Development and Validation of a Prognostic Index for 1-Year Mortality in
Older Adults After Hospitalization


Author Information <http://jama.ama-assn.org/issues/v285n23/rfull/#aainfo>
Louise C. Walter, MD; Richard J. Brand, PhD; Steven R. Counsell, MD; Robert
M. Palmer, MD, MPH; C. Seth Landefeld, MD; Richard H. Fortinsky, PhD;
Kenneth E. Covinsky, MD, MPH
Context  For many elderly patients, an acute medical illness requiring
hospitalization is followed by a progressive decline, resulting in high
rates of mortality in this population during the year following discharge.
However, few prognostic indices have focused on predicting posthospital
mortality in older adults.
Objective  To develop and validate a prognostic index for 1 year mortality
of older adults after hospital discharge using information readily available
at discharge.
Design  Data analyses derived from 2 prospective studies with 1-year of
follow-up, conducted in 1993 through 1997.
Setting and Patients  We developed the prognostic index in 1495 patients
aged at least 70 years who were discharged from a general medical service at
a tertiary care hospital (mean age, 81 years; 67% female) and validated it
in 1427 patients discharged from a separate community teaching hospital
(mean age, 79 years; 61% female).
Main Outcome Measure  Prediction of 1-year mortality using risk factors such
as demographic characteristics, activities of daily living (ADL) dependency,
comorbid conditions, length of hospital stay, and laboratory measurements.
Results  In the derivation cohort, 6 independent risk factors for mortality
were identified and weighted using logistic regression: male sex (1 point);
number of dependent ADLs at discharge (1-4 ADLs, 2 points; all 5 ADLs, 5
points); congestive heart failure (2 points); cancer (solitary, 3 points;
metastatic, 8 points); creatinine level higher than 3.0 mg/dL (265 µmol/L)
(2 points); and low albumin level (3.0-3.4 g/dL, 1 point; <3.0 g/dL, 2
points). Several variables associated with 1-year mortality in bivariable
analyses, such as age and dementia, were not independently associated with
mortality after adjustment for functional status. We calculated risk scores
for patients by adding the points of each independent risk factor present.
In the derivation cohort, 1-year mortality was 13% in the lowest-risk group
(0-1 point), 20% in the group with 2 or 3 points, 37% in the group with 4 to
6 points, and 68% in the highest-risk group (>6 points). In the validation
cohort, 1-year mortality was 4% in the lowest-risk group, 19% in the group
with 2 or 3 points, 34% in the group with 4 to 6 points, and 64% in the
highest-risk group. The area under the receiver operating characteristic
curve for the point system was 0.75 in the derivation cohort and 0.79 in the
validation cohort.
Conclusions  Our prognostic index, which used 6 risk factors known at
discharge and a simple additive point system to stratify medical patients 70
years or older according to 1-year mortality after hospitalization, had good
discrimination and calibration and generalized well in an independent sample
of patients at a different site. These characteristics suggest that our
index may be useful for clinical care and risk adjustment.
JAMA. 2001;285:2987-2994
JOC01917
People aged 65 years or older make up about 13% of the US population, but
they account for 37% of discharges from acute care hospitals. 1
<http://jama.ama-assn.org/issues/v285n23/rfull/#r1>  For many elderly
patients, an acute medical illness requiring hospitalization is followed by
a progressive physical decline, resulting in high rates of mortality during
the year following discharge. 2
<http://jama.ama-assn.org/issues/v285n23/rfull/#r2>  Since hospitalization
is frequently a major health transition for older adults, reassessing goals
of care at this juncture is often necessary. Prognostic information can
provide the basis for discussions about the goals of care and therapy. 3
<http://jama.ama-assn.org/issues/v285n23/rfull/#r3>  However, few prognostic
indices have focused on prediction of posthospital mortality in the elderly
population.
A prognostic index that estimates long-term mortality in older adults
following hospitalization may be useful to clinicians for many reasons. Such
an index can provide objective prognostic estimates to supplement
clinicians' intuition and judgment when counseling patients and their
families about the meaning of health problems and utility of treatment
options. Prognostic indices also can be useful in identifying groups at high
risk for poor outcomes in whom targeted treatment interventions may be
indicated 4 <http://jama.ama-assn.org/issues/v285n23/rfull/#r4>  or for whom
palliative care may be most appropriate. 5
<http://jama.ama-assn.org/issues/v285n23/rfull/#r5>
Also, prognostic indices are essential for comparing outcomes among
different physicians, hospitals, or systems of care. 6
<http://jama.ama-assn.org/issues/v285n23/rfull/#r6>  For example, indices
that correct for baseline risk differences among patients are needed to draw
fair inferences from observed mortality data about the quality of patient
care provided by different health plans following hospitalization. Fair
comparisons can stimulate improvements in quality of care, but such
comparisons are not possible without accurate methods of risk adjustment. 7
<http://jama.ama-assn.org/issues/v285n23/rfull/#r7>
The few prognostic indices that stratify hospitalized general medical
patients into risk groups for long-term mortality have a number of
limitations. Some only apply to the critically ill, 8-10
<http://jama.ama-assn.org/issues/v285n23/rfull/#r8>  or require complex
calculations and data that would not be routinely available to clinicians.
Only a few include functional status, 11-13
<http://jama.ama-assn.org/issues/v285n23/rfull/#r11>  despite its
association with mortality in older patients who are hospitalized. 14
<http://jama.ama-assn.org/issues/v285n23/rfull/#r14> , 15
<http://jama.ama-assn.org/issues/v285n23/rfull/#r15>  Also, many indices
have not been developed for ethnically diverse groups of patients or
validated in independent samples, limiting their generalizability. 16
<http://jama.ama-assn.org/issues/v285n23/rfull/#r16>
To address these issues, we developed a prognostic index for 1-year
mortality following hospital discharge in a large heterogeneous group of
older adults with medical illnesses, in whom we measured multiple potential
prognostic factors, including functional status. We then validated the index
in an independent sample. Our goal was to provide an accurate and
easy-to-use index that could stratify older adults into groups by their risk
of mortality after hospital discharge.



