Non-invasive Diagnostic Model Research Articles

A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels.

Background and Aims: Chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) levels are at risk of disease progression. Currently, liver biopsy is suggested to identify this population. We aimed to establish a non-invasive diagnostic model to identify patients with significant liver inflammation.Method: A total of 504 CHB patients who had undergone liver biopsy with normal ALT levels were randomized into a training set (n = 310) and a validation set (n = 194). Independent variables were analyzed by stepwise logistic regression analysis. After the predictive model for diagnosing significant inflammation (Scheuer's system, G ≥ 2) was established, a nomogram was generated. Discrimination and calibration aspects of the model were measured using the area under the receiver operating characteristic curve (AUC) and assessment of a calibration curve. Clinical significance was evaluated by decision curve analysis (DCA).Result: The model was composed of 4 variables: aspartate aminotransferase (AST) levels, γ-glutamyl transpeptidase (GGT) levels, hepatitis B surface antigen (HBsAg) levels, and platelet (PLT) counts. Good discrimination and calibration of the model were observed in the training and validation sets (AUC = 0.87 and 0.86, respectively). The best cutoff point for the model was 0.12, where the specificity was 83.43%, the sensitivity was 77.42%, and the positive likelihood and negative likelihood ratios were 4.67 and 0.27, respectively. The model's predictive capability was superior to that of each single indicator.Conclusion: This study provides a non-invasive approach for predicting significant liver inflammation in CHB patients with normal ALT. Nomograms may help to identify target patients to allow timely initiation of antiviral treatment.

Urinary sediment microRNAs can be used as potential noninvasive biomarkers for diagnosis, reflecting the severity and prognosis of diabetic nephropathy

BackgroundPatients with both diabetes mellitus (DM) and kidney disease could have diabetic nephropathy (DN) or non-diabetic renal disease (NDRD). IgA nephropathy (IgAN) and membranous nephropathy (MN) are the major types of NDRD. No ideal noninvasive diagnostic model exists for differentiating them. Our study sought to construct diagnostic models for these diseases and to identify noninvasive biomarkers that can reflect the severity and prognosis of DN.MethodsThe diagnostic models were constructed using logistic regression analysis and were validated in an external cohort by receiver operating characteristic curve analysis method. The associations between these microRNAs and disease severity and prognosis were explored using Pearson correlation analysis, Cox regression, Kaplan–Meier survival curves, and log-rank tests.ResultsOur diagnostic models showed that miR-95-3p, miR-185-5p, miR-1246, and miR-631 could serve as simple and noninvasive tools to distinguish patients with DM, DN, DM with IgAN, and DM with MN. The areas under the curve of the diagnostic models for the four diseases were 0.995, 0.863, 0.859, and 0.792, respectively. The miR-95-3p level was positively correlated with the estimated glomerular filtration rate (p < 0.001) but was negatively correlated with serum creatinine (p < 0.01), classes of glomerular lesions (p < 0.05), and scores of interstitial and vascular lesions (p < 0.05). However, the miR-631 level was positively correlated with proteinuria (p < 0.001). A low miR-95-3p level and a high miR-631 level increased the risk of progression to end-stage renal disease (p = 0.002, p = 0.011).ConclusionsThese four microRNAs could be noninvasive tools for distinguishing patients with DN and NDRD. The levels of miR-95-3p and miR-631 could reflect the severity and prognosis of DN.

A noninvasive diagnostic model for significant liver fibrosis in patients with chronic hepatitis B based on CHI3L1 and routine clinical indicators.

