RAPD markers as predictors of infectious hypodermal and hematopoietic necrosis virus (IHHNV) resistance in shrimp (Litopenaeus stylirostris).

Random amplified polymorphic DNA (RAPD) fingerprints of two shrimp populations (Litopenaeus stylirostris) were compared to find genetic marker(s) that may be associated with infectious hypodermal and hematopoietic necrosis virus (IHHNV) resistance or susceptibility. Of the 100 10-mer random primers and 100 intersimple-sequence repeat (ISSR) primers screened, five provided markers specific to the Super Shrimp population and three provided markers specific to the wild caught population. The two populations were further characterized for relative viral load (reported as cycle threshold, CT) using real-time quantitative PCR with primers specific to the IHHNV genome. The beta-actin gene was amplified to serve as a control for normalization of the IHHNV viral load. The mean viral load was significantly lower (C(T) = 34.58; equivalent to 3.3 x 10(1) copies of IHHNV genome/ng DNA) in Super Shrimp than in the wild caught population (CT = 23.49; equivalent to 4.2 x 10(4) copies/ng DNA; P < 0.001; CT values are inversely related to viral load). A preliminary prediction model was created with Classification and Regression Tree (CART) software (Salford Systems, San Diego, Calif.), where the resultant decision tree uses the presence or absence of seven RAPD markers as predictors of the relative viral load.     

Viral interference between infectious hypodermal and hematopoietic necrosis virus and white spot syndrome virus in Litopenaeus vannamei

White spot syndrome virus (WSSV) is highly virulent and has caused significant production losses to the shrimp culture industry over the last decade. Infectious hypodermal and hematopoietic necrosis virus (IHHNV) also infects penaeid shrimp and, while being less important than WSSV, remains a major cause of significant production losses in Litopenaeus vannamei (also called Penaeus vannamei) and L. stylirostris (also called Penaeus stylirostris). These 2 viruses and their interactions were previously investigated in L. stylirostris. We report here laboratory challenge studies carried out to determine if viral interference between IHHNV and WSSV also occurs in L. vannamei, and it was found that experimental infection with IHHNV induced a significant delay in mortality following WSSV challenge. L. vannamei infected per os with IHHNV were challenged with WSSV at 0, 10, 20, 30, 40 and 50 d post-infection. Groups of na?ve shrimp infected with WSSV alone died in 3 d whereas shrimp pre-infected with IHHNV for 30, 40 or 50 d died in 5 d. Real-time PCR analysis showed that the delay correlated to the IHHNV load and that WSSV challenge induced a decrease in IHHNV load, indicating some form of competition between the 2 viruses.     

Detection and quantification of infectious hypodermal and hematopoietic necrosis virus in penaeid shrimp by real-time PCR

A real-time PCR method using a fluorogenic 5' nuclease assay and a PE Applied Biosystems GeneAmp 5700 sequence detector was developed to detect infectious hypodermal and hematopoietic necrosis virus (IHHNV) in penaeid shrimp. A pair of PCR primers to amplify an 81 bp DNA fragment and a fluorogenic probe (TaqMan probe) were selected from ORF1 (open reading frame 1) of the IHHNV genome. The primers and TaqMan probe used in this assay were shown to be specific for IHHNV and did not react with either hepatopancreatic parvovirus (HPV), white-spot syndrome virus (WSSV), or shrimp DNA. A plasmid, pIHHNV-P4, containing the target IHHNV sequence was constructed and used as a positive control. The concentration of pIHHNV-P4 was determined through spectrophotometric analysis and the plasmid was used for quantitative studies. This real-time PCR assay had a detection limit of 10 copies and a log-linear range up to 5 x 10(7) copies of IHHNV DNA. The assay was then used to quantify IHHNV in infected shrimp collected from 5 locations: Hawaii, Panama, Mexico, Guam, and the Philippines. The quantitative analysis showed that wild-caught, large juvenile Penaeus stylirostris collected from the Gulf of California (Mexico) in 1996 were naturally infected with IHHNV and contained up to 10(9) copies of IHHNV microg(-1) of DNA. Similar quantities of IHHNV were detected in hatchery-raised, small juvenile P. stylirostris collected from Guam in 1995 and in farm-raised, post-larval P. monodon from the Philippines in 1996. Laboratory-infected P. stylirostris contained approximately 10(8) copies of IHHNV 31 d after being fed with IHHNV-infected shrimp tissue. In contrast, individuals of Super Shrimp, a line of P. stylirostris selected for IHHNV resistance, showed no signs of infection 32 d after ingesting IHHNV-infected shrimp tissue. Laboratory-infected P. vannamei also contained approximately 10(8) copies of IHHNV 30 d after being fed infected shrimp tissue. A time-course study of IHHNV replication in juvenile P. vannamei showed that the doubling time in the exponential growth phase was approximately 22 h.     