METHODS



Participants

This study includes individuals enrolled in 2 randomized trials of an
intervention to improve functional outcomes of hospitalized older adults.
The trials were conducted at the University Hospitals of Cleveland (UHC), a
tertiary care hospital, and the Akron City Hospital (ACH), a community
teaching hospital in Ohio, between 1993 and 1997. Each trial enrolled
patients who were aged 70 years or older and who were admitted to the
general medical service. Patients admitted to intensive care units (ICUs) or
subspecialty services or elective admissions were excluded, as were patients
with lengths of stay fewer than 2 days. Study protocols randomly selected a
subset of eligible patients to be representative of the general medical
wards since it was not possible to enroll all eligible patients because of
logistic constraints. Of a possible 11 475 eligible patients, 3163 were
randomly selected for enrollment. The demographic, clinical, and functional
characteristics of patients enrolled in the study were similar to those not
enrolled. 17 <http://jama.ama-assn.org/issues/v285n23/rfull/#r17>  After 1
year, there was no difference in mortality or functional status between the
control and intervention groups, so they were combined for this analysis.
We used patients from the UHC to derive the prediction model and then used
patients from the ACH to validate the model. The UHC trial enrolled 1632
patients and the ACH trial enrolled 1531 patients. The potential analytic
cohorts for this study included the 1565 UHC patients and 1482 ACH patients
who survived to hospital discharge. We excluded 70 patients (4%) in the UHC
cohort because they were missing data on comorbid conditions or functional
status, leaving 1495 patients, and 55 patients (4%) from the ACH cohort who
were missing data on these risk factors, leaving 1427 patients.
Data Collection and Measurements

