A novel prognostic model to predict outcome of artificial liver support system treatment

Changes in TNF-α, IL-33, and MIP-1α before and after artificial liver support treatment and their prognostic value

OBJECTIVE

To investigate the effect of ALST (artificial liver support treatment) on inflammatory factors and prognosis in patients with ACLF (acute-on-chronic liver failure).

METHODS

Data of ACLF patients admitted to the No. 2 People's Hospital of Lanzhou from June 2020 to January 2023 were retrospectively analyzed. Patients were compared before and after ALST in terms of ALT (Alanine Aminotransferase), AST (Aspartate Aminotransferase), TBil (Total Bilirubin), Cr (Creatinine), INR (International Normalized Ratio), MELD (Model for End-Stage Liver Disease) scores, as well as TNF-α (Tumor Necrosis Factor-α), IL-33 (Interleukin-33), and MIP-1α (Macrophage Inflammatory Protein-1 α) levels. The ROC (receiver operating characteristic) curve was used to analyze the efficacy of the above indicators in predicting 90-day mortality in patients.

RESULTS

After the treatment, the levels of ALT, AST, TBil, Cr, INR, and MELD score were significantly lower than those before treatment (all P<0.001). Also, the levels of TNF-α, IL-33, and MIP-1α were substantially lower than those before treatment (all P<0.001). TNF-α, IL-33, and MIP-1α were positively correlated with MELD score before and after the treatment (all P<0.01). TNF-α, IL-33, MIP-1α, and MELD score were significantly higher in the death group than in the survival group (all P<0.01). The ROC curves showed that MELD (AUC=0.857), TNF-α (AUC=0.836), IL-33 (AUC=0.749), and MIP-1α (AUC=0.746) had high efficacy in predicting patients' 90-day mortality.

CONCLUSION

ALST can significantly reduce TNF-α, IL-33, and MIP-1α levels in patients with ACLF, and postoperative TNF-α, IL-33, and MIP-1α levels have a high predictive value for patients' prognosis.

Characteristics of circulating immune cells in HBV-related acute-on-chronic liver failure following artificial liver treatment

BACKGROUND AND AIM

Liver failure, which is predominantly caused by hepatitis B (HBV) can be improved by an artificial liver support system (ALSS). This study investigated the phenotypic heterogeneity of immunocytes in patients with HBV-related acute-on-chronic liver failure (HBV-ACLF) before and after ALSS therapy.

METHODS

A total of 22 patients with HBV-ACLF who received ALSS therapy were included in the study. Patients with Grade I according to the ACLF Research Consortium score were considered to have improved. Demographic and laboratory data were collected and analyzed during hospitalization. Immunological features of peripheral blood in the patients before and after ALSS were detected by mass cytometry analyses.

RESULTS

In total, 12 patients improved and 10 patients did not. According to the immunological features data after ALSS, the proportion of circulating monocytes was significantly higher in non-improved patients, but there were fewer γδT cells compared with those in improved patients. Characterization of 37 cell clusters revealed that the frequency of effector CD8+ T (P = 0.003), CD4+ TCM (P = 0.033), CD4+ TEM (P = 0.039), and inhibitory natural killer (NK) cells (P = 0.029) decreased in HBV-ACLF patients after ALSS therapy. Sub group analyses after treatment showed that the improved patients had higher proportions of CD4+ TCM (P = 0.010), CD4+ TEM (P = 0.021), and γδT cells (P = 0.003) and a lower proportion of monocytes (P = 0.012) compared with the non-improved patients.

CONCLUSIONS

Changes in effector CD8+ T cells, effector and memory CD4+ T cells, and inhibitory NK cells are associated with ALSS treatment of HBV-ACLF. Moreover, monocytes and γδT cells exhibited the main differences when patients obtained different prognoses. The phenotypic heterogeneity of lymphocytes and monocytes may contribute to the prognosis of ALSS and future immunotherapy strategies.

Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis

PURPOSE

This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis.

METHODS

We prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1-year follow-up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet-50 to analyze 2D SWE images, a SWE-based deep learning signature (DLswe) was developed for 1-year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts.

RESULTS

The C-index for DLswe was 0.748 (95% CI 0.666-0.829) and 0.744 (95% CI 0.623-0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C-index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763-0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707-0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable.

CONCLUSIONS

The 2D SWE-based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.

A multi-subgroup predictive model based on clinical parameters and laboratory biomarkers to predict in-hospital outcomes of plasma exchange-centered artificial liver treatment in patients with hepatitis B virus-related acute-on-chronic liver failure

Background

Postoperative risk stratification is challenging in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) who undergo artificial liver treatment. This study characterizes patients' clinical parameters and laboratory biomarkers with different in-hospital outcomes. The purpose was to establish a multi-subgroup combined predictive model and analyze its predictive capability.