Classification Models for Neurocognitive Impairment in HIV Infection Based on Demographic and Clinical Variables

Objective

We used demographic and clinical data to design practical classification models for prediction of neurocognitive impairment (NCI) in people with HIV infection.

Methods

The study population comprised 331 HIV-infected patients with available demographic, clinical, and neurocognitive data collected using a comprehensive battery of neuropsychological tests. Classification and regression trees (CART) were developed to obtain detailed and reliable models to predict NCI. Following a practical clinical approach, NCI was considered the main variable for study outcomes, and analyses were performed separately in treatment-naïve and treatment-experienced patients.

Results

The study sample comprised 52 treatment-naïve and 279 experienced patients. In the first group, the variables identified as better predictors of NCI were CD4 cell count and age (correct classification [CC]: 79.6%, 3 final nodes). In treatment-experienced patients, the variables most closely related to NCI were years of education, nadir CD4 cell count, central nervous system penetration-effectiveness score, age, employment status, and confounding comorbidities (CC: 82.1%, 7 final nodes). In patients with an undetectable viral load and no comorbidities, we obtained a fairly accurate model in which the main variables were nadir CD4 cell count, current CD4 cell count, time on current treatment, and past highest viral load (CC: 88%, 6 final nodes).

Conclusion

Practical classification models to predict NCI in HIV infection can be obtained using demographic and clinical variables. An approach based on CART analyses may facilitate screening for HIV-associated neurocognitive disorders and complement clinical information about risk and protective factors for NCI in HIV-infected patients.     

Importance of physical and hydraulic characteristics to unionid mussels: a retrospective analysis in a reach of large river

Interest in understanding physical and hydraulic factors that might drive distribution and abundance of freshwater mussels has been increasing due to their decline throughout North America. We assessed whether the spatial distribution of unionid mussels could be predicted from physical and hydraulic variables in a reach of the Upper Mississippi River. Classification and regression tree (CART) models were constructed using mussel data compiled from various sources and explanatory variables derived from GIS coverages. Prediction success of CART models for presence–absence of mussels ranged from 71 to 76% across three gears (brail, sled-dredge, and dive-quadrat) and 51% of the deviance in abundance. Models were largely driven by shear stress and substrate stability variables, but interactions with simple physical variables, especially slope, were also important. Geospatial models, which were based on tree model results, predicted few mussels in poorly connected backwater areas (e.g., floodplain lakes) and the navigation channel, whereas main channel border areas with high geomorphic complexity (e.g., river bends, islands, side channel entrances) and small side channels were typically favorable to mussels. Moreover, bootstrap aggregation of discharge-specific regression tree models of dive-quadrat data indicated that variables measured at low discharge were about 25% more predictive (PMSE = 14.8) than variables measured at median discharge (PMSE = 20.4) with high discharge (PMSE = 17.1) variables intermediate. This result suggests that episodic events such as droughts and floods were important in structuring mussel distributions. Although the substantial mussel and ancillary data in our study reach is unusual, our approach to develop exploratory statistical and geospatial models should be useful even when data are more limited. Handling editor: D. Dudgeon     

Impacts of predictor variables and species models on simulating Tamarix ramosissima distribution in Tarim Basin,northwestern China

Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin, Northwest China. Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities. However, the uncertainty in the data and models inevitably reduces their prediction power. The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T. ramosissima distribution, to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T. ramosissima in this basin.Methods Three models—the generalized linear model (GLM), classification and regression tree (CART) and Random Forests—were selected and were processed on the BIOMOD platform. The presence/absence data of T. ramosissima in the Tarim Basin, which were calculated from vegetation maps, were used as response variables. Climate, soil and digital elevation model (DEM) data variables were divided into four datasets and then used as predictors. The four datasets were (i) climate variables, (ii) soil, climate and DEM variables, (iii) principal component analysis (PCA)-based climate variables and (iv) PCA-based soil, climate and DEM variables.Important findings The results indicate that predictive variables for species distribution models should be chosen carefully, because too many predictors can reduce the prediction power. The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models. Our results implied that it is better to reduce the correlating predictors before model processing. The Random Forests model was more precise than the GLM and CART models. The best model for T. ramosissima was the Random Forests model with climate predictors alone. Soil variables considered in this study could not significantly improve the model's prediction accuracy for T. ramosissima. The potential distribution area of T. ramosissima in the Tarim Basin is ~3.57 × 10 4 km 2, which has the potential to mitigate global warming and produce bioenergy through restoring T. ramosissima in the Tarim Basin.     

Comparison of discriminant function and classification tree analyses for age classification of marmots

We evaluated the predictive power of two classification techniques, one parametric – discriminant function analysis (DFA) and the other non-parametric – classification and regression tree analysis (CART), in order to provide a non-subjective quantitative method of determining age class in Vancouver Island marmots ( Marmota vancouverensis ) and hoary marmots ( Marmota caligata ). For both techniques we used morphological measurements of known-age male and female marmots from two independent population studies to build and test predictive models of age class. Both techniques had high predictive power (69–86%) for both sexes and both species. Overall, the two methods performed identically with 81% correct classification. DFA was marginally better at discriminating among older more challenging age classes compared to CART. However, in our test samples, cases with missing values in any of the discriminant variables were deleted and hence unclassified by DFA, whereas CART used values from closely correlated variables to substitute for the missing values. Therefore, overall, CART performed better (CART 81% vs DFA 76%) because of its ability to classify incomplete cases. Correct classification rates were approximately 10% higher for hoary marmots than for Vancouver Island marmots, a result that could be attributed to different sets of morphological measurements. Zygomatic arch breadth measured in hoary marmots was the most important predictor of age class in both sexes using both classification techniques. We recommend that CART analysis be performed on data-sets with incomplete records and used as a variable screening tool prior to DFA on more complete data-sets.     

Susceptibility to an inoculum of infectious hypodermal and haematopoietic necrosis virus (IHHNV) in three batches of whiteleg shrimp Litopenaeus vannamei (Boone, 1931)

The present study evaluated the susceptibility of three different batches of whiteleg shrimp Litopenaeus vannamei from Mexico to an inoculum of infectious hypodermal and haematopoietic necrosis virus (IHHNV). Each of the three shrimp batches came from a different hatchery. Because of their origin, it was possible that the genetic makeup of these batches was different among each other. The three batches tested showed differences in IHHNV susceptibility. Here, susceptibility is defined as the capacity of the host to become infected, and it can be measured by the infectivity titer. Susceptibility to IHHNV was observed in decreasing order in shrimp from batch 1 (hatchery from El Rosario, Sinaloa), batch 3 (hatchery from Nayarit) and batch 2 (hatchery from El Walamo, Sinaloa), respectively. The largest susceptibility difference between batches was 5012 times, and that between early and late juveniles from the same batch was 25 times. These results indicate that within a species, susceptibility to a pathogen such as IHHNV can have large differences. Susceptibility to pathogens is an important trait to consider before performing studies on pathogenesis. It may influence virological parameters such as speed of replication, pathogenicity and virus titer. In order to evaluate the potential use of IHHNV as a natural control agent against white spot syndrome virus (WSSV), it is necessary to know host susceptibility and the kinetics of IHHNV infection. These features can help to determine the conditions in which IHHNV could be used as antagonist in a WSSV infection.     

