Context Hip fracture is a common clinical problem that leads to considerable
mortality and disability. A need exists for a practical means to monitor and
improve outcomes, including function, for patients with hip fracture.
Objectives To identify and compare the importance of significant prefracture predictors
of functional status and mortality at 6 months for patients hospitalized with
hip fracture and to compare risk-adjusted outcomes for hospitals providing
Design Prospective study with data obtained from medical records and through
structured interviews with patients and proxies.
Setting and Participants A total of 571 adults aged 50 years or older with hip fracture who were
admitted to 4 New York, NY, metropolitan hospitals between August 1997 and
Main Outcome Measures In-hospital and 6-month mortality; locomotion at 6 months; and adverse
outcomes at 6 months, defined as death or needing assistance to ambulate,
compared by hospital, adjusting for patient risk factors.
Results The in-hospital mortality rate was 1.6%. At 6 months, the mortality
rate was 13.5%, and another 12.8% needed total assistance to ambulate. Laboratory
values were strong predictors of mortality but were not significantly associated
with locomotion. Age and prefracture residence at a nursing home were significant
predictors of locomotion (P = .02 for both) but were
not significantly associated with mortality. Adjustment for baseline characteristics
either substantially augmented or diminished interhospital differences in
outcomes. Two hospitals had 1 outcome (functional status or mortality) that
was significantly worse than the overall mean while the other outcome was
nonsignificantly better than average.
Conclusions Mortality and functional status ideally should be considered both together
and individually to distinguish effects limited to one or the other outcome.
Hospital performance for these 2 measures may differ substantially after adjustment,
probably because different processes of care are important to each outcome.
Hip fracture is a common and important cause of mortality and loss of
function. An estimated 350 000 hip fractures occur annually in the United
States, and the total inpatient cost of caring for these patients is nearly
$6 billion per year exclusive of physician charges.1
After a short hospital stay, patients with hip fracture may receive medical
and rehabilitative services from varying combinations of acute rehabilitation,
nursing home, and home care services—often from several different and
unconnected providers. Among patients discharged following hospitalization
for hip fracture, only 60% will have recovered their prefracture walking ability
by 6 months,2 and 24% of patients will have
died by 12 months.3 Consequently, there is
a need for a practical means for improving outcomes, including function, of
An important step in understanding and improving these outcomes is being
able to adjust the outcomes for baseline patient characteristics—beyond
the control of clinicians—that influence the outcomes and may differ
by provider. Research of this type has been conducted for a broad range of
clinical problems; however, much of this research has focused on risk-adjusted
mortality or complications. In recent years, measures of patient functional
outcomes have been proposed as a means to assess the effectiveness and quality
of care, but the research on risk-adjusted functional outcomes is limited.
For many clinical problems, using functional outcomes in this way has been
complicated by the possibly delayed impact of medical care on functional outcomes
and the difficulty of identifying an appropriate time period for assessing
baseline function in patients with chronic conditions. These issues are less
of a problem for patients with hip fracture.4
For hip fracture, studies have identified the patient factors related to the
recovery of functional status or mortality.2,5-31
However, the vast majority of these studies have considered function or mortality
independently, and none has reported on how risk-adjusted outcomes could be
obtained to assess the effectiveness or quality of care.
The objectives of this study are (1) to develop and test statistical
models that identify significant risk factors for 6-month mortality and compromised
functional status for patients with hip fracture; (2) to examine alternative
ways for risk-adjusting the outcomes of function and mortality for a clinical
problem for which both outcomes are clearly important and neither can be meaningfully
ignored; and (3) to compare the risk-adjusted outcomes by the hospital providing
Patients hospitalized for hip fracture between August 1997 and August
1998 in 4 hospitals in the New York metropolitan area were eligible for the
study. Exclusions consisted of patients who were younger than 50 years; whose
fracture occurred as an inpatient; who were transferred from another hospital;
or who sustained concurrent major internal injuries, pathological fractures,
fractures not limited to the pelvis or acetabulum, fractures 2 cm or more
below the trochanter, bilateral hip fractures, prior surgery on the same hip,
or previous ipsilateral hip fracture.
