Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease
BackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1497916/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850248530604589056 |
|---|---|
| author | Hanyu Luo Benlong Wang Rui Cao Jun Feng |
| author_facet | Hanyu Luo Benlong Wang Rui Cao Jun Feng |
| author_sort | Hanyu Luo |
| collection | DOAJ |
| description | BackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575 elderly patients with CHD admitted to the Affiliated Lu’an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used.ResultsThe XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets.ConclusionTyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies. |
| format | Article |
| id | doaj-art-0bc4fe814fbd422793ddc5b96f88a2ee |
| institution | OA Journals |
| issn | 2297-055X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-0bc4fe814fbd422793ddc5b96f88a2ee2025-08-20T01:58:42ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2024-12-011110.3389/fcvm.2024.14979161497916Construction and validation of a readmission risk prediction model for elderly patients with coronary heart diseaseHanyu LuoBenlong WangRui CaoJun FengBackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575 elderly patients with CHD admitted to the Affiliated Lu’an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used.ResultsThe XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets.ConclusionTyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies.https://www.frontiersin.org/articles/10.3389/fcvm.2024.1497916/fullcoronary heart diseasereadmissionprediction modelXGBoostTyG-BMI |
| spellingShingle | Hanyu Luo Benlong Wang Rui Cao Jun Feng Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease Frontiers in Cardiovascular Medicine coronary heart disease readmission prediction model XGBoost TyG-BMI |
| title | Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease |
| title_full | Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease |
| title_fullStr | Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease |
| title_full_unstemmed | Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease |
| title_short | Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease |
| title_sort | construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease |
| topic | coronary heart disease readmission prediction model XGBoost TyG-BMI |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1497916/full |
| work_keys_str_mv | AT hanyuluo constructionandvalidationofareadmissionriskpredictionmodelforelderlypatientswithcoronaryheartdisease AT benlongwang constructionandvalidationofareadmissionriskpredictionmodelforelderlypatientswithcoronaryheartdisease AT ruicao constructionandvalidationofareadmissionriskpredictionmodelforelderlypatientswithcoronaryheartdisease AT junfeng constructionandvalidationofareadmissionriskpredictionmodelforelderlypatientswithcoronaryheartdisease |