Methods

We enrolled HBV-ACLF patients who received plasma exchange (PE)-centered artificial liver support system (ALSS) therapy from May 6, 2017, to April 6, 2022. There were 110 patients who died (the death group) and 110 propensity score-matched patients who achieved satisfactory outcomes (the survivor group). We compared baseline, before ALSS, after ALSS, and change ratios of laboratory biomarkers. Outcome prediction models were established by generalized estimating equations (GEE). The discrimination was assessed using receiver operating characteristic analyses. Calibration plots compared the mean predicted probability and the mean observed outcome.

Results

We built a multi-subgroup predictive model (at admission; before ALSS; after ALSS; change ratio) to predict in-hospital outcomes of HBV-ACLF patients who received PE-centered ALSS. There were 110 patients with 363 ALSS sessions who survived and 110 who did not, and 363 ALSS sessions were analyzed. The univariate GEE models revealed that several parameters were independent risk factors. Clinical parameters and laboratory biomarkers were entered into the multivariate GEE model. The discriminative power of the multivariate GEE models was excellent, and calibration showed better agreement between the predicted and observed probabilities than the univariate models.

Conclusions

The multi-subgroup combined predictive model generated accurate prognostic information for patients undergoing HBV-ACLF patients who received PE-centered ALSS.

Low high-density lipoprotein cholesterol levels predicting poor outcomes in patients with hepatitis B virus-related acute-on-chronic liver failure

Background

Lipid profile disorders frequently occur in patients with advanced liver diseases. High-density lipoprotein cholesterol (HDL-C) levels decrease rapidly during acute conditions of some diseases, and HDL-C levels may be related to mortality in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF).

Materials and methods

A retrospective cohort study was conducted on 200 subjects with HBV-ACLF. The patients were separated into non-survivors and survivors according to their 28-day outcome. Univariate and multivariate Cox regression analyses were performed to identify predictors of mortality, and the performance of these predictors was evaluated by receiver operating characteristic (ROC) curve analysis. Kaplan-Meier analysis was performed to draw survival curves of HDL-C.

Results

The 28-day mortality in the cohort was 27.0%. HDL-C levels differed markedly between non-survivors and survivors. In the multivariate analysis, HDL-C, the Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), and Chinese Group on the Study of Severe Hepatitis B-ACLF II (COSSH-ACLF II) scores were identified as independent predictors for mortality (HR = 0.806, 95% CI: 0.724-0.898; HR = 1.424, 95% CI: 1.143-1.775; HR = 1.006, 95% CI: 1.002-1.007; and HR = 1.609, 95% CI: 1.005-2.575, respectively). Patients with lower HDL-C levels had a worse prognosis than those with higher HDL-C levels. In ROC analysis, the prognostic accuracy for mortality was similar between HDL-C (AUROC: 0.733) and the CTP, MELD, and COSSH-ACLF II scores (AUROC: 0.753; 0.674 and 0.770, respectively).

Conclusion

The HDL-C level may serve as a potential indicator for the prognosis of HBV-ACLF and can be used as a simple marker for risk assessment and selection of therapeutic options.

The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique

PURPOSE

Several mathematical models have been developed to estimate individualized chances of assisted reproduction techniques (ART) success, although with limited clinical application. Our study aimed to develop a decisional algorithm able to predict pregnancy and live birth rates after controlled ovarian stimulation (COS) phase, helping the physician to decide whether to perform oocytes pick-up continuing the ongoing ART path.

METHODS

A single-center retrospective analysis of real-world data was carried out including all fresh ART cycles performed in 1998-2020. Baseline characteristics, ART parameters and biochemical/clinical pregnancies and live birth rates were collected. A seven-steps systematic approach for model development, combining linear regression analyses and decision trees (DT), was applied for biochemical, clinical pregnancy, and live birth rates.

RESULTS

Of fresh ART cycles, 12,275 were included. Linear regression analyses highlighted a relationship between number of ovarian follicles > 17 mm detected at ultrasound before pick-up (OF17), embryos number and fertilization rate, and biochemical and clinical pregnancy rates (p < 0.001), but not live birth rate. DT were created for biochemical pregnancy (statistical power-SP:80.8%), clinical pregnancy (SP:85.4%), and live birth (SP:87.2%). Thresholds for OF17 entered in all DT, while sperm motility entered the biochemical pregnancy's model, and female age entered the clinical pregnancy and live birth DT. In case of OF17 < 3, the chance of conceiving was < 6% for all DT.

CONCLUSION

A systematic approach allows to identify OF17, female age, and sperm motility as pre-retrieval predictors of ART outcome, possibly reducing the socio-economic burden of ART failure, allowing the clinician to perform or not the oocytes pick-up.