Dataset Information

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

ABSTRACT: BACKGROUND:The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. METHODS AND RESULTS:Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). CONCLUSIONS:Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.

SUBMITTER: Mortazavi BJ

PROVIDER: S-EPMC5459389 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Analysis of Machine Learning Techniques for Heart Failure Readmissions.

Mortazavi Bobak J BJ Downing Nicholas S NS Bucholz Emily M EM Dharmarajan Kumar K Manhapra Ajay A Li Shu-Xia SX Negahban Sahand N SN Krumholz Harlan M HM

Circulation. Cardiovascular quality and outcomes 20161108 6

<h4>Background</h4>The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions.<h4>Methods and results</h4>Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectivenes ...[more]

PMID: 28263938

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Project description:BackgroundHeart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization.ObjectiveMachine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs).MethodsWe used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared.ResultsA total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions.ConclusionsWe predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.

Project description:BackgroundHeart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission.MethodsWe used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system.ResultsData from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital.ConclusionsDeep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.

Project description:To phenotype mechanistic differences between heart failure with reduced (HFrEF) and preserved (HFpEF) ejection fraction, a closed-loop model of the cardiovascular system coupled with patient-specific transthoracic echocardiography (TTE) and right heart catheterization (RHC) data was used to identify key parameters representing haemodynamics. Thirty-one patient records (10 HFrEF, 21 HFpEF) were obtained from the Cardiovascular Health Improvement Project database at the University of Michigan. Model simulations were tuned to match RHC and TTE pressure, volume, and cardiac output measurements in each patient. The underlying physiological model parameters were plotted against model-based norms and compared between HFrEF and HFpEF. Our results confirm the main mechanistic parameter driving HFrEF is reduced left ventricular (LV) contractility, whereas HFpEF exhibits a heterogeneous phenotype. Conducting principal component analysis, k -means clustering, and hierarchical clustering on the optimized parameters reveal (i) a group of HFrEF-like HFpEF patients (HFpEF1), (ii) a classic HFpEF group (HFpEF2), and (iii) a group of HFpEF patients that do not consistently cluster (NCC). These subgroups cannot be distinguished from the clinical data alone. Increased LV active contractility ( p<0.001 ) and LV passive stiffness ( p<0.001 ) at rest are observed when comparing HFpEF2 to HFpEF1. Analysing the clinical data of each subgroup reveals that elevated systolic and diastolic LV volumes seen in both HFrEF and HFpEF1 may be used as a biomarker to identify HFrEF-like HFpEF patients. These results suggest that modelling of the cardiovascular system and optimizing to standard clinical data can designate subgroups of HFpEF as separate phenotypes, possibly elucidating patient-specific treatment strategies. KEY POINTS: Analysis of data from right heart catheterization (RHC) and transthoracic echocardiography (TTE) of heart failure (HF) patients using a closed-loop model of the cardiovascular system identifies key parameters representing haemodynamic cardiovascular function in patients with heart failure with reduced and preserved ejection fraction (HFrEF and HFpEF). Analysing optimized parameters representing cardiovascular function using machine learning shows mechanistic differences between HFpEF groups that are not seen analysing clinical data alone. HFpEF groups presented here can be subdivided into three subgroups: HFpEF1 described as 'HFrEF-like HFpEF', HFpEF2 as 'classic HFpEF', and a third group of HFpEF patients that do not consistently cluster. Focusing purely on cardiac function consistently captures the underlying dysfunction in HFrEF, whereas HFpEF is better characterized by dysfunction in the entire cardiovascular system. Our methodology reveals that elevated left ventricular systolic and diastolic volumes are potential biomarkers for identifying HFrEF-like HFpEF patients.

Project description:Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF. Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms. Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation. Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups. Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.

Dataset Information

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

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Analysis of Machine Learning Techniques for Heart Failure Readmissions.

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