In consideration of the limitations of liver biopsy, the past years have seen a great advance in the application of noninvasive indices in assessing liver fibrosis. However, the accuracies of the existing indices to determine liver fibrosis in patients with chronic hepatitis B (CHB) are still unsatisfactory. Here, we established a noninvasive diagnostic model for assessing significant liver fibrosis (SLF) in CHB patients based on serum chitinase 3-like 1 (CH3L1) and routine clinical indicators. The clinical data of 337 CHB patients treated at Xiamen Hospital of Traditional Chinese Medicine from December 1, 2019, to September 30, 2020, were collected in this cross-sectional study. All the enrolled cases were randomly divided into a training cohort (n=270) and a validation cohort (n=67). The training cohort was further divided into a non-significant liver fibrosis (NSLF) group (stages S0-S1; n=189; used as the control group) and an SLF group (stage S2-S4; n=81) based on the Scheuer scoring system. Univariate and multivariate logistic regression analyses were performed to screen for independent predictors of SLF in CHB patients and to establish a diagnostic model. The results of univariate and multivariate logistic regression analysis showed that CHI3L1, AFP and PLT were independent predictors of SLF in CHB patients, and the diagnostic model was established as follows: CHI3L1/AFP/PLT (CAP) = 0.600 × CHI3L1/upper limit of normal (ULN) + 0.252 × AFP/ULN - 1.424 × PLT/lower limit of normal (LLN) - 1.223. The area under the receiver operating characteristic (AUROC) of this model for the diagnosis of SLF in the training cohort and the validation cohort was 0.805 and 0.819, respectively, showing no statistically significant difference (P>0.05), and the AUROC for the diagnosis of SLF in the whole cohort was significantly higher than those of other noninvasive markers including aspartate transaminase to platelet ratio index (APRI), fibrosis 4 score (FIB-4) and CHI3L1 (all P<0.05). The newly established model has a good diagnostic efficacy for SLF in CHB patients and is superior to other noninvasive markers including APRI, FIB-4, and CHI3L1. Thus, it can be used as a noninvasive diagnostic index for liver fibrosis in CHB patients.

A salivary microbiome-based auxiliary diagnostic model for type 2 diabetes mellitus

ObjectiveStudies have shown that oral microbiota composition is altered in type 2 diabetes mellitus, implying that it is a potential biomarker for diabetes. This study aimed at constructing a noninvasive auxiliary diagnostic model for diabetes based on differences in the salivary microbial community. DesignSalivary microbiota from 24 treatment-naive type 2 diabetes mellitus patients and 21 healthy populations were detected through 16S rRNA gene sequencing, targeting the V3/V4 region using the MiSeq platform. Salivary microbiome diversity and composition were analyzed so as to establish a diagnostic model for type 2 diabetes. ResultsSalivary microbiome for treatment-naive type 2 diabetes mellitus patients was imbalanced with certain taxa, including Slackia, Mitsuokella, Abiotrophia, and Parascardovia that being significantly dominant, while the abundance of Moraxella was high in healthy controls. Diabetic patients exhibited varying levels of Prevotella nanceiensis and Prevotella melaninogenica which were negatively correlated with glycosylated hemoglobin and fasting blood glucose levels, as well as fasting blood glucose levels, respectively. Based on differences in salivary microbiome composition between diabetic and healthy groups, we developed a diagnostic model that can be used for the auxiliary diagnosis of type 2 diabetes mellitus with an accuracy of 80 %. ConclusionsThese findings elucidate on the differences in salivary microbiome compositions between type 2 diabetic and non-diabetic populations, and the diagnostic model provides a promising approach for the noninvasive auxiliary diagnosis of diabetes mellitus.

A non-invasive differential diagnostic model for light chain cast nephropathy in newly diagnosed multiple myeloma patients with renal involvement: a multicenter study

Light chain cast nephropathy is the most common form of renal lesion in multiple myeloma. Kidney impairment caused by light chain cast nephropathy can be reversed and survival can be improved if early diagnosis is available. It is thus of imperative importance to develop a non-invasive method to diagnose light chain cast nephropathy once the kidney biopsy is not always applicable. We consecutively screened newly diagnosed multiple myeloma patients with kidney biopsies from 4 centers in China. Kidney pathologies were reviewed and clinical presentations were recorded. Then a diagnostic model was established by logistic regression and the predictive values were assessed. Between 1 June 1999 and 30 June 2019, a kidney biopsy was performed in 94 patients with newly diagnosed multiple myeloma, and light chain cast nephropathy was the most common pattern, seen in 52% of biopsied patients. The diagnostic model was established by multivariate logistic regression analysis as P(z) = 1/(1 + e-z) and z = -0.093 Hemoglobin (g/L) + 0.421 Serum albumin (g/L) + 3.463 Acute kidney injury (0/1) -9.207 High-density lipoprotein (mmol/L). If P(z) ≥ 0.55, the diagnosis pointed to light chain cast nephropathy; if P(z) < 0.55, the diagnosis favored non-light chain cast nephropathy. The area under the receiver operating characteristic curves was 0.981 (95% CI 0.959, 1.000). The model had a sensitivity of 93.9%, a specificity of 95.6%, a positive predictive value of 96.0%, a negative predictive value of 94.0%, and a total consistency of 95.0%. We built a novel, non-invasive diagnostic model through a multicenter study, which may be helpful in the diagnosis of light chain cast nephropathy in newly diagnosed multiple myeloma patients.