A total of 804 patients with hip fracture were admitted to the 4 hospitals.
Of these patients, 650 (81%) met the eligibility criteria, and 571 (88%) of
those patients gave informed consent for participation in the study.
A variety of patient characteristics and risk factors were collected
from medical records and patient or proxy interviews during and after hospitalization
for hip fracture. Information obtained through these interviews included the
patient's functional status and type of residence prior to the fracture, as
well as whether the patient suffered from dementia or had a paid helper in
his/her home prior to the fracture. Medical records were used to capture comorbid
conditions, vital signs, laboratory value indicators, demographics, and the
presence of dementia.
After discharge from the hospital, functional status information was
obtained by interviewers at 6 months. For cognitively compromised patients,
alternate respondents who were most familiar with the patient (usually a spouse
or close relative) were identified and interviewed to obtain information about
the patient's functional status before and after the fracture. Prior studies
indicate that proxies can provide reliable estimates for the areas of physical
function measured in this study.30,32,33
Patient Characteristics and Predictors of Outcome
The independent variables that were explored in the study included demographic
measures (age, sex, race), prefracture locomotion, reliance on help from others
prior to fracture (nursing home residence, paid helper at home), dementia,
presence of chronic medical conditions, physiological function, type of fracture/displacement
(femoral neck/displaced, femoral neck/nondisplaced, intertrochanteric), and
type of procedure (hemiarthroplasty, internal fixation). Prefracture locomotion
was measured by eliciting information for the locomotion subscale of the Functional
Independence Measure (FIM)23-26
relative to function just prior to the fracture.
The impact of chronic conditions was measured by modifying the RAND
comorbidity score27 to tailor the relative
importance of each condition to outcomes for patients with hip fracture (available
from the authors on request). Also, a modified APACHE (Acute Physiology and
Chronic Health Evaluation) score28 (without
Glasgow Coma Scale, which was not considered relevant for patients with hip
fracture) was used to capture the impact of patients' vital signs, laboratory
studies, and mental status on outcomes.
There were 3 outcomes of interest in the study, each measured at 6 months
following hospitalization: mortality, locomotion, and adverse outcome (defined
as mortality or needing total assistance to ambulate).
Locomotion (ability to walk and climb stairs) was measured using a modified
version of the locomotion subscale of the FIM, which rates the patient's independence
in traveling 45 m (150 ft) walking or in a wheelchair and in going up and
down 12 to 14 stairs.23-26
Each of the measures in the locomotion subscale (walking, climbing stairs)
is scored from 1 to 7, with 1 indicating a patient requiring total assistance
and 7 indicating a completely independent patient. Thus, the total locomotion
score ranges from 2 to 14, with higher scores being indicative of better functioning.
"Needing total assistance to ambulate," which is part of one of the outcome
measures used in the study, was defined as needing total assistance from another
person to walk 45 m (150 ft).
The relationships between several available patient risk factors and
each of the outcomes were examined using χ2 tests. Patient
risk factors, which were collected at admission based on patient status prior
to the fracture, included age, sex, race, functional status (locomotion subscale
of FIM), dementia, admission from a nursing home, paid helper required to
care for the patient, modified APACHE score,28
and modified RAND comorbidity score.27
Next, relationships between the entire group of risk factors and the
outcome measures were explored. For postfracture locomotion, the relationship
with the group of patient risk factors was examined using a multiple linear
regression model, with locomotion subscale at 6 months as the dependent variable.
