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Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis

Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.


Results
Baseline characteristics of the derivation and validation cohorts. A total of 699 hospitalised HBV-ACLF patients were initially screened and enrolled; 365 patients in the derivation cohort and 124 in the validation cohort were ultimately included (Fig. 1). The baseline characteristics of both cohorts of patients are listed in Table 1 and Supplementary Table 1. Most patients in the derivation cohort were male and had lower rates of liver cirrhosis (P < 0.05). The derivation cohort had lower ALB, glucose, BUN and NH3 levels, and a lower WBC count, than the validation cohort, which in turn had lower D-dimer, ALP, Hb and Plt levels than the deviation cohort (all P < 0.05). No significant differences in terms of model scores or mortality were evident between the two cohorts. The characteristics of the derivation and validation cohorts stratified by 28-day mortality are shown in Table 2. The cohorts were similar in terms of the variables significantly influencing survival, i.e. the HE proportion; WBC count; the levels of ALT, TBil, INR, PT, fibrinogen, D-dimer, Hb and NH3; and the scores on all systems tested (all P < 0.05). No significant differences were found between ALSS sessions and mortality in both two cohorts (Supplementary Table 2).
CART and LRM-Z analysis in the derivation cohort. In CART analysis, HE status served as the initial variable. After HE separation, a PT of 27.8 s was chosen as the second split variable in patients lacking HE. When the PT ≤ 27.8 s, the next best predictor was the TBil level, at an optimal cut-off of 455 μmol/L. No additional node afforded any increment in risk discrimination. Therefore, four subgroups of patients, differing significantly in terms of 28-day mortality, were generated by three predictive variables identified via CART analysis: subgroup 1 (patients with HE); subgroup 2 (patients lacking HE but with a PT > 27.8 s); subgroup 3 (patients lacking HE and with a PT ≤ 27.8 s, but a TBil level > 455 μmol/L); and subgroup 4 (patients lacking HE with a PT ≤ 27.8 s, and a TBil level ≤ 455 μmol/L) (Fig. 2). Subgroups 1 and 2 were combined to form a high-risk group, with mortality rates ranging from 63.7 to 87.2%, whereas subgroups 3 and 4 were combined to form a low-risk group, with mortality rates ranging from 10.2 to 39.5%. HBV-ACLF patients in the high-risk group exhibited a 9.883-fold (95% CI: 6.002-16.275-fold, P < 0.001) increased in 28-day mortality (compared to those in the low-risk group) after ALSS-treatment (Table 3). Detailed descriptions of LRM-Z analysis are given in the Supplemental Results and Supplementary Table 3.
Validation and comparison. CART analysis was validated for its efficiency of risk stratification efficiency in an independent validation cohort containing 124 subjects. Using the flow chart of the classification tree, each patient was allocated to a subgroup ( Supplementary Fig. 1). All patients were also stratified into lowand high-risk groups. Compared to those in the low-risk group, patients in the high-risk group exhibited an 8.485-fold (95% CI: 3.726-19.202-fold, P < 0.001) increase in 28-day mortality, similar to what was found in the derivation cohort (Table 3).

