Home Hepatitis A novel predictive score for citrate accumulation among patients receiving artificial liver support system therapy with regional citrate anticoagulation

A novel predictive score for citrate accumulation among patients receiving artificial liver support system therapy with regional citrate anticoagulation

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A novel predictive score for citrate accumulation among patients receiving artificial liver support system therapy with regional citrate anticoagulation

Patient characteristics

A total of 480 patients treated with ALSS therapy were initially screened and enrolled (Fig. 1). Patients treated with non-DPMAS plus PE therapy (N = 7) or non-RCA (N = 9) were excluded from the study. Patients with liver cancer (N = 18) and those without HBV infection (N = 108) were also excluded. A total of 338 patients were enrolled and randomly divided into a derivation cohort (N = 230) and a validation cohort (N = 108) with a ratio of 2:1 using SPSS software. All patients were followed up 2 h after RCA-ALSS therapy.

Figure 1

Flow diagram of patient selection. Of the 480 patients in our database that received ALSS therapy, 142 were excluded from the study. The remaining 338 patients were randomly divided into a derivation cohort (N = 230) and a validation cohort (N = 108). ALSS artificial liver support system, DPMAS double plasma molecular adsorption system, PE plasma exchange, RCA regional citrate anticoagulation, HBV hepatitis B virus.

The patient characteristics are shown in Table 1. There were no significant differences between the two cohorts in gender, age, causes of liver disease, usage of antiviral agents, or laboratory parameters before the initial ALSS therapy. The Model for End-Stage Liver Disease (MELD) score17 and the proportion who met the HBV-ACLF criteria18 were similar in the two cohorts. The overall rates of longer duration of citrate accumulation (LDCA) were not significantly different between the two cohorts. There were no significant differences in indicators representing that patients received similar RCA, such as intracorporeal Catot before RCA-ALSS therapy, intracorporeal and extracorporeal Caion during RCA-ALSS therapy, and intracorporeal Catot and Caion 2 h after RCA-ALSS therapy (Table 1).

Table 1 Characteristics of derivation and validation cohorts.

Development of R-CA model in derivation cohort

The derivation cohort was analyzed for the predictors of LDCA on the basis of baseline parameters by logistic regression analysis. In multivariate analysis, four baseline variables were found to be independently associated with LDCA: gender, international normalized ratio (INR) of prothrombin time (PT) (PT-INR), serum creatinine, and serum chloride (Table 2). A predictive R-CA model, Logit (P) = 5.380 + 1.173 × Gender + 0.797 × PT-INR + 1.863 × Serum creatinine (× ULN)# − 0.109 × Serum chloride (mmol/L) [gender: female = 2, male = 0; #Statistical analysis using relative values adjusted by gender (a multiple of upper limit of normal)], was developed by using multivariate logistic regression analysis with the backward stepwise (likelihood ratio) method.

Table 2 Predictors for LDCA in derivation cohort.

Testing of R-CA model in validation cohort

Before developing a simplified predictive score, the four independent predictors were tested in the validation cohort. Gender, PT-INR, serum creatinine, and serum chloride were verified as independent predictors of LDCA based on the results of the multivariate analysis of the validation cohort (Table 3). The R-CA model showed a good predictability with AUROC of 0.848 (95% CI 0.795–0.892, p = 0.000) and 0.856 (95% CI 0.776–0.916, p = 0.000) in the derivation and validation cohorts, respectively. The expected LDCA rates and observed LDCA rates from the derivation cohort (R2 = 0.909, p = 0.000) matched with the validation cohort (R2 = 0.778, p = 0.007; Fig. 2A).

Table 3 Testing predictors for LDCA in validation cohort.
Figure 2
figure2

Linear correlation lines of expected LDCA rate and observed LDCA rate of R-CA model and R-CA score. (A) The linear correlation lines of expected and observed LDCA rates in the derivation and validation cohorts based on the R-CA model. (B) The linear correlation lines of expected and observed LDCA rates in the derivation and validation cohorts based on the R-CA score. The expected and observed LDCA rates of the derivation cohort match those of the validation cohort. LDCA longer duration of citrate accumulation, R-CA model logistic regression model of risk predictors for citrate accumulation, R-CA score risk score for citrate accumulation.