Noninvasive diagnostic models for chronic hepatitis B liver fibrosis

Hepatitis B virus (HBV) infection is still a public problem that seriously threatens human health. Evaluation of liver fibrosis progression with an efficient noninvasive model is of great significance for condition assessment, disease management, and prognostic evaluation in patients with chronic HBV infection. This article reviews the noninvasive models commonly used in the diagnosis of liver fibrosis in recent years, summarizes the research background, methods, related studies, and advantages and disadvantages of these models, and analyzes the current research status and possible development trends of liver fibrosis assessment models. Recent studies have shown that although current models are not perfect for Chinese patients with chronic HBV infection as the main predisposing factor for liver fibrosis, the excellent performance of noninvasive models in liver fibrosis assessment provides a reference for the assessment of liver fibrosis in patients with chronic HBV infection and can replace liver biopsy to a certain extent.

Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches

Background: Colorectal cancer (CRC) is one of the most frequent causes of gastrointestinal tumors. Due to the invasiveness of the current diagnostic methods, there is an urgent need to develop non-invasive diagnostic approaches for CRC. The exact mechanisms and the most important genes associated with the development of CRC are not fully demonstrated. Objectives: This study aimed to identify differentially expressed miRNAs (DEMs), key genes, and their regulators associated with the pathogenesis of CRC. The signaling pathways and biological processes (BPs) that were significantly affected in CRC were also indicated. Moreover, two non-invasive models were constructed for CRC diagnosis. Methods: The miRNA dataset GSE59856 was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify DEMs in CRC patients compared with healthy controls (HCs). A protein-protein interaction (PPI) network was built and analyzed. Significant clusters in the PPI networks were identified, and the BPs and pathways associated with these clusters were studied. The hub genes in the PPI network, as well as their regulators were identified. Results: A total of 569 DEMs were demonstrated with the criteria of P value &lt;0.001. A total of 110 essential genes and 30 modules were identified in the PPI network. Functional analysis revealed that 1005 BPs, 9 molecular functions (MFs), 14 cellular components (CCs), and 887 pathways were significantly affected in CRC. A total of 22 transcription factors (TFs) were demonstrated as the regulators of the hubs. Conclusion: Our results may provide new insight into the pathogenesis of CRC and advance the diagnostic and therapeutic methods of the disease. However, confirmation is required in the future.

A machine learning approach identified a diagnostic model for pancreatic cancer through using circulating microRNA signatures

Late diagnosis of pancreatic cancer (PC) due to the limited effectiveness of modern testing approaches, causes many patients to miss the chance of surgery and consequently leads to a high mortality rate. Pivotal improvements in circulating microRNA expression levels in PC patients make it possible to diagnose and treat patients at earlier stages. A list of circulating miRNAs was identified in this study using bioinformatics methods in association with pancreatic cancer through analyzing four GEO microarray datasets. The value of top miRNAs was then assessed via using a machine learning method. Taking the advantage of a combinatorial approach consisting of Particle Swarm Optimization (PSO) + Artificial Neural Network (ANN) and Neighborhood Component Analysis (NCA) iterations on a collection of top differentially expressed circulating miRNAs in PC patients, facilitated ranking them by significance. MiRNA’s functional analysis in the final index was performed by predicting target genes and constructing PPI networks. Remarkably, the final model consist of miR-663a, miR-1469, miR-92a-2-5p, miR-125b-1-3p and miR-532–5p showed great diagnostic results on investigated cases and the validation set (Accuracy: 0.93, Sensitivity: 0.93, and Specificity: 0.92). Kaplan-Meier survival assessments of the top-ranked miRNAs revealed that three miRNAs, hsa-miR-1469, hsa-miR-663a and hsa-miR-532–5p, had meaningful associations with the prognosis of patients with pancreatic cancer. This miRNA index may serve as a non-invasive and potential PC diagnostic model, although experimental testing is needed.

Development and validation of an optimized integrative model using urinary chemokines for noninvasive diagnosis of acute allograft rejection.