Fit of the model was assessed using the R2
value, and the significance of each independent variable was assessed using
the t statistic for the variable. The model included
only patients who were alive at 6 months. For the 2 binary outcome measures,
the multivariate relationship with patient risk factors was examined using
multiple logistic regression models. The binary dependent variable was the
presence or absence of the outcome, and the independent variables were the
same ones used in the linear regression models above. Model fit was assessed
using the C statistic34 to measure each model's
discrimination, and the Hosmer-Lemeshow statistic35
was used to measure its calibration. A proportional hazards model that predicts
time until death was also used and yielded results that were very similar
to those of the logistic regression model.
Independent variables used in all of the models included those mentioned
above in the univariate analyses. Age, locomotion subscale, modified RAND
comorbidity score, and modified APACHE score were treated as continuous variables.
A quadratic term was tested for age, but it proved not to be significant.
Also, age categories were used in lieu of a continuous measure, but they yielded
a worse model fit (eg, R2 value for the
linear regression models) than the linear term for age. Interactions between
age and sex were also tested and were found not to be statistically significant.
Use of long-term care services was treated as a variable with 3 categories
(admission from a nursing home, paid helper required to care for the patient
at home prior to the fracture, and all other [the reference category]).
The next stage of the data analyses consisted of adding an indicator
(dummy) variable for 3 of the 4 hospitals to each of the statistical models
described above. This was done to assess the performance of each hospital
with regard to each outcome measure after adjusting for differences in patient
severity of illness prior to the hip fracture. The hospital dummy variables
were coded using effects rather than reference coding so that each hospital's
performance could be compared with the overall average performance rather
than the performance of an arbitrarily chosen reference hospital. P values were used to identify the significance of each hospital's
deviation from the overall average performance, and the correspondence of
each hospital's performance across all outcome measures was examined. Statistical
significance was set at P<.05.
In an effort to explore the reasons for outcome differences among hospitals,
we explored whether the differences could be explained by choice of discharge
destination or type of surgical procedure. We first added discharge destination
(home, nursing home, acute rehabilitation hospital) to the statistical models
to determine if destination was a significant predictor of each of the 3 outcomes,
and to determine if interhospital outcome variations would be diminished when
controlling for discharge destination. When we proceeded to examine the effect
of surgical procedure, we found that internal fixation was used for nearly
all (95%) of intertrochanteric fractures. Also, displaced femoral neck fractures
were usually (90% of the time) treated with arthroplasty, and nondisplaced
femoral neck fractures were almost always treated with internal fixation.
Consequently, it was impossible to include both the fracture characteristics
and type of surgery in the models because there was not sufficient variation
in the choice of treatment. To examine the significance of fracture type/procedure
type, we designated intertrochanteric fracture as the reference category and
defined femoral neck fracture/internal fixation and femoral neck fracture/hemiarthroplasty
as indicator variables in the models.
Statistical analyses were conducted with SAS version 6.12 (SAS Inc,
Relationship Between Individual Patient Risk Factors and Outcomes
The in-hospital mortality rate was 1.6%, the 6-month mortality rate
was 13.5%, and the 6-month adverse outcome (dead or needing total assistance
to ambulate) was 26.3%. Of the patients who died within 6 months after admission,
the percentages who died each month were 20%, 10%, 20%, 17%, 13%, and 20%,
respectively. The mean locomotion score at 6 months for survivors was 8.06
(SD = 4.01).
Table 1 presents the relative
frequencies of patient risk factors and their bivariate relationships with
the 3 outcome measures. Of the patients with hip fracture in the study, 81%
were women and 92% were white; prior to the fracture, 12% resided in a nursing
home, 36% had a paid helper in the home, and 25% had dementia. Significant
risk factors for mortality were race, presence of dementia, type of fracture/displacement,
use of long-term care services, prefracture function, RAND score, and APACHE
score. All of those risk factors were also significantly related to adverse
outcome except sex, the APACHE score, and type of fracture/displacement. Locomotion
scores were significantly different by use of long-term care services, dementia,
type of fracture/displacement, function, comorbidity, RAND score, and age.
