Development and validation of a nomogram for predicting advanced liver fibrosis in patients with chronic hepatitis B.

Background: The progression of chronic hepatitis B (CHB) to liver fibrosis and even cirrhosis is often unknown to patients, but noninvasive markers capable of effectively identifying advanced liver fibrosis remains absent. Objective: Based on the results of liver biopsy, we aimed to construct a new nomogram to validate the stage of liver fibrosis in CHB patients by the basic information of CHB patients and routine laboratory tests. Methods: Patients with CHB diagnosed for the first time in the First Affiliated Hospital of Anhui Medical University from 2010 to 2018 were selected, and their basic information, laboratory tests and liver biopsy information were collected. Eventually, 974 patients were enrolled in the study, while all patients were randomized into a training cohort (n = 732) and an internal validation cohort (n = 242) according to a 3:1 ratio. In the training cohort, least absolute shrinkage and selection operator (Lasso) regression were used for predictor variable screening, and binary logistic regression analysis was used to build the diagnostic model, which was ultimately presented as a nomogram. The predictive accuracy of the nomograms was analyzed by running operating characteristic curve (ROC) to calculate area under curve (AUC), and the calibration was evaluated. Decision curve analysis (DCA) was used to determine patient benefit. In addition, we validated the built models with internal as well as external cohort (n = 771), respectively. Results: Ultimately, the training cohort, the internal validation cohort, and the external validation cohort contained sample sizes of 188, 53, and 149, respectively, for advanced liver fibrosis. Gender, albumin (Alb), globulin (Glb), platelets (PLT), alkaline phosphatase (AKP), glutamyl transpeptidase (GGT), and prothrombin time (PT) were screened as independent predictors. Compared with the aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and King's score, the model in the training cohort (AUC = 0.834, 95% CI 0.800-0.868, p < 0.05) and internal validation cohort (AUC = 0.804, 95% CI 0.742-0.866, p < 0.05) showed the best discrimination and the best predictive performance. In addition, DCA showed that the clinical benefit of the nomogram was superior to the APRI, FIB-4 and King's scores in all cohorts. Conclusion: This study constructed a validated nomogram model with predictors screened from clinical variables which could be easily used for the diagnosis of advanced liver fibrosis in CHB patients.

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.

Background: Prevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential. Objective: An XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM. Methods: All data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort. Results: A total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa]. Conclusions: XGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.

NLR, A Convenient Early-Warning Biomarker of Fatal Outcome in Patients With Severe Fever With Thrombocytopenia Syndrome.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that greatly threatens public health. This study aimed to examine a convenient early-warning biomarker of fatal outcomes in patients with SFTS to reduce mortality. Methods: A retrospective cohort study was performed, and patients with confirmed SFTS were enrolled in the top two hospitals in Anhui Province, China from 1 May 2016 to 31 October 2019. The clinical symptoms, laboratory indicators, and treatment data of patients with SFTS were evaluated. All patients with SFTS were followed up till 28 days from the start of admission. The laboratory indicators that could be used to predict the fatal outcome were identified. Results: A total of 228 patients with SFTS were enrolled, 177 patients were enrolled in the survival group, and 51 patients in the death group. The median age of all 228 patients with SFTS was 63 years. Five laboratory indicators (SFTSV viral load, neutrophil to lymphocyte ratio (NLR), aspartate transaminase (AST)/alanine aminotransferase (ALT), ALT, and blood urea nitrogen (BUN)) were identified as the predicting factors of the fatal outcome of patients with SFTS. The area under the receiver operating characteristic (ROC) curve (AUC) of SFTSV viral load was the highest (0.919), then NLR (0.849), followed by AST/ALT (0.758), AST (0.738), and BUN (0.709). The efficacy of SFTVS viral load and NLR in predicting fatal outcomes was significantly higher than AST/ALT, AST, and BUN. The Kaplan-Meier survival curves show that the case fatality rate was significantly increased in patients whose SFTSV viral load was higher than 500,000 or NLR higher than 2.0. Gamma-globulin treatment showed a significant difference between the survival group and the death group, and the duration of gamma-globulin that had been proposed should not be <3 days. Conclusion: The SFTSV viral load and NLR showed great efficacy in predicting the fatal outcome of patients with SFTS, and NLR is a convenient and efficient early-warning biomarker that helps healthcare workers focus on patients with high risks of fatal outcomes. The efficacy of gamma-globulin provided a new idea for the treatment of SFTS, which needs further analysis in future studies.