Dataset Information

Current NAFLD guidelines for risk stratification in diabetic patients have poor diagnostic discrimination.

ABSTRACT: Patients with type 2 diabetes (T2D) are at risk for non-alcoholic fatty liver disease (NAFLD) and associated complications. This study evaluated the performance of international (EASL-EASD-EASO) and national (DGVS) guidelines for NAFLD risk stratification. Patients with T2D prospectively underwent ultrasound, liver stiffness measurement (LSM) and serum-based fibrosis markers. Guideline-based risk classification and referral rates for different screening approaches were compared and the diagnostic properties of simplified algorithms, genetic markers and a new NASH surrogate (FAST score) were evaluated. NAFLD risk was present in 184 of 204 screened patients (age 64.2?±?10.7 years; BMI 32.6?±?7.6 kg/m2). EASL-EASD-EASO recommended specialist referral for 60-77% depending on the fibrosis score used, only 6% were classified as low risk. The DGVS algorithm required LSM for 76%; 25% were referred for specialised care. The sensitivities of the diagnostic pathways were 47-96%. A simplified referral strategy revealed a sensitivity/specificity of 46/88% for fibrosis risk. Application of the FAST score reduced the referral rate to 35%. This study (a) underlines the high prevalence of fibrosis risk in T2D, (b) demonstrates very high referral rates for in-depth hepatological work-up, and (c) indicates that simpler referral algorithms may produce comparably good results and could facilitate NAFLD screening.

SUBMITTER: Blank V

PROVIDER: S-EPMC7591877 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Current NAFLD guidelines for risk stratification in diabetic patients have poor diagnostic discrimination.

Blank Valentin V Petroff David D Beer Sebastian S Böhlig Albrecht A Heni Maria M Berg Thomas T Bausback Yvonne Y Dietrich Arne A Tönjes Anke A Hollenbach Marcus M Blüher Matthias M Keim Volker V Wiegand Johannes J Karlas Thomas T

Scientific reports 20201027 1

Patients with type 2 diabetes (T2D) are at risk for non-alcoholic fatty liver disease (NAFLD) and associated complications. This study evaluated the performance of international (EASL-EASD-EASO) and national (DGVS) guidelines for NAFLD risk stratification. Patients with T2D prospectively underwent ultrasound, liver stiffness measurement (LSM) and serum-based fibrosis markers. Guideline-based risk classification and referral rates for different screening approaches were compared and the diagnosti ...[more]

PMID: 33110165

Similar Datasets

Project description:Background & aimsHepatocellular carcinoma (HCC) risk varies dramatically in patients with cirrhosis according to well-described, readily available predictors. We aimed to develop simple models estimating HCC risk in patients with alcohol-related liver disease (ALD)-cirrhosis or non-alcoholic fatty liver disease (NAFLD)-cirrhosis and calculate the net benefit that would be derived by implementing HCC surveillance strategies based on HCC risk as predicted by our models.MethodsWe identified 7,068 patients with NAFLD-cirrhosis and 16,175 with ALD-cirrhosis who received care in the Veterans Affairs (VA) healthcare system in 2012. We retrospectively followed them for the development of incident HCC until January 2018. We used Cox proportional hazards regression to develop and internally validate models predicting HCC risk using baseline characteristics at entry into the cohort in 2012. We plotted decision curves of net benefit against HCC screening thresholds.ResultsWe identified 1,278 incident cases of HCC during a mean follow-up period of 3.7 years. Mean annualized HCC incidence was 1.56% in NAFLD-cirrhosis and 1.44% in ALD-cirrhosis. The final models estimating HCC were developed separately for NAFLD-cirrhosis and ALD-cirrhosis and included 7 predictors: age, gender, diabetes, body mass index, platelet count, serum albumin and aspartate aminotransferase to √alanine aminotransferase ratio. The models exhibited very good measures of discrimination and calibration and an area under the receiver operating characteristic curve of 0.75 for NAFLD-cirrhosis and 0.76 for ALD-cirrhosis. Decision curves showed higher standardized net benefit of risk-based screening using our prediction models compared to the screen-all approach.ConclusionsWe developed simple models estimating HCC risk in patients with NAFLD-cirrhosis or ALD-cirrhosis, which are available as web-based tools (www.hccrisk.com). Risk stratification can be used to inform risk-based HCC surveillance strategies in individual patients or healthcare systems or to identify high-risk patients for clinical trials.Lay summaryPatients with cirrhosis of the liver are at risk of getting hepatocellular carcinoma (HCC or liver cancer) and therefore it is recommended that they undergo surveillance for HCC. However, the risk of HCC varies dramatically in patients with cirrhosis, which has implications on if and how patients get surveillance, how providers counsel patients about the need for surveillance, and how healthcare systems approach and prioritize surveillance. We used readily available predictors to develop models estimating HCC risk in patients with cirrhosis, which are available as web-based tools at www.hccrisk.com.

Project description:BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717-0.817), AUC of 0.780(95%CI: 0.721-0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Project description:BACKGROUND:Guidelines from the United Kingdom and the United States on risk stratification after polypectomy differ, as do recommended surveillance intervals. OBJECTIVE:To compare risk for advanced colorectal neoplasia at 1-year colonoscopy among patients cross-classified by U.S. and U.K. surveillance guidelines. DESIGN:Pooled analysis of 4 prospective studies between 1984 and 1998. SETTING:Academic and private clinics in the United States. PATIENTS:3226 postpolypectomy patients with 6- to 18-month follow-up colonoscopy. MEASUREMENTS:Rates of advanced neoplasia (an adenoma ?1 cm, high-grade dysplasia, >25% villous architecture, or invasive cancer) at 1 year, compared across U.S. and U.K. risk categories. RESULTS:Advanced neoplasia was detected 1 year after polypectomy in 3.8% (95% CI, 2.7% to 4.9%) of lower-risk patients and 11.2% (CI, 9.8% to 12.6%) of higher-risk patients by U.S. criteria. According to U.K. criteria, 4.4% (CI, 3.3% to 5.4%) of low-risk patients, 9.9% (CI, 8.3% to 11.5%) of intermediate-risk patients, and 18.7% (CI, 14.8% to 22.5%) of high-risk patients presented with advanced neoplasia; U.K. high-risk patients comprised 12.1% of all patients. All U.S. lower-risk patients were low-risk by U.K. criteria; however, more patients were classified as low-risk, because the U.K. guidelines do not consider histologic features. Higher-risk U.S. patients were distributed across the 3 U.K. categories. Among all patients with advanced neoplasia, 26.3% were reclassified by the U.K. criteria to a higher-risk category and 7.0% to a lower-risk category, with a net 19.0% benefiting from detection 2 years earlier. Overall, substitution of U.K. for U.S. guidelines resulted in an estimated 0.03 additional colonoscopy every 5 years per patient. LIMITATIONS:Patients were enrolled 15 to 20 years ago, and quality measures for colonoscopy were unavailable. Patients lacking follow-up colonoscopy or with surveillance colonoscopy after 6 to 18 months and those with cancer or insufficient baseline adenoma characteristics were excluded (2076 of 5302). CONCLUSION:Application of the U.K. guidelines in the United States could identify a subset of high-risk patients who may warrant a 1-year clearing colonoscopy without substantially increasing rates of colonoscopy. PRIMARY FUNDING SOURCE:European Union Public Health Programme.

Dataset Information

Current NAFLD guidelines for risk stratification in diabetic patients have poor diagnostic discrimination.

Publications

Current NAFLD guidelines for risk stratification in diabetic patients have poor diagnostic discrimination.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets