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Bedside Index for Severity in Acute Pancreatitis (BISAP)

Abstract
Objectives The aim of this study was to evaluate the diagnostic performance of Bedside Index for Severity in Acute Pancreatitis (BISAP) score for predicting severe acute pancreatitis (SAP) in the early phase.
Method The PubMed, Cochrane library and EMBASE databases were searched until May 2014. The strict selection criteria and exclusion criteria were determined, and we applied hierarchic summary receiver operating characteristic (HSROC) model and bivariate random effects models to assess thediagnosibility of the BISAP score for predicting SAP. We obtained pooled summary statistics for sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and calculated the area under the HSROC curve (AUC). The 95% confidence intervals (CI) for each diagnostic test measure were also calculated. Publication bias was assessed using Deek’s funnel plot asymmetry test. Statistical analyses were performed using the STATA12.0 software.
Results The pooled sensitivity, specificity, PLR, NLR, and DOR were 64.82%, 83.62%, 3.96, 0.42 and 9.41, respectively. The AUC was 0.77 and the HSROC curve for individual studies was generated and analyzed to explore the influence of threshold effects.
Conclusion We confirmed that BISAP score is an accurate means to predict SAP in the early phase.
Keywords: BISAP, HSROC curve, severe acute pancreatitis, acute pancreatitis
Introduction
Acute pancreatitis (AP) is an inflammatory condition of the pancreas with a clinical course that varies from mild to severe and characterized by activation of pancreatic enzymes to cause self-digestion of the pancreas 1. Generally, AP is mild, self-limiting, and requires no special treatment and ranges about 80-90% of patients with only minimal or transitional systemic manifestations, but about 20%-30% of patients develop a severe disease that can progress to systemic inflammation and cause pancreatic necrosis, multi-organ failure, and potentially death 1-4. So it is important to have an early, quick, and accurate risk stratification of AP patients, which would permit evidence-based early initiation of intensive care therapy for patients with severe AP (SAP) to prevent adverse outcomes and allow treatment of mild AP (MAP) on the common ward. Early identification of patients with SAP would allow the clinician to consider more aggressive interventions within a time frame that could prevent possible complications.
Currently, there are a variety of score systems developed for the early detection of SAP, such as Ranson’s score 5, acute physiology and chronic health examination (APACHE) II 6, 7 and computed tomography severity index (CTSI) 8. Also there are many inflammation markers such as C-reactive protein (CRP), interleukin-6 (IL-6) and others 9, 10. Several studies show that cytokines play an important role in the cascading inflammatory responses 11 and it may act as mediators of distant organ complications in SAP. So the levels of cytokine in serum may also reflect the degree of the inflammatory response 12. In 2008, Wu et al. 13 proposed a new prognostic scoring system, the bedside index of severity in acute pancreatitis (BISAP), is a simple and accurate  method that can predict the clinical severity of AP within 24 h of presentation. BISAP incorporates five parameters: blood urea nitrogen > 25 mg/dL, impaired mental status, systemic inflammatory response syndrome (SIRS), age > 60 years, and detection of pleural effusion by imaging 14.
Unfortunately there has been no systematic or meta-analytic review of cross-sectional studies of this scoring system. The purpose of this study was to aggregate the reported data across the different studies and to assess the ability of the BISAP score to predict SAP.
Materials and methods
2.1 Literature search
The search was performed on three databases: PubMed, Cochrane library and EMBASE. These databases were searched from the first date available in each database up to May 2014, using the search terms ‘acute pancreatitis’ AND (‘BISAP’ OR ‘bedside index of severity in acute pancreatitis’). Once articles had been collected, bibliographies were then hand-searched for additional references.
2.2 Inclusion and Exclusion Criteria
To be included in this meta-analysis, studies must meet the following criteria: (1) studies evaluate the BISAP score for predicting SAP; (2) the subjects were diagnosed with AP; (3) prospective study; (4) the absolute numbers of true positive (TP), false negative (FN), false positive (FP), and true negative (TN) test results were available or derivable from the article; (5) the clinical result of patients was indicated as SAP.
