The last year of life is
often filled with trips to the emergency room, admissions to the
hospital—frequently the ICU—and multiple visits to medical specialists. The
treatments patients endure during that final year are burdensome, invasive, and
costly. And in the end, they die anyway.
The problem with this kind of analysis is that it starts
with the time of death and works backwards. But we don’t know in advance who is
going to die. What about all the people who undergo aggressive treatments and
don’t die? Isn’t it possible that they live longer, and sometimes better,
because of all those doctors and hospitals? We will all die eventually and the
very old will die sooner rather than later. The challenge is to predict how we
will get from here to there so that we can make reasonable choices along the
way. A new study in the BMJ offers a possible means of figuring that out.
We’ve known for some time that older people follow
different trajectories near the end of life, and that a useful way to
characterize those trajectories is by the extent of dependence and disability.
A rough approximation of what happens is:
A more refined description
suggests that there are five distinct “trajectories of disability” in the last
year of life and that particular medical conditions—heart failure, cancer, or
frailty—do not alone determine the path. The new study indicates that a
powerful determinant of the path, independent of the medical condition that
proves to be the cause of death, is hospitalization.
The authors had the opportunity to analyze data from an
ongoing longitudinal study of 754 community-dwelling older people over the age
of 70 who were initially independent in four essential activities of daily
life: bathing, dressing, walking, and going from lying to sitting and sitting
to standing. A comprehensive home-based assessment was conducted at baseline
for every patient and then every 18 months for over ten years, as well as
telephone interviews along the way. The evaluation included mental status,
chronic conditions, and physical performance. Data was available on 582
decedents.
Using a complex modeling procedure called “trajectory
modeling” which is a form of a complicated process known as “latent class
analysis,” the authors ended up expanding their earlier classification of 5
trajectories to 6. At one extreme is the total absence of disability in the
year prior to death (17.2% of decedents). At the other extreme is persistent,
severe disability (28.1%) or the presence of marked disability a full year
before death, disability that didn’t get any better. In between are
catastrophic disability (11.1%), in which a patient becomes acutely disabled,
for example from a stroke; and three forms of progressive disability:
accelerated disability (9.6%), progressive mild disability (11.1%), and
progressive severe disability (23%).
The striking result of the analysis is that without
exception, the course of disability closely tracked hospitalization. No matter
how the authors adjusted their analysis to account for possible confounders,
the results remained unchanged. For every trajectory, being admitted to the
hospital in a given month had a strong, independent effect on the severity of
disability.
Now it’s possible that it was the acute problem leading to
hospitalization, not the ensuing hospitalization, that caused the functional
decline. The conclusion may be that we need to redouble our efforts to make
hospital care for older people better, to try to improve over the modest
progress we have made to date with ACOVE (acute care for vulnerable elders)
units and fall prevention protocols. Or the conclusion might be, as the authors
suggest, that patients admitted to the hospital with progressive, severe
disability or with persistent severe disability, it would be best to suggest a palliative
approach to medical care.
Whatever else we take away from this intriguing study, we
should recognize what was perhaps obvious all along: it is often difficult and
frequently impossible to predict from a single point in time what a given
patient’s trajectory will look like. But if we consider two or three points in
time and ask what the patient’s function is like over time, we can have a far
better idea. Just as we cannot determine the slope of a line from just one
point but we can calculate the slope from any two points—and we need more
points to define more complicated curves—so, too, will we do better at prognosticating
if we see patients as dynamic rather than static.