Predictors of Mortality
We obtained data from standardized interviews with patients and surrogates
and from medical records. We interviewed surrogate respondents when the
patient scored more than 5 errors on the 10-point Short Portable Mental
Status Questionnaire 18 <http://jama.ama-assn.org/issues/v285n23/rfull/#r18>
or was too ill to communicate at the time of admission (40%). We interviewed
participants at both admission and discharge. The interviews included
demographic characteristics and reports of independence in 5 activities of
daily living (ADLs): bathing, dressing, using the toilet, transferring from
bed to chair, and eating. We used a modified version of the Katz Index of
ADLs 19 <http://jama.ama-assn.org/issues/v285n23/rfull/#r19>  to assess
independence in ADLs by asking the patient or surrogate at the time of
discharge whether the patient needed help from another person to perform
each activity. A patient who required personal assistance to perform a
particular ADL was classified as dependent in that ADL. A patient who used
an assistive device to perform an ADL but did not require help from another
person was considered independent.
Information obtained from medical records by trained chart abstractors
included laboratory values on admission comprising the APACHE (Acute
Physiology and Chronic Health Evaluation) II score, 20
<http://jama.ama-assn.org/issues/v285n23/rfull/#r20>  medical diagnoses
comprising the Charlson comorbidity index, 21
<http://jama.ama-assn.org/issues/v285n23/rfull/#r21>  reason for admission,
length of hospital stay, and discharge destination. We used laboratory
values from the time of admission, because in clinical practice they are
routinely obtained at that time but not always at the time of discharge.
We grouped the risk factors that we hypothesized were associated with 1-year
mortality into 4 broad categories: demographic variables, medical diagnoses,
functional status, and laboratory values. Race was identified by the
patient. Specific risk factors were chosen based on clinical relevance,
previous studies of predictors of mortality, and prevalence greater than 10%
in our sample.
Age was coded into 5-year intervals. Length of hospital stay was divided
into 7 days or fewer or more than 7 days, based on the mean length of stay.
Categorical variables, such as comorbid conditions, were coded as present or
absent, except that cancer was coded as absent or solitary or metastatic
solid tumor. Hematologic malignancies were coded as solitary cancer.
Functional status was categorized as totally independent (independent in all
ADLs), partially dependent (dependent in 1-4 ADLs), or totally dependent
(dependent in all ADLs). Analyses that used individual ADL items or total
ADL scores produced models with virtually the same discrimination as our
final model. Creatinine and albumin levels were also recoded into intervals
based on clinically relevant cut points. 22
<http://jama.ama-assn.org/issues/v285n23/rfull/#r22> , 23
<http://jama.ama-assn.org/issues/v285n23/rfull/#r23>
Definition of Outcome
The outcome of interest was defined as death within 1 year after hospital
discharge. We also used Kaplan-Meier curves to examine the performance of
our prognostic index over time. We obtained information about vital status
through follow-up interviews with participants and family members and a
search of the National Death Index. 24
<http://jama.ama-assn.org/issues/v285n23/rfull/#r24>  Deaths were classified
based on matches of the National Death Index record with the subject
according to name, sex, date of birth, and Social Security number. We
achieved 100% follow-up for vital status.
Model Derivation
We measured the bivariable relationship between each risk factor and
mortality in the derivation cohort using logistic regression models
containing only the risk factor of interest. We then entered all risk
factors associated with 1-year mortality (at P<.20) into a multivariable
logistic regression model with backward elimination (P<.05 to retain) to
select the final set of risk factors. The same multivariable model was
chosen using forward selection (P<.05 to enter). After developing the final
model, we assessed interactions between sex and age with other risk factors.
None were significant at P<.05.
We describe the results of our predictive model in 2 ways. First, we
estimated the predicted risk of death for each subject, based on the final
logistic regression model, and divided the subjects into quartiles of risk.
Second, we constructed a bedside risk scoring system in which we assigned
points to each risk factor by dividing each betacoefficient in the final
model by the lowest betacoefficient (male sex) and rounding to the nearest
integer. 25 <http://jama.ama-assn.org/issues/v285n23/rfull/#r25>  A risk
score was assigned to each subject by adding up the points for each risk
factor present. Subjects were then divided into approximate quartiles based
on their risk scores.
The predictive accuracy of the logistic model and the point scoring system
was determined by comparing predicted vs observed mortality in the ACH
validation cohort (calibration), and by calculating the area under the
receiver operating characteristic (ROC) curves (discrimination) in both the
derivation and validation cohorts. Discrimination reflects the ability of
the prognostic index to distinguish between patients at high and low risk of
death and is often described in terms of the area under the ROC curve (ROC
area), which is related to the relative probability that in all possible
pairs of patients in which one patient lives and the other dies, a higher
risk was assigned to the patient who died than to the one who lived. 26
<http://jama.ama-assn.org/issues/v285n23/rfull/#r26>  We chose to validate
our predictive model at a different site (ACH) than where it was developed
(UHC) since this form of prospective validation not only tests the accuracy
of the model but also tests its geographic and methodologic
transportability. 16 <http://jama.ama-assn.org/issues/v285n23/rfull/#r16> ,
27 <http://jama.ama-assn.org/issues/v285n23/rfull/#r27> , 28
<http://jama.ama-assn.org/issues/v285n23/rfull/#r28>