Studies were excluded if one of the following existed: (1) the numbers of TP, FN, FP, and TN test results were not derivable from the article; (2) cross-sectional study; (3) non-original articles, such as review, meeting abstract, case report and comment; (4) duplicate of previous publications and data description is not clear.
2.3 Data extraction and quality assessment
All data were extracted independently by two authors according to the inclusion criteria listed above. Disagreements were resolved by discussion or solved by consultation of a third reviewer. The following characteristics were collected from each study: the first author, year of publication, source, experiment design, sample size, the reference standard (gold standard), the numbers of TP, FN, FP, and TN and others. The QADAS (Quality Assessment of Diagnostic Accuracy Studies) criteria were used to assess the quality of diagnostic accuracy studies included in this meta-analysis 15.
Statistical analyses
Hierarchical summary receiver operating characteristic (HSROC) modeland bivariate random effects model were performed in STATA 12.0 (StataCorp, College Station, TX, USA) software using the program ‘metandi’ to generate pooled accuracy estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and calculated the area under the HSROC curve (AUC) 16. The HSROC curve for individual studies was generated and analyzed to explore the influence of threshold effects. The 95% confidence intervals (CI) for each diagnostic test measure were also calculated. Publication bias was assessed using Deek’s funnel plot asymmetry test 17.
Results
3.1 Eligible Studies
The process of selecting studies for the meta-analysis was shown in Fig. 1. There were 32 studies potentially eligible studies identified. Of these, 14 studies were excluded after screening based on abstracts or titles to avoid obvious irrelevance. Finally, 9 studies 14, 18-25 met the inclusion criteria and were included in the meta-analysis. The data collected from the related studies was summarized in Table 1. Among these studies, kim et al. 20 reported the results of the meta-analysis with the cutoff values set at 2and 3, respectively. All patients were recruited within 24 h from the time of admission or transfer and used for the calculation of the BISAP scores. All included citations were prospective cohort studies. The absolute numbers of TP, FN, FP, and TN were calculated by sample size and the degree of sensitivity and specific.
A summary of the quality of the studies was displayed in table 2. The included studies were not descript the tenth quality indicator (were the index test results interpreted without knowledge of the results of the reference?) and the eleventh quality indicator (were the reference standard results interpreted without knowledge of the results of the index test?) 15. At the same time, there are some studies not described in detail for eliminate and exit objects.
3.2 Meta-analysis
The results of the HSROC model were show in Table 3. The pooled sensitivity of BISAP testing for the diagnosis of SAP was 64.82% (95% CI: 54.47%-73.74%), and the specificity was 83.62% (95%CI: 70.03%-91.77%). The pooled DOR was 9.41 (95%CI: 5.38-16.45), the PLR was 3.96 (95%CI: 2.27-6.89), and the NLR was 0.42 (95%CI: 0.34-0.52). The AUC of the HSROC was 0.77 (95%CI: 0.73-0.80) (Fig. 2). The I2 index of heterogeneity was 95% (95% CI, 91%-99%).
3.3 Subgroup Analyses
There was a negative correlation between the logits of sensitivity and specificity (Spearman correlation coefficient, 20.09), indicating the present of an importanteffect of the diagnostic threshold (cutoff level) on the performance of BISAP score. The following cutoffs were selected for subgroups analysis (Table 4).
Analysis of studies that set the BISAP cutoff point at 2, the pooled sensitivity, specificity, PLR, NLR, and DOR were 67.30% (95%CI: 60.53%-73.42%), 78.28% (95%CI: 68.86%-85.46%), 3.10 (95%CI: 2.12-4.52), 0.42 (95%CI: 0.34-0.51) and 7.42 (95%CI: 4.39-12.54), respectively. The AUC of the HSROC was 0.70(95%CI: 0.66-0.74).