Table 2 presents the results
of a linear regression model that predicts the FIM locomotion subscale on
the basis of several prefracture patient risk factors. The R2 value was .38 and the only significant predictors of
postfracture locomotion were prefracture locomotion (P<.001),
age (P = .02), and whether patients had resided in
a nursing home prior to the fracture (P = .02).
Table 3 presents the results
of a logistic regression model that predicts mortality at 6 months and adverse
outcome using the same patient risk factors that were examined in Table 2. The fit of the logistic regression
model for mortality was good, with a c statistic
of 0.770 and a P value for the Hosmer-Lemeshow test
of .18. As indicated in Table 2,
lower prefracture locomotion, higher modified APACHE score, and paid helper
at home prior to the fracture were significantly related to higher mortality
at 6 months at the .05 level. The fit of the logistic regression model that
predicts adverse outcome (mortality or needing total assistance to ambulate
at 6 months) was even better, with a c statistic
of 0.813 and a Hosmer-Lemeshow P value of .13. The
adverse outcome rate at 6 months was 26.3%, with 13.5% of the patients having
died and another 12.8% needing total assistance to ambulate. The same 3 patient
risk factors as in the mortality model (prefracture locomotion, paid helper
at home prior to the fracture, and modified APACHE score) were significant,
and dementia was also significantly associated with the outcome.
Table 4 presents the unadjusted
difference between each hospital's performance and the overall performance
as well as the results after adjusting for differences in patient prefracture
characteristics by adding indicator variables for the hospitals to the statistical
models in Table 2 and Table 3.
Adjustment for baseline patient characteristics either substantially
augmented or diminished interhospital differences in outcomes for each of
the 4 hospitals. Also, some hospitals performed markedly different with respect
to functional status and mortality. Hospital C's unadjusted locomotion score
was significantly lower than average, and its risk-adjusted score was also
significantly lower than average (−0.69, P
= .005). Two other hospitals (B and D) had unadjusted scores that were significantly
different than average, although these differences were not significant after
For the other 2 outcomes, both of which are binary, the odds ratio (OR)
and its associated P value were used to evaluate
the association with outcomes. For mortality within 6 months, patients in
hospital A had significantly higher adjusted odds of dying than the entire
population (OR, 1.92; 95% confidence interval [CI], 1.04-3.57]; P = .04), although its unadjusted odds were not significantly higher.
Patients in hospital D had the best risk-adjusted results, with odds of mortality
relative to other patients of 0.42 (95% CI, 0.16-1.07; P = .07). Also, hospital D's unadjusted odds of mortality were significantly
lower than those of the entire population. Hospital B had significantly higher
unadjusted odds, but they were no longer significant after adjustment.
The combined outcome of death or needing total assistance to ambulate
at the end of 6 months demonstrated more accentuated outcomes, with patients
in hospital D having significantly lower adjusted odds (OR, 0.34; 95% CI,
0.16-0.72; P = .005) and patients in hospital A having
an OR of an adverse outcome that remained significant (OR, 2.59; 95% CI, 1.55-4.34; P<.001). Without adjustment, the ORs for hospitals D
and A were also significant. Hospital B had significantly higher odds before
adjustment, but they were no longer significant after the adjustment process.
When the discharge destination variables were added to the set of independent
variables in Table 4 (not shown
in the table), the results remained essentially the same for death and the
combined outcome of death or needing total assistance to ambulate. For example,
the mortality ORs for the 4 hospitals were 1.92 vs 1.83, 1.43 vs 1.21, 0.88
vs 1.04, and 0.42 vs 0.47, respectively. Additionally, the new variables "discharge
to acute rehabilitation" and "discharge to nursing home" did not yield significantly
different outcomes than "discharge to home." For locomotion status at 6 months,
"discharge to a nursing home" proved to be associated with significantly lower
FIM scores than the reference (discharge to home). "Discharge to acute rehabilitation"
and "discharge to home" did not have significantly different results from
one another. Also, hospital C no longer had significantly lower locomotion
status after 6 months after controlling for discharge destination. Thus, hospital
C's locomotion results may be partially explained by the tendency of hospital
C to discharge a high percentage of its patients to nursing homes (81% vs
45% for the other hospitals combined). In the case of procedure/fracture type,
these variables were not significantly associated with any of the 3 outcomes
after controlling for preoperative severity of illness and had no effect on
the interhospital differences among these outcomes.
