Dataset Information

A novel nomogram to predict all-cause readmission or death risk in Chinese elderly patients with heart failure.

ABSTRACT:

Aims

Elderly patients with heart failure (HF) are associated with frequent all-cause readmission or death. The present study sought to develop an accurate and easy-to-use model to predict all-cause readmission or death risk in Chinese elderly patients with HF.

Methods and results

This was a prospective cohort study in patients with HF aged 65 or older. Demographic, co-morbidity, laboratory, and medication data were collected. A Cox regression model was used to identify factors for the prediction of readmission or death at 30 days and 1 year. A nomogram was developed with bootstrap validation. Of the included 854 patients, the cumulative all-cause readmission and mortality rates were 10.5% and 11.6% at 30 days and 34.9% and 19.7% at 1 year, respectively. The independent risk factors associated with both 30 day and 1 year readmission or death were older age, stroke, diastolic blood pressure < 60 mmHg, body mass index ? 18.5 kg/m² , lower estimated glomerular filtration rate, and BNP > 400 pg/mL (all P < 0.05). Anaemia, abnormal neutrophils, and admission without angiotensin-converting enzyme inhibitors/angiotensin receptor blockers were the specific independent risk factors of 30 day all-cause readmission or death (all P < 0.05), whereas serum sodium ? 140 mmol/L and admission without beta-blockers were the specific independent risk factors of 1 year all-cause readmission or death (all P < 0.05). The C-index of the 30 day and 1 year diagnosis prediction model was 0.778 [95% confidence interval (CI) 0.693-0.862] and 0.738 (95% CI 0.640-0.836), respectively.

Conclusions

We developed accurate and easy-to-use nomograms to predict all-cause readmission or death in Chinese elderly patients with HF. The nomograms will assist in reducing the all-cause readmission and mortality rates.

SUBMITTER: Yang M

PROVIDER: S-EPMC7261546 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

A novel nomogram to predict all-cause readmission or death risk in Chinese elderly patients with heart failure.

Yang Mengxi M Tao Liyuan L An Hui H Liu Gang G Tu Qiang Q Zhang Hu H Qin Li L Xiao Zhu Z Wang Yu Y Fan Jiaxai J Feng Dongping D Liang Yan Y Ren Jingyi J

ESC heart failure 20200421 3

<h4>Aims</h4>Elderly patients with heart failure (HF) are associated with frequent all-cause readmission or death. The present study sought to develop an accurate and easy-to-use model to predict all-cause readmission or death risk in Chinese elderly patients with HF.<h4>Methods and results</h4>This was a prospective cohort study in patients with HF aged 65 or older. Demographic, co-morbidity, laboratory, and medication data were collected. A Cox regression model was used to identify factors for ...[more]

PMID: 32319228

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Project description:Background: The existing prediction models lack the generalized applicability for chronic heart failure (CHF) readmission. We aimed to develop and validate a widely applicable nomogram for the prediction of 180-day readmission to the patients. Methods: We prospectively enrolled 2,980 consecutive patients with CHF from two hospitals. A nomogram was created to predict 180-day readmission based on the selected variables. The patients were divided into three datasets for development, internal validation, and external validation (mean age: 74.2 ± 14.1, 73.8 ± 14.2, and 71.0 ± 11.7 years, respectively; sex: 50.2, 48.8, and 55.2% male, respectively). At baseline, 102 variables were submitted to the least absolute shrinkage and selection operator (Lasso) regression algorithm for variable selection. The selected variables were processed by the multivariable Cox proportional hazards regression modeling combined with univariate analysis and stepwise regression. The model was evaluated by the concordance index (C-index) and calibration plot. Finally, the nomogram was provided to visualize the results. The improvement in the regression model was calculated by the net reclassification index (NRI) (with tenfold cross-validation and 200 bootstraps). Results: Among the selected 2,980 patients, 1,696 (56.9%) were readmitted within 180 days, and 1,502 (50.4%) were men. A nomogram was established by the results of Lasso regression, univariate analysis, stepwise regression and multivariate Cox regression, as well as variables with clinical significance. The values of the C-index were 0.75 [95% confidence interval (CI): 0.72-0.79], 0.75 [95% CI: 0.69-0.81], and 0.73 [95% CI: 0.64-0.83] for the development, internal validation, and external validation datasets, respectively. Calibration plots were provided for both the internal and external validation sets. Five variables including history of acute heart failure, emergency department visit, age, blood urea nitrogen level, and beta blocker usage were considered in the final prediction model. When adding variables involving hospital discharge way, alcohol taken and left bundle branch block, the calculated values of NRI demonstrated no significant improvements. Conclusions: A nomogram for the prediction of 180-day readmission of patients with CHF was developed and validated based on five variables. The proposed methodology can improve the accurate prediction of patient readmission and have the wide applications for CHF.

Project description:Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human-machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human-machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human-machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human-machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Dataset Information

A novel nomogram to predict all-cause readmission or death risk in Chinese elderly patients with heart failure.

Aims

Methods and results

Conclusions

Publications

A novel nomogram to predict all-cause readmission or death risk in Chinese elderly patients with heart failure.

Similar Datasets

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