Analysis of studies that set the BISAP cutoff point at 3, the pooled sensitivity, specificity, PLR, NLR, and DOR were 61.18% (95%CI: 41.20%-78.00%), 88.64% (95%CI: 88%-97.18%), 5.39 (95%CI: 1.80-16.12), 0.44 (95%CI: 0.30-0.64), and (95%CI: 4.44-34.03), respectively. The AUC of the HSROC was 0.78 (95%CI: 0.75-0.82).
3.4 Publication Bias
Deeks’ Funnel Plot Asymmetry Test for the overall analysis showed that no significant publication bias was found (P = 0.359, Fig. 3).
Discussion
Generally, Ranson, APACHE II, and CTSI scoring systems have been used to evaluate the severity of AP 22, 23. However, these techniques all have their inherent strengths and weaknesses. For example, the Ranson’s score 5 is relatively accurate at classifying the severity of AP, but the evaluation cannot be completed until 48 h, which will miss the potential for early treatment and increase mortality. The APACHE II system 6, 7 allows the determination of disease on the first day of admission and is more accurate than Ranson’s score, but complexity is its major drawback. CTSI 26, 27 is calculated based on CT findings of some local complications and cannot reflect the systemic inflammatory response. Recently, the BISAP score has been proposed as an accurate method for early identification of patients at risk for in hospital mortality 13. Several studies showed that BISAP score is a reliable and accurate means for predicting the severity of AP in the early phase 18, 22, 23. But these studies are not systematic, so we collect the reported data across the different studies and apply HSROC model and bivariate random effects model to assess the ability of the BISAP score to predict SAP. The pooled sensitivity, specificity, PLR, NLR, and DOR were 64.82%, 83.62%, 3.96, 0.42 and 9.41, respectively. The AUC of the HSROC was 0.77. Our meta-analysis indicated that BISAP score is a reliable and accurate means to predict SAP.
This meta-analysis assessed the diagnostic performance of BISAP in 1972 individuals from 9 research studies 14, 18-25. The results show that the performance of BISAP to predict the severity of AP has a good specificity, but moderate sensitivity in predicting SAP. In addition, compared with other scoring systems in predicting SAP, BISAP has a higher specificity but a lower sensitivity 21-23, 28. The low sensitivity may be caused by these factors. First, the characteristics of study participants are differences (cultural and geographical differences), such as lifestyle, race, and genetic basis. Second, etiologic distribution may also explain the noted differences. Third, the different definitions of SAP may also be a reason for these variations.
The HSROC curve presents a global summary of test performance and shows the tradeoff between sensitivity and specificity. The summary DOR and the AUC of the HSROC were 9.41 and 0.77, respectively.The predictive accuracy of BISAP scoring system was measured by AUC. An AUC of 1.0 represents a perfect test, whereas an AUC of 0.5 represents a test that performs no better than chance 29. The result revealed that the discrimination of disease severity was good in our study, which is similar to other reports. DOR is a single indicator of test accuracy that combines the sensitivity and specificity data into a single number. The DOR of a test is the ratio of the odds of positive test results in the patient with disease relative to the odds of positive test results in the patient without disease. The value of a DOR ranges from 0 to infinity, with higher values indicating better discriminatory test performance (higher accuracy).A DOR of 1.0 indicates that a test does not discriminate between patients with the disorder and those without it 30. In the present meta-analysis, we found that the pooled DOR was 9.41, also indicated a high level of overall accuracy.
