity; mean age; male/female ratio; cut-off value, clinical end-points, and several data about the statistical analysis were reviewed for the risk of bias assessment.

Data Analysis

To construct 2 × 2 contingency tables, true positive, false positive, false negative, and true negative values were abstracted. These served as input to fit Hierarchial Summary Receiver Operating Characteristics (HSROC) curves and estimate summary sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals (CI). For each method and outcome, we collected the AUC values and their CIs as well and performed a meta-analysis using the random effect model to gain pooled AUC estimates with 95% CI.

The statistical analysis was performed with Stata 14 software using the metandi module1. Heterogeneity was assessed using the I2 measure and the corresponding chi2 test, p < 0.1 indicates significant heterogeneity. Based on the Cochrane Handbook, I2 = 100% × (Q–df)/Q represents the magnitude of the heterogeneity (moderate: 30–60%, substantial: 50–90%, considerable: 75–100%) (Higgins and Green, 2011).

Risk of Bias and Applicability Assessment

The Prediction model Risk Of Bias ASsessment Tool (PROBAST) (Wolff et al., 2019) was used to assess the risk of bias and applicability of primary studies in accordance with the recommendation of the Cochrane Collaboration. This tool is able to assess the risk of bias based on the following four domains: participants, predictors, outcome, and analysis. It includes also concerns regarding applicability in three domains: participants, predictors and outcome.

Results

Study Selection and Characteristics

The systematic search strategy identified 319 articles after removal of duplicates. The search provided 123 articles in Pubmed, 279 studies in Embase and 25 articles in Cochrane library databases. The flow chart (Figure 1) shows the study selection process: 148 articles were screened by abstract and after excluding 86 studies, 62 full-text articles were assessed for eligibility. A further 32 articles were excluded, because of the lack of reporting of the outcome of interest or inappropriate patient population. In our meta-analysis 30 articles were included (Simchuk et al., 2000; Chatzicostas et al., 2003; Gurleyik et al., 2005; Vriens et al., 2005; Huang et al., 2010; Papachristou et al., 2010; Bollen et al., 2011, 2012; Fabre et al., 2012; Lautz et al., 2012; Jakchairoongruang and Arjhansiri, 2013; Khanna et al., 2013; Park et al., 2013; Xu et al., 2014; Banday et al., 2015; Cho et al., 2015; Qiu et al., 2015; Sharma et al., 2015; Yue et al., 2015; Alper et al., 2016; Hashimoto et al., 2016; Lee et al., 2016; Raghuwanshi et al., 2016; Yadav et al., 2016; Yang et al., 2016; Zhao et al., 2016; Biberoglu et al., 2017; Kumar et al., 2017; Sahu et al., 2017; Fei et al., 2018), which contained the data of 5,988 patients. The main characteristics of the included studies are shown in Table 2.

