Risk Stratification Model for 30-Day Heart Failure Readmission in a Multiethnic South East Asian Community

Leong, Kui Toh Gerard; Wong, Lai Yin; Aung, Khin Chaw Yu; MacDonald, Michael R.; Yan, Chaowu; Sheldon, Lee; Chow, Wai Leng; Doddamani, Sanjay; Richards, A. Mark

doi:10.1016/j.amjcard.2017.01.026

Cited by 25 publications

(36 citation statements)

References 25 publications

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“…The HF population in China differs from that in Western countries, including notably more stroke and a lower BMI, 1,9,22 making data derived from Western populations likely to be not applicable. In addition, previously published studies on HF readmission or death risk model have mainly depended on cardiac conditions, 5,6,23 whereas they have focused less on the impact of noncardiac factors such as stroke and anaemia, which were the major factors for all‐cause readmission or death in elderly patients with HF.…”

Section: Discussionmentioning

confidence: 99%

“…As many of readmissions and deaths are possibly predictable and preventable, 3,4 an easy‐to‐use and accurate model to predict the risk of all‐cause readmission or death has been noted 5–7 . However, it is suboptimal using a one‐size‐fits‐all approach to predict the risk of readmission or death for all patients with HF.…”

Section: Introductionmentioning

confidence: 99%

“…Although prior studies have suggested that patients are readmitted disproportionately early, no study has rigorously distinguished the weight of risk factors between the precise timing of 30 day and 1 year readmission or death. In addition, prior prediction models using complex methods were only modest discriminative ability 5,6,16 . Hence, these limitations emphasize the need for a user‐friendly model to accurately target elderly patients with multimorbidity.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Tao

et al. 2020

ESC Heart Failure

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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 resultsThis was a prospective cohort study in patients with HF aged 65 or older. Demographic, comorbidity, 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/m 2 , 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 Cindex 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yang

Tao

et al. 2020

ESC Heart Failure

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“…About 5% of all urgent hospitalisations in USA and Europe are because of AHF and AHF is threatening lives of millions of peoples around the universe. With a high percentage (about 10) HF patients would come up with in-hospital death through admission (Leong, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The highest amount of ACC and AUC was about 84% and 0.78 respectively for the purposed Composite model. In 2017, Leong et al (2017) worked on predicting the risk of 30-day readmission of the HF patients. LR was applied to the dataset and the AUC with the amount of 0.76 was presented as the¯nal result.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Readmission of Heart Failure Patients through Data Analytics

Sohrabi

Vanani

Gooyavar

et al. 2019

J. Info. Know. Mgmt.

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Reducing the costs and improving the quality of treatment in hospital systems as well as demands for better treatment from patients in order to keep them away from readmissions are two main issues healthcare systems have faced. In order to solve such challenges, predicting the occurrence of re-hospitalisation with data mining techniques would be so worthwhile. In this study, we are seeking to predict the occurrence of re-hospitalisation of the heart failure patients in two time-horizons (1-month and 3-month) via deployment of classification algorithms (i.e. decision trees, artificial neural networks, support vector machines and logistic regression). Two criterions (as main criterions) such as AUC (area under curve) and ACC (accuracy) have been calculated and assessed for classifying the prediction-power of the models in each time-horizon (outcome/target). We also have calculated some other criterions such as recall, precision and F1-Score. Then, we identified the importance and contribution of the variables for each outcome. Therefore, the variables whose contribution/importance changes over time are differentiated. It is noteworthy to say that this study is done under the scrutiny of an expert cardiologist. Trained nurses and expert cardiologist monitored the dataset every day, which was a hard and valuable measure to conduct. Finally, the dataset does not have missing values and noises. This research can be the basis for prospective medical studies and projects.

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