Since the HSROC curve and theDOR are not easy to interpret or use in clinical practice, and likelihood ratios are considered to be more clinically meaningful, we also presented both PLR and NLR as our measures of diagnostic accuracy. Likelihood ratios of > 10 or < 0.1 generate large and often conclusive shifts from pre-test to posttest probability (indicating high accuracy) 31. The PLR and NLR value were 3.96 and 0.42, respectively. This result performed similar to traditional scoring systems in predicting SAP and suggested that the accuracy of still need to improve. But BISAP is relatively simple and had greater accuracy than other scoring systems, making it a promising method of predicting SAP 14, 19, 21, 28. Furthermore,it may be combined in medical decision-making at the extreme of the prediction range, such as enrollment criteria for clinical trials, and as triaging intensive care unit admission 32, 33.
We also explored systematically the issue of heterogeneity by use of subgroup analysis. In our analysis, the diagnostic threshold presented an important effect on the performance of BISAP score. The results demonstrated that a BISAP score of 3 had greater accuracy and high predictive value than a score of 2 for predicting SAP.
Our meta-analysis had several limitations. First, when the BISAP scoring system converts continuous variables into binary values of equal weight, it fails to capture synergistic or multiplicative effects based on the interactions of interdependent systems 21. Future research could focus on comprehensive reassessment of the pathologic mechanisms of AP with attention to the effects of preexisting risk factors (e.g. age, obesity, genetic) and well-defined end points, identification of accurate biomarkers to assess activity on these pathways, and mathematical models that have strong predictive accuracy.
Second, the exclusion of conference abstracts, letters to the editor, and non-English-language studies might have led to publication bias, which was not found in the present review. However, a review of these abstracts and letters suggested that the overall results were similar to the results in the English language studies included. Third, there is a risk for publication bias in which positive results or results with ‘expected’ findings are more likely to be published. We made every possible effort to minimize this type of bias by contacting investigators in the field of BISAP. If editors were more likely to publish manuscripts showing the ‘expected’ results of a good diagnostic performance for BISAP, then our results may be overestimating the real diagnostic performance of BISAP.
In conclusion, we confirmed that BISAP score is an accurate means to predict SAP in the early phase. Due to simplicity and easily obtained parameters, BISAP score should gain broad acceptance in routine use not by replacing clinical assessment, but rather by complementing and objectifying it.

Physiochemical Analysis of Dye Soil

INTRODUCTION
Decline in the quality of soil by the act of human and nature is called soil pollution or soil contamination. The main factors of soil pollution include excessive erosion, excessive use of soil fertilizers, pesticides, insecticides, herbicides, urban and industrial wastes, forest fires, water logging, leaching and drought etc. Studies have shown that soil contamination is increasing day by day in rural and urban areas. Faulty disposal of industrial and urban areas wastes and use of urban sewage for irrigation badly affect the physical and chemical properties of the soils. Some researchers have found that excessive use of chemical fertilizers and dyes and their improper disposal have increased the alkalinity of soils in the fringe areas of the city. Nature has an amazing ability to cope up with small amount of water wastes and pollution, but it would be hazardous if billions of gallons of waste water and other products produced everyday are not treated before releasing them back to the environment. The quantities and characteristics of discharged effluent vary from industry to industry depending on the water consumption and average daily product. Dye effluent mainly contains large amount of total dissolved solids limiting the industrial and agricultural use of water and high levels of chemical oxygen demand and biological oxygen demand.
Soil is also one of the vital resources on living planet Earth. It is heterogeneous in nature. Many scientists have documented adverse effects of different industrial effluents on the growth of plants and dye waste water has also been found toxic to several crop plants. One of the main sources with severe pollution problems worldwide is the textile industries and its dye containing wastewater. 10-25% of textile dyes are lost during the dyeing process and 2-20% is discharged as aqueous effluents in different environmental components. Waste water from any industries is often rich in color, containing residues of reactive dyes, organic chemicals and bleaching agents. Heavy metals like zinc, copper, manganese, iron etc. are present in the dye effluents. The present investigation was aimed to know the effect of dye industrial effluent on water and soil quality. It have been reported that the major problem associated with textile processing effluents is presence of heavy metal ions, which arise from material used in the dyeing process or in a considerably high amount, from metal containing dye. Most of the heavy metals are essential for growth of organisms but are only required in low concentrations. The present study was carried out to characteristic dyeing industry effluent in turn to their physicochemical properties and to evaluate the impact of industrial effluent on the soil near small dyeing industries in Sanganer industrial area, Jaipur (Rajasthan). Jaipur is situated in the eastern part of Rajasthan, surrounded on three sides by the rugged Aravali hills and is located at 26°55′N 75°49′E (26.92°N 75.82°E).