Figure 1

Table 2

Study numberReferencesStudy periodStudy designCountryGroupsEvaluation time, hDefinition of SAPSample sizeAgeMale/female ratioSeverityMortality
1Cho et al., 20152011–2012ProspectiveSouth KoreaCTSI<48 of admission2012 revised Atlanta16162.3a ± 16.1c102/59+
Ranson<48 of admission
BISAP<24 of admission
APACHE II<24 of admission
CRPOn admission and 24 h after admission
2Gurleyik et al., 20052001–2005ProspectiveTurkeyCTSI<120 after the onset of symptoms1992 Atlanta55Mild: 56.1a (28–98)e severe: 60.3a (23–77)e18/37+
APACHE II48
CRPat 48 h
3Lee et al., 20162010–2013ProspectiveSouth KoreaCTSIOn admission2012 revised Atlanta146Mild: 48.8a ± 16.4c, moderate: 49.3a ± 18.5c, severe: 63a ± 22.8c92/54+
RansonOn admission
BISAPOn admission
APACHE IIOn admission
CRPOn admission and 24 h after admission
4Qiu et al., 20152008–2014RetrospectiveChinaCTSI<48 h of admission2012 revised Atlanta12957a ± 17.3c468/441+
Ranson<48 h of admission
BISAP<24 h of admission
CTSI<48 h of admission780
Ranson<48 h of admission
BISAP<24 h of admission
5Yue et al., 20152011–2013ProspectiveChinaCTSI<48 h of admission2009 Atlanta16954.3a ± 16.7c98/71+
Ranson<48 h
APACHE II0., and 72 h
6Biberoglu et al., 20172010–2011RetropsectiveTurkeyCTSINANA76Presented in intervals29/47+
7Bollen et al., 20112005–2007ProspectiveNACTSI0–168 h, median 48 h after onset of symptoms2009 Atlanta19653a (21–94)e107/89++
MCTSI0–168 h, median 48 h after onset of symptoms
APACHE IINA
8Sharma et al., 20152013–2014RetropsectiveIndiaCTSI72–240 h after onset of symptoms2012 revised Atlanta10540.6a ± 12.99NA65/40+
MCTSI72–240 h after onset of symptoms
BISAPNA
9Yang et al., 20162007–2015RetropsectiveChinaMCTSI<72 after onset of symptoms2008 Atlanta32644a (14–85)e184/142++
BISAP<24 of admission
Ranson<48 of admission
APACHE II<24 of admission
10Bollen et al., 20122.5 year periodProspectiveUSACTSI<24 of admission2009 Atlanta13154b (21–91)e84/66++
MCTSI<24 of admission131
APACHE IINA131/159
BISAPNA131/159
11Alper et al., 20162011–2014ProspectiveTurkeyCTSIWithin 72–96 h after admissionCTSI >6 and/or modified Glasgow score >3187mild 57a ± 17.9NA, severe 60.1a ± 15.9NA111/76++
12Zhao et al., 20162012–2014ObservationalChinaAPACHE IIAt admission = <72 h after onset of symptoms2012 revised Atlanta74NANA+
CTSIAt admission = <72 h after onset of symptoms
CRP<72 h
13Fabre et al., 20122003–2007RetrospectiveFranceRansonNA1992 Atlanta48133 monthsb (24.9–233.5)e23/25+
CTSI17
14Simchuk et al., 20001992–1997RetrospectiveUSACTSI––26857a (18–93)e147/121+
15Jakchairoongruang and Arjhansiri, 20132005–2010RetrospectiveThailandCTSI––7247.7a (6–89)e39/33+
16Hashimoto et al., 20162002–2012RetrospectiveJapanRansonNADeBanto et al.376b (5–12)e15/22+
CTSI33
17Park et al., 20132007–2010RetrospectiveKoreaBISAP<24 of admissionBased on organ failure and/or local complications30352NA ± 17NA216/87++
Ranson<48 of admission
APACHE II<24 of admission
CTSIWithin 7 days of admission
CRPInitial
CRPAfter 48 h
18Khanna et al., 20132010–2012ProspectiveIndiaBISAP<24 of admissionPresence of organ failure for more than 48 h and local complications7240.5a (18–76)e37/35++
APACHE II<24 of admission72
Ranson<48 of admission72
CTSIOn day 454
CRPOn day 260
19Lautz et al., 20122000–2009RetrospectiveUSACTSIAt presentationDeBanto et al.6412.3bNA+
RansonNA64
20Chatzicostas et al., 20031999–2001ProspectiveGreeceCTSI<72 h after admission (median time 62 h)1992 Atlanta7863.8a (25–93)e42/36+
APACHE II<24 of admission
Ranson<48 of admission
21Xu et al., 20142012RetropsectiveChinaCTSIWithin 3–5 days after admission2008 Atlanta25751.2a ± 12.3NA (23–89)e196/61+
22Yadav et al., 20162012–2014ProspectiveIndiaBISAP<24 of admissionPersistent organ failure for more than 48 h11938.94a ± 14.59c84/35++
Ranson<48 of admission
CTSIWithin the first 7 days of hospitalization
23Papachristou et al., 20102003–2007ProspectiveUSABISAP<24 of admissionPresence of organ failure for more than 48 h18551.7a(15–90)e94/91++
Ranson<48 of admission
APACHE II<24 of admission
CTSIWithin 48 h from admission
24Vriens et al., 20051994–2002ProspectiveNetherlandsCTSIWithin 48 h after admission, 80% within 12 hNA7961a (15–86)e39/40+
Ranson<48 of admission
25Raghuwanshi et al., 20162013–2015ProspectiveIndiaCTSINA2012 revised Atlanta50NANA+
MCTSI
26Banday et al., 20152012–2013ProspectiveIndiaMCTSINANA5042,32a (17–80)e33/17+
27Huang et al., 20102007–2009ProspectiveChinaCTSI<24 h after onset of symptoms1992 Atlanta187mild: 60.4a ± 6.7c severe: 59.5a ± 7.1c,112/75+
28/AFei et al., 20182013–2016RetrospectiveChinaBISAPOn admission2012 revised Atlanta107347.3a ± 7.1c615/458+
CTSI
AAPCHE II
Ranson
28/B2012–2016BISAP32653.6a ± 8.7c168/158
CTSI
APACHE II
Ranson
29Kumar et al., 20172014–2016ProspectiveNepalCTSIAfter 48 h after arrival to hospitalAtlanta12546.78a ± 14.16c74/51+
CRPat 48 h
RansonAfter 48 h of admission
30Sahu et al., 20172014–2016ProspectiveIndiaCTSIMedian of 6 days; range of 5–11 days2012 revised Atlanta6037a (19–65)e36/24+
mCTSIMedian of 6 days; range of 5–11 days

