3.1 Basic characteristics of patients with HBV-ACLF in derivation cohort.
A total of 642 HBV-ACLF patients were collected from 3 centers. According to the inclusion and exclusion criteria, 65 patients were excluded（Figure 1）. Finally, 577 patients with HBV-ACLF were enrolled in the study. Among them, 421 patients from the affiliated hospital of southwest medical university were derivation cohort, and basic characteristics of patients were listed in Table 1.Besides, in the derivation cohort, 307 (72.92%) patients complicated with bacterial infection, in whom 245(58.19%) had a single infection site, 41(9.73%) had 2 infection sites, and 21 (4.99%) had ≥3 infection sites on admission. The most frequent infection was pneumonia (n=188, 44.66%), followed by SBP (n=167, 39.67%) and intestinal infection (n=36, 8.55%).
3.2 Comparison of inflammatory markers and routine hematological parameters between survivors and non-survivors.
In order to identify indicators with statistical differences, inflammatory markers and routine hematological parameters in the survivors and non-survivors were analyzed. For the inflammatory markers, compared with the survivors, the level of NLR, MLR, PLR, RLR and RDW increased (P≤0.001) (Table 1); For the routine hematological parameters, compared with the survivors, the serum Na, PTA and lymphocytes were lower, while WBC, neutrophils, monocytes, TBIL, Cr, cyst-c, PT, INR, MELD scores, MELD-Na and CTP were higher (P＜0.05). Moreover, the incidence of hepatic encephalopathy was elevated in the non-survivors (P<0.05) (Table 1).
3.3 Univariate and multivariate cox regression analysis of survival and death in HBV-ACLF patients
Univariate regression analysis was performed on statistic significant indicators in table 1, and multivariate cox regression analysis was performed on the indicators with significant difference in univariate analysis (P<0.05), including TBIL, Cr, Cyst-c, INR, PTA, WBC, neutrophils, RDW, NLR, RLR, PLR, MLR. The multivariate cox regression results indicated that RDW, NLR, TBIL, INR, Cr were risk factors for 90-day death in HBV-ACLF patients (P<0.05). In addition, RDW and NLR were significantly positively correlated with MELD scores (P<0.05), suggesting that high RDW, NLR might be closely associated with the prognosis of the patients with HBV-ACLF (Figure 2a and 2b). We further identified the patients with HBV-ACLF based on the cut-off values of NLR and RDW to graph the Kaplan-Meier survival curves. The results showed that the patients with NLR＞4.09 and RDW＞16.10 had a more worse prognosis(Figure 2c and 2d).
3.4 Establishing a new prognostic model combining inflammatory markers with hematological parameters in patients with HBV-ACLF by Cox regression
The two inflammatory markers RDW, NLR and other three hematological parameters TBIL, INR, Cr had been found to be related to the prognosis of patients with HBV-ACLF in forward analysis. Based on the regression coefficient (Beta coefficient) as the weight of the risk factor (Table 2), the following model was established:
COXRNTIC=0.053×RDW+0.027×NLR+0.003×TBIL+0.317×INR+0.003×Cr with a cut-off value of 3.08 (sensitivity: 77.89%, specificity: 86.04%). The model was able to predict 190 patients alive and 155 dead, accurately classifying 81.95% of the patients in this study (Table 3).
3.5 Comparison of predictive value of MELD score, MELD-Na, CTP and RNTIC for prognosis of patients with HBV-ACLF
Receiver operating characteristic (ROC) curves for parameters including MELD scores, MELD-Na, CTP and RNTIC were shown in Figure 1c. RNTIC had a higher area under the ROC curve (AUC) for identifying poor prognosis than the other four (p<0.001, Table 3). We further identified the patients with HBV-ACLF based on the cut-off values of RNTIC, MELD, MELD-Na and CTP and graphed the Kaplan-Meier survival curves. The results showed RNTIC was more efficient to predict the patients’ prognosis than other indicators (Figure 3a).
3.6 External validation of RNTIC.
In order to test the model, 180 patients were enrolled from the other two hospitals. According to the inclusion and exclusion criteria, 156 patients were admitted to the validation cohort (Figure 1) with a 90-day mortality rate at 35.89%. Comparisons of demographics and baseline clinical characteristics of the patients in the derivation and validation cohort were summarized in Table 4. The AUC of the RNTIC was higher than MELD, MELD-Na and CTP (P<0.05, Figure3b, Table3), which proved this model also had an efficient ability on the prediction of the 90-day death in patients with HBV-ACLF in the validation cohort.