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Prediction of Hospital Readmission in Heart Failure Patients : = A Data-Driven Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prediction of Hospital Readmission in Heart Failure Patients :/
其他題名:	A Data-Driven Analysis.
作者:	Hu, Qinyi.
面頁冊數:	1 online resource (56 pages)
附註:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30000250click for full text (PQDT)
ISBN:	9798379563615

Prediction of Hospital Readmission in Heart Failure Patients : = A Data-Driven Analysis.
Hu, Qinyi.

Prediction of Hospital Readmission in Heart Failure Patients :A Data-Driven Analysis. - 1 online resource (56 pages)

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--The University of Texas School of Public Health, 2023.

Includes bibliographical references

Background and aims: The high rate of readmissions after heart failure (HF) hinders the patients' recovery, and increases their financial burdens. Therefore, it is important for clinicians and researchers to identify risk factors of heart-failure (DHF) hospitalization. We explored the relationship of HF readmission with HF types, age, gender, race, type-2 diabetes Mellitus (T2DM), hypertension medications, and vital signs.Methods: Data source was the electronic health records provided by the Cerner Health Facts database, a comprehensive dataset that includes de-identified patient information, with healthcare records over 63 million patients for 85 systems with 750 hospitals and healthcare facilities in the United States from 2000 to 2018. Patients who have at least one International Classification of Disease 9 diagnosis code of HF, and at least one HF medication and hospitalization record were identified as the study cohort. Age, ethnicity, heart-failure types, Type 2 diabetes Mellitus, hypertension medication intake, and vital signs were considered as potential risk factors. Missing data was imputed by MICE package. Purposeful variable selection was used for the variable selection of predict model. Stepwise selection by Akaike information criterion (AIC) and lasso regression method were performed as comparisons of purposeful variable selection.Results: In total, 135,253 inpatients are included, of which 96627 (%) patients are identified as HF readmission patients, and 38626 (%) patients are not readmitted for HF. Age, gender, race, HF types, ACE inhibitors intake, Beta blockers intake, Diuretics intake, Calcium channel blockers intake, Angiotensin receptor blockers intake, Antiadrenergic inhibitors intake, mean measurement of systolic blood pressure, Body Mass Index (BMI) and height are predictors of HF readmission in a logistic regression model. Area under Receiver operator characteristics (ROC) curve is 0.539, so the model is a bad discriminatory performance.Conclusion: Heart failure readmission is associated with patients' age, gender, race, heart failure types, systolic blood pressure, Body Mass Index (BMI) height, and intake of hypertension medications including ACE inhibitors, Beta blockers, Diuretics, Calcium channel blockers, Angiotensin receptor blockers, and Antiadrenergic inhibitors. Future improvements are needed to enhance the predictive ability of the model.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379563615Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Heart failure patientsIndex Terms--Genre/Form:

542853
Electronic books.

Prediction of Hospital Readmission in Heart Failure Patients : = A Data-Driven Analysis.
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