A practical approach to identifying mortality-related factors in established long-term care residents.
OBJECTIVE
Determining prognosis is an important part of medical planning for long-term care residents. Clarifying the resident characteristics associated with increased mortality has received little attention from investigators, and many approaches that have been suggested are unsuitable for widespread use. Using a readily available database, we sought to determine factors associated with 1-year mortality in established long-term care residents.


METHODS
A retrospective cohort study.


METHODS
A 725-bed long-term care facility.


METHODS
We examined Minimum Data Set (MDS) information on 780 residents from April 1994 through August 1997. The association between death and 65 resident factors, covering a broad array of physical, functional, medical, and psychosocial measures, was examined initially in bivariate proportional hazards models. Putative factors with P values < .10 in bivariate analysis were considered in the multivariate analysis. Using these factors, we employed a forward step-wise multivariate proportional hazards regression method to select the set of factors associated independently with mortality at a P value < .05. A mortality score was developed by assigning points to each factor based on the risk ratio in the multivariate proportional hazards model. The performance characteristics of the model were examined using logistic regression.


RESULTS
Forty-four of the 65 factors examined were associated with 1-year mortality in bivariate proportional hazards analysis. Eight of these 44 factors were associated with 1-year mortality in the multivariate proportional hazards regression. These factors were functional impairment, weight loss, shortness of breath, male gender, low body mass index, swallowing problems, congestive heart failure, and advanced age. A higher mortality score was associated with a higher death rate in the subsequent year. The model demonstrated good performance with an area under the ROC curve of 0.77.


CONCLUSIONS
Using a widely available database that requires no additional medical testing or staff training, a useful model for identifying factors associated with 1-year mortality in established long-term care residents can be developed. Widespread use of such a practical approach to assess mortality risk could be of benefit to patients, their families, and physicians for informing care plan decisions.
Flacker
J M
JM
Hebrew Rehabilitation Center for Aged Research, and Harvard Medical School Division on Aging, Boston, Massachusetts 02131, USA.
Kiely
D K
DK
D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008134:Long-Term Care; D008297:Male; D009026:Mortality; D015999:Multivariate Analysis; D009735:Nursing Homes; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D012307:Risk Factors