The urinary chemokines CXCL9 and CXCL10 are promising noninvasive diagnostic markers of acute rejection (AR) in kidney recipients, but their levels might be confounded by urinary tract infection (UTI) and BK virus (BKV) reactivation. Multiparametric model development and validation addressed these confounding factors in a training set of 391 samples, optimizing the diagnostic performance of urinary chemokines. CXCL9/creatinine increased in UTI and BKV viremia with or without nephropathy (BKVN) (no UTI/leukocyturia/UTI: -0.10/1.61/2.09, P=.0001 and no BKV/viremia/BKVN: -0.10/1.90/2.29, P<.001) as well as CXCL10/creatinine (1.17/2.09/1.98, P<.0001 and 1.13/2.21/2.51, P<.001, respectively). An optimized 8-parameter model (recipient age, sex, estimated glomerular filtration rate, donor specific antibodies, UTI, BKV blood viral load, CXCL9, and CXCL10) diagnosed AR with high accuracy (area under the curve [AUC]: 0.85, 95% confidence interval [CI]: 0.80-0.89) and remained highly accurate at the time of screening (AUC: 0.81, 95% CI: 0.48-1) or indication biopsies (AUC: 0.85, 95% CI: 0.81-0.90) and within the first year (AUC: 0.86, 95% CI: 0.80-0.91) or later (AUC: 0.90, 95% CI: 0.84-0.96), achieving AR diagnosis with an AUC of 0.85 and 0.92 (P<.0001) in 2 external validation cohorts. Decision curve analyses demonstrated the clinical utility of the model. Considering confounding factors rather than excluding them, we optimized a noninvasive multiparametric diagnostic model for AR of kidney allografts with unprecedented accuracy.

Total bile acid to platelet ratio: A noninvasive index for predicting liver fibrosis in primary biliary cholangitis.

The aim of the study was to develop a new early noninvasive diagnostic model for primary biliary cholangitis (PBC).A total of 118 PBC patients who had undergone a liver biopsy were enrolled in the study, and were randomized into a model group (78 patients) and a validation group (40 patients). The patients' histological stages were based on the classifications of the Scheuer's stage. All common parameters and liver pathological results were analyzed. And total bile acid to platelet ratio, aspartate aminotransferase to platelet ratio index, fibrosis index based on 4 factors and red cell distribution width to platelet ratio were calculated.There were 106 (89.8%) women and 12 men in this study, and the number of patients in Scheuer stage I, II, III, and IV hepatic fibrosis was 52 (44.1%), 36 (30.5%), 26 (22.0%), and 4 (3.4%), respectively. The areas under the receiver operating characteristic curves of the total bile acid to platelet ratio (TPR), the aspartate aminotransferase to platelet ratio index, the fibrosis index based on 4 factors , and the red cell distribution width to platelet ratio for predicting advanced liver fibrosis were 0.771, 0.715, 0.618, and 0.517 respectively. The areas under the receiver operating characteristic curves of the TPR was higher than other non-invasive serological models.As a simple, inexpensive and easily accessible non-invasive liver fibrosis diagnostic model, the TPR may be a new noninvasive marker for predicting histologic severity of PBC.

Establishment and preliminary application of serum Golgi protein 73 based noninvasive diagnostic model for compensated stage hepatitis B cirrhosis

Objective: To establish and evaluate diagnostic efficacy and applicability of serum Golgi protein (GP) 73 based non-invasive diagnostic model with other conventional serological indicators for compensated stage hepatitis B cirrhosis. Methods: 666 cases with chronic hepatitis B (CHB) who had visited to the Fifth Medical Center of People's Liberation Army General Hospital from January 2010 to December 2017 were selected as the study subjects, and were classified according to compensated stage cirrhosis into clinical and pathological diagnosis group based on whether or not the liver histological examination was performed. A diagnostic model of compensated stage hepatitis B cirrhosis in the clinical diagnosis group was established. The current clinically used diagnostic model of liver cirrhosis, aspartate aminotransferase/platelet ratio index (APRI), fibrosis index (FIB)-4 and liver stiffness measurement (LSM) were compared. Eventually, the diagnostic model was verified step by step by pathological diagnosis group. Results: The area under the receiver operating characteristic curve (AUC) of GP73 and APRI, FIB-4, and LSM for cirrhosis patients in the clinical diagnosis group were 0.842, 0.857, 0.864, and 0.832, respectively. The diagnostic efficiency of the four indicators were of similar (P value > 0.05). A diagnostic model of compensated stage hepatitis B cirrhosis (GAPA) using logistic regression analysis was established: LogitP = 1/ [1 + exp (1.614-0.054 × GP73-0.045 × Age + 0.030 × PLT-0.015 × ALP)]. The AUC of the model was as high as 0.940 and the optimal cut-off value were 0.41. The corresponding diagnostic sensitivity and specificity were 0.92 and 0.82, respectively. The diagnostic efficiency was better than that of APRI, FIB-4, LSM and GP73 alone (P < 0.05). The AUC of GAPA was 0.877 in the pathological diagnosis group, which was similar to the diagnostic efficacy of LSM (0.891) and FIB-4 (0.847) (P > 0.1), but still superior to that of APRI (0.811) and GP73 alone (0.780) (P < 0.001). Conclusion: GAPA, a diagnostic model for compensated stage hepatitis B cirrhosis established in this study, has a good diagnostic efficacy in both the clinical and pathological diagnosis group, and has certain auxiliary diagnostic value in the areas where resources are relatively scarce or where LSM has not been developed.