Weighing risk factors associated with bee colony collapse disorder by classification and regression tree analysis

Colony collapse disorder (CCD), a syndrome whose defining trait is the rapid loss of adult worker honey bees, Apis mellifera L., is thought to be responsible for a minority of the large overwintering losses experienced by U.S. beekeepers since the winter 2006-2007. Using the same data set developed to perform a monofactorial analysis (PloS ONE 4: e6481, 2009), we conducted a classification and regression tree (CART) analysis in an attempt to better understand the relative importance and interrelations among different risk variables in explaining CCD. Fifty-five exploratory variables were used to construct two CART models: one model with and one model without a cost of misclassifying a CCD-diagnosed colony as a non-CCD colony. The resulting model tree that permitted for misclassification had a sensitivity and specificity of 85 and 74%, respectively. Although factors measuring colony stress (e.g., adult bee physiological measures, such as fluctuating asymmetry or mass of head) were important discriminating values, six of the 19 variables having the greatest discriminatory value were pesticide levels in different hive matrices. Notably, coumaphos levels in brood (a miticide commonly used by beekeepers) had the highest discriminatory value and were highest in control (healthy) colonies. Our CART analysis provides evidence that CCD is probably the result of several factors acting in concert, making afflicted colonies more susceptible to disease. This analysis highlights several areas that warrant further attention, including the effect of sublethal pesticide exposure on pathogen prevalence and the role of variability in bee tolerance to pesticides on colony survivorship.     

Microevolutionary Patterns and Molecular Markers: The Genetics of Geographic Variation in Ascaris suum

Molecular markers have been used only rarely to characterize the population genetic structure of nematodes. Published studies have suggested that different taxa may show distinct genetic architectures. Isoenzyme and RAPD markers have been used to investigate geographic variation of Ascaris suum at the level of infrapopulations (nematodes within individual hosts), within localities, and among geographic regions. Independent estimates of genetic differentiation among population samples based on isoenzyme and RAPD data showed similar patterns and substantial correlation. Heterozygote deficiencies within infrapopulations and large values for inbreeding coefficients among infrapopulations suggested that the composition of these populations was not consistent with a model of random recruitment from a large panmictic pool of life-cycle stages. Both isoenzyme and RAPD markers revealed moderate levels of genetic differentiation among samples representing infrapopulations and localities. Of total gene diversity, 9.4% (isoenzyme) and 9.2% (RAPD) was partitioned among infrapopulations. Geographic localities accounted for 7.8% (isoenzyme) and 6.2% (RAPD) of total diversity. Only infrapopulations from the same farm had low levels of differentiation.     

Log-linear models for gene mapping with affected sib pair data

In genome-wide screens of genetic marker loci, non-mendelian inheritance of a marker is taken to indicate its vicinity to a disease locus. Heritable complex traits are thought to be under the influence of multiple possibly interacting susceptibility loci yet the most frequently used methods of linkage and association analysis focus on one susceptibility locus at a time. Here we introduce log-linear models for the joint analysis of multiple marker loci and interaction effects between them. Our approach focuses on affected sib pair data and identical by descent (IBD) allele sharing values observed on them. For each heterozygous parent, the IBD values at linked markers represent a sequence of dependent binary variables. We develop log-linear models for the joint distribution of these IBD values. An independence log-linear model is proposed to model the marginal means and the neighboring interaction model is advocated to account for associations between adjacent markers. Under the assumption of conditional independence, likelihood methods are applied to simulated data containing one or two susceptibility loci. It is shown that the neighboring interaction log-linear model is more efficient than the independence model, and incorporating interaction in the two-locus analysis provides increased power and accuracy for mapping of the trait loci.     

Survival analysis with time-varying regression effects using a tree-based approach

Nonproportional hazards often arise in survival analysis, as is evident in the data from the International Non-Hodgkin's Lymphoma Prognostic Factors Project. A tree-based method to handle such survival data is developed for the assessment and estimation of time-dependent regression effects under a Cox-type model. The tree method approximates the time-varying regression effects as piecewise constants and is designed to estimate change points in the regression parameters. A fast algorithm that relies on maximized score statistics is used in recursive segmentation of the time axis. Following the segmentation, a pruning algorithm with optimal properties similar to those of classification and regression trees (CART) is used to determine a sparse segmentation. Bootstrap resampling is used in correcting for overoptimism due to split point optimization. The piecewise constant model is often more suitable for clinical interpretation of the regression parameters than the more flexible spline models. The utility of the algorithm is shown on the lymphoma data, where we further develop the published International Risk Index into a time-varying risk index for non-Hodgkin's lymphoma.     

