A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients
Background: Although many risk prediction models have been released internationally, the application of these models in the Chinese population still has some limitations.Aims: The purpose of the study was to establish a heart failure (HF) prognosis model suitable for the Chinese population.Methods:...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:ffc5ab9160aa4fcbbe5e81309b813b2e2021-11-18T06:54:08ZA Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients2297-055X10.3389/fcvm.2021.761605https://doaj.org/article/ffc5ab9160aa4fcbbe5e81309b813b2e2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.761605/fullhttps://doaj.org/toc/2297-055XBackground: Although many risk prediction models have been released internationally, the application of these models in the Chinese population still has some limitations.Aims: The purpose of the study was to establish a heart failure (HF) prognosis model suitable for the Chinese population.Methods: According to the inclusion criteria, we included patients with chronic heart failure (CHF) who were admitted to the Department of Cardiac Rehabilitation of Tongji Hospital from March 2007 to December 2018, recorded each patient's condition and followed up on the patient's re-admission and death. All data sets were randomly divided into derivation and validation cohorts in a ratio of 7/3. Least absolute shrinkage and selection operator regression and Cox regression were used to screen independent predictors; a nomogram chart scoring model was constructed and validated.Results: A total of 547 patients were recruited in this cohort, and the median follow-up time was 519 days. The independent predictors screened out by the derivation cohort included age, atrial fibrillation (AF), percutaneous coronary intervention (PCI), diabetes mellitus (DM), peak oxygen uptake (peak VO2), heart rate at the 8th minute after the cardiopulmonary exercise peaked (HR8min), C-reaction protein(CRP), and uric acid (UA). The C indexes values of the derivation and the validation cohorts were 0.69 and 0.62, respectively, and the calibration curves indicate that the model's predictions were in good agreement with the actual observations.Conclusions: We have developed and validated a multiple Cox regression model to predict long-term mortality and readmission risk of Chinese patients with CHF.Registration Number: ChicTR-TRC-00000235.Bo ZhuangTing ShenDejie LiYumei JiangGuanghe LiQian LuoYishan JinZiwei ShanLin CheLemin WangLiang ZhengYuqin ShenFrontiers Media S.A.articlerisk predictionmortalityhospitalizationheart failureChinese populationDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021) |
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| topic |
risk prediction mortality hospitalization heart failure Chinese population Diseases of the circulatory (Cardiovascular) system RC666-701 |
| spellingShingle |
risk prediction mortality hospitalization heart failure Chinese population Diseases of the circulatory (Cardiovascular) system RC666-701 Bo Zhuang Ting Shen Dejie Li Yumei Jiang Guanghe Li Qian Luo Yishan Jin Ziwei Shan Lin Che Lemin Wang Liang Zheng Yuqin Shen A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients |
| description |
Background: Although many risk prediction models have been released internationally, the application of these models in the Chinese population still has some limitations.Aims: The purpose of the study was to establish a heart failure (HF) prognosis model suitable for the Chinese population.Methods: According to the inclusion criteria, we included patients with chronic heart failure (CHF) who were admitted to the Department of Cardiac Rehabilitation of Tongji Hospital from March 2007 to December 2018, recorded each patient's condition and followed up on the patient's re-admission and death. All data sets were randomly divided into derivation and validation cohorts in a ratio of 7/3. Least absolute shrinkage and selection operator regression and Cox regression were used to screen independent predictors; a nomogram chart scoring model was constructed and validated.Results: A total of 547 patients were recruited in this cohort, and the median follow-up time was 519 days. The independent predictors screened out by the derivation cohort included age, atrial fibrillation (AF), percutaneous coronary intervention (PCI), diabetes mellitus (DM), peak oxygen uptake (peak VO2), heart rate at the 8th minute after the cardiopulmonary exercise peaked (HR8min), C-reaction protein(CRP), and uric acid (UA). The C indexes values of the derivation and the validation cohorts were 0.69 and 0.62, respectively, and the calibration curves indicate that the model's predictions were in good agreement with the actual observations.Conclusions: We have developed and validated a multiple Cox regression model to predict long-term mortality and readmission risk of Chinese patients with CHF.Registration Number: ChicTR-TRC-00000235. |
| format |
article |
| author |
Bo Zhuang Ting Shen Dejie Li Yumei Jiang Guanghe Li Qian Luo Yishan Jin Ziwei Shan Lin Che Lemin Wang Liang Zheng Yuqin Shen |
| author_facet |
Bo Zhuang Ting Shen Dejie Li Yumei Jiang Guanghe Li Qian Luo Yishan Jin Ziwei Shan Lin Che Lemin Wang Liang Zheng Yuqin Shen |
| author_sort |
Bo Zhuang |
| title |
A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients |
| title_short |
A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients |
| title_full |
A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients |
| title_fullStr |
A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients |
| title_full_unstemmed |
A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients |
| title_sort |
model for the prediction of mortality and hospitalization in chinese heart failure patients |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/ffc5ab9160aa4fcbbe5e81309b813b2e |
| work_keys_str_mv |
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