In this study we developed a new prognostic model, the PROMPT, to predict six-month mortality in community-dwelling elderly patients with self-reported declining health. The model was developed using a large diverse sample, and utilized 11 total variables, including HRQOL, ascertained by patient self-report. The model demonstrated good calibration and discrimination overall. Importantly, diagnostic performance at various thresholds of estimated risk was superior to existing non-disease-specific models. Specificity was high, and at strata of estimated six-month mortality risk of 40% or more, the model yielded positive likelihood ratios of moderate to large magnitude (5.0 or more) with respect to clinical prediction—exceeding the performance of previous models and increasing the post-test odds of death to an extent generally considered useful in clinical decision making. The model’s positive predictive value in our study population was also high and commensurate to estimated risk; 53% of patients in the 50% risk stratum died by six months, and the proportions of observed deaths were correspondingly greater in higher risk strata. On extended observation, over half of all patients with estimated six-month mortality risk of 30% or more died by 12 months.
These promising performance characteristics are particularly noteworthy given the types of variables used in our model, and the nature of our study population. Unlike prior prognostic efforts, the PROMPT included no physiologic or laboratory data and relatively little clinical data regarding disease characteristics or health services utilization. The study population was clinically heterogeneous, ambulatory, community-dwelling, and relatively healthy, with a lower pretest mortality risk, compared with populations included in other prognostic modeling efforts and for whom hospice care is typically considered by clinicians. Yet, in spite of these significant constraints on prognostic power, the PROMPT still demonstrated superior performance compared to existing tools such as the SUPPORT model and NHO guidelines. This supports the model’s robustness and potential transportability to more narrowly-defined populations with higher pretest probabilities of mortality in which prognostic performance would likely be more optimal. These conclusions remain preliminary, however, since our model has yet to be externally validated and directly compared to other tools.
The primary limitation of the PROMPT is one shared by all existing prognostic tools for predicting short-term mortality: insufficient sensitivity to “rule out” death in a substantial proportion of patients. This is not surprising given that there are undoubtedly numerous causal factors and trajectories in the dying process,72,73
and no prognostic model has accounted for them all. For the PROMPT, furthermore, comorbidities were ascertained by self-report only, and some important ones, e.g., dementia, renal and liver disease, were not assessed. A substantial proportion of surveys (38%) also were completed by proxy, presumably because patients were too ill or impaired to do so themselves. Finally, our selection of patients on the basis of self-reported decline in health likely also led to the inclusion of patients with acute, self-limited conditions with little impact on mortality. All of these factors likely limit the PROMPT’s sensitivity and use as an exclusive means of determining hospice eligibility because this would result in the denial of hospice services for a majority of dying patients. This limitation has led other modelers to conclude that the goal of determining individuals’ risk of six-month mortality is unrealistic.19, 25
Yet, we believe our modeling effort offers important insights for future research, and that the PROMPT has significant potential utility for clinical care. Our study adds to mounting evidence of the prognostic power of HRQOL. The PROMPT’s superior overall performance compared with efforts incorporating disease and physiologic variables alone supports the hypothesis that as death approaches, HRQOL assumes greater prognostic significance.32, 33
The prominent role of similar HRQOL variables in other prognostic models in elderly patients with advanced illness24, 74
further bears this out, supporting the PROMPT’s validity and the value of integrating HRQOL in future modeling efforts.
Furthermore, despite its low sensitivity in ruling out imminent death, the PROMPT has significant potential to improve end-of-life care given the prevailing underutilization of hospice services, overutilization of life-prolonging interventions, and lack of more accurate, evidence-based and explicit prognostic methods. These circumstances alone raise the possibility that use of the model could increase hospice utilization and advance care planning. Yet, the PROMPT’s greatest potential value lies in its ability to confirm a poor six-month prognosis. Its very high specificity across a range of estimated mortality risks (97% or more for all estimated risk cutpoints of 40% or greater) makes the PROMPT an extremely valuable tool for “ruling in” imminent death, with very few false positives. From both an ethical and a clinical standpoint, this function has at least as much clinical importance as ruling out death. The potential harm of a false negative estimate of six-month mortality is overly aggressive care at the end of life. Although undesirable, this outcome is arguably more tolerable than the potential irreversible harm of a false positive estimate: mistakenly labeling patients as “dying” and forgoing potentially beneficial or curative interventions. This ethical concern may be an important reason for physicians’ clinical reluctance to render prognoses,22, 75
and patients’ reluctance to accept them.76
The PROMPT’s ability to identify imminently dying patients with very few false positives addresses this concern, providing physicians and patients with the necessary reassurance to make critical decisions about end-of-life care.
A final limitation of the PROMPT’s performance is its weaker calibration at higher estimated risk levels, at which it overestimated mortality. This is likely a consequence of the small number of total deaths in these subgroups, reflecting the low overall mortality rate of the study sample (15%). In sicker populations with a higher pretest likelihood of mortality, it is possible that the model’s calibration would be improved.
However, this remains to be seen, and further evaluation is needed before the PROMPT can be implemented clinically. Although the large size and geographic and clinical heterogeneity of the study population enhances the model’s generalizability, it needs to be validated prospectively in other populations with differing comorbidities and experiences with health care. The target population of any predictive model determines both its clinical appropriateness and performance characteristics, including sensitivity and specificity,77
and ours consisted of community-dwelling elders with self-reported declining health. However, the PROMPT might be more accurate and useful in alternative populations, for example, patients identified on the basis of comorbidities and health care utilization (as in the SUPPORT study19, 21
and more recent prognostic model efforts in nursing home patients with dementia24
), or physicians’ own prognostic estimates,78, 79
recently operationalized through what has been termed the “surprise question:”80–82
“Would I be surprised if this patient died in the next 12 months?” Applying our tool in such selected populations—with higher pre-test probabilities of six-month mortality—would likely improve prognostic performance. Future research also might fruitfully examine whether testing strategies combining multiple prognostic tools and factors could further enhance prognostic power. For example, sensitivity might be increased by employing the PROMPT in parallel with other approaches, such as physicians’ prognostic estimates or the CMS Local Coverage Determination guidelines83
used by hospice providers to determine hospice eligibility.
Finally, more work is needed not only to evaluate and improve the PROMPT, but to understand the optimal means and outcomes of applying prognostic models clinically. The landmark SUPPORT study demonstrated that simply providing physicians with prognostic information may not alter end-of-life decision making or patterns of care.84
Various factors including physician and patient attitudes14, 22
and the structures and processes of health care may limit effective utilization of prognostic models. The feasibility of implementing a PRO-based model such as ours, which utilizes 11 variables but 28 individual data elements, remains to be determined. These and other potential barriers need to be better understood, along with the appropriate methods for communicating prognostic information in a sensitive, comprehensible manner. The current effort provides a foundation for such work and for efforts to refine existing models and to determine optimal strategies for their implementation.