Establishment of Multiple Myeloma Diagnostic Model Based on Logistic Regression in Clinical Laboratory.

Due to the insidious onset of multiple myeloma (MM), missed diagnosis and misdiagnosis have a serious impact on the health of MM patients. Simple, rapid, and valid laboratory screening is critical for MM clinical diagnosis. We used routine laboratory tests to establish a simple, inexpensive, and non-invasive diagnostic model for MM based on logistic regression. In the retrospective analysis, a total of 273 newly diagnosed MM inpatients and 288 non-MM participants, from January 2016 to December 2018 in Beijing Chaoyang hospital, Capital Medical University, were divided into training set and validation set. Age, gender, and the related routine laboratory tests for MM, including albumin (ALB), globulin (GLB), lactate dehydrogenase (LDH), creatinine (Cr), calcium (Ca2+), hemoglobin (Hb) and platelet (PLT), were analyzed by multivariate logistic regression to develop a diagnostic model. A diagnostic model was calculated using the formula MM index=-((-18×gender-3×ALB-Hb)/10), based on the logistic regression. The MM index [22 (20 - 25)] of MM patients was significantly lower than that of non-MM [30 (29 - 31)] in the training set (p < 0.001). It showed an excellent diagnostic performance in diagnosing MM through a receiver operating characteristic (ROC) curve, and its corresponding sensitivity, specificity, and area under the curve (AUC) were 95.6%, 96.7%, and 0.982 (0.968, 0.997), respectively. At a diagnostic risk threshold of 28, the model identified MM with a sensitivity of 95.6% and a specificity of 98.1% by using independent validation data. There was a significant positive correlation (r = 0.845, p < 0.001) between the DS grading and the MM index among all the participants. The established diagnostic model of MM index can successfully identify newly diagnosed MM from healthy controls. The diagnostic model of MM index may also act as a predictor of the severity of MM without therapy.

Establishment and evaluation of a novel and non-invasive diagnostic model on cirrhotic patients

Objective To establish and evaluate a novel and non-invasive diagnostic model using routine laboratory serological indexes in cirrhotic patients. Methods A retrospective study was conducted on 1044 consecutive patients with hepatocellular carcinoma (HCC) treated by hepatectomy in the Affiliated Tumor Hospital of Guangxi Medical University from September 2013 to December 2016. These patients were divided into a training cohort (n=783) and a validation cohort (n=261) using the 3∶1 matching principle. Logistic regression analysis was used to identify independent risk factors related to occurrence of cirrhosis in the training cohort, and then a PPH score was established. The accuracy of the model in predicting cirrhosis in two groups was evaluated respectively by the area under the receiver operating characteristic curve (AUC) and goodness of fit, and compared with the following commonly used predictive systems: the model for end-stage liver disease (MELD) score, fibrosis index based on 4 factor score (FIB-4), Forns score and aspartate aminotransferase to platelet ratio index score (APRI). Results Univariate and multivariate Logistic regression analysis in the training cohort showed prothrombin time, platelet count and hepatitis B surface antigen positivity were closely related to occurrence of cirrhosis. The accuracy of the PPH score (AUC=0.705) in diagnosing cirrhosis in the training cohort was significantly better than the MELD score (AUC=0.557), APRI score (AUC=0.598), FIB-4 score (AUC=0.597) and Forns score (AUC=0.665). Similar results were obtained in the validation cohort (AUC: 0.702 vs 0.554 vs 0.624 vs 0.634 vs 0.580). The goodness of fit indicated that there was no significant difference between the actual and predicted values of cirrhosis in the two cohorts, and the model was in good agreement. Conclusions A novel and non-invasive model for the diagnosis of cirrhosis was successfully established. The accuracy of this model in diagnosing cirrhosis was better than the MELD, APRI, Fib-4 and Forns scores. This model has significance in guiding clinical treatment decision in HCC patients with cirrhosis. Key words: Carcinoma, hepatocellular; Liver cirrhosis; Biological markers; Non-invasive diagnosis model

A non-invasive diagnostic model of immunoglobulin A nephropathy and serological markers for evaluating disease severity

