OBJECTIVE To esta blish the method for simultaneous determination of 25 components （such as berberine ， magnoflorine and hydroxysafflor yellow A ）in Bawei xiaobopi capsules. METHODS High-performance liquid chromatography- tandem triple quadrupole mass spectrometry （HPLC-QqQ-MS）method was adopted. The determination was performed on WondaSil C18-WR column with mobile phase consisted of 0.1％ formic acid solution-methanol （gradient elution ）at the flow rate of 0.5 mL/min. The column temperature was 25 ℃，and sample size was 5 μL. Electrospray ionization source was scanned in positive and negative ion mode at the same time ，with multiple reaction monitoring. The capillary voltage was 4 000 V（+）and 2 500 V（－）. The drying gas flow rate was 11 L/min with the temperature of 300 ℃. The pressure was 15 psi. RESULTS Totally 25 components of Bawei xiaobopi capsules had good linear relationship within a certain range ，such as magnolflorine ，jatrorrhizine，berberine， palmatine，bufotenine，bufotenidine，piperine，glycyrrhizic acid ，ferulic acid ，ferulic acid 4-O-β-D-glucopyranoside，hydroxysafflor yellow A ，chlorogenic acid ，gallic acid ，chebulagic acid ，corilagin，ellagic acid ，liquiritigenin，liquiritin，rutin，quercetin， glycocholic acid ，cholic acid ，glycochenodeoxycholic acid ，glycodeoxycholic acid ，ursodeoxycholic acid （r≥0.999 0）. The limits of quantitation were 0.62-554.50 ng/mL；the limits of detection were 0.18-166.30 ng/mL.RSDs of precision ，repeatability and stability（24 h）tests were all lower than 3.00％. The recovery rates were 80％-115％（all RSDs lower than 3.00％，n＝6）. The contents of above 25 components were 16.94-20.82，3.78-5.17，9.11-11.43，0.24-0.30，0.20-0.39，0.74-1.16，0.79-0.89，3.26-3.35， 0.48-0.66，11.96-13.35，2.30-3.12，0.19-0.21，6.07-8.83，10.42-10.48，1.43-1.64，4.17-4.76，0.14-0.15，0.46-0.52，0.04，0.01， 0.59-0.63，0.20-0.23，0.02，0.15-0.16，0.01 mg/g，respectively. CONCLUSIONS Established method is simple ，sensitive and stable，and can be used for content determination of 25 components in Bawei xiaobopi capsules simultaneously.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.