Prediction model for cardiovascular events or all-cause mortality in incident dialysis patients

Abstract

Some variables including age, comorbidity of diabetes, and so on at dialysis initiation are associated with patient prognosis. Cardiovascular (CV) events are a major cause of death, and adequate models that predict prognosis in dialysis patients are warranted. Therefore, we created models using some variables at dialysis initiation. We used a database of 1,520 consecutive dialysis patients (median age, 70 years; 492 women [32.4%]) from a multicenter prospective cohort study. We established the primary endpoint as a composite of the incidence of first CV events or all-cause death. A multivariable Cox proportional hazard regression model was used to construct a model. We considered a complex and a simple model. We used area under the receiver operating characteristic curve (AUROC) to assess and compare the predictive performances of the prediction models and evaluated the improvement in discrimination using the complex model versus the simple model using net reclassification improvement (NRI). We then assessed integrated discrimination improvement (IDI) to evaluate improvements in average sensitivity and specificity. Of 392 deaths, 152 were CV-related. Totally, 506 CV events occurred during the follow-up period (median 1,285 days). Finally, 692 patients reached the primary endpoint. Baseline data were set at dialysis initiation. AUROC for the primary endpoint was 0.737 (95% confidence interval [CI], 0.712–0.761) in the simple model and 0.765 (95% CI, 0.741–0.788) in the complex model. There were significant intergroup differences in NRI (0.44; 95% CI, 0.34–0.53; p < 0.001) and IDI (0.02; 95% CI, 0.02–0.03; p < 0.001). We prepared a Shiny R application for each model to automatically calculate the predicted occurrence probability ( https://statacademy.shinyapps.io/App_inaguma_20190717/ ). The complex model made more accurate predictions than the simple model. However, the intergroup difference was not significant. Hence, the simple model was more useful than the complex model. The tool was useful in a real-world clinical setting because it required only routinely available variables. Moreover, we emphasized that the tool could predict the incidence of CV events or all-cause mortality for individual patients. In the future, we must confirm its external validity in other prospective cohorts.

Development of a prediction model to estimate the 5-year risk of cardiovascular events and all-cause mortality in haemodialysis patients: a retrospective study

“…Acute myocardial infarction was defined as myocardial ischaemia indicated by symptoms, electrocardiogram or cardiac biomarkers, narrowing/obstruction indicated by coronary angiography, percutaneous coronary intervention or coronary artery bypass surgery ( Kimura et al, 2019 ). Stroke was defined as bleeding or infarction on computed tomography (CT) or magnetic resonance imaging (MRI) accompanied by neurological symptoms ( Inaguma et al, 2019 ). Peripheral vascular events were defined as the amputation of fingers and limbs secondary to vascular disease, peripheral arterial bypass or angioplasty ( O’Hare & Johansen, 2001 ).…”

“…Although some models that include HD-specific risk factors have been created for the haemodialysis population, the majority were used to predict early death after dialysis initiation ( Inaguma et al, 2019 ), had a shorter follow-up time ( Matsubara et al, 2017 ), or needed complex or expensive indicators ( Floege et al, 2015 ). No simple widely used clinical tool is currently available.…”

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

“…For example, consider MTS signals collected for dialysis patients. Dialysis is an important renal replacement therapy for purifying the blood of patients whose kidneys are not working normally (Inaguma et al 2019) have routines of dialysis, blood tests, chest X-ray, etc., which record data such as venous pressure, glucose level, and cardiothoracic ratio (CTR). These signal sources may have different frequencies.…”

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

“…For example, consider MTS signals collected for dialysis patients. Dialysis is an important renal replacement therapy for purifying the blood of patients whose kidneys are not working normally (Inaguma et al 2019). Dialysis patients have routines of dialysis, blood tests, chest X-ray, etc., which record data such as venous pressure, glucose level, and cardiothoracic ratio (CTR).…”