Discussion
ACLF is one of the most intractable clinical problems worldwide, characterised by severe hepatic abnormalities and rapid disease progression 16 . The short-term mortality rate of HBV-ACLF is extremely high; it is essential to stratify patients by their current condition and possible prognosis to select appropriate treatment strategies 17 . Liver transplantation is optimal, but is compromised by donor organ scarcity and the need to select patients carefully 18,19 . ALSS therapies have been considered useful to replace liver function, affording an opportunity for hepatic recovery or stabilising the clinical status prior to transplantation 20 . However, the optimal timing of ALSS treatment and the target population remain remains unclear. Clinicians are in urgent need of a better method to correctly identify patients that would benefit from such treatment, to avoid unnecessary clinical burdens 21 . It is essential to screen patients with ACLF and in terms of factors that would allow them to benefit from ALSS therapy. Here, we established and validated a CART approach toward analysis and identification of predictive factors in subgroups of patients with HBV-ACLF who would benefit from ALSS therapy.
HE is the most common complication of HBV-ACLF. Previous studies reported that HE in hospitalised ACLF patients was associated with a high mortality rate 22 . Another study indicated that HE obviously affected the clinical prognosis of such patients 23 . Here, LRM-Z confirmed that HE was independently prognostic of 28-day mortality. In CART analysis, HE was the first variable split. HBV-ACLF patients with HE were allocated to subgroup 1; the 28-day mortalities were 87.2 and 87.5% in the derivation and validation cohorts, respectively. The high 28-day mortality rate of subgroup 1 suggests that HBV-ACLF patients with HE might have difficulty in earning benefits from ALSS therapy. It is generally accepted that hyperbilirubinaemia and coagulopathy are the two most prominent features of liver failure, as indicated by both official criteria and studies on ACLF diagnosis and prognosis worldwide 2,3,24,25 . PT and the TBil level were positively associated with mortality risk in both CART and LRM-Z analyses. PT was the second variable split in CART analysis and was also an independent prognostic factor in the LRM-Z model, suggesting a positive correlation between PT and ACLF mortality, consistent with data from previous studies 26,27 . It is also well received for TBil level to be the third split variable. Our results are in line with previous studies reporting that the TBil level was independently prognostic of ACLF 28,29 . CART analysis also stratified subjects into low-and high-risk groups that exhibited significant differences. Patients in the low-risk group exhibited lower than usual 28-day mortality compared to most patients with HBV-ACLF 4 , strongly indicating that ALSS therapy may improve overall survival in such patients. However, patients in the high-risk group may be difficult to earn benefits from ALSS therapy; LT is required as soon as possible.
Compared to traditional multivariate models, CART analysis has unique advantages. First, CART analysis can process high-dimensional data (even highly skewed data) when the sample size is low 30 . CART analysis can calculate probabilities and impurities using the non-missing values; missed values are ignored 31 . From our results, CART is very comparable to models using logistic regression in terms of predictive value of mortality. However, models constructed with the aid of various logistic regression coefficients are very complex, and clinical utility is compromised. Given its simple classification parameters and cut-off values, the CART model is simple and user-friendly in the hands of clinicians. Third, the CART analysis stratified patients into low-and high-risk groups exhibiting significant differences. Thus, the model facilitates subgroup/risk stratification in a manner similar to how clinicians make decisions, which may improve the management of hospitalised patients with HBV-ACLF. Patients at lower risk can be reassured that ALSS therapy will play a positive and active role in their treatment programs, but unnecessary ALSS therapy should be avoided, and LT prioritised in patients at higher risk.
Our study had certain limitations. First, potential confounders in small data samples may cause the significance of included risk factors to be overestimated, thus influencing the actual risk, which may explain why the prognostic factors identified by the CART model and LRM-Z differed 32 . Further work with a larger population is needed. Second, the CART was built in a single centre and validated in another centre, using homogeneous data. The multicentre adaptability and feasibility of CART requires further verification. Third, only HBV-infected patients were included. To generalise its use, the CART model requires further validation in patients with ACLF of diverse aetiologies. Thus, multicentre, prospective studies with larger patient populations are needed to further verify the applicability of our model.
However, the CART model is a novel, validated, user-friendly bedside tool that can screen HBV-ACLF patients in terms of eligibility for ALSS therapy. HBV-ACLF patients lacking HE and with a PT ≤ 27.8 s may benefit from such therapy. In this group, the benefit may be more pronounced when the TBil level ≤ 455 µmol/L. The CART model helps physicians correctly identify patients at lower risk (facilitating appropriate ALSS use as part of a treatment program), and to prioritise liver transplantation for patients at higher risk.