Basic characteristics of the included studies.

CTSI, computed tomography severity index; MCTSI, modified computed tomography severity index; BISAP, bedside index of severity in acute pancreatitis; APACHE II, Acute Physiology And Chronic Health Examination II; N/A, not applicable;

a

mean;

b

median;

c

standard difference;

d

standard error of mean;

e

range.

Prediction of Mortality

From the 30 articles, 11 contained data on AUC for the prediction of mortality (Figure 2). Table 3 summarizes the study numbers, sample sizes, AUC, and heterogeneity data of the different severity scores based on the outcome of AP. For CTSI based on data from 10 articles, the pooled AUC for mortality was 0.79 (CI 0.73–0.86; heterogeneity I2 = 83%, p < 0.001). Eight articles included AUC data for mortality for BISAP, the pooled AUC was 0.87 (CI 0.83–0.90; heterogeneity I2 = 0%, p = 0.578). The pooled AUC for mCTSI was 0.80 (CI 0.72–0.89; heterogeneity I2 = 79.4%, p = 0.001) according to five studies. Only two studies reported AUC data predicting mortality for CRP level, and the pooled AUC was 0.73 (CI 0.66–0.81; heterogeneity I2 = 0%, p = 0.708) Six articles included AUC data for mortality for Ranson score with a pooled AUC of 0.87 (CI 0.81–0.92; heterogeneity I2 = 65.6%, p = 0.013) and also for APACHE II score with a pooled AUC of 0.91 (CI 0.88–0.93; heterogeneity I2 = 4.8%, p = 0.386).

Figure 2

Table 3

ScoreStudy numberSample sizeAUC (CI)Heterogeneity
MORTALITY
CTSI101,4890.79 (0.73–0.86)I2 = 83%, p < 0.001
BISAP81,3700.87 (0.83–0.90)I2 = 0%, p = 0.578
mCTSI58180.80 (0.72–0.89)I2 = 79.4%, p = 0.001
CRP23630.73 (0.66–0.81)I2 = 0%, p = 0.708
Ranson61,1340.87 (0.81–0.92)I2 = 65.6%, p = 0.013
APACHE II61,2130.91 (0.88–0.93)I2 = 4.8%, p = 0.386
SEVERITY
CTSI182,5350.80 (0.76–0.85)I2 = 86.2%; p < 0.001
BISAP101,8980.79 (0.72–0.86)I2 = 89.7%, p < 0.001
mCTSI36530.83 (0.75–0.91)I2 = 68.1%; p = 0.043
CRP68690.73 (0.64–0.83)I2 = 77%, p = 0.001
Ranson142,1190.81 (0.75–0.87)I2 = 87.5%, p < 0.001
APACHE II111,1980.80 (0.77–0.83)I2 = 36.8%, p = 0.105

Summary table of mortality and severity data based on the forest plots.

