TY - JOUR
T1 - Predicting death in the nursing home
T2 - Development and validation of the 6-month minimum data set mortality risk index
AU - Porock, Davina
AU - Oliver, Debra Parker
AU - Zweig, Steve
AU - Rantz, Marilyn
AU - Mehr, David
AU - Madsen, Richard
AU - Petroski, Greg
N1 - Funding Information:
ACKNOWLEDGMENTS The members of the University of Missouri MDS and Nursing Home Quality Research Team gratefully acknowledge the support of the Missouri Department of Health and Senior Services. Research activities were partially supported by contract #C-5-31455 from the Missouri Division of Aging to the Sinclair School of Nursing and Biostatistics Group of the School of Medicine, University of Missouri-Columbia. Opinions are those of the authors and do not represent the Missouri Department of Health and Senior Services.
PY - 2005/4
Y1 - 2005/4
N2 - Background: Currently, 24% of all deaths nationally occur in nursing homes making this an important focus of care. However, many residents are not identified as dying and thus do not receive appropriate care in the last weeks and months of life. The aim of our study was to develop and validate a predictive model of 6-month mortality risk using functional, emotional, cognitive, and disease variables found in the Minimum Data Set. Methods. This retrospective cohort study developed and validated a clinical prediction model using stepwise logistic regression analysis. Our study sample included all Missouri long-term-care residents (43,510) who had a full Minimum Data Set assessment transmitted to the Federal database in calendar year 1999. Death was confirmed by death certificate data. Results. The validated predictive model with a c-statistic of .75 included the following predictors: a) demographics (age and male sex); b) diseases (cancer, congestive heart failure, renal failure, and dementia/Alzheimer's disease); c) clinical signs and symptoms (shortness of breath, deteriorating condition, weight loss, poor appetite, dehydration, increasing number of activities of daily living requiring assistance, and poor score on the cognitive performance scale); and d) adverse events (recent admission to the nursing home). A simple point system derived from the regression equation can be totaled to aid in predicting mortality. Conclusions. A reasonably accurate, validated model has been produced, with clinical application through a scored point system, to assist clinicians, residents, and family members in defining good goals of care around end-of-life care.
AB - Background: Currently, 24% of all deaths nationally occur in nursing homes making this an important focus of care. However, many residents are not identified as dying and thus do not receive appropriate care in the last weeks and months of life. The aim of our study was to develop and validate a predictive model of 6-month mortality risk using functional, emotional, cognitive, and disease variables found in the Minimum Data Set. Methods. This retrospective cohort study developed and validated a clinical prediction model using stepwise logistic regression analysis. Our study sample included all Missouri long-term-care residents (43,510) who had a full Minimum Data Set assessment transmitted to the Federal database in calendar year 1999. Death was confirmed by death certificate data. Results. The validated predictive model with a c-statistic of .75 included the following predictors: a) demographics (age and male sex); b) diseases (cancer, congestive heart failure, renal failure, and dementia/Alzheimer's disease); c) clinical signs and symptoms (shortness of breath, deteriorating condition, weight loss, poor appetite, dehydration, increasing number of activities of daily living requiring assistance, and poor score on the cognitive performance scale); and d) adverse events (recent admission to the nursing home). A simple point system derived from the regression equation can be totaled to aid in predicting mortality. Conclusions. A reasonably accurate, validated model has been produced, with clinical application through a scored point system, to assist clinicians, residents, and family members in defining good goals of care around end-of-life care.
UR - http://www.scopus.com/inward/record.url?scp=19044364143&partnerID=8YFLogxK
U2 - 10.1093/gerona/60.4.491
DO - 10.1093/gerona/60.4.491
M3 - Article
C2 - 15933390
AN - SCOPUS:19044364143
SN - 1079-5006
VL - 60
SP - 491
EP - 498
JO - Journals of Gerontology - Series A Biological Sciences and Medical Sciences
JF - Journals of Gerontology - Series A Biological Sciences and Medical Sciences
IS - 4
ER -