Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China

Zhou, Ying; Chen, Si; Rao, Zhenqi; Yang, Dong Heon; Liu, Xiang; Dong, Nianguo; Li, Fēi

doi:10.1016/j.ijcard.2021.07.024

Cited by 22 publications

(45 citation statements)

References 17 publications

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“…Moreover, compared with traditional statistical methods, ML algorithms can handle missing data more efficiently because they do not rely on data distribution assumptions and are capable of more complex calculations. Clinical models constructed by ML have been used to predict short-term mortality in cardiac surgery with the performance regarding AUC ranging from 0.77 to 0.92 ( 19 , 20 , 39 – 47 ). Zhou et al ( 39 ) and Ong et al ( 40 ).…”

Section: Discussionmentioning

confidence: 99%

“…Clinical models constructed by ML have been used to predict short-term mortality in cardiac surgery with the performance regarding AUC ranging from 0.77 to 0.92 ( 19 , 20 , 39 – 47 ). Zhou et al ( 39 ) and Ong et al ( 40 ). Found that the RF models predict short-term mortality better than other models in cardiac surgical procedures.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery

Peng²,

Zhang³

et al. 2022

Front. Cardiovasc. Med.

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Objective:This study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery.MethodsThe Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA).ResultsOf 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings.ConclusionsThe Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery

Peng²,

Zhang³

et al. 2022

Front. Cardiovasc. Med.

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“…Zhou et al did demonstrate the effectiveness of ML models for assessing the prognosis of heart transplantation patients in a predominantly Chinese population, however, any consequential contribution to generalisability was limited by the small sample size (381 patients) as well as the focus on the short-term prognosis, thus highlighting the need for further studies before wider implementation of ML models. 15 Moreover, the heterogeneity in the ML methods used as well as what the endpoints measured limits comparison between the current studies, and hence the question of what the best model(s) are remains to be solved. The need for constant updatability to the various novel interventions is another area that must be considered when implementing ML algorithms-previous studies have incorporated data from a 30-or 40-year period and in this time, novel interventions such as the left ventricular assist devices have significantly improved patient survival as well as changes to organ allocation sequences.…”

Section: Challenges With the Implementation Of MLmentioning

confidence: 98%

“…There were 12 studies that discussed the use of machine learning in predicting mortality in heart transplantation patients, [9][10][11][12][13][14][15][16][17][18][19][20] comprising 463 807 patients and included a conglomerate of different modeling methods. There was 1 study that discussed the use of machine learning in predicting graft failure in heart transplantation, 17 the study comprised 15 236 patients.…”

Section: Prediction Of Graft Failure and Mortalitymentioning

confidence: 99%

“…29 Two biomarkers found to have a major influence on graft failure were pre-transplant creatinine and bilirubin. 12,14,15,17,18 Previous studies have highlighted this relationship as creatinine and bilirubin are useful indicators to assess overall kidney and liver health, both of which are crucial in cardiovascular health in the post-transplant patient. 30 Additionally, transplantation itself could affect kidney function due to reduced renal blood flow and the side effects of potent immunosuppressive drugs post-transplantation.…”

Section: Other Major Factorsmentioning

confidence: 99%

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Machine learning and artificial intelligence in cardiac transplantation: A systematic review

et al. 2022

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Background This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. Methods A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021. Results Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk. Conclusion ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.

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Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

Hu,

Zhang,

Wei

et al. 2024

Pharmacoepidemiology and Drug

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BackgroundDistinguishing warfarin‐related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization.MethodCardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine‐learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score.ResultsA total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817–0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell.ConclusionA model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

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Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China

Cited by 22 publications

References 17 publications

Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery

Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery

Machine learning and artificial intelligence in cardiac transplantation: A systematic review

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

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