Development and Validation of a Nomogram Model for Predicting the Risk of Readmission in Patients with Heart Failure with Reduced Ejection Fraction within 1 Year

Hu, Yue; Wang, Xiaotong; Xiao, Shengjue; Huan, Chunyan; Wu, Huimin; Xu, Tao; Guo, Minjia; Hong, Zhu; Pan, Defeng

doi:10.1155/2022/4143173

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…Heart failure (HF) is the final clinical outcome of various serious heart diseases and is characterized by severe systolic dysfunction and prolonged diastolic time. [ 1 ] In developed countries, the prevalence of HF is 1.5%, and the prevalence in people aged 70 years and older is over 10%. [ 2 ] Most patients admitted to hospital with noticeable clinical manifestations have already developed moderate-to-severe HF.…”

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confidence: 99%

Effects of recombinant human brain natriuretic peptide combined with tolvaptan on cardiac and renal function and serum inflammatory factors in patients with severe heart failure

Yang,

Zhang,

Guo

et al. 2023

Medicine

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This study examined the effects of recombinant human brain natriuretic peptide (rhBNP) combined with tolvaptan on cardiac and renal function and serum inflammatory factors in patients with severe heart failure (HF). This retrospective study included 90 patients with severe HF who were treated at our hospital between January 2019 and August 2021. Patients treated with tolvaptan tablets were assigned to the control group, and those treated with rhBNP combined with tolvaptan were assigned to the observation group. Efficacy, cardiac function, levels of inflammatory factors, renal function, 6 minutes walking test, Minnesota Living with Heart Failure Questionnaire score, and adverse reactions were assessed. The curative effect (97.78% vs 77.78%) and improvement in cardiac function were greater in the observation group than in the control group (P < .05). Decreased levels of inflammatory factors were seen in both groups after treatment, and the levels of tumor necrosis factor-α, interleukin-33, and intercellular adhesion factor-1 in the observation group were lower than those in the control group (P < .05). The 6 minutes walking test was higher and the Minnesota Living with Heart Failure Questionnaire score was lower in the observation group compared with the control group (P < .05). The incidence of adverse reactions such as dry mouth, nausea, polyuria, hypotension, and headache in the observation group was lower than that in the control group (P < .05). In conclusion, for patients with severe HF, rhBNP combined with tolvaptan can improve cardiac function, alleviate symptoms of dyspnea, protect renal function, and reduce serum inflammatory factor levels when compared with tolvaptan alone.

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Section: Introductionmentioning

confidence: 99%

Effects of recombinant human brain natriuretic peptide combined with tolvaptan on cardiac and renal function and serum inflammatory factors in patients with severe heart failure

Yang,

Zhang,

Guo

et al. 2023

Medicine

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SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

Luo,

Xiang,

Zeng

et al. 2024

Sci Rep

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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.

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A dynamic nomogram for predicting in-hospital major adverse cardiovascular and cerebrovascular events in patients with both coronary artery disease and atrial fibrillation: a multicenter retrospective study

Jian,

Zhang,

Zhang

et al. 2024

Coronary Artery Disease

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Background and objective Patients with both coronary artery disease (CAD) and atrial fibrillation (AF) are at a high risk of major adverse cardiovascular and cerebrovascular events (MACCE) during hospitalization. Accurate prediction of MACCE can help identify high-risk patients and guide treatment decisions. This study was to elaborate and validate a dynamic nomogram for predicting the occurrence of MACCE during hospitalization in Patients with CAD combined with AF. Methods A total of 3550 patients with AF and CAD were collected. They were randomly assigned to a training group and a validation group in a ratio of 7 : 3. Univariate and multivariate analyses were utilized to identify risk factors (P < 0.05). To avoid multicollinearity and overfit of the model, the least absolute shrinkage and selection operator was conducted to further screen the risk factors. Calibration curves, receiver operating characteristic curves, and decision curve analyses are employed to assess the nomogram. For external validation, a cohort consisting of 249 patients was utilized from the Medical Information Mart for Intensive Care IV Clinical Database, version 2.2. Results Eight indicators with statistical differences were screened by univariate analysis, multivariate analysis, and the least absolute shrinkage and selection operator method (P < 0.05). The prediction model based on eight risk factors demonstrated good prediction performance in the training group, with an area under the curve (AUC) of 0.838. This performance was also maintained in the internal validation group (AUC = 0.835) and the external validation group (AUC = 0.806). Meanwhile, the calibration curve indicates that the nomogram was well-calibrated, and decision curve analysis revealed that the nomogram exhibited good clinical utility. Conclusion The nomogram we constructed may aid in stratifying the risk and predicting the prognosis for patients with CAD and AF.

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Development and Validation of a Nomogram Model for Predicting the Risk of Readmission in Patients with Heart Failure with Reduced Ejection Fraction within 1 Year

Cited by 4 publications

References 47 publications

Effects of recombinant human brain natriuretic peptide combined with tolvaptan on cardiac and renal function and serum inflammatory factors in patients with severe heart failure

Effects of recombinant human brain natriuretic peptide combined with tolvaptan on cardiac and renal function and serum inflammatory factors in patients with severe heart failure

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

A dynamic nomogram for predicting in-hospital major adverse cardiovascular and cerebrovascular events in patients with both coronary artery disease and atrial fibrillation: a multicenter retrospective study

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