Based on the above results of the meta-analytical calculations the APACHE II scoring system had significantly higher predictive accuracy for mortality than CTSI or CRP level (p = 0.001; p < 0.001, respectively). However, CTSI was not different from the BISAP, mCTSI, CRP or Ranson criteria in the prediction of mortality of AP, and these scores can be classified as good and fair.

Prediction of Severity

AUC data for severity were included in 19 studies (Figure 3).

Figure 3

The pooled AUC for severity based on data from 18 articles was 0.80 (CI 0.76–0.85) for CTSI with high heterogeneity (I2 = 86.2%; p < 0.001). The pooled AUC for BISAP was 0.79 (CI 0.72–0.86, heterogeneity I2 = 89.7%, p < 0.001). The pooled AUC for mCTSI from three studies was 0.83 (CI 0.75–0.91) with substantial heterogeneity (I2 = 68.1%; p = 0.043). The pooled AUC was 0.73 (CI 0.64–0.83) for CRP level (heterogeneity: I2 = 77%, p = 0.001), 0.81 (CI 0.75–0.87) for Ranson score (heterogeneity I2 = 87.5%, p < 0.001), and 0.80 (CI 0.77–0.83) for APACHE II score (heterogeneity I2 = 36.8%, p = 0.105).

There was no statistical difference between the severity predicting values of the different scoring systems. The heterogeneity values were I2 = 86.2%, p < 0.001; I2 = 89.7%, p < 0.001; I2 = 68.1%, p = 0.043; I2 = 77%, p = 0.001; I2 = 87.5%, p < 0.001; I2 = 36.8%, p = 0.105 for CTSI, BISAP, mCTSI, CRP, Ranson, and APACHE II scores, respectively. The heterogeneity across the studies was significant in all scoring systems or predicting values, except for the APACHE II score. Based on the results of the meta-analytical calculations, the severity prediction values of the included scoring systems are not different.

HSROC Analysis of Different Scoring Systems for Predicting Mortality and Severity of AP

Figure 4A shows the application of CTSI in the form of HSROC curves for mortality [sensitivity: 0.88 (CI 0.69–0.97); specificity: 0.61 (CI 0.52–0.70); DOR: 12.84 (CI 4.19–39.41)] and Figure 4B for severity, respectively [sensitivity: 0.81 (CI 0.73–0.87); specificity: 0.82 (CI 0.73–0.88); DOR: 19.1 (CI 10.29–35.45)]. Figure 5A illustrates the bivariate HSROC curve of BISAP score for mortality [sensitivity: 0.88 (CI 0.71–0.96); specificity: 0.77 (CI 0.70–0.83); DOR: 24.74 (CI 9.44–64.81)] and Figure 5B for severity, respectively [sensitivity: 0.73 (CI 0.53–0.87); specificity: 0.80 (CI 0.72–0.88); DOR: 11.71 (CI 4.49–30.61)]. Figure 6A shows the application of mCTSI in the form of HSROC curves for mortality [sensitivity: 0.95 (CI 0.76–0.99); specificity: 0.36 (CI 0.16–0.63); DOR: 10.32 (CI 2.11–50.53)] and Figure 6B for severity, respectively [sensitivity: 0.88 (CI 0.47–0.98); specificity: 0.80 (CI 0.56–0.92); DOR: 29.07 (CI 3.36–251.91)]. Figure 7 illustrates the bivariate HSROC curve of CRP score for severity [sensitivity: 0.71 (CI 0.59–0.81); specificity: 0.87 (CI 0.66–0.96); DOR: 16.75 (CI 3.49–80.48)]. Figure 8A illustrates the bivariate HSROC curve of Ranson score for mortality [sensitivity: 0.91 (CI 0.70–0.98); specificity: 0.72 (CI 0.66–0.79); DOR: 28.72 (CI 7.57–109.05)] and Figure 8B for severity, respectively [sensitivity: 0.79 (CI 0.69–0.86); specificity: 0.78 (CI 0.71–0.84); DOR: 13.32 (CI 7.33–24.24)]. Figure 9A illustrates the bivariate HSROC curve of APACHE II score for mortality [sensitivity: 0.92 (CI 0.70–0.98); specificity: 0.79 (CI 0.66–0.88); DOR: 45.08 (CI 11.4–178.2)] and Figure 9B for severity, respectively [sensitivity: 0.71 (CI 0.60–0.79); specificity: 0.80 (CI 0.71–0.88); DOR: 9.94 (CI 6.45–15.30)].

