Development of a Preoperative Transthoracic Echocardiography-Based Nomogram for Predicting 1-Year Cardiovascular Events in End-Stage Renal Disease Kidney Transplant Recipients.
Pan M, Xu L, et al. • Echocardiography (Mount Kisco, N.Y.) • 2026
A preoperative transthoracic echocardiography-based nomogram incorporating IVSTd, pulmonary hypertension, aortic valve calcification, and diabetes history demonstrated reliable discrimination for predicting 1-year cardiovascular events in kidney transplant recipients, with training AUC of 0.776 and testing AUC of 0.749.
Key Findings
Results
The overall 1-year post-transplant cardiovascular event incidence was 17.55% in ESRD kidney transplant recipients.
96 out of 547 patients developed cardiovascular events within 1 year post-transplant.
This was a single-center cohort study enrolling ESRD patients receiving kidney transplantation from 2021 to 2022.
Cardiovascular events were identified as the primary endpoint of the study.
The authors note that cardiovascular events are 'the top cause of morbidity and mortality in transplant recipients.'
Results
Four independent risk factors for 1-year post-transplant cardiovascular events were identified: interventricular septal thickness at end-diastole (IVSTd), pulmonary hypertension (PH), aortic valve calcification (AVC), and history of diabetes.
Risk factors were screened using LASSO regression followed by multivariate logistic regression.
Three of the four independent predictors (IVSTd, PH, AVC) are echocardiographic parameters measurable via preoperative transthoracic echocardiography.
Diabetes history was the sole clinical (non-echocardiographic) independent predictor retained in the final model.
Both preoperative TTE parameters and clinical/laboratory data were collected and analyzed.
Results
The nomogram demonstrated good discriminative performance in both training and testing sets.
Training set AUC was 0.776 (95% CI 0.711–0.841), with sensitivity of 61.8% and specificity of 79.4%.
Testing set AUC was 0.749 (95% CI 0.618–0.880).
Model performance was validated using ROC analysis, calibration curves, and decision curve analysis (DCA).
The dataset was split into training and testing sets to allow internal validation of the model.
Results
Calibration curves showed favorable consistency between predicted and observed cardiovascular event probabilities.
Calibration was assessed as a component of model validation alongside discrimination and clinical utility.
The authors describe the calibration curves as showing 'favorable consistency.'
Both training and testing sets underwent calibration curve assessment.
Results
Decision curve analysis demonstrated stable clinical net benefit of the nomogram across a threshold probability range of 7% to 68%.
DCA was used to evaluate the clinical utility and net benefit of the predictive model.
The model provided net benefit across a wide threshold range of 7%–68%, suggesting practical applicability across a broad range of clinical risk tolerance.
DCA was performed as part of a three-pronged validation strategy alongside ROC and calibration curves.
Background
The study aimed to provide a non-invasive preoperative tool for cardiovascular risk stratification in kidney transplant candidates.
Transthoracic echocardiography is described as a non-invasive modality integrated into the predictive model.
The model was designed for use in the preoperative period to stratify post-transplant cardiovascular risk.
The authors describe the model as having 'reliable discrimination and clinical value for non-invasive cardiovascular risk stratification in KT candidates.'
547 ESRD patients from a single center were enrolled, limiting generalizability without external validation.
What This Means
This research suggests that a relatively simple scoring tool (nomogram) built from routine preoperative heart ultrasound (echocardiography) measurements, combined with a patient's diabetes history, can help predict which kidney transplant recipients are at higher risk of experiencing a serious heart or blood vessel problem within the first year after surgery. The study followed 547 patients with end-stage kidney disease who received a kidney transplant, and found that about 1 in 6 patients (17.55%) experienced a cardiovascular event within one year. Four specific factors were found to independently predict this risk: the thickness of the heart's inner wall (IVSTd), elevated blood pressure in the lungs (pulmonary hypertension), calcium deposits on the aortic heart valve (aortic valve calcification), and a history of diabetes — all of which can be assessed before surgery using non-invasive methods.
The predictive model performed reasonably well, correctly distinguishing higher- from lower-risk patients about 75–78% of the time in both the group used to build the model and a separate group used to test it. The model also showed good agreement between predicted and actual event rates, and decision curve analysis confirmed it would provide meaningful clinical benefit across a wide range of risk thresholds used by clinicians.
This research suggests that incorporating standard echocardiographic measurements into preoperative evaluations could help transplant teams identify patients who may need closer cardiovascular monitoring or additional preventive interventions after transplant. Because the study was conducted at a single center, future research involving multiple centers would be needed to confirm whether the model performs equally well in broader, more diverse patient populations.
Pan M, Xu L, Cai Y, Li Y, Deng W, Ou A, et al.. (2026). Development of a Preoperative Transthoracic Echocardiography-Based Nomogram for Predicting 1-Year Cardiovascular Events in End-Stage Renal Disease Kidney Transplant Recipients.. Echocardiography (Mount Kisco, N.Y.). https://doi.org/10.1111/echo.70633