Development of R-CA score

Following the validation of our predictive model, the four individual parameters were scored. An ordinal grading system (0–2) with distinct hazard ratios (HR) on logistic regression was performed by comprehensively considering their cut-off values of AUROCs predicting the probability of LDCA, ACLF diagnostic criteria19,20, and clinically significant values (Tables 4, 5). The total R-CA score ranges from a minimum of 0 to a maximum of 8. The scoring parameters are easy to collect laboratory measurements or clinical features that showed a distinct hazard ratio on logistic regression in derivation and validation cohorts (all HR > 2 and all p = 0.000; Table 6).

Table 4 Simplified univariate predictors for LDCA in derivation and validation cohorts.
Table 6 Predictive model for LDCA in derivation and validation cohorts, and that among patients with or without liver cirrhosis.

The R-CA scores and LDCA rates showed a linear correlation in the derivation cohort (R2 = 0.912, p = 0.000; Fig. 3). A linear regression equation was developed: LDCA rate = 12.8% × R-CA score − 1.2%. The expected LDCA rates and observed LDCA rates based on the R-CA scores in the derivation cohort (R2 = 0.845, p = 0.000) matched those of the validation cohort (R2 = 0.842, p = 0.000; Fig. 2B).

Figure 3
figure3

Linear regression lines of R-CA score and observed LDCA rate in the derivation cohort. A linear regression equation was developed for the R-CA scores and LDCA in derivation. LDCA longer duration of citrate accumulation, R-CA score risk score for citrate accumulation.

Evaluation of R-CA model and R-CA score as predictors of LDCA in derivation and validation cohorts

Our R-CA model and its simplified R-CA score were compared with other potential predictors, such as the MELD score, PT-INR and lactate (Fig. 4A,B and Table 7). AUROCs of the R-CA model and the R-CA score in the derivation cohort were 0.848 and 0.803, and those in the validation cohort were 0.856 and 0.816, respectively. Our R-CA model was found to be as capable as the R-CA score, and superior to the MELD score (AUROC = 0.725) and other univariate predictors (AUROCs < 0.700), in predicting LDCA (p = 0.369, p = 0.022 and p ≤ 0.001, respectively). The R-CA score was as capable as the MELD score in predicting LDCA (p = 0.174) and superior to other univariate predictors in predicting LDCA (p < 0.05).

Figure 4
figure4

Receiver operating curves (ROC) for the abilities of risk models to predict LDCA. ROC for risk models predicting 3-month mortality in the derivation cohort (A), validation cohort (B), patients with liver cirrhosis (C), and patients without liver cirrhosis (D). Our R-CA model and R-CA score were as capable as or superior to all other models in predicting LDCA. R-CA model logistic regression model of risk predictors for citrate accumulation, R-CA score risk score for citrate accumulation, MELD Model for End-Stage Liver Disease, PT-INR international normalized ratio (INR) of prothrombin time (PT).

Table 7 Comparison of the predictive values of R-CA model, R-CA score, and other predictors in derivation and validation cohorts.

Less than 10% of patients who had an R-CA model ≤  − 1.00 or an R-CA score of 0–2 experienced LDCA. R-CA model ≤  − 1.00 had an AUROC of 0.848, a sensitivity of 74.1%, a specificity of 88.6%, a positive predictive value of 66.7%, and a negative predictive value of 91.8%. R-CA score of 0–2 had an AUROC of 0.803, a sensitivity of 70.4%, a specificity of 84.1%, a positive predictive value of 57.6%, and a negative predictive value of 90.2% (Table 8). Although patients with MELD score ≤ 26.6, PT-INR ≤ 2.43 and lactate ≤ 2.65 had high negative predictive values of LDCA, their AUROCs were all less than 0.750 (Table 8).

Table 8 Predictive values of predictors based on their maximum area of AUROCs in derivation cohort and patients with liver cirrhosis.