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Sensitivity, specificity, and DOR data of all scores predicting mortality and severity are summarized in Table 4. In summary, the sensitivity for the prediction of mortality of AP was the highest for mCTSI, Ranson, and APACHE II scores. While the specificity for prediction of mortality was the best for APACHE II, BISAP, and Ranson scores. The sensitivity for the prediction of severity of CTSI, mCTSI, and Ranson scores were the highest, while the specificity for prediction of severity were the highest for CRP and CTSI.

Table 4

ScoreSensitivitySpecificityDOR
MORTALITY
CTSI0.88 (0.69–0.97)0.61 (0.52–0.70)12.84 (4.19–39.41)
BISAP0.88 (0.71–0.96)0.77 (0.70–0.83)24.74 (9.44–64.81)
MCTSI0.95 (0.76–0.99)0.36 (0.16–0.63)10.32 (2.11–50.53)
Ranson0.91 (0.70–0.98)0.72 (0.66–0.79)28.72 (7.57–109.05)
APACHE II0.92 (0.70–0.98)0.79 (0.66–0.88)45.08 (11.4–178.2)
SEVERITY
CTSI0.81 (0.73–0.87)0.82 (0.73–0.88)19.10 (10.29–35.45)
BISAP0.73 (0.53–0.87)0.80 (0.72–0.88)11.71 (4.49–30.61)
MCTSI0.88 (0.47–0.98)0.80 (0.56–0.92)29.07 (3.36–251.91)
CRP0.71 (0.59–0.81)0.87 (0.66–0.96)16.75 (3.49–80.48)
Ranson0.79 (0.69–0.86)0.78 (0.71–0.84)13.32 (7.33–24.24)
APACHE II0.71 (0.60–0.79)0.80 (0.71–0.88)9.94 (6.45–15.30)

Summary table of mortality and severity data based on the HSROC curves.

Quality Assessment and Risk of Bias

In this study, the two main outcomes were mortality and severity of the disease, therefore, we included the data in two different tables. The majority of the studies included in our analysis met the predefined criteria of the definition of AP and contained all grades of severities, therefore the risk of bias regarding the included populations was deemed as low. The data on CTSI from all studies were significantly limited by the timeframe the CT was done from either the admission or the onset of the symptoms and the diagnosis of AP. Therefore, this is the main limitation of the applicability of our results on CTSI. The result of the risk of bias and applicability assessment is showed in Tables 5A,B.

