Related Experiment Video
Updated: Feb 12, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Can Serum Cystatin C predict long-term survival in cardiac surgery patients?
Valentina Rovella1, Giulia Marrone1,2, Mariarita Dessì3
1Department of Medicine, Hypertension and Nephrology Unit, University Hospital Tor Vergata, Rome 00133, Italy.
Insights
Serum Cystatin C (sCysC) effectively predicts cardiovascular mortality in cardiac surgery patients. Elevated sCysC levels are a significant indicator of mortality risk, surpassing serum creatinine in predictive value.
Area of Science:
- Cardiology
- Nephrology
- Biomarkers
Background:
- Renal dysfunction is a known risk factor for adverse outcomes in cardiac surgery.
- Serum Cystatin C (sCysC) is recognized for detecting early renal dysfunction.
- The prognostic value of sCysC for cardiovascular outcomes remains less explored.
Purpose of the Study:
- To investigate the prognostic significance of serum Cystatin C (sCysC) for predicting cardiovascular mortality in patients undergoing cardiac surgery.
Main Methods:
- A cohort of 424 cardiac surgery patients were analyzed.
- Renal function, inflammatory markers, and demographic data were assessed at admission.
- Kaplan-Meier survival analysis and multivariate Cox-Proportional Hazard Models (CPHM) were employed to identify mortality predictors.
Main Results:
- Serum Cystatin C (sCysC) emerged as a significant independent predictor of cardiovascular mortality (p<0.00001).
- Age was also a significant predictor of mortality (p=0.039).
- In comparison, serum creatinine (sCrea) was a less significant predictor when sCysC was considered.
Conclusions:
- Elevated serum Cystatin C (sCysC) levels are a valuable biomarker for cardiovascular mortality risk in cardiac surgery patients.
- sCysC demonstrates superior prognostic capability compared to serum creatinine for this patient group.
Abstract:
Renal dysfunction is a risk factor for morbidity and mortality in cardiac surgery patients. Serum Cystatin C (sCysC) is a well-recognized marker of early renal dysfunction but few reports evaluate its prognostic cardio-vascular role. The aim of the study is to consider the prognostic value of sCysC for cardiovascular mortality. Four hundred twenty-four cardiac-surgery patients (264 men and 160 women) were enrolled. At admission, all patients were tested for renal function and inflammatory status. Patients were subdivided in subgroups according to the values of the following variables: sCysC, serum Creatinine (sCrea), age, high sensitivity-C Reactive Protein, fibrinogen, surgical procedures and Kaplan-Meier cumulative survival curves were plotted. The primary end-point was cardiovascular mortality. In order to evaluate the simultaneous independent impact of all measured variables on survival we fitted a multivariate Cox-Proportional Hazard Model (CPHM). In Kaplan-Meier analysis 124 patients (29.4%) reached the end-point. In multivariate CPHM, the only significant predictors of mortality were sCysC (p<0.00001, risk ratio: 1.529, CI: 1.29-1.80) and age (p=0.039, risk ratio: 1.019, CI: 1.001-1.037). When replacing sCysC with sCrea, the only significant predictor of mortality was sCrea (p=0.0026; risk ratio 1.20; CI: 1.06-1.36). Increased levels of sCysC can be considered a useful biomarker of cardiovascular mortality in cardiac-surgery patients.
Related Concept Videos
Predicting Molecular Geometry
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Survival Tree
Building a Survival Tree
Constructing a...
Long-term Depression
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...

