干旱区生态环境知识资源中心(Arid): Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data

Arid

DOI	10.1097/NNR.0000000000000328
	Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data
	Sullivan, Suzanne S.1; Hewner, Sharon 2; Chandola, Varun 3; Westra, Bonnie L.4
通讯作者	Sullivan, Suzanne S.
来源期刊	NURSING RESEARCH
ISSN	0029-6562
EISSN	1538-9847
出版年	2019
卷号	68 期号:2 页码:156-166
英文摘要	Background: Newer analytic approaches for developing predictive models provide a method of creating decision support to translate findings into practice. Objectives: The aim of this study was to develop and validate a clinically interpretable predictive model for 12-month mortality risk among community-dwelling older adults. This is done by using routinely collected nursing assessment data to aide homecare nurses in identifying older adults who are at risk for decline, providing an opportunity to develop care plans that support patient and family goals for care. Methods: A retrospective secondary analysis of Medicare and Medicaid data of 635,590 Outcome and Assessment Information Set (OASIS-C) start-of-care assessments from January 1, 2012, to December 31, 2012, was linked to the Master Beneficiary Summary File (2012-2013) for date of death. The decision tree was benchmarked against gold standards for predictive modeling, logistic regression, and artificial neural network (ANN). The models underwent k-fold cross-validation and were compared using area under the curve (AUC) and other data science metrics, including Matthews correlation coefficient (MCC). Results: Decision tree variables associated with 12-month mortality risk included OASIS items: age, (M1034) overall status, (M1800-M1890) activities of daily living total score, cancer, frailty, (M1410) oxygen, and (M2020) oral medication management. The final models had good discrimination: decision tree, AUC = .71, 95% confidence interval (CI) [. 705, .712], sensitivity = .73, specificity = .58, MCC = .31; ANN, AUC = .74, 95% CI [. 74,.74], sensitivity = .68, specificity = .68, MCC = .35; and logistic regression, AUC = .74, 95% CI [.735, .742], sensitivity = .64, specificity = .70, MCC = .35. Discussion: The AUC and 95% CI for the decision tree are slightly less accurate than logistic regression and ANN; however, the decision tree was more accurate in detecting mortality. The OASIS data set was useful to predict 12-month mortality risk. The decision tree is an interpretable predictive model developed from routinely collected nursing data that may be incorporated into a decision support tool to identify older adults at risk for death.
英文关键词	decision support decision trees end-of-life care informatics machine learning precision health predictive modeling
类型	Article
语种	英语
国家	USA
开放获取类型	Green Accepted
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000461444300431
WOS关键词	END ; CARE ; PATTERNS ; COMPLEXITY
WOS类目	Nursing
WOS研究方向	Nursing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217795
作者单位	1.SUNY Buffalo, Buffalo, NY USA; 2.SUNY Buffalo, Sch Nursing, 3435 Main St,201A Wende Hall, Buffalo, NY 14214 USA; 3.SUNY Buffalo, Dept Comp Sci & Engn, Buffalo, NY USA; 4.Univ Minnesota, Sch Nursing, Minneapolis, MN 55455 USA
推荐引用方式 GB/T 7714	Sullivan, Suzanne S.,Hewner, Sharon,Chandola, Varun,et al. Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data[J],2019,68(2):156-166.
APA	Sullivan, Suzanne S.,Hewner, Sharon,Chandola, Varun,&Westra, Bonnie L..(2019).Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data.NURSING RESEARCH,68(2),156-166.
MLA	Sullivan, Suzanne S.,et al."Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data".NURSING RESEARCH 68.2(2019):156-166.