Dataset Information

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.

SUBMITTER: Inaguma D

PROVIDER: S-EPMC6705850 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Inaguma Daijo D Morii Daichi D Kabata Daijiro D Yoshida Hiroyuki H Tanaka Akihito A Koshi-Ito Eri E Takahashi Kazuo K Hayashi Hiroki H Koide Shigehisa S Tsuboi Naotake N Hasegawa Midori M Shintani Ayumi A Yuzawa Yukio Y

PloS one 20190822 8

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 p ...[more]

PMID: 31437231

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Project description:AimWe assess the improvement in discrimination afforded by the addition of the computed tomography risk markers thoracic aorta calcium (TAC), aortic valve calcification (AVC), mitral annular calcification (MAC), pericardial adipose tissue volume (PAT), and liver attenuation (LA) to the Framingham risk score (FRS) + coronary artery calcium (CAC) for incident coronary heart disease (CHD) and incident cerebrovascular disease (CVD) in a multiethnic cohort.Methods and resultsA total of 5745 participants were enrolled, with 2710 at intermediate Framingham risk, 210 CVD events, and 155 CHD events). Over 9 years of follow up, 251 had adjudicated CHD, 346 had CVD events, and 321 died. The data were analysed using Cox proportional hazard, receiver operator curve (ROC), and net reclassification improvement (NRI) analyses. In the whole cohort and also when the analysis was restricted to only the intermediate-risk participants, CAC, TAC, AVC, and MAC were all significantly associated with incident CVD, incident CHD, and mortality, and CAC had the strongest association. When added to the FRS, CAC had the highest area under the curve (AUC) for the prediction of incident CVD and incident CHD; LA had the least. The addition of TAC, AVC, MAC, PAT, and LA to FRS + CAC all resulted in a significant reduction in AUC for incident CHD (0.712 vs. 0.646, 0.655, 0.652, 0.648, and 0.569; all p < 0.01, respectively) in participants with intermediate FRS. The addition of CAC to FRS resulted in an NRI of 0.547 for incident CHD in the intermediate-risk group. The NRI when TAC, AVC, MAC, PAT, and LA were added to FRS + CAC were 0.024, 0.026, 0.019, 0.012, and 0.012, respectively, for incident CHD in the intermediate-risk group. Similar results were obtained for incident CVD in the intermediate-risk group and also when the whole cohort was used instead of the intermediate FRS group.ConclusionsThe addition of CAC to the FRS provides superior discrimination especially in intermediate-risk individuals compared with the addition of TAC, AVC, MAC, PAT, or LA for incident CVD and incident CHD. Compared with FRS + CAC, the addition of TAC, AVC, MAC, PAT, or LA individually to FRS + CAC worsens the discrimination for incident CVD and incident CHD. These risk markers are unlikely to be useful for improving cardiovascular risk prediction.

Dataset Information

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

Publications

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

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