Methods
Patients. Patients were screened at two different medical centres operating identical medical record systems.
Medical data prior to ALSS therapy were collected from patient records, and derivation and validation cohorts were defined (derivation cohort:    www.nature.com/scientificreports www.nature.com/scientificreports/ Diagnostic criteria of complications. Liver cirrhosis was diagnosed based on symptoms and signs of portal hypertension and findings on ultrasonography, computed tomography or magnetic resonance imaging. Ascites was confirmed via paracentesis, abdominal imaging and other clinical evidence. HE assessment and grading employed the West Haven criteria 33 . Gastrointestinal haemorrhage was diagnosed by a positive faecal occult blood test or the presence of blood in vomit. Infections included spontaneous bacterial peritonitis, pulmonary infections and urinary tract infections, and were explored via imaging and laboratory culture 34 . Organ failure was diagnosed using the chronic liver failure-sequential organ failure assessment (CLIF-SOFA) score 2 .
Treatments. Standard medical therapy. All patients received standard medical therapy including bed rest, adequate nutritional support and single or combination of antiviral drugs. Sodium restriction, diuretics and paracentesis combined with albumin administration was used for ascites; Patients with hepatic encephalopathy received lactulose and L-ornithine aspartate; Appropriate antibiotics were applied for infections and adjusted based on the laboratory culture; Gastrointestinal haemorrhage were treated with somatostatin, pituitrin, proton pump inhibitors and necessary endoscopic therapy.
ALSS therapy. All patients received uniformed plasma exchange (PE) plus plasma bilirubin adsorption (PBA) ALSS therapy. For patients with HE, plasma perfusion (PP) was used as part of the ALSS therapy regimen 10 . The total exchanged plasma volume was 2500-3500 mL, with the exchange rate was 20-25 mL/min. The flow rate of blood was 100-130 ml/min. Dexamethasone (5 mg) and heparin (2500 U) were injected routinely before ALSS therapy. Protamine sulphate (20-50 mg) was used for neutralization in every session. Each session of ALSS therapy lasted for 4-6 hours and was repeated every 2-4 days. ALSS therapy was discontinued when the overall improvement in the patient's status and TB < 200 µmol/L or conditions such as bleeding and circulatory abnormalities that did not allow further ALSS therapy 35 . A total of 752 sessions (average 2 sessions/patient, ranging from 1 to 5 sessions) of ALSS therapy in derivation cohort, while a total of 262 sessions (average 2 sessions/ patient, ranging from 1 to 4 sessions) of ALSS therapy were performed in validation cohort (Supplementary Table 5).
ALSS therapy was initiated within 2 days of admission. The date of diagnosis of HBV-ACLF was the follow-up commencement date.       www.nature.com/scientificreports www.nature.com/scientificreports/ hepatitis B virus-deoxyribonucleic acid (HBV-DNA), white blood cell (WBC), hemoglobin (Hb) and platelet count (Plt). All laboratory data were collected at the time of hospital admission or prior to ALSS therapy.
The scores of published prognostic models (MELD, iMELD, CLIF-C ACLF and COSSH-ACLF) were calculated using the following formulas: MELD score 9  Construction of the CART and LRM-Z. Using selected variables, CART analysis divided all data into two homologous groups exhibiting different survival outcomes; the best splits and cut-off values were derived for each variable 4,36-38 . Then, the algorithm allocated data by reference to the best overall split of all best splits to a parent node, which then produce two child nodes exhibiting higher homogeneities. This process was repeated using both tree-building and -pruning until statistical analysis indicated that no further reduction in node impurity was possible or that pre-specified stop criteria had been met 39 . This generated several subgroups with predicted mortality rates. We used CART analysis to identify and screen HBV-ACLF patients eligible for ALSS therapy. Two risk groups were identified base on the mortality rates of subgroups. The ability of CART to stratify ALSS-treated HBV-ACLF patients into subgroups by mortality risks was tested using the independent validation cohort. Patients from this cohort were allocated to subgroups using the flow chart of the CART tree. Mortality was calculated for each subgroup, and odds ratios (ORs) and 95% confidence interval (CI) were calculated when risk groups were compared in both cohorts. Detailed descriptions of the LRM-Z construction are given in the Supplemental Methods.

Statistical analysis. Normality of distribution was explored for all variables. Continuous variables that
were normally distributed were expressed as mean ± standard deviations and other variables as median with interquartile ranges. Categorical variables are expressed as percentages and counts. Student's t-test or the Mann-Whitney U-test was used to compare continuous variables. The Pearson chi-squared test or Fisher's exact test was employed to compare categorical variables and proportions between groups, as appropriate. A P-value < 0.05 was considered statistically significant. The capacities of various scoring systems to differentiate survivors from non-survivors were assessed by evaluating areas under receiver operating characteristic curves (auROCs). CART analysis was performed with the C50 package in R version 3.4.3 (http://www.r-project.org/). Other statistical analyses was performed using SPSS ver. 18.0 software (SPSS Inc., Chicago, IL, USA). ROC curves were drawn with the aid of MedCalc ver. 18.2 software (Mariakerke, Belgium).