The value of NLR versus MLR in the short-term prognostic assessment of HBV-related acute-on-chronic liver failure

Predicting survival in sepsis: The prognostic value of NLR and BAR ratios

BACKGROUND

Due to the high-risk nature of sepsis, emergency departments urgently need a simple evaluation method to assess the degree of inflammation and prognosis in sepsis patients, providing a reference for diagnosis and treatment.

OBJECTIVE

To investigate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) combined with the blood urea nitrogen-to-serum albumin ratio (BAR) in sepsis.

METHODS

A total of 377 sepsis patients admitted to Lishui People's Hospital from June 2022 to June 2023 were selected as the study subjects. Based on their prognosis, they were divided into a survival group (255 cases) and a death group (82 cases). The clinical data of the two groups were compared. Multivariate logistic analysis was used to identify factors influencing sepsis prognosis, and ROC curve analysis was used to assess the predictive efficacy of NLR, BAR, and their combination.

RESULTS

Compared with survivors, non-survivors had higher NLR and BAR, with statistically significant differences (p< 0.05). After adjusting for confounding factors, NLR (OR = 1.052) and BAR (OR = 1.095) were found to be independent prognostic factors for sepsis patients (both p< 0.05). The AUC of NLR combined with BAR was 0.798 (95% CI 0.745-0.850, p< 0.05), higher than the AUC of NLR alone (0.776) and BAR alone (0.701).

CONCLUSIONS

The combination of NLR and BAR has a high predictive value for the prognosis of sepsis patients. Its simple calculation makes it particularly suitable for use in emergency departments.

The Impact of Liver Failure on the Immune System

Liver failure profoundly affects the immune system, leading to dysregulation of innate and adaptive immune response. This review explores the intricate relationship between liver function and immune homeostasis. The role of the liver as a central hub in immune response initiation is elucidated, emphasizing its involvement in hepatic inflammation induction and subsequent systemic inflammation. Cytokines, chemokines, growth factors, and lipid mediators orchestrate these immune processes, serving as both prognostic biomarkers and potential therapeutic targets in liver failure-associated immune dysregulation, which might result from acute-on-chronic liver failure (ACLF) and cirrhosis. Furthermore, the review delves into the mechanisms underlying immunosuppression in liver failure, encompassing alterations in innate immune cell functions such as neutrophils, macrophages, and natural killer cells (NK cells), as well as perturbations in adaptive immune responses mediated by B and T cells. Conclusion: Understanding the immunological consequences of liver failure is crucial for developing targeted therapeutic interventions and improving patient outcomes in liver disease management.

Low platelet to high-density lipoprotein ratio predicts poor short-term prognosis in hepatitis B-related acute-on-chronic liver failure

BACKGROUND

Acute-on-chronic liver failure (ACLF) is characterized by a systemic inflammatory response, predominantly associated with hepatitis B virus in the Asia-Pacific region, with a high short-term mortality rate. The platelet to high-density lipoprotein ratio (PHR) has been used to predict the prognosis of patients with various inflammatory diseases. We aim to is to use the PHR to predict the short-term prognosis of patients with HBV-ACLF.

METHOD

In this study, we retrospectively analyzed clinical data from 270 HBV-ACLF patients. Using logistic regression, we identified independent risk factors for short-term mortality and developed a prognostic model. This model was then validated, compared, and its clinical utility assessed via decision curve analysis (DCA).

RESULTS

Among the 270 HBV-ACLF patients, 98 patients died within 28 days. The deceased group exhibited a higher proportion of severe hepatic encephalopathy and ascites. Additionally, there was a statistically significant difference (P = 0.046) in the novel inflammation scoring system, PHR, between the two groups. Following stringent variable selection, PHR was identified as a predictive factor for short-term mortality in HBV-ACLF patients using logistic regression analysis (OR: 0.835 (0.756-0.999), P = 0.009), and it exhibited a synergistic effect with certain traditional scores. The prognostic model constructed based on PHR demonstrated a superior ability to predict short-term mortality compared to traditional scores such as Child-Turcotte-Pugh (AUC: 0.889). Evaluation using calibration curves and decision curve analysis (DCA) suggested its practical utility.

CONCLUSION

PHR can predict short-term mortality in patients, with a low PHR upon admission being associated with an increased risk of death.

Construction of a predictive model for acute liver failure after hepatectomy based on neutrophil-to-lymphocyte ratio and albumin-bilirubin score

BACKGROUND

Acute liver failure (ALF) is a common cause of postoperative death in patients with hepatocellular carcinoma (HCC) and is a serious threat to patient safety. The neutrophil-to-lymphocyte ratio (NLR) is a common inflammatory indicator that is associated with the prognosis of various diseases, and the albumin-bilirubin score (ALBI) is used to evaluate liver function in liver cancer patients. Therefore, this study aimed to construct a predictive model for postoperative ALF in HCC tumor integrity resection (R0) based on the NLR and ALBI, providing a basis for clinicians to choose appropriate treatment plans.

AIM

To construct an ALF prediction model after R0 surgery for HCC based on NLR and ALBI.

METHODS

In total, 194 patients with HCC who visited The First People's Hospital of Lianyungang to receive R0 between May 2018 and May 2023 were enrolled and divided into the ALF and non-ALF groups. We compared differences in the NLR and ALBI between the two groups. The risk factors of ALF after R0 surgery for HCC were screened in the univariate analysis. Independent risk factors were analyzed by multifactorial logistic regression. We then constructed a prediction model of ALF after R0 surgery for HCC. A receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the value of the prediction model.

RESULTS

Among 194 patients with HCC who met the standard inclusion criteria, 46 cases of ALF occurred after R0 (23.71%). There were significant differences in the NLR and ALBI between the two groups (P < 0.05). The univariate analysis showed that alpha-fetoprotein (AFP) and blood loss volume (BLV) were significantly higher in the ALF group compared with the non-ALF group (P < 0.05). The multifactorial analysis showed that NLR, ALBI, AFP, and BLV were independent risk factors for ALF after R0 surgery in HCC. The predictive efficacy of NLR, ALBI, AFP, and BLV in predicting the occurrence of ALT after R0 surgery for HCC was average [area under the curve (AUC)NLR = 0.767, AUCALBI = 0.755, AUCAFP = 0.599, AUCBLV = 0.718]. The prediction model for ALF after R0 surgery for HCC based on NLR and ALBI had a better predictive efficacy (AUC = 0.916). The calibration curve and actual curve were in good agreement. DCA showed a high net gain and that the model was safer compared to the curve in the extreme case over a wide range of thresholds.

CONCLUSION

The prediction model based on NLR and ALBI can effectively predict the risk of developing ALF after HCC R0 surgery, providing a basis for clinical prevention of developing ALF after HCC R0 surgery.

Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study

Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925-0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.