RESULTS



Characteristics of Participants

The mean (SD) age of patients in the UHC derivation cohort was 81 (7) years.
Sixty-seven percent were women, 60% were white, and 30% were discharged to a
nursing home or skilled nursing facility. Forty-one percent were independent
in all ADLs at discharge, 32% were dependent in 1 to 4 ADLs, and 27% were
dependent in all ADLs ( Table 1
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t1.html> ). During
1-year follow-up, 492 patients (33%) died.
The mean (SD) age of patients in the ACH validation cohort was 79 (7) years.
Sixty-one percent were women, 88% were white, and 14% were discharged to a
nursing home or skilled nursing facility. Fifty percent were independent in
all ADLs at discharge, 35% were dependent in 1 to 4 ADLs, and 15% were
dependent in all ADLs ( Table 1
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t1.html> ). During
the year following hospital discharge, 398 patients (28%) died.
Bivariable Results

Risk factors associated with 1-year mortality in the bivariable analyses
(P<.20) included age of 80 years or older, male sex, history of myocardial
infarction, congestive heart failure, cerebrovascular disease, dementia,
cancer, ADL function at discharge, length of hospital stay of more than 7
days, discharge to a nursing home or skilled nursing facility, creatinine
level of 1.5 mg/dL (132.6 µmol/L) or more, and albumin level of less than
4.0 g/dL ( Table 2
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t2.html> ).
Multivariable Results

Six of these 12 risk factors were independently associated with mortality in
multivariable analysis ( Table 3
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t3.html> ),
including 1 demographic variable (male sex), 2 medical diagnoses (congestive
heart failure and cancer), functional dependency in any ADL at discharge,
and 2 laboratory values (creatinine level >3.0 mg/dL [265.2 µmol/L] and
albumin level 3.4 g/dL). Many of the risk factors significantly associated
with 1-year mortality in bivariable analyses were not independently
associated with 1-year mortality after adjustment for discharge functional
status. These included age, dementia, and discharge to a nursing home.
By quartiles of predicted risk, 1-year mortality ranged from 13% in the
lowest-risk quartile to 63% in the highest-risk quartile in the derivation
cohort and from 9% to 64% in the validation cohort ( Table 4
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t4.html> ). There
was good calibration of the model, with close agreement between observed and
predicted mortality. The discrimination of the final model was better in the
validation cohort (ROC area = 0.80) than in the derivation cohort (ROC area
= 0.75). The model also retained good discrimination in the validation
cohort within sex and age subgroups. The ROC area was 0.80 for women, 0.78
for men, 0.79 for patients aged 70 to 79 years, and 0.79 for patients aged
80 years or older.
Bedside Risk Scoring System