Evaluation of R-CA model and R-CA score as predictors of LDCA among patients with or without liver cirrhosis

The R-CA model and R-CA score also showed a distinct hazard ratio on logistic regression among patients with or without liver cirrhosis (all HR > 2 and all p = 0.000; Table 6). The expected LDCA rates and observed LDCA rates based on the R-CA model among patients with liver cirrhosis (R2 = 0.951, p = 0.000) matched with patients without liver cirrhosis (R2 = 0.754, p = 0.001; Fig. 5A). The expected LDCA rates and observed LDCA rates based on the R-CA scores among patients with liver cirrhosis (R2 = 0.799, p = 0.001) also matched those of patients without liver cirrhosis (R2 = 0.640, p = 0.006; Fig. 5B).

Figure 5
figure5

Linear correlation lines of expected LDCA rate and observed LDCA rate of R-CA model and R-CA score among patients with or without liver cirrhosis. (A) The linear correlation lines of expected and observed LDCA rates among patients with or without liver cirrhosis based on the R-CA model. (B) The linear correlation lines of expected and observed LDCA rates among patients with or without liver cirrhosis based on the R-CA score. The expected and observed LDCA rates of patients with liver cirrhosis match those of patients without liver cirrhosis. LDCA longer duration of citrate accumulation, R-CA model logistic regression model of risk predictors for citrate accumulation, R-CA score risk score for citrate accumulation.

AUROCs of the R-CA model and the R-CA score among patients with liver cirrhosis were 0.851 and 0.804, and those among patients without liver cirrhosis were 0.836 and 0.781, respectively (Fig. 4C,D, and Table 9). Our R-CA model was found to be as capable as the R-CA score, and superior to the MELD score (AUROC = 0.712) and other univariate predictors (AUROCs < 0.700), in predicting LDCA (p = 0.280, p = 0.005 and p = 0.000, respectively). The R-CA score was as capable as the MELD score in predicting LDCA (p = 0.075) and superior to other univariate predictors in predicting LDCA (p < 0.01).

Table 9 Comparison of the predictive values of R-CA model, R-CA score, and other predictors among patients with or without liver cirrhosis.

About 12.5% of cirrhotic patients who had an R-CA model ≤  − 1.39 or an R-CA score of 0–2 experienced LDCA. R-CA model ≤  − 1.39 had an AUROC of 0.851, a sensitivity of 76.5%, a specificity of 80.5%, a positive predictive value of 61.2%, and a negative predictive value of 89.5%. R-CA score of 0–2 had an AUROC of 0.804, a sensitivity of 70.6%, a specificity of 81.1%, a positive predictive value of 60.0%, and a negative predictive value of 87.3% (Table 8). Although cirrhotic patients with MELD score ≤ 26.6, PT-INR ≤ 2.22 and lactate ≤ 2.65 had high negative predictive values of LDCA, their AUROCs were all less than 0.750 (Table 8).

Correlation between R-CA model, R-CA score, LDCA and disease severity

R-CA model, R-CA score, LDCA, and Catot/Caion 2 h after RCA-ALSS therapy were positively correlated with disease severity rated by MELD score in derivation and validation cohorts and among patients with or without liver cirrhosis with all the p < 0.01 (Fig. 6, Table 10).

Figure 6
figure6

Correlation between R-CA model, R-CA score, LDCA and disease severity in derivation cohort. R-CA model, R-CA score and LDCA are positively correlated with disease severity rated by MELD score. MELD Model for End-Stage Liver Disease, R-CA model logistic regression model of risk predictors for citrate accumulation, R-CA score risk score for citrate accumulation, LDCA longer duration of citrate accumulation, Catot total calcium, Caion ionized calcium, Catot/Caion Catot to Caion ratio, RCA regional citrate anticoagulation, ALSS therapy artificial liver support system therapy.

Table 10 Correlation of R-CA model, R-CA score, LDCA and disease severity rated by MELD score in derivation and validation cohorts, and that among patients with or without liver cirrhosis.

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