Microsatellite and RAPD polymorphisms in Ontario corn hybrids are related to the commercial sources and maturity ratings

Random-amplified polymorphic DNA (RAPD) and microsatellite markers were used to estimate the genetic relationships among 37 Ontario corn hybrids. Almost all (95%) of the 160 RAPD fragments and all of the 79 microsatellite alleles were polymorphic across the 37 hybrids. Similarity values among the hybrids ranged from 31% to 86% when based on the RAPD data. The similarities based on microsatellite markers ranged from 12% to 77%. The genetic diversity revealed by microsatellite marker analysis was higher than that obtained from RAPD analysis. The similarity matrices for the microsatellite data and the RAPD data were moderately correlated (0.43). Cluster analyses based on either type of marker showed that most of the hybrids from the same company were closely related to each other. Both dendrograms clustered similar pairs or groups of hybrids. A principal component analysis, based on the combined RAPD and microsatellite data, yielded a good separation of the hybrids with Ontario Corn Heat Unit (OCHU) values <2800 from those with OCHU values >2800. Seventeen RAPD markers and 5 microsatellite markers were significantly associated with the OCHU ratings of the hybrids.     

Applying ecological site concepts and state‐and‐transition models to a grazed riparian rangeland

Ecological sites and state‐and‐transition models are useful tools for generating and testing hypotheses about drivers of vegetation composition in rangeland systems. These models have been widely implemented in upland rangelands, but comparatively, little attention has been given to developing ecological site concepts for rangeland riparian areas, and additional environmental criteria may be necessary to classify riparian ecological sites. Between 2013 and 2016, fifteen study reaches on five creeks were studied at Tejon Ranch in southern California. Data were collected to describe the relationship between riparian vegetation composition, environmental variables, and livestock management; and to explore the utility of ecological sites and state‐and‐transition models for describing riparian vegetation communities and for creating hypotheses about drivers of vegetation change. Hierarchical cluster analysis was used to classify the environmental and vegetation data (15 stream reaches × 4 years) into two ecological sites and eight community phases that comprised three vegetation states. Classification and regression tree (CART) analysis was used to determine the influence of abiotic site variables, annual precipitation, and cattle activity on vegetation clusters. Channel slope explained the greatest amount of variation in vegetation clusters; however, soil texture, geology, watershed size, and elevation were also selected as important predictors of vegetation composition. The classification tree built with this limited set of abiotic predictor variables explained 90% of the observed vegetation clusters. Cattle grazing and annual precipitation were not linked to qualitative differences in vegetation. Abiotic variables explained almost all of the observed riparian vegetation dynamics—and the divisions in the CART analysis corresponded roughly to the ecological sites—suggesting that ecological sites are well‐suited for understanding and predicting change in this highly variable system. These findings support continued development of riparian ecological site concepts and state‐and‐transition models to aid decision making for conservation and management of rangeland riparian areas.     

Comparison of RAPD and ISSR markers for genetic diversity analysis among different endangered Mangifera indica genotypes of Indian Gir forest region

The genetic variability and relationships among 20 Mangifera indica genotypes representing 15 endangered and 5 cultivars, obtained from Indian Gir forest region, were analyzed using 10 random amplified polymorphic DNA (RAPD) and 21 inter simple sequence repeat (ISSR) markers. RAPD markers were more efficient than the ISSR assay with regards to polymorphism detection. Also, the average numbers of polymorphic loci per primer, average polymorphic information content (PIC) and primer index (PI) values were more for RAPD than for ISSR. But, total number of genotype specific marker loci, Nei’s genetic diversity (h), Shannon’s information index (I), total heterozygosity (Ht), average heterozygosity (Hs) and mean coefficient of gene differentiation (Gst) were more for ISSR as compared to RAPD markers. The regression test between the two Nei’s genetic diversity indexes showed low regression between RAPD and ISSR based similarities but maximum for RAPD and RAPD + ISSR based similarities. The pattern of clustering of genotypes within groups was not similar when RAPD and ISSR derived dendrogram were compared. Thus, both the markers were equally important for genetic diversity analysis in M. indica.     

