東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction of Hospital Readmission in Heart Failure Patients :/
Reminder of title:	A Data-Driven Analysis.
Author:	Hu, Qinyi.
Description:	1 online resource (56 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Biostatistics. -
Online resource:	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.
LDR:03773nmm a2200373K 4500 001 2362794
005 20231109093728.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798379563615
035 $a (MiAaPQ)AAI30000250
035 $a AAI30000250
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Hu, Qinyi. $3 3703534
245 1 0 $a Prediction of Hospital Readmission in Heart Failure Patients : $b A Data-Driven Analysis.
264 0 $c 2023
300 $a 1 online resource (56 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Wu, Hulin;Chan, Wenyaw.
502 $a Thesis (M.S.)--The University of Texas School of Public Health, 2023.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Biostatistics. $3 1002712
650 4 $a Nutrition. $3 517777
653 $a Heart failure patients
653 $a Hospital readmission
653 $a Patients' recovery
653 $a Electronic health records
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0308
690 $a 0570
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Texas School of Public Health. $b Biostatistics. $3 1018615
773 0 $t Masters Abstracts International $g 84-11.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30000250 $z click for full text (PQDT)