The points assigned to each of the final 6 risk factors in the bedside
scoring system are listed in Table 3
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t3.html> . A risk
score was calculated for each patient by adding the points of each risk
factor that was present. For example, a 70 year-old man (1 point) admitted
to a general medical service with functional dependency in 3 ADLs (2
points), an albumin level of 2.9 g/dL (2 points), and a normal creatinine
level would have a risk score of 5 points. Derivation cohort risk scores
ranged from 0 to 16 points (mean [SD], 4.0 [3]).
Patients were divided by risk scores into 4 risk groups of roughly equal
size. In the UHC derivation cohort, mortality ranged from 13% in the
lowest-risk group (0-1 point) to 68% in the high-risk group (>6 points).
Within these groups, patients with 0 points had a mortality rate of 11%
(22/197) while patients with more than 9 points had a mortality rate of 82%
(55/67). Similar results were seen in the validation cohort, except that the
low-risk group had only a 4% mortality ( Table 4
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_t4.html> ). The
point system had better discrimination in the validation cohort (ROC area =
0.79) than the derivation cohort (ROC area = 0.75). Kaplan-Meier survival
curves of the 4 risk groups in the validation cohort demonstrate that the
groups have markedly different survival trajectories and that the mortality
differences between risk groups are persistent over the 1 year of follow-up
( Figure 1
<http://jama.ama-assn.org/issues/v285n23/fig_tab/joc01917_f1.html> ). In
addition, the point system retained good discrimination in age and sex
subgroup analyses (ROC area = 0.79 for women, 0.78 for men, 0.79 for
patients aged 70-79 years, and 0.79 for patients aged 80 years or older).



