Machine Learning for Prediction of Outcomes in Cardiogenic Shock
ObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.849688/full |
| _version_ | 1828532109420003328 |
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| author | Fangning Rong Huaqiang Xiang Lu Qian Yangjing Xue Kangting Ji Ripen Yin |
| author_facet | Fangning Rong Huaqiang Xiang Lu Qian Yangjing Xue Kangting Ji Ripen Yin |
| author_sort | Fangning Rong |
| collection | DOAJ |
| description | ObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS.MethodsWe extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established using the training set. Through the comparison of area under the receiver operating characteristic (ROC) curve (AUC), C index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and median improvement in risk score, the best model was selected. Then for external validation, compared the best model with the simplified acute physiology score II (SAPSII) and the CardShock risk score.ResultsA total of 919 patients were included in the study, of which 804 patients were in the training set and 115 patients were in the verification set. Using the training set, we built three models: the cox regression model including 6 predictors, the LASSO regression model including 4 predictors, and the CoxBoost model including 16 predictors. Among them, the CoxBoost model had good discrimination [AUC: 0.730; C index: 0.6958 (0.6657, 0.7259)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of other models were all<0. In the validation set, the CoxBoost model was also well-discriminated [AUC: 0.770; C index: 0.7713 (0.6751, 0.8675)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of SAPS II and the CardShock risk score were all < 0. And we constructed a dynamic nomogram to visually display the model.ConclusionIn conclusion, this study showed that in predicting the 30-day mortality of elderly CS patients, the CoxBoost model was superior to the Cox regression model, LASSO regression model, SAPS II, and the CardShock risk score. |
| first_indexed | 2024-12-11T22:48:31Z |
| format | Article |
| id | doaj.art-89d73b5e50974b24ba6e340cbd0abe91 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-12-11T22:48:31Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-89d73b5e50974b24ba6e340cbd0abe912022-12-22T00:47:32ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-05-01910.3389/fcvm.2022.849688849688Machine Learning for Prediction of Outcomes in Cardiogenic ShockFangning RongHuaqiang XiangLu QianYangjing XueKangting JiRipen YinObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS.MethodsWe extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established using the training set. Through the comparison of area under the receiver operating characteristic (ROC) curve (AUC), C index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and median improvement in risk score, the best model was selected. Then for external validation, compared the best model with the simplified acute physiology score II (SAPSII) and the CardShock risk score.ResultsA total of 919 patients were included in the study, of which 804 patients were in the training set and 115 patients were in the verification set. Using the training set, we built three models: the cox regression model including 6 predictors, the LASSO regression model including 4 predictors, and the CoxBoost model including 16 predictors. Among them, the CoxBoost model had good discrimination [AUC: 0.730; C index: 0.6958 (0.6657, 0.7259)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of other models were all<0. In the validation set, the CoxBoost model was also well-discriminated [AUC: 0.770; C index: 0.7713 (0.6751, 0.8675)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of SAPS II and the CardShock risk score were all < 0. And we constructed a dynamic nomogram to visually display the model.ConclusionIn conclusion, this study showed that in predicting the 30-day mortality of elderly CS patients, the CoxBoost model was superior to the Cox regression model, LASSO regression model, SAPS II, and the CardShock risk score.https://www.frontiersin.org/articles/10.3389/fcvm.2022.849688/fullcardiogenic shockintensive care unitmachine learningCoxBoostpredictive model |
| spellingShingle | Fangning Rong Huaqiang Xiang Lu Qian Yangjing Xue Kangting Ji Ripen Yin Machine Learning for Prediction of Outcomes in Cardiogenic Shock Frontiers in Cardiovascular Medicine cardiogenic shock intensive care unit machine learning CoxBoost predictive model |
| title | Machine Learning for Prediction of Outcomes in Cardiogenic Shock |
| title_full | Machine Learning for Prediction of Outcomes in Cardiogenic Shock |
| title_fullStr | Machine Learning for Prediction of Outcomes in Cardiogenic Shock |
| title_full_unstemmed | Machine Learning for Prediction of Outcomes in Cardiogenic Shock |
| title_short | Machine Learning for Prediction of Outcomes in Cardiogenic Shock |
| title_sort | machine learning for prediction of outcomes in cardiogenic shock |
| topic | cardiogenic shock intensive care unit machine learning CoxBoost predictive model |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.849688/full |
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