TABLE 5A

StudyRisk of biasApplicability concernsOverall
Partici-pantsPredictorsOutcome mortalityAna-lysisPartici-pantsPredictorsOutcome mortalityRisk of biasAppli-cability
CTSIBISAPMCTSICRPRan-sonApache IICTSIBISAPMCTSICRPRan-sonApache II
Biberoglu et al., 2017+?N/AN/AN/AN/AN/A–––?N/AN/AN/AN/AN/A–+?
Bollen et al., 2011––N/A–N/AN/A?–––+N/A+N/AN/A?––+
Sharma et al., 2015––––N/AN/AN/AN/A––+?+N/AN/AN/AN/A–+
Yang et al., 2016–N/A––N/A–––?+N/A––N/A––––+
Bollen et al., 2012––––N/AN/A––––+––N/AN/A–––+
Alper et al., 2016+–N/AN/AN/AN/AN/A––+–N/AN/AN/AN/AN/A–+–
Simchuk et al., 2000–?N/AN/AN/AN/AN/A–––?N/AN/AN/AN/AN/AN/A??
Jakchairoongruang and Arjhansiri, 2013–?N/AN/AN/AN/AN/A–++?N/AN/AN/AN/AN/A–++
Park et al., 2013–––N/A––––––+–N/A–––––+
Khanna et al., 2013–+–N/A+––––––N/AN/A––––+–
Yadav et al., 2016+––N/AN/A–N/A–––+–N/AN/A–N/A–++
Papachristou et al., 2010–+–N/AN/A–––––+–N/AN/A–––++
Vriens et al., 2005––N/AN/AN/A–N/AN/A+–+N/AN/AN/A–N/A––+
Raghuwanshi et al., 2016+?N/A?N/AN/AN/A–??+N/A+N/AN/AN/A–++
Banday et al., 2015–N/AN/A?N/AN/AN/A–?–N/AN/A+N/AN/AN/A–?+
Sahu et al., 2017––N/A–N/AN/AN/A–?–+N/A+N/AN/AN/A–?+

The PROBAST Tool for mortality.

TABLE 5B

StudyRisk of biasApplicability concernsOverall
Partici-pantsPredictorsOutcome severityAna-lysisPartici-pantsPredictorsOutcome severityRisk of biasAppli-cability
CTSIBISAPMCTSICRPRan-sonApache IICTSIBISAPMCTSICRPRan-sonApache II
Cho et al., 2015–––N/A––––––––N/A––––––
Gurleyik et al., 2005––N/AN/A–N/A––––+N/AN/A–N/A–––+
Lee et al., 2016–––N/A––––?–+–N/A––––?+
Qiu et al., 2015–––N/AN/A–N/A–––––N/AN/A–N/A–––
Yue et al., 2015–+N/AN/AN/A–––––+N/AN/AN/A–––++
Bollen et al., 2011––N/A–N/AN/A?–––+N/A+N/AN/A?––+
Yang et al., 2016–N/A––N/A–––?+N/A––N/A––––+
Bollen et al., 2012––––N/AN/A––––+––N/AN/A–––+
Alper et al., 2016––N/AN/AN/AN/AN/A+–+–N/AN/AN/AN/AN/A–++
Zhao et al., 2016––N/AN/A–N/A–––+–N/AN/A–N/A–––+
Fabre et al., 2012–+N/AN/AN/A–N/A–?+N/AN/AN/AN/A?N/A–++
Hashimoto et al., 2016–?N/AN/AN/A?N/AN/A++?N/AN/AN/A?N/A?++
Park et al., 2013–––N/A––––––+–N/A–––––+
Khanna et al., 2013–+–N/A+––––––N/AN/A––––+–
Lautz et al., 2012+–N/AN/AN/A?N/A–?++N/AN/AN/A?N/AN/A++
Chatzicostas et al., 2003––N/AN/AN/A–––?––N/AN/AN/A–––––
Xu et al., 2014+–N/AN/AN/AN/AN/A––––N/AN/AN/AN/AN/A–+–
Yadav et al., 2016+––N/AN/A–N/A–––+–N/AN/A–N/A–++
Papachristou et al., 2010–+–N/AN/A–––––+–N/AN/A–––++
Huang et al., 2010––N/AN/AN/AN/AN/A–––+N/AN/AN/AN/AN/A––+
Fei et al., 2018–?–N/AN/A–––––+–N/AN/A–––?+
Kumar et al., 2017–?N/AN/A––N/A?–––N/AN/A––N/A–?–

The PROBAST Tool for severity.

red, high risk of bias or concern; yellow, unclear risk of bias or concern; green, low risk of bias or concern; +, high risk of bias or concern; ?, unclear risk of bias or concern; −, low risk of bias or concern.