MATERIAL AND METHOD
Soil Sample Collection
Various sites of capital city of Rajasthan (i.e. Jaipur) were checked and analyzed to collect the suitable samples for the analysis of physiochemical analysis of dye soil and after testing many samples three soil samples were collected from nearby agricultural field receiving textile effluent in Sanganer area Jaipur, Rajasthan. These samples were collected in the month of February 2015 in sterile polypropylene zip lock bags from a depth of 10-15cm. Samples were collected from three different sites in which two were from industrial land and were termed as Polluted Sample-A and Polluted sample-B respectively while one sample were collected from the same site but of normal soil and termed as Controlled Sample. Samples were allowed to dry for one week in sun light under constant study and care was taken to prevent addition of any of other pollutant while drying. When particles of soils shattered from each other after proper drying many experimental analysis were conducted in the laboratory with the help of various modern lab equipment. The collected soil samples have been analyzed for physicochemical parameters like pH, organic carbon, electric conductivity, nitrogen, calcium, magnesium, potassium, phosphorus, water.
1.Study of soil colour Requirements
Munsell’s soil color chart, soil samples etc.

Principle
Color of soil is varying from one environment to another and this soil color is usually expressed in terms of symbols as given in Munsell’s chart. Compare the particles of soil with Munsell’s standard color chart. The chip with which the soil color match is taken out and symbols indicate on the chart is noted which gives the color of soil. Procedure: All the three samples were collected and compared on the Munsell’s soil chart with the help of chip. Three different colors were observed of three different samples. Observations:
S. No.
Sample type
Soil color
1
Polluted sample-A
Strong Brown
2
Polluted Sample-B
Reddish yellow
3
Controlled sample
Very Pale brown
Polluted sample-A was observed of strong brown and polluted sample-B was reddish yellow and controlled was of very pale brown. 2. Study of Soil Texture Principle Soil constitutes unconsolidated minerals particles of different size. As per the international society of soil science, the weathered minerals are classified as follows: – Gravel = >2.0 mm diameter Coarse sand = 2.0-0.2 mm diameter Fine sand = 0.2-0.02 mm diameter Silt = 0.02-0.002 mm diameter Clay = <0.002 mm diameter
Requirements
Soil samples, brown paper, sieves of different pore size, Balance.
Procedure Soil samples were gently crushed and passed successfully through a series of sieves of different mesh size. Different size minerals particles obtained are weighed. Now, based on the relative sizes of sieves and by comparing with the soil texture principle three sizes was considered as mentioned in the below table. Observations
Polluted Sample-A
Polluted sample-B
Controlled sample
Total Weight
266.62 gm
243.34gm
113.65 gm
Gravel
20.05%
15.66%
07.83%
Silt
03.19%
03.17%
02.26%
Clay
68.29%
73.81%
89.02%
Gravel was weighed found 20.05%, 15.66% and 07.83% in polluted sample- A and B and controlled respectively. Silt was weighed and found 03.19%, 03.17% and 02.26% in polluted sample-A and B and in controlled respectively. Clay was weighed and found 68.29%, 73.81% and 89.02% in polluted sample-A and B and in controlled sample respectively. 3. Study of soil pH
Requirement
Electric pH meter, soil samples, Distilled water, Buffer solutions, Filter paper.
