TY - JOUR
T1 - Predicting time to nursing home care and death in individuals with Alzheimer disease
AU - Stern, Yaakov
AU - Tang, Min Xing
AU - Albert, Marilyn S.
AU - Brandt, Jason
AU - Jacobs, Diane M.
AU - Bell, Karen
AU - Marder, Karen
AU - Sano, Mary
AU - Devanand, Devangere
AU - Albert, Steven M.
AU - Bylsma, Frederick
AU - Tsai, Wei Yann
N1 - Funding Information:
R.S.E. acknowledges support from DOE grant DESC0001101, M.S. from the Royal Society, A.G. from the Israeli Science Foundation and a European Union Marie Curie fellowship. Support for program GO 11721 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA Contract NAS5-26555. The National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, provided staff, computational resources, and data storage for this project. P.E.N. acknowledges support from the US Department of Energy Scientific Discovery through Advanced Computing program under contract DE-FG02-06ER06-04. S.B.C. acknowledges generous support from Gary and Cynthia Bengier and the Richard and Rhoda Goldman Foundation.
PY - 1997/3/12
Y1 - 1997/3/12
N2 - Objective.-To develop and validate an approach that uses clinical features that can be determined in a standard patient visit to estimate the length of time before an individual patient with Alzheimer disease (AD) requires care equivalent to nursing home placement or dies. Design.- Prospective cohort study of 236 patients, followed up semiannually for up to 7 years. A second validation cohort of 105 patients was also followed. Setting.-Three AD research centers. Patients.-All patients met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD and had mild dementia at the initial visit. Intervention.- Predictive features, ascertained at the initial visit, were sex, duration of illness, age at onset, modified Mini-Mental State Examination (mMMS) score, and the presence or absence of extrapyramidal signs or psychotic features. Main Outcome Measures.-(1) Requiring the equivalent of nursing home placement and (2) death. Results.-Prediction algorithms were constructed for the 2 outcomes based on Cox proportional hazard models. For each algorithm, a predictor index is calculated based on the status of each predictive feature at the initial visit. A table that specifies the number of months in which 25%, 50%, and 75% of patients with any specific predictor index value are likely to reach the end point is then consulted. Survival curves for time to need for care equivalent to nursing home placement and for time to death derived from the algorithms for selected predictor indexes fell within the 95% confidence bands of actual survival curves for patients. When the predictor variables from the initial visit for the validation cohort patients were entered into the algorithm, the predicted survival curves for time to death fell within the 95% confidence bands of actual survival curves for the patients. Conclusions.-The prediction algorithms are a first but promising step toward providing specific prognoses to patients, families, and practitioners. This approach also has clear implications for the design and interpretation of clinical trials in patients with AD.
AB - Objective.-To develop and validate an approach that uses clinical features that can be determined in a standard patient visit to estimate the length of time before an individual patient with Alzheimer disease (AD) requires care equivalent to nursing home placement or dies. Design.- Prospective cohort study of 236 patients, followed up semiannually for up to 7 years. A second validation cohort of 105 patients was also followed. Setting.-Three AD research centers. Patients.-All patients met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD and had mild dementia at the initial visit. Intervention.- Predictive features, ascertained at the initial visit, were sex, duration of illness, age at onset, modified Mini-Mental State Examination (mMMS) score, and the presence or absence of extrapyramidal signs or psychotic features. Main Outcome Measures.-(1) Requiring the equivalent of nursing home placement and (2) death. Results.-Prediction algorithms were constructed for the 2 outcomes based on Cox proportional hazard models. For each algorithm, a predictor index is calculated based on the status of each predictive feature at the initial visit. A table that specifies the number of months in which 25%, 50%, and 75% of patients with any specific predictor index value are likely to reach the end point is then consulted. Survival curves for time to need for care equivalent to nursing home placement and for time to death derived from the algorithms for selected predictor indexes fell within the 95% confidence bands of actual survival curves for patients. When the predictor variables from the initial visit for the validation cohort patients were entered into the algorithm, the predicted survival curves for time to death fell within the 95% confidence bands of actual survival curves for the patients. Conclusions.-The prediction algorithms are a first but promising step toward providing specific prognoses to patients, families, and practitioners. This approach also has clear implications for the design and interpretation of clinical trials in patients with AD.
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U2 - 10.1001/jama.277.10.806
DO - 10.1001/jama.277.10.806
M3 - Article
C2 - 9052710
AN - SCOPUS:8044242205
SN - 0098-7484
VL - 277
SP - 806
EP - 812
JO - JAMA
JF - JAMA
IS - 10
ER -