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Development and validation of mortality prediction models for heart transplantation using nutrition-related
Shirui Qian1, Bingxin Cao1, Ping Li1
1Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Insights
A new model using nutrition-related indicators predicts heart transplant mortality risk. This tool helps clinicians identify high-risk patients for targeted preventative therapy, aiming to reduce post-transplant death.
Area of Science:
- Cardiology
- Transplantation Medicine
- Medical Informatics
Background:
- Heart transplantation (HT) outcomes can be influenced by patient nutritional status.
- Predicting post-transplant mortality is crucial for patient management and resource allocation.
Purpose of the Study:
- To develop and validate a mortality prediction model for heart transplantation (HT) using nutrition-related indicators.
- To provide clinicians with a tool to identify patients at high risk of death after HT.
Main Methods:
- A prediction model was developed and validated in adult Chinese HT recipients (2015-2020).
- 428 subjects were randomly assigned to derivation (70%) and validation (30%) cohorts.
- The likelihood-ratio test with Akaike information was used for indicator selection; model performance was assessed using AUC, C-index, and reclassification metrics.
Main Results:
- The final model included age, nutritional risk index (NRI), serum creatine, and triglyceride.
- In the derivation cohort, the model achieved an AUC of 0.76 and C-statistic of 0.72; in the validation cohort, AUC was 0.71.
- The multivariable model significantly improved discrimination and reclassification compared to models using fewer variables.
Conclusions:
- The developed model accurately predicts mortality risk after heart transplantation.
- This tool can aid clinicians in identifying patients at high risk for targeted interventions, potentially reducing postoperative mortality.
Objective:
We sought to develop and validate a mortality prediction model for heart transplantation (HT) using nutrition-related indicators, which clinicians could use to identify patients at high risk of death after HT.
Method:
The model was developed for and validated in adult participants in China who received HT between 1 January 2015 and 31 December 2020. 428 subjects were enrolled in the study and randomly divided into derivation and validation cohorts at a ratio of 7:3. The likelihood-ratio test based on Akaike information was used to select indicators and develop the prediction model. The performance of models was assessed and validated by area under the curve (AUC), C-index, calibration curves, net reclassification index, and integrated discrimination improvement.
Result:
The mean (SD) age was 48.67 (12.33) years and mean (SD) nutritional risk index (NRI) was 100.47 (11.89) in the derivation cohort. Mortality after HT developed in 66 of 299 patients in the derivation cohort and 28 of 129 in the validation cohort. Age, NRI, serum creatine, and triglyceride were included in the full model. The AUC of this model was 0.76 and the C statistics was 0.72 (95% CI, 0.67-0.78) in the derivation cohort and 0.71 (95% CI, 0.62-0.81) in the validation cohort. The multivariable model improved integrated discrimination compared with the reduced model that included age and NRI (6.9%; 95% CI, 1.8%-15.1%) and the model which only included variable NRI (14.7%; 95% CI, 7.4%-26.2%) in the derivation cohort. Compared with the model that only included variable NRI, the full model improved categorical net reclassification index both in the derivation cohort (41.8%; 95% CI, 9.9%-58.8%) and validation cohort (60.7%; 95% CI, 9.0%-100.5%).
Conclusion:
The proposed model was able to predict mortality after HT and estimate individualized risk of postoperative death. Clinicians could use this model to identify patients at high risk of postoperative death before HT surgery, which would help with targeted preventative therapy to reduce the mortality risk.
