Development and Validation of a Prognostic Index for 1-Year
Mortality in Older Adults After Hospitalization
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
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 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 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 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
indicated4 or for whom
palliative care may be most appropriate.5
Also, prognostic indices are essential for
comparing outcomes among different physicians, hospitals, or systems of care.6 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
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 or require complex
calculations and data that would not be routinely available to clinicians. Only
a few include functional status,11-13 despite its
association with mortality in older patients who are hospitalized.14, 15 Also, many indices
have not been developed for ethnically diverse groups of patients or validated
in independent samples, limiting their generalizability.16
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 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
Questionnaire18 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 ADLs19 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 medical diagnoses
comprising the Charlson comorbidity index,21 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, 23
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 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 
coefficient in the final model by the lowest coefficient (male sex) and rounding to the nearest integer.25 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 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, 27, 28
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).
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).
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).
Multivariable Results
Six of these 12 risk factors were independently associated with mortality in
multivariable analysis (Table 3),
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).
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.
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).
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).
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 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 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, 31 While physicians
often fail to assess their patients' functional status,32 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
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 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 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, 34, 35
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 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, 11, 13 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
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, 11, 36 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 This is despite
evidence that most patients would like clinicians to discuss prognosis with
them.3, 8 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 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 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 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, 28
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]).
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.