Key result
The CALIBRA risk algorithm accurately predicted 1-year cardiovascular hospitalizations in non-dialysis dependent chronic kidney disease patients, significantly outperforming established scores like FHS, ASCVD, and INDANA.
Why the study?
Existing cardiovascular prognostic models appear not to be entirely adequate for patients suffering from chronic kidney disease.
Does the CALIBRA risk algorithm improve prediction of yearly cardiovascular hospitalizations compared to established risk scores in patients with non-dialysis dependent CKD?
Observational (n=27,694)
Yes
Does the CALIBRA risk algorithm improve prediction of yearly cardiovascular hospitalizations compared to established risk scores in patients with non-dialysis dependent CKD?
Effect estimate: AUC 0.79 (95% CI 0.76-0.81)
p-value: p=<0.001
The CALIBRA risk algorithm provides more accurate prediction of cardiovascular hospitalizations in non-dialysis CKD patients compared to traditional risk scores like FHS and ASCVD.
May improve CV hospitalization prediction in non-dialysis CKD; leaves open need for prospective validation before practice change.
Background and Objectives Cardiovascular (CV) disease is the main cause of morbidity and mortality in patients suffering from chronic kidney disease (CKD). Although it is widely recognized that CV risk assessment represents an essential prerequisite for clinical management, existing prognostic models appear not to be entirely adequate for CKD patients. We derived a literature-based, naïve-bayes model predicting the yearly risk of CV hospitalizations among patients suffering from CKD, referred as the CArdiovascular, LIterature-Based, Risk Algorithm (CALIBRA). Methods CALIBRA incorporates 31 variables including traditional and CKD-specific risk factors. It was validated in two independent CKD populations: the FMC NephroCare cohort (European Clinical Database, EuCliD®) and the German Chronic Kidney Disease (GCKD) study prospective cohort. CALIBRA performance was evaluated by c-statistics and calibration charts. In addition, CALIBRA discrimination was compared with that of three validated tools currently used for CV prediction in CKD, namely the Framingham Heart Study (FHS) risk score, the atherosclerotic cardiovascular disease risk score (ASCVD), and the Individual Data Analysis of Antihypertensive Intervention Trials (INDANA) calculator. Superiority was defined as a ΔAUC>0.05. Results CALIBRA showed good discrimination in both the EuCliD® medical registry (AUC 0.79, 95%CI 0.76-0.81) and the GCKD cohort (AUC 0.73, 95%CI 0.70-0.76). CALIBRA demonstrated improved accuracy compared to the benchmark models in EuCliD® (FHS: ΔAUC=-0.22, p<0.001; ASCVD: ΔAUC=-0.17, p<0.001; INDANA: ΔAUC=-0.14, p<0.001) and GCKD (FHS: ΔAUC=-0.16, p<0.001; ASCVD: ΔAUC=-0.12, p<0.001; INDANA: ΔAUC=-0.04, p<0.001) populations. Accuracy of the CALIBRA score was stable also for patients showing missing variables. Conclusion CALIBRA provides accurate and robust stratification of CKD patients according to CV risk and allows score calculations with improved accuracy compared to established CV risk scores also in real-world clinical cohorts with considerable missingness rates. Our results support the generalizability of CALIBRA across different CKD populations and clinical settings.
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Neri et al. (2022) conducted an observational in Non-Dialysis Dependent Chronic Kidney Disease (NDD-CKD) (n=27,694). CALIBRA risk algorithm vs. FHS, ASCVD, and INDANA risk scores was evaluated on Cardiovascular hospitalization within 1 year (AUC 0.79, 95% CI 0.76-0.81, p=<0.001). The CALIBRA risk algorithm accurately predicted 1-year cardiovascular hospitalizations in non-dialysis dependent chronic kidney disease patients, significantly outperforming established scores like FHS, ASCVD, and INDANA.
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