COMMENT



We have developed a prognostic index that can be used as a simple point
scoring system at the bedside to stratify elderly medical patients into
high-, intermediate-, and low-risk groups for mortality during the year
following hospital discharge. This index includes risk factors from each of
the 4 domains that we hypothesized were associated with 1-year mortality:
demographic variables, medical diagnoses, functional status, and laboratory
values. This finding is consistent with the clinical scenario that in many
older adults the cause of death is multifactorial. 29
<http://jama.ama-assn.org/issues/v285n23/rfull/#r29>  Our index emphasizes
the importance of considering multiple domains when assessing prognosis in
older patients and adds to our understanding of the complexity of mortality
prediction in the elderly population.
Our study, by demonstrating the prognostic importance of ADL function,
provides further evidence supporting routine assessment of functional status
in hospitalized older adults. Consistent with other studies, we found that
measures of functional status add important information about risk for
1-year mortality beyond that provided by medical diagnoses or physiologic
measures. 13-15 <http://jama.ama-assn.org/issues/v285n23/rfull/#r13>  This
is probably because functional status reflects the severity and end result
of many different illnesses and psychosocial factors. However, the
importance of assessing functional status extends well beyond its value as a
prognostic measure. Assessing ADL function of hospitalized older adults is
essential for providing quality care after discharge. Without assessing ADL
function, it is difficult to advise a patient about long-term care needs,
assess the need for home care and other supportive services, or evaluate the
needs of a patient's caregiver. 30
<http://jama.ama-assn.org/issues/v285n23/rfull/#r30> , 31
<http://jama.ama-assn.org/issues/v285n23/rfull/#r31>  While physicians often
fail to assess their patients' functional status, 32
<http://jama.ama-assn.org/issues/v285n23/rfull/#r32>  the ADL questions we
asked in this study took only a few minutes to administer. Also, the ease of
reviewing functional information routinely obtained by other disciplines,
such as nursing or physical therapy, should improve as more hospitals are
developing systematic methods for measuring and recording functional status
in older adults. 13 <http://jama.ama-assn.org/issues/v285n23/rfull/#r13>
Only 2 of the medical diagnoses from the Charlson comorbidity index
(congestive heart failure and cancer) remained independently associated with
mortality. Other illnesses, such as dementia and cerebrovascular disease,
which were highly associated with mortality in bivariate analyses, no longer
added to the prognostic estimate after adjustment for functional status.
This suggests that decrements in functional status reflect the severity of
dementia and cerebrovascular disease better than they reflect the severity
of congestive heart failure or cancer.
Additional risk factors that remained associated with an increased risk for
mortality after adjustment for comorbid illness and functional status
included male sex and laboratory values for creatinine and albumin. Others
have argued that the association between creatinine and mortality may be
explained by the direct negative effects of renal dysfunction on multiple
organ systems or may be reflective of generalized decreased tissue
perfusion. 33 <http://jama.ama-assn.org/issues/v285n23/rfull/#r33>  Albumin
also is a strong predictor of mortality in this and other studies probably
because it is both a marker of malnutrition as well as general disease
severity. 23 <http://jama.ama-assn.org/issues/v285n23/rfull/#r23>  In
contrast, age did not add to the predictive power of our index after we
adjusted for comorbidity and functional status. This suggests that the
association of older age with mortality may be explained by greater disease
burden and functional impairment in older patients consistent with other
studies. 12 <http://jama.ama-assn.org/issues/v285n23/rfull/#r12> , 34
<http://jama.ama-assn.org/issues/v285n23/rfull/#r34> , 35
<http://jama.ama-assn.org/issues/v285n23/rfull/#r35>
By combining functional status, comorbid illnesses, sex, and laboratory
values, our index performed better in predicting 1-year mortality than other
available prognostic indices that focus only on comorbid illnesses or
physiologic measures. For example, the Charlson comorbidity index had a ROC
curve area of 0.68 for 1-year mortality in the validation cohort, and APACHE
II, a physiologic index developed for ICU patients, had a ROC area of 0.59.
15 <http://jama.ama-assn.org/issues/v285n23/rfull/#r15>  Since mortality in
older adults is often dependent on many factors, it makes sense that an
index combining multiple domains of risk would have better discrimination
than indices that consider only a single domain.
In comparison with other prognostic indices that consider multiple domains
of risk, 8 <http://jama.ama-assn.org/issues/v285n23/rfull/#r8> , 11
<http://jama.ama-assn.org/issues/v285n23/rfull/#r11> , 13
<http://jama.ama-assn.org/issues/v285n23/rfull/#r13>  our index is easier to
use while maintaining prognostic accuracy. Our prognostic index, based on 6
risk factors and an additive point system, performed well in stratifying
older adults into risk groups for 1-year mortality. Our index had good
discrimination, with large differences in 1-year mortality between the
low-risk and high-risk groups. Our index was successfully validated in an
independent patient sample from a different site with no decrement in
discrimination (ROC area = 0.79) and only a mild decrease in calibration,
demonstrating our index's generalizability to another location and patient
group. 16 <http://jama.ama-assn.org/issues/v285n23/rfull/#r16>
Clinicians should use our index to supplement and lend confidence to their
judgments about prognosis, rather than to replace their clinical judgment.
Previous work suggests that clinicians' abilities to estimate prognoses are
about equal to that of prognostic indices. However, combining prognostic
indices and clinician estimates results in more accurate estimates than
either alone. 8 <http://jama.ama-assn.org/issues/v285n23/rfull/#r8> , 11
<http://jama.ama-assn.org/issues/v285n23/rfull/#r11> , 36
<http://jama.ama-assn.org/issues/v285n23/rfull/#r36>  Further, a recent
survey of clinicians suggests that many clinicians do not fully consider
prognosis in their clinical decision making and avoid discussing prognosis
with patients because they lack confidence in their prognostic estimates. 37
<http://jama.ama-assn.org/issues/v285n23/rfull/#r37>  This is despite
evidence that most patients would like clinicians to discuss prognosis with
them. 3 <http://jama.ama-assn.org/issues/v285n23/rfull/#r3> , 8
<http://jama.ama-assn.org/issues/v285n23/rfull/#r8>  One use of objective
prognostic indices may be to increase clinicians' confidence in their own
prognostic estimates, enhancing their willingness to discuss prognosis with
their patients.
Many patients may be concerned about their prognosis when they experience a
major event like hospitalization. Our index may be useful to clinicians in
initiating and guiding discussions about prognosis with patients at both low
and high risk for 1-year mortality. For example, an 80-year-old woman
admitted for pneumonia with no ADL dependencies at discharge and no major
comorbid conditions may be relieved to know that her 1-year risk of death is
similar to an 80-year-old woman living in the general community who has not
been hospitalized (<10%). 38
<http://jama.ama-assn.org/issues/v285n23/rfull/#r38>  In contrast, an
80-year-old man who is dependent in 3 ADLs at discharge, has a creatinine
level of 3.5 mg/dL (309.4 µmol/L) and an albumin level of 2.8 g/dL has a
greater than 60% risk of death in the ensuing year. Such information may
stimulate a conversation about the goals of care.
Our study has several limitations. First, we did not have information about
clinical care or patient preferences after discharge so that in some cases
poor survival may have been affected by decisions to limit treatment. Also,
there are different ways to ask about ADLs. For example, inquiring about
difficulty instead of dependence would have resulted in higher levels of ADL
impairment. 39 <http://jama.ama-assn.org/issues/v285n23/rfull/#r39>  Users
of our index should be aware that the performance of our index will differ
if the way of inquiring about ADLs is changed from our method. Finally,
since the patients were involved in a study to improve functional outcomes,
it is possible that the selection process for the study or the process of
being observed in a study could affect the generalizability of our index.
However, this seems unlikely because the intervention did not significantly
improve outcomes after 1 year and the patients randomly selected for the
study were representative of those admitted to the medical services of the 2
hospitals. 17 <http://jama.ama-assn.org/issues/v285n23/rfull/#r17>  As with
all prognostic indices, the true validity and generalizability of our index
needs to be established by cumulative testing to determine if the index
remains accurate in other locations and groups of patients. 16
<http://jama.ama-assn.org/issues/v285n23/rfull/#r16> , 28
<http://jama.ama-assn.org/issues/v285n23/rfull/#r28>
In summary, our index provides a potentially useful prognostic tool to
estimate the likelihood of 1-year mortality after hospitalization for older
medical patients. The index uses 6 risk factors, all of which are easily
available at hospital discharge, and a simple additive point system. The
index had good discrimination and calibration, and it generalized well in an
independent sample of patients at a different site. These characteristics
suggest that our index may be useful for guiding clinical care and for risk
adjustment.



