Current issues of ACP Journal Club are published in Annals of Internal Medicine


Prognosis

Algorithms provided useful information about the course of Alzheimer disease

ACP J Club. 1997 Sep-Oct;127:43. doi:10.7326/ACPJC-1997-127-2-043


Source Citation

Stern Y, Tang MX, Albert MS, et al. Predicting time to nursing home care and death in individuals with Alzheimer disease. JAMA. 1997 Mar 12;277:806-12.


Abstract

Objective

To assess a clinical approach for estimating the length of time from presentation of symptoms of Alzheimer disease to admission in a nursing home (or equivalent) or death.

Design

Inception cohort followed for up to 7 years.

Setting

3 referral centers in the United States.

Patients

236 patients (mean age 73 y, 59% women) who met the National Institute of Neurological and Communicative Disorders and Stroke—Alzheimer's Disease and Related Disorders Association criteria for probable Alzheimer disease and had mild dementia according to a modified Mini-Mental State Examination (MMSE). Exclusion criteria were history of substance abuse, schizophrenia, schizoaffective disorder, or major affective disorder; electroconvulsive therapy in the previous 2 years or ≥ 10 treatments at any time; stroke or Hachinski Ischemic Score of ≥ 5; or receipt of neuroleptic medications within 1 month of initial evaluation.

Assessment of prognostic factors

At the initial evaluation of patients, the following predictors were assessed: duration of illness; age at onset (young [< 65 y] or old [≥ 65 y]); non-drug-induced extrapyramidal signs (mild to moderate severity of ≥ 1 of hypophonia, masked faces, resting tremor, rigidity, bradykinesia or hypokinesia, or posture and gait); psychosis (presence of delusions or hallucinations); and cognition.

Main outcome measures

Requirement for care in a nursing home (or equivalent) and death. Predictor algorithms for the outcomes were developed based on Cox proportional hazard analysis. The algorithm included a patient's predictor index and the estimated survival curve for a representative range of values.

Main results

164 patients (69%) were judged to have reached a point at which they required care in a nursing home or equivalent. The predictors associated with reaching this end point were presence of extrapyramidal signs (relative risk [RR] 2.56, 95% CI 1.49 to 4.41); presence of psychotic symptoms (RR 1.50, CI 1.04 to 2.15); and young age at disease onset (RR 1.62, CI 0.99 to 2.67). The predictor associated with reduced risk for the need for institutionalized care was higher modified MMSE score at presentation (RR 0.93, CI 0.90 to 0.96). 99 patients (42%) died. Increased mortality was associated with presence of extrapyramidal signs (RR 3.61, CI 2.12 to 6.14). Mortality was decreased in women (RR 0.49, CI 0.32 to 0.77) and in patients with longer duration of illness (RR 0.90, CI 0.83 to 0.98). Algorithm-derived survival curves for a specific predictor index were compared with survival curves of patients who had similar predictor indexes at their initial visit. For placement in a nursing home (or equivalent) and death, nearly all patient data points fell within the 95% confidence bands of the curves generated from the data.

Conclusion

Predictor algorithms for Alzheimer disease were useful for providing information about time to requirement for admission to a nursing-home (or equivalent) or death.

Sources of funding: National Institutes of Health and Banbury Fund.

For article reprint: Dr. Y. Stern, Sergievsky Center, 630 West 168th Street, New York, NY 10032, USA. FAX 212-305-2426.


Commentary

It is encouraging to read a study that explores aspects of prognosis for patients with dementia. Certainly, dementia is a common problem in the "young-old," such as the patients studied by Stern and colleagues, although the risk for developing dementia is higher in the "old-old" (1). The outcomes of nursing home admission or death are important from the standpoint of planning, policy development, and cost containment for cognitively impaired patients.

The study raises interesting questions about delineating potential sources of bias in the provision or acceptance for persons with dementia (2). One can anticipate that the authors will extend the work done by Shapiro and Tate (3), among others, by exploring socioeconomic status, education, marital status, and use of community long-term care services as additional factors in prognosticating admission to a nursing home or death.

It is interesting to note that a clinical, low-technology approach was used to address this multidimensional problem. Readers may use the results of this study to help identify which of their patients with Alzheimer dementia will need institutional care sooner than others.

Albert J. Kirshen, MD
University of TorontoToronto, Ontario, Canada


References

1. Canadian study of health and aging: study methods and prevalence of dementia. Can Med Assoc J. 1994;150:899-913.

2. Mittelman MS, Ferris SH, Shulman E, Steinberg G, Levin B. A family intervention to delay nursing home placement of patients with Alzheimer disease: a randomized controlled trial. JAMA. 1996;276:1725-31.

3. Shapiro E, Tate R. Who is really at risk of institutionalization? Gerontologist. 1988; 28:237-45.