We developed statistical models for predicting functional status and
mortality, both separately and together, for patients with hip fracture. These
models were then used to assess hospital outcomes. In the study, patients
with hip fracture who were admitted to 4 New York metropolitan hospitals were
similar to patients in other studies that were more national in scope. For
example, compared with the study by Carson et al36
involving 20 hospitals in 4 regions of the United States, our patient sample
included 81.4% women (vs 78.4%), 46.9% were 80 to 89 years old (vs 43.1%),
91.9% were white (vs 86.5%), 24.7% had dementia (vs 26.1%), and 47.6% had
cardiovascular disease (vs 43.1%).
The findings of the study are important in 2 respects. First, significant
patient risk factors were identified for 3 outcomes (mortality, functional
status, and a combination of the 2: death or needing total assistance to ambulate),
and statistical models based on these risk factors were then used to report
risk-adjusted hospital outcomes for hip fracture. In fact, for all 3 of the
performance measures, there was at least 1 hospital with significantly high
or low unadjusted outcomes that did not prove to be statistically significant
after adjustment for differences in patient severity of illness. Thus, the
study demonstrates that there is a need to risk-adjust when assessing hospital
outcomes for hip fracture.
Second, with respect to hospital performance, our study demonstrates
that performance on one outcome is not necessarily related to performance
on another. As indicated, 1 hospital had a significantly higher risk-adjusted
mortality rate but not a significantly worse risk-adjusted functional status.
Another hospital had a significantly worse risk-adjusted functional status
but not a significantly higher risk-adjusted mortality rate. This indicates
that both mortality and functional status measures are needed to adequately
assess hospital performance, and that the processes of care that are associated
with lower mortality rates are not identical to the processes associated with
better functional status.
There are several possible explanations for the hospital-specific results
that we observed. The hospitals in our study differed in the baseline characteristics
of the patients that were admitted, and it is possible that our models did
not fully adjust for differences in these patient characteristics. Our analyses,
however, included all the major variables that have been shown in the literature
to be associated with poor functional outcomes in hip fracture. Consistent
with these other reports, our study found that low levels of function prior
to the fracture and older age are predictive of poor postfracture functioning,2,5,6,11,13-15,37
and the presence of compromised vital signs low levels of prefracture function
are predictive of postfracture mortality.3,16-28
Further, the fit of the prediction models was consistent with that of other
multivariate outcome models that have been developed for clinical problems.
For these reasons, we do not believe that residual unaccounted differences
in the patient populations are a major explanation for the observed hospital
We believe that the differences in outcomes may reflect differences,
in part, in the inpatient and postacute care services received by these patients.
In the inpatient acute care of these patients, interventions (supported by
evidence that ranges from randomized trials to expert opinion) are available
to prevent or treat many of the common complications encountered in these
patients, including thrombophlebitis, wound infection, urinary tract infections,
urinary retention, pressure ulcers, cardiac complications, and delirium.38 After the acute hospital episode, the typical patient
with hip fracture continues to receive postacute rehabilitative services for
several weeks in skilled nursing facilities, acute rehabilitation units, home
health care programs, or a combination of these, and the hospital to which
a patient is initially admitted may have considerable influence on the type
of postacute services that are made available.39-41
As an example, the percentage of patients discharged to acute rehabilitation
facilities ranged from 6% to 80% among the 4 study hospitals, and patients
with hip fracture who were treated in the 2 hospitals discharging the largest
proportion of patients to acute rehabilitation facilities (80% and 50%) also
had higher, but not significantly higher, levels of locomotion at 6 months
than patients discharged from the other 2 study hospitals where a much smaller
proportion were discharged to this care setting (10% and 6%). Also, we found
that the hospital that had significantly lower risk-adjusted locomotion status
in this study had a much higher percentage of patients discharged to nursing
homes, and when discharge destination was included in the risk-adjustment
process, the hospital no longer had a statistically different locomotion status
than other hospitals.