Discussion

Summary of Main Findings

Severe acute pancreatitis is a serious state with high mortality and it requires high costs of the health care system. By more accurate prediction of the severity on admission, the risk of mortality can be reduced with the immediate optimal therapy.

Acute pancreatitis is diagnosed on the basis of the presence of two or more of the following three criteria: abdominal pain consistent with the diagnosis elevated pancreatic enzymes to a level of more than three times the upper normal value, and characteristic findings on abdominal imaging. Different radiological modalities (ultrasound, CT) are not only necessary to make the diagnosis of AP, but by the visualization of the gallbladder and biliary tract, they can reveal its etiology as biliary or non-biliary. Furthermore, by using morphological scoring systems e.g., CTSI or mCTSI, obtaining a CT scan can be useful for assessing the severity of AP.

This is the first meta-analysis, which quantifies the accuracy of CTSI and mCTSI scores for the prediction of the severity and mortality of AP, and compares them with other commonly used scoring systems. Two previous meta-analyses (Gao et al., 2015; Yang and Li, 2016) assessed the predictive accuracy of the BISAP score, but these articles did not contain CTSI nor mCTSI. Yang and Li (2016) found that the pooled sensitivity and specificity of the BISAP for the prediction of SAP were 0.65 (CI: 0.54–0.74) and 0.84 (CI: 0.70–0.92), respectively, the pooled AUC was 0.77 (CI: 0.73–0.80). Gao et al. (2015) calculated the pooled sensitivity as 0.51 (CI: 0.43–0.60), the specificity as 0.91 (CI: 0.89–0.92), the AUC was 0.87. Based on our results, we calculated 0.73 (CI: 0.53–0.87) for sensitivity, 0.80 (CI: 0.72–0.88) for specificity, and our pooled AUC was 0.79. The results are similar, the difference in specificity between our results and those of Gao et al. (2015) may be explained by the higher numbers of articles included in our analysis.

In our meta-analysis, APACHE II proved to be the most accurate scoring system for the prediction of mortality. It is the most widely used mortality prediction score in critically ill patients, however, it contains 12 points, including numerous clinical parameters, hence its application can be cumbersome and it limits its widespread use. In addition, APACHE II is designed for patients admitted to the intensive care unit, therefore it is not suitable for the early prediction of severity of AP. The AUC's of BISAP, mCTSI, and Ranson scores overlapped with APACHE II, while those of CTSI and CRP were mildly weaker.

Computed tomography severity index is accurate to predict severity, and its accuracy did not differ from the other scoring systems. However, the Ranson, APACHE II, and BISAP scores include several clinical parameters. There is a good correlation between morphological severity according to CT scoring systems and clinical scoring systems using clinical data and laboratory parameters.

The most recent guidelines of AP recommend a CT scan 72–96 h after the onset of the symptoms (Working Group IAP/APA Acute Pancreatitis Guidelines., 2013; Hritz et al., 2015), because pancreatic parenchymal necrosis in contrast-enhanced CT rarely appears within 48 h (Ryu, 2009). The guidelines allow an earlier CT scan in case of diagnostic uncertainty.

However, the contrast-enhanced CT examination cannot always be performed in every patient. In extreme obese patients, body weight, and size preclude the CT investigation. The contrast-enhanced CT assessment requires an intravenous injection of iodinated contrast medium for the detection of hypoperfused areas in pancreas parenchyma, therefore intravenous contrast media allergy, impaired renal function, and hyperthyroidism are contraindications.

Because of the risk of radiation exposure, repeated CT scans should be avoided, and should be reserved for patients who fail to improve clinically. CT examination had shown an advantage in evaluation of local complications (Ju et al., 2006), which can modify therapeutical strategy.