Principle and procedure
Nature of the soil reaction is measured on a scale of 0 to 14. This scale is actually the negative logarithm of hydrogen ion concentration and therefore, as the pH value increases from 1 to 6.9, the acidity or actual H Ion concentration decreases. pH 7 is represent the natural condition and beyond it is alkaline For determination of pH prepared a soil water suspension made by mixing soil sample in distilled water (1.5 ratios). The suspension was shaken thoroughly. The supernatant liquid was tested for pH using an electricpH meter. Observations:
Samples
Readings
Concordant Reading
Polluted Sample-A
08.33
08.37
08.38
08.42
Polluted Sample-B
06.22
06.75
06.94
07.1
Controlled Sample
07.92
07.78
07.71
07.71
4. Study of soil moisture context
Principle
Soil moisture is a measure of soil water available to plant root. Moisture content is very much related to size and shape of mineral particles and the amount of pore spaces. It is an useful parameter to measure plant’s soil-moisture relationship.
Requirements
Soil samples, glass container, oven, balance etc
Procedure
Soil moisture content was generally measured by gravimetric method. In this method a soil was sampled, put into a container, weighed in the sampled condition and oven dried to constant weight (kept in oven at 105C – 110C). The samples were weighed again after drying.
Calculation:
It was generally estimated as per sent moisture content on oven dry weight basis. First of all the container weight was subtracted to get weighed of moist and dry soil.
Actual weight of moist soil = Total moist weight – weight of container= x gm
Actual weight of dry soil = Total dry weight – weight of container = y gm
% moisture contain= x-y / y * 100 Observations:
Sample name
Total moist soil weight
Total dry soil weight
% moisture contained
Polluted sample- A
20g
19.70
1.52%
Polluted sample –B
20g
19.87
0.65%
Controlled sample
20g
19.92
0.40%
Moisture content was observed and it is found that there is a ver little difference found in the three samples whether it is of polluted sample or controlled.
5. Study of conductivity of soil
Principle
Total soluble salt determined by prepared a soil suspension in distilled water and found out its electrical conductivity with the help of a conductivity meter. Conductivity depends upon ionic concentration which is directly dependent upon the total salt concentration and its dissociability. The conductivity value of the soil suspension thus obtained is compared with the value of known concentration of chemical solutions to get total salt concentration. Observations: Conductivity was measured in ds/m and was found a little difference in all the three samples this is may be because of the soil samples was from the same site of Sanganer industrial area of dyes companies.
Sample name
Conductivity
(dS/m)
Polluted sample – A
0.19
Polluted sample- B
0.26
Controlled
0.51
6. Study of organic carbon in soil
Principle
The determination of organic carbon by Walkley and Black’s Rapid titration method, which is also known as Wet-digestion method Involves in the estimation of organic – carbon in soil. A known weight of soil is treated with an excess volume of K2CR2O7 the presence of concentrated H2SO4. The soil is digested by heated by heat of dilution of H2SO4 and organic carbon was thus oxidizedto CO2. The excess of K2CR2O7 is not reduced by organic matter is titrated back against a standard solution of ferrous ammonium sulphate in the presence of phosphoric acid and diphenylamine indicator.
Requirement
Glassware – Conical flask, pipette, burette, analytical balance,
Reagents –
1N potassium dichromate
0.5N ferrous ammonium sulphate ( Mohar’s salt)
Concentrated sulphuric acid
Orthophosphoric acid
Diphenylamine indicator
Procedure:
1g of soil in a 500ml conical flask was taken, 10ml of 1nK2CR2O7 and 20 ml of concentrated H2SO4was added. The flask was shaken and allowed to rest for 30min. 200 ml of water was added to dilute the suspension, 10 ml of 85% phosphoric acid and 1ml of diphenylamine indicator was added. Titrated the solution with ferrous ammonium sulphate. Similarly blank titration was carried out.
Calculation:
% of organic –C in the soil = 0.5 * (B-S) * 1* 0.003 * (100/W)
Where,
B

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