Stability analysis of mixtures of mutagenetic trees

Background  

Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned.     

Developing a discrimination rule between breast cancer patients and controls using proteomics mass spectrometric data: a three-step approach

To discriminate between breast cancer patients and controls, we used a three-step approach to obtain our decision rule. First, we ranked the mass/charge values using random forests, because it generates importance indices that take possible interactions into account. We observed that the top ranked variables consisted of highly correlated contiguous mass/charge values, which were grouped in the second step into new variables. Finally, these newly created variables were used as predictors to find a suitable discrimination rule. In this last step, we compared three different methods, namely Classification and Regression Tree (CART), logistic regression and penalized logistic regression. Logistic regression and penalized logistic regression performed equally well and both had a higher classification accuracy than CART. The model obtained with penalized logistic regression was chosen as we hypothesized that this model would provide a better classification accuracy in the validation set. The solution had a good performance on the training set with a classification accuracy of 86.3%, and a sensitivity and specificity of 86.8% and 85.7%, respectively.     

Detection and analysis of genetic variation in Hordeum spontaneum populations from Israel using RAPD markers

Randomly amplified polymorphic DNA (RAPD) markers were used to analyse genetic diversity within and between Hordeum spontaneum populations sampled from Israel. Nei's index of genetic differentiation was used to partition diversity into within and between population components. Fifty-seven per cent of the variation detected was partitioned within 10 H. spontaneum populations. Using principal component and multiple regression analysis, part of the variation detected between populations was seen to be associated with certain ecogeographical factors. Fifty-eight per cent of the distribution of the phenotypic frequencies of three RAPD phenotypes detected using a single primer in 20 H. spontaneum populations could be accounted for by four ecogeographical variables, suggesting adaptive variation at certain RAPD loci.     

The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables

This research assesses the potential impact of weekly weather variability on the incidence of cryptosporidiosis disease using time series zero-inflated Poisson (ZIP) and classification and regression tree (CART) models. Data on weather variables, notified cryptosporidiosis cases and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Both time series ZIP and CART models show a clear association between weather variables (maximum temperature, relative humidity, rainfall and wind speed) and cryptosporidiosis disease. The time series CART models indicated that, when weekly maximum temperature exceeded 31°C and relative humidity was less than 63%, the relative risk of cryptosporidiosis rose by 13.64 (expected morbidity: 39.4; 95% confidence interval: 30.9–47.9). These findings may have applications as a decision support tool in planning disease control and risk-management programs for cryptosporidiosis disease.     

Risk stratification of spontaneous bacterial peritonitis in cirrhosis with ascites based on classification and regression tree analysis

Risk stratification for spontaneous bacterial peritonitis (SBP) in patients with cirrhosis and ascites helps guide care. Existing prediction models, such as end-stage liver disease (MELD) score, are accurate but controversial in clinical practice. We developed and validated a practical user-friendly bedside tool for SBP risk stratification of patients with cirrhosis and ascites. Using classification and regression tree (CART) analysis, a model was developed for prediction of SBP in cirrhosis with ascites. The CART model was derived on data collected from 676 patients admitted from January 2007 to December 2009 retrospectively, and then was prospectively tested in another independent 198 inpatients between January 2010 and December 2010. The accuracy of CART model was evaluated using the area under the receiver operating characteristic curve. The performance of the model was further validated by comparing its predictive accuracy with that of the MELD score. Furthermore, the model was used to stratify SBP among patients with MELD scores under 15. CART analysis identified four variables for prediction of SBP: creatinine, total bilirubin, prothrombin time and white blood cell count, and three risk groups: low (2.0%), intermediate (27.5–33.3%) and high (60.6–86.4%) risk. The accuracy of CART model (0.881) exceeded that of MELD (0.791). Subjects in the intermediate risk and high risk groups had 22.21-fold (95% confident interval (CI), 9.98–49.45) and 173.50-fold (95% CI, 77.68–634.33) increased risk of SBP, respectively, comparing with the low risk group. Similar results were found when this risk stratification was applied to the validation cohort. Cirrhotic patients with ascites at low, intermediate, and high risk for SBP can be easily identified using CART model, which provides clinicians with a validated, practical bedside tool for SBP risk stratification.