Contrary to the evaluation of Ranson, BISAP, and APACHE II scores, contrast-enhanced CT assessment, and CTSI calculation require radiological expertise.

Limitations

Three of the articles contained data of children (Fabre et al., 2012; Lautz et al., 2012; Hashimoto et al., 2016), in 2 from these articles the DeBanto score was used for evaluating the severity of AP, which is a specific score for pediatric pancreatitis. The AP population of the studies is not necessarily representative for the whole AP population, because the CT scan is mostly performed in the more severe cases. In several studies not all etiologies of AP were included, Yang et al. included only patients with hyperlipidemic etiology while Alper et his coworkers included only biliary AP cases. Fifteen of the included studies were retrospectively designed, and these might have caused selection bias. The time of CTSI and mCTSI was not the same in the studies, in several studies it included a longer delay, while in others it was carried out on admission, leading to higher heterogeneity. While APACHE II, Ranson, BISAP and CRP values were established mostly on admission or within the first 48 h, the optimal timing of the CT examination 72–96 h after the onset of the symptoms and in several studies it was performed later than the other prognostic scores. This can limitate the prognostic score of the CTSI because the other scores can predict earlier severity or mortality with similar accuracy. It is also a good question if the radiologist can judge the time point of necrosis development. In populations with previous necrotizing pancreatitis, the severity cannot be accurately assessed.

For the value of predicting mortality, a considerable heterogeneity for CTSI and a substantial heterogeneity for Ranson score can be observed, while for the severity predicting value of AP a considerable heterogeneity for CTSI, BISAP, CRP, and Ranson scores and substantial heterogeneity for mCTSI can be noticed. We suspect that the confounder factors, that cause high heterogeneity among the studies are because of different population in terms of ethnicity, BMI, age (etc.), and etiology, the different timing and interpretation of imaging modalities, and potential inter observer variability between the radiologists interpreting the CT images. Because of the long delay characterizing the studies, the severity was assessed according to several Atlanta classifications and definitions.

Conclusions

Implications for Practice

In the prediction of mortality in AP, CTSI was revealed as equally valuable as BISAP, mCTSI, CRP, or Ranson score, only APACHE II score overcame its predicting ability. Considering severity, there was no difference in the prediction value of the scores. If CT scans are performed, CTSI and mCTSI can be easily calculated and should be used in addition to the other scoring systems.

Implications for Research

Further research is warranted for the assessment of the effect of early CT and its predictive value in AP.

Statements

Data availability statement

The datasets generated for this study are available on request to the corresponding author.

Author contributions

ÉV and PH designed the research and the study concept. ÉV and AM performed the acquisition of data. PM and AS analyzed and interpreted the data. ÉV, AM, PM, AS, and PH wrote the article. AG, MB, BE, and LC supervised the study. BM, BE, JT, PS, and IR made a critical revision of the manuscript for important intellectual content. All of the authors gave their final approval of the version of the article to be published.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2019.01002/full#supplementary-material

Footnotes

1.^https://www.stata-journal.com/sjpdf.html?articlenum=st0163

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Summary

Keywords

acute pancreatitis, CT-severity index, accuracy, severity, mortality

Citation

Mikó A, Vigh É, Mátrai P, Soós A, Garami A, Balaskó M, Czakó L, Mosdósi B, Sarlós P, Erőss B, Tenk J, Rostás I and Hegyi P (2019) Computed Tomography Severity Index vs. Other Indices in the Prediction of Severity and Mortality in Acute Pancreatitis: A Predictive Accuracy Meta-analysis. Front. Physiol. 10:1002. doi: 10.3389/fphys.2019.01002

Received

04 June 2019

Accepted

19 July 2019

Published

27 August 2019

Volume

10 - 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

Updates

Copyright

© 2019 Mikó, Vigh, Mátrai, Soós, Garami, Balaskó, Czakó, Mosdósi, Sarlós, Erőss, 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: Péter Hegyi

This article was submitted to Gastrointestinal Sciences, a section of the journal Frontiers in Physiology

†These authors have contributed equally to this work

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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