Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure

Tan, Boyu; Gu, Jun-yuan; H, Wei; Chen, Li; Yan, Sulan; Deng, Nan

doi:10.1186/s12911-019-0915-8

Cited by 21 publications

(31 citation statements)

References 35 publications

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“…A previous EVEREST analysis provided a comprehensive description of various factors in the course of HF patients who experience 90‐day all‐cause rehospitalization or death after discharge. 12 In addition, another moderately predictive model (AUC 0.730) looking at 90‐day all‐cause readmission identified the Charleston comorbidity index, NT pro‐BNP, and certain hematologic parameters as predictors in a Chinese cohort of HF patients after discharge 13 …”

Section: Discussionmentioning

confidence: 99%

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Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis

Sarijaloo

Park

Zhong

et al. 2020

Clinical Cardiology

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Readmission or death soon after heart failure (HF) admission is a significant problem. Traditional analyses for predicting such events often fail to consider the gamut of characteristics that may contribute– tending to focus on 30‐day outcomes even though the window of increased vulnerability may last up to 90 days. Risk assessments incorporating machine learning (ML) methods may be better suited than traditional statistical analyses alone to sort through multitude of data in the electronic health record (EHR) and identify patients at higher risk. Hypothesis ML‐based decision analysis may better identify patients at increased risk for 90‐day acute HF readmission or death after incident HF admission. Methods and Results Among 3189 patients who underwent index HF hospitalization, 15.2% experienced primary or acute HF readmission and 11.5% died within 90 days. For risk assessment models, 98 variables were considered across nine data categories. ML techniques were used to help select variables for a final logistic regression (LR) model. The final model's AUC was 0.760 (95% CI 0.752 to 0.767), with sensitivity of 83%. This proved superior to an LR model alone [AUC 0.744 (95% CI 0.732 to 0.755)]. Eighteen variables were identified as risk factors including dilated inferior vena cava, elevated blood pressure, elevated BUN, reduced albumin, abnormal sodium or bicarbonate, and NT pro‐BNP elevation. A risk prediction ML‐based model developed from comprehensive characteristics within the EHR can efficiently identify patients at elevated risk of 90‐day acute HF readmission or death for whom closer follow‐up or further interventions may be considered.

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Section: Discussionmentioning

confidence: 99%

“…However, window of vulnerability is likely longer, with the spike in event rates occurring out 90 days post‐discharge and plateauing thereafter 5,7 . Few existing HF risk prediction algorithms have explored readmission and death assessed over the post‐discharge phase transition out to 90 days 12‐14 …”

Section: Introductionmentioning

confidence: 99%

Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis

Sarijaloo

Park

Zhong

et al. 2020

Clinical Cardiology

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“…Among all diagnosis-specific studies, half of them (50%) built models to predict readmission among patients with heart-specific conditions. Among the 18 studies focused on heart conditions, 13 papers predicted readmissions among the HF population [ 56 – 60 , 63 , 65 – 71 ], 2 worked on AMI cohorts [ 61 , 62 ], 2 developed models on general cardiovascular disease patients [ 55 , 72 ], and 1 worked on the stroke population [ 64 ]. As shown in Fig.…”

Section: Application To Readmissionmentioning

confidence: 99%

“…Even with the emergence of the ML algorithm, 29 out of 36 articles adopted traditional statistical methods. Among these studies, ~ 90% used LR either as a baseline [ 56 , 58 , 60 , 62 – 64 , 68 , 73 , 74 , 76 – 78 , 83 , 85 – 87 ] or the main model in prediction [ 60 , 69 , 71 , 82 , 88 – 90 ], and 3 studies derived their own risk scores on the basis of LR variable coefficients [ 61 , 66 , 84 ]. In the remaining 3 papers, the prognosis of readmission was carried out with Cox regression survival analysis.…”

Section: Application To Readmissionmentioning

confidence: 99%

Current Trends in Readmission Prediction: An Overview of Approaches

et al. 2021

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Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.

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“…However, Cotter et al [ 7 ] concluded that the LACE index performed poorly in predicting 30-day readmission with the area under the receiver operating characteristic curve (AUC) of 0.55, while that of the logistic regression (LR) model was 0.57. Regression analysis method is a process of estimating the probability of target variables given some linear combination of the predictors, and has been widely applied to predict the readmission risk [ 8 , 9 ]. However, it is difficult to solve the nonlinear problem or multicollinearity among risk factors based on detailed clinical data.…”

Section: Introductionmentioning

confidence: 99%

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

Zhang

Qiu

et al. 2020

BMC Med Inform Decis Mak

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Background Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. Methods In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. Results The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). Conclusion It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.

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Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure

Cited by 21 publications

References 35 publications

Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis

Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis

Current Trends in Readmission Prediction: An Overview of Approaches

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

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