ALICE-PROTECT Study Yields Online Risk Prediction Tool in Diabetic Nephropathy From ESH 2016 | LB 1: Jean-Pierre Fauvel, MD CHU Lyon, Hôpital E Herriot,

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Presentation transcript:

ALICE-PROTECT Study Yields Online Risk Prediction Tool in Diabetic Nephropathy From ESH 2016 | LB 1: Jean-Pierre Fauvel, MD CHU Lyon, Hôpital E Herriot, Lyon, France

Overview Online risk prediction tool created to aid optimizing treatment of diabetic nephropathy ALICE-PROTECT study data of patients with type 2 diabetes (T2D) and diabetic nephropathy used for Bayesian modeling Online tool predicts 2-year risk of cardiovascular (CV) event Access for online calculator: https://www.hed.cc/?s=cvevent&t=CV%20Event

ALICE-PROTECT Study Prospective, observational study Primary outcome: number of patients at 2 years with blood pressure <130/80 mmHg and proteinuria <0.5 g daily 986 patients, mean age 70 years, mean eGFR 42 ml/min/1.73 m2, 66% patients had proteinuria >1 g daily 630 patients alive at 2 years; 39 patients had CV event during Year 1; 26 patients died from CV cause Reference: Joly D et al. Diabetes Res Clin Pract 2015

Proportion of Patients with a Cardiovascular Event in ALICE-PROTECT % of the population

Variables in Bayesian Model Patient Characteristics Age, sex, body mass index, blood pressure, ethnicity, smoking habits Medical History Stroke, sleep apnea, peripheral arterial disease, ischemic heart disease, heart failure, diabetes duration, hypertension duration, retinopathy

Variables in Bayesian Model Biology eGFR, potassium, low-density lipoprotein cholesterol, HbA1c, proteinuria, hemoglobin Treatment Renin angiotensin system blockers, ASE, insulin, statin, diuretics, antithrombotic agent

Variables in Bayesian Model Created Bayesian network to simulate data, using original data from ALICE-PROTECT study Simulation calibrated with 2000 simulated individual data, 1000 with and 1000 without a CV event; multiple links found between variables Bayesian network mimics usual medical thinking by physicians, analyzes large number of variables Used increasingly as diagnostic tools for medical decision making

ALICE-PROTECT Study Yields Online Risk Prediction Tool in Diabetic Nephropathy From ESH 2016 | LB 1: Jean-Pierre Fauvel, MD CHU Lyon, Hôpital E Herriot, Lyon, France