Background:Immunoglobulin A nephropathy (IgAN) is the most common pathological type of glomerular disease. Kidney biopsy, the gold standard for IgAN diagnosis, has not been routinely applied in hospitals worldwide due to its invasion nature. Thus, we aim to establish a non-invasive diagnostic model and determine markers to evaluate disease severity by analyzing the serological parameters and pathological stages of patients with IgAN.Methods:A total of 272 biopsy-diagnosed IgAN inpatients and 518 non-IgA nephropathy inpatients from the Department of Nephrology of Chinese People's Liberation Army General Hospital were recruited for this study. Routine blood examination, blood coagulation testing, immunoglobulin-complement testing, and clinical biochemistry testing were conducted and pathological stages were analyzed according to Lee grading system. The serological parameters and pathological stages were analyzed. The receiver operating characteristic (ROC) analysis was performed to estimate the diagnostic value of the clinical factors. Logistic regression was used to establish the diagnostic model.Results:There were 15 significantly different serological parameters between the IgAN and non-IgAN groups (all P < 0.05). The ROC analysis was performed to measure the diagnostic value for IgAN of these parameters and the results showed that the area under the ROC curve (AUC) of total protein (TP), total cholesterol (TC), fibrinogen (FIB), D-dimer (D2), immunoglobulin A (IgA), and immunoglobulin G (IgG) were more than 0.70. The AUC of the “TC + FIB + D2 + IgA + age” combination was 0.86, with a sensitivity of 85.98% and a specificity of 73.85%. Pathological grades of I, II, III, IV, and V accounted for 2.21%, 17.65%, 62.50%, 11.76%, and 5.88%, respectively, with grade III being the most prevalent. The levels of urea nitrogen (UN) (13.57 ± 5.95 vs. 6.06 ± 3.63, 5.92 ± 2.97, 5.41 ± 1.73, and 8.41 ± 3.72 mmol/L, respectively) and creatinine (Cr) (292.19 ± 162.21 vs. 80.42 ± 24.75, 103.79 ± 72.72, 96.41 ± 33.79, and 163.04 ± 47.51 μmol/L, respectively) were significantly higher in grade V than in the other grades, and the levels of TP (64.45 ± 7.56, 67.16 ± 6.94, 63.22 ± 8.56, and 61.41 ± 10.86 vs. 37.47 ± 5.6 mg/d, respectively), direct bilirubin (DB) (2.34 ± 1.23, 2.58 ± 1.40, 1.91 ± 0.97, and 1.81 ± 1.44 vs. 0.74 ± 0.57 μmol/L, respectively), and IgA (310.35 ± 103.78, 318.48 ± 107.54, 292.58 ± 81.85, and 323.29 ± 181.67 vs. 227.17 ± 68.12 g/L, respectively) were significantly increased in grades II–V compared with grade I (all P < 0.05).Conclusions:The established diagnostic model that combined multiple factors (TC, FIB, D2, IgA, and age) might be used for IgAN non-invasive diagnosis. TP, DB, IgA, Cr, and UN have the potential to be used to evaluate IgAN disease severity.

Clinical value of noninvasive method in diagnosing hepatic fibrosis about chronic HBV carriers

Objective: To compare the clinical value of FibroScan, FIB-4, APRI and AAR diagnosing hepatic fibrosis in chronic hepatitis B virus (HBV) carriers. Methods: A total of 213 patients with chronic HBV carriers diagnosed by clinical and liver biopsy were selected. And according to HBeAg status, 149 patients were divided into HBeAg-positive group and 64 patients were divided into HBeAg-negative group. The liver stiffness measurements (LSM) was measured by FibroScan (FS), FIB-4, APRI and AAR values were calculated using FIB-4, APRI and AAR formula. And all patients underwent liver biopsy in the same period. According to the degree of hepatic fibrosis in Knodell, one decision point was set: significant hepatic fibrosis (S ≥ 2). The Spearman correlation analysis method was used to analyze the correlation of indicators and the area under receiver operator characteristic curves (AUROCs) of LSM, FIB-4, APRI and AAR were drawn according to liver biopsy pathology results as gold standard. The value of LSM, FIB-4, APRI and AAR diagnosing hepatic fibrosis in chronic HBV carriers was retrospectively analyzed. Retrospective analysis of FS, FIB-4, APRI and AAR were divided into 149 HBeAg-positive chronic HBV carriers (HBeAg-positive group) and 64 HBeAg-negative chronic HBV carriers (HBeAg) in 213 patients with chronic HBV carriers and HBeAg Negative group) in the diagnosis of liver fibrosis. Results: The LSM of 213 patients with chronic HBV carriers, 149 patients with HBeAg-positive chronic HBV carriers and 64 patients with HBeAg-negative chronic HBV carriers were significantly correlated with liver fibrosis grade≥ 2 (P < 0.001). Regardless of HBeAg status, only LSM in the three groups had moderate evaluation efficacy for evaluating significant fibrosis(S≥2), and the positive predictive value was more than 94%, but the diagnostic accuracy was not high, the minimum was 46.31% (HBeAg-positive group), the maximum value of 67.19% (HBeAg-negative group), while the remaining three kinds of serum noninvasive liver fibrosis diagnostic model indicators and diagnostic efficacy are low. The LSM in the three groups showed a significant positive correlation with liver fibrosis grade (S)≥2. Conclusion: LSM is more accurate than FIB-4, APRI and AAR in diagnosing chronic HBV carriers. Dynamically monitoring changes of LSM can earlier understand the progress of liver fibrosis than the three kinds of serology noninvasive diagnostic model and is contributed to the choice of liver biopsy timing.

Effect of Percutaneous Coronary Intervention on Heart Rate Variability in Coronary Artery Disease Patients.

Patients with coronary artery disease (CAD) have a state of autonomic imbalance with a sympathetic predominance. Autonomic dysfunction has been linked to an increased risk of cardiovascular morbidity and mortality. Heart rate variability (HRV) analysis is one of the most encouraging non-invasive diagnostic models and is increasingly used for the assessment of autonomic dysfunction. Percutaneous coronary intervention (PCI) is considered the gold standard in CAD treatment. Revascularisation through PCI eliminates the state of sympathetic hyperactivity, restores the normal cardiac autonomic modulation that can be assessed by HRV measurement.

The relationship between serum hepatitis B surface antigen levels and liver pathology during the natural history of chronic hepatitis B

Objective To investigate the relationship between hepatitis B surface antigen (HBsAg) levels and liver pathology at different phases of natural history in chronic hepatitis B (CHB) patients, and to establish a non-invasive liver fibrosis diagnostic model based on HBsAg quantification. Methods A total of 145 CHB patients were enrolled and underwent liver biopsy from January 2013 to January 2015, among which 73 patients were hepatitis B e antigen (HBeAg) positive. HBsAg levels and HBV DNA levels were compared between patients at different phases of natural history and between patients with different HBeAg statuses. Logistic analysis was used to analyze the risk factors associated with fibrosis in HBeAg-positive patients, and to evaluate the predictive value of non-invasive liver fibrosis diagnostic model based on HBsAg quantification. Analysis of variance was used for statistical analysis, and t test analysis was used for the comparison between two independent samples. Results The serum HBsAg levels at the immunologic tolerance phase, immunologic clearance phase, low copy phase and reactivation phase of CHB patients were (4.29±0.69), (3.56±0.61), (3.22±0.64), and (3.54±0.50) lg IU/mL, respectively (F=16.72, P<0.01), and the HBV DNA levels were (8.48±0.58), (6.69±1.44), (3.80±0.59), and (6.21±1.06) lg IU/mL, respectively (F=76.73, P<0.01). In HBeAg-positive CHB patients with liver inflammation stage (G)≤G1, G2, G3 and G4, the serum HBsAg levels were (4.44±0.65), (4.00±0.72), (3.74±0.62), and (3.28±0.50) lg IU/mL, respectively (F=9.198, P<0.01). In HBeAg-positive CHB patients with liver fibrosis stage (S)≤S1, S2, S3, and S4, the serum HBsAg levels were (4.55±0.54), (4.04±0.89), (3.59±0.63), and (3.34±0.50) lg IU/mL, respectively (F=10.66, P<0.01). Logistic analysis showed that age (OR=1.091, 95%CI: 1.013—1.175) and HBsAg level (OR=0.190, 95%CI: 0.066—0.542) were independent factors for predicting fibrosis stage. The area under receiver operating characteristic curve of the non-invasive fibrosis model based on age and HBsAg level was 0.849, which was higher than aspartate aminotransferase to platelet ratio index (0.749) and fibrosis index based on the 4 factors (0.763). Conclusions The serum HBsAg levels are significantly different among the different phases of natural history in CHB patients. The serum HBsAg levels decline with the progression of liver fibrosis in HBeAg-positive CHB patients. The non-invasive diagnostic model that based on HBsAg quantification could be used to evaluate the stage of liver fibrosis. Key words: Hepatitis B, chronic; Hepatitis B surface antigens; Natural history; Pathological damage

