Prognostic Indices for Older Adults

Abstract

Context To better target services to those who may benefit, many guidelines recommend incorporating life expectancy into clinical decisions. Objectives We conducted a systematic review to help physicians assess the quality and limitations of prognostic indices for mortality in older adults. Data Sources We searched MEDLINE, EMBASE, Cochrane, and Google Scholar through November 2011. Study Selection We included indices if they were validated and predicted absolute risk of mortality in patients whose average age was ≥ 60. We excluded indices that estimated ICU, disease-specific, or in-hospital mortality. Data Extraction For each prognostic index, we extracted data on clinical setting, potential for bias, generalizability, and accuracy. Results We reviewed 21,593 titles to identify 16 indices that predict risk of mortality from 6-months to 5 years for older adults in a variety of clinical settings: the community (six indices), the nursing home (two indices), and the hospital (eight indices). At least 1 measure of transportability was tested for all but 3 indices. By our measures, no study was free from potential bias. While 13 indices had c-statistics ≥ 0.70, none of the indices had c-statistics ≥ 0.90. Only two indices were independently validated by investigators who were not involved in the index’s development. Conclusion We identified several indices for predicting overall mortality in different patient groups; future studies need to independently test their accuracy in heterogeneous populations and their ability to improve clinical outcomes before their widespread use can be recommended.

Receipt of Cancer Screening Is a Predictor of Life Expectancy

“…Finally, it is possible that life expectancy measures other than the one employed might be less likely to show selection bias by screening behavior. 31 In summary, cancer screening behavior was a strong predictor of life expectancy, independent of comorbidity and other variables included in an algorithm for estimating life expectancy from administrative data. This means that patients and physicians are making somewhat more appropriate choices about screening than are indicated by the method for estimating life expectancy from administrative data.…”

Development and Validation of a 10-Year Mortality Prediction Model: Meta-Analysis of Individual Participant Data From Five Cohorts of Older Adults in Developed and Developing Countries

“…However, all available mortality prediction models have been developed using data from a single study, but all from the United States and Western Europe (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17), which greatly limits their validity for use even in other high-income countries, let alone in the developing countries. For instance, a model developed using data from the Health and Retirement Study (HRS) in the United States showed poor performance when it was applied to older adults in England (18).…”

“…For instance, a model developed using data from the Health and Retirement Study (HRS) in the United States showed poor performance when it was applied to older adults in England (18). In addition, a recent meta-analysis concluded that none of the prediction models are ready for use in clinical practice, because their validity has not been tested in other populations (15). Another limitation of the existing prediction models is the need for laboratory tests, which increases the costs and reduces the feasibility of using them in developing countries (10,12,19,20).…”

A Framework for Crafting Clinical Practice Guidelines that are Relevant to the Care and Management of People with Multimorbidity

“…More prediction instruments may become available for particular disease clusters and may be considered in CPG development, but some have cautioned about the utility of instruments for decision-making for an individual patient. 56,57 Not all indices transparently report the uncertainty surrounding the prediction estimates. 58 A number of innovations are needed going forward.…”