Author/Article Information


Author Affiliations: Division of Geriatrics, San Francisco VA Medical Center
and University of California, San Francisco (Drs Walter, Landefeld, and
Covinsky); Department of Epidemiology and Biostatistics, University of
California, San Francisco (Dr Brand); Division of General Internal Medicine
and Geriatrics, Indiana University School of Medicine, Indianapolis (Dr
Counsell); Section of Geriatric Medicine, Cleveland Clinic Foundation,
Cleveland, Ohio (Dr Palmer); and Center on Aging and Division of Geriatrics,
University of Connecticut Health Center, Farmington (Dr Fortinsky).

Corresponding Author and Reprints: Louise C. Walter, MD, Division of
Geriatrics, VA Medical Center 111G, 4150 Clement St, San Francisco, CA 94121
(e-mail: [log in to unmask] <mailto:[log in to unmask]> ).
Author Contributions: Study concept and design: Walter, Landefeld, Covinsky.
Acquisition of data: Walter, Counsell, Palmer, Landefeld, Fortinsky.
Analysis and interpretation of data: Walter, Brand, Counsell, Palmer,
Landefeld, Covinsky.
Drafting of the manuscript: Walter, Palmer, Covinsky.
Critical revision of the manuscript for important intellectual content:
Brand, Counsell, Palmer, Landefeld, Fortinsky, Covinsky.
Statistical expertise: Brand, Covinsky.
Obtained funding: Counsell, Palmer, Landefeld.
Administrative, technical, or material support: Counsell, Palmer, Landefeld.
Funding/Support: This work was supported by grants from the National
Institute on Aging to the Claude Pepper Older American Independence Center
at Case Western Reserve University, Cleveland, Ohio (NIA AG10418); the Summa
Health System Foundation, Akron, Ohio; and a Hartford Foundation Center of
Excellence grant to the University of California, San Francisco. Dr Walter
was supported in part by a grant from the John A. Hartford Foundation,
University of California, San Francisco, the Geriatrics Center of
Excellence, and a T-32 Training grant (Research Training in Geriatric
Medicine) from the National Institute on Aging. Dr Covinsky was supported in
part by an independent investigator award (K02HS00006-01) from the Agency
for Healthcare Research and Quality and is a Paul Beeson Faculty Scholar in
Aging Research.
Previous Presentation: An abstract of this study was presented at the annual
meetings of the Society of General Internal Medicine in Boston, Mass, and
American Geriatrics Society in Nashville, Tenn, May 2000.
Acknowledgment: We thank Warren S. Browner, MD, MPH, for his review of the
manuscript.




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Edward E. Rylander, M.D.
Diplomat American Board of Family Practice.
Diplomat American Board of Palliative Medicine.