Mo1547 - Volatile Organic Compounds Predict Response to Both Low Fodmap Diet and Probiotics in Irritable Bowel Syndrome: A Randomised Controlled Trial

Introduction Dietary intervention is an effective therapy for irritable bowel syndrome (IBS), with restriction of fermentable carbohydrates (low FODMAP diet, LFD) and microbial therapies such as probiotics showing efficacy in clinical trials. The aim of this study was to investigate whether a non-invasive diagnostic model using faecal volatile organic compounds (VOCs) in IBS could be used to identify (i) what factors at baseline are associated with response to dietary interventions; and (ii) what factors at end of treatment are associated with response. Method Adults fulfilling Rome III criteria for IBS were recruited to a 2 × 2 factorial randomised controlled trial and randomised to a low FODMAP diet (LFD) or placebo dietary advice (sham diet) and to multi-strain probiotic (VSL#3) or placebo for 4 weeks. Response to either intervention was defined as a reduction of ≥50 points for the validated IBS symptom scoring system (IBS-SSS). Faecal VOCs were analysed by a gas chromatography – sensor device (Odoreader). VOC profiles were processed using an in-house pipeline involving wavelet transformation followed by feature selection based on random forest. Using the selected features, a partial least squares (PLS) classifier was constructed to classify VOC profiles from responders and non-responders and accuracies determined using 10-fold cross-validation. Results A total of 95 patients completed the study of whom 93 (63 females, 68%) provided sufficient sample for analysis. More patients responded to LFD (37/46, 80%) compared to sham (21/47, 45%)(p Conclusion Faecal VOC profiling is a low cost, non-invasive tool that may help predict response to dietary intervention in IBS patients as well as shedding light on mechanisms underpinning response. Clinical trials using this algorithm are needed, and if successful, will pave the way for personalised treatment plans in IBS. Disclosure of Interest None Declared

126 - Diagnosis of Myelodysplastic Syndromes without a Bone Marrow Biopsy: A Non-Invasive Diagnostic Model

126 - Diagnosis of Myelodysplastic Syndromes without a Bone Marrow Biopsy: A Non-Invasive Diagnostic Model

Establishment and evaluation of noninvasive diagnostic models for liver fibrosis in patients with chronic hepatitis B

Objective: To establish the model of liver fibrosis based on noninvasive indices, and to investigate the diagnostic value of this model. Methods: A total of 838 patients with chronic hepatitis B (CHB) who underwent liver biopsy in our hospital from March 2003 to October 2013 were selected, and the results of blood tests and B-ultrasound were collected. The correlation between these indices and liver fibrosis stage was analyzed. A logistic regression analysis was performed to establish a predictive model, and the value of this model was examined in validation group. The t-test, Mann-Whitney U non-parametric test, and chi-square test were used for data analysis. A Spearman rank correlation analysis was used for bivariate correlation analysis, and a dichotomous logistic stepwise regression analysis was used for multivariate analysis. Results: In the model group, a model (FV) consisting of age, platelet count (PLT), γ-glutamyl transferase (GGT), albumin/globulin ratio (A/G), and splenic square area (SSA) was established. The areas under the receiver operating characteristic curve (AUROCs) of the model FV were 0.892, 0.910, and 0.915, respectively, in diagnosing significant liver fibrosis (S2-4), progressive liver fibrosis (S3-4), and early-stage liver cirrhosis (S4), with sensitivities of 77.6%, 83.7%, and 86.0%, respectively, specificities of 89.7%, 84.5%, and 83.7%, respectively, and accuracy of 82.1%, 84.2%, and 84.2%, respectively. There were no significant differences in AUROCs between the validation group and the model group (Z = 0.360, 0.885, and 0.046, all P > 0.05). In all patients, FV had significantly higher AUROCs in the diagnosis of liver fibrosis than FIB4 index and S index (Z = 4.569/3.423, 5.640/4.709, and 4.652/4.439, all P < 0.05). With < 0.374 and ≥ 0.577 as the cut-off values for the exclusion and diagnosis of significant liver fibrosis, 61.1% (512/838) of all patients could avoid liver biopsy, and the accuracy was 92.6% (474/512). Conclusion: The noninvasive model based on age, PLT, GGT, A/G, and SSA can accurately predict liver fibrosis degree in patients with CHB with good reproducibility; therefore, it can be used for dynamic monitoring of liver fibrosis degree in clinical practice.