This suggests that the hospital's practice of discharging a high percentage
of patients to nursing homes was associated with the lower locomotion of its
patients 6 months after discharge. However, these data do not provide definitive
evidence of less effectiveness of discharges to nursing homes relative to
acute rehabilitation facilities or home. For example, we were not able to
determine reliably at the time of hospital discharge whether a patient was
sent to a traditional type of nursing home unit or to a subacute type of unit,
nor were we able to determine the patient's ambulatory status on admission
to the postacute facility. Our study also did not have sufficient detail about
the lengths and nature of postacute rehabilitation sessions. Thus, the information
we have on postdischarge rehabilitation is incomplete and limited in detail.
Our findings have implications for ongoing efforts by providers, accrediting
agencies, employers, and other parties to better understand and improve outcomes
of health care. Specifically, we believe that greater attention needs to be
paid, not only to preventing hip fracture, but also to preventing the mortality
and morbidity that results once a patient has fractured a hip—an issue
that has not been on the quality improvement agenda of most health care organizations.
Hip fractures are common, costly, and have serious health consequences. With
changes in the financing and delivery of care in the last 2 decades, this
is a clinical problem where care is becoming increasingly fragmented among
different settings (hospital, nursing home, rehabilitation, and home care)
and providers (orthopedic surgeons, physiatrists, and primary care physicians),
with resulting diffusion in the role and responsibilities for the patient's
care. The expansion in the Balanced Budget Act of prospective payment to postacute
service providers will only increase the fragmentation and the boundaries
that providers will set on their responsibilities for the episode of care.
It should be noted that the issues encountered by these patients are not specific
to hip fracture and are likely to be generalizable to the situation of other
patients (eg, with acute strokes and other illnesses) who, made vulnerable
from the combination of acute and chronic illness, are set on their own to
navigate the postacute care system after a short stay in the hospital for
management of what is only the initial phase of their acute illness.
In parallel with efforts to improve and better coordinate the health
care services received by patients with hip fracture, there is a need for
clinical research to better understand the efficacy of interventions that
might increase survival and improve functional outcomes. Across all of these
treatment and care management areas, studies evaluating functional limitation
and disability as outcomes are unusual. For example, virtually all studies
on timing of hip fracture surgery evaluate the effect of early surgery only
on mortality. Thus, in research, as well as in the management of the 350 000
cases of hip fracture annually in the United States, there is a need for collaborative
efforts to provide interventions known to be effective, to coordinate the
care provided, and to expand the knowledge base of how survival and functional
outcomes can be improved.
Hospital Inpatient Statistics, 1996. Washington, DC: Agency for Health Care Policy and Research; 1999.
AHCPR publication 99-0034.
Magaziner J, Hawkes W, Hebel JR.
et al. Recovery from hip fracture in eight areas of function. J Gerontol A Biol Sci Med Sci.2000;55:M498-M507.Google Scholar
Magaziner J, Simonsick EM, Kashner TM.
et al. Predictors of functional recovery one year following hospital discharge
for hip fracture: a prospective study. J Gerontol.1990;45:M101-M107.Google Scholar
Young Y, Brant L, German P.
et al. A longitudinal examination of functional recovery among older people
with subcapital hip fractures. J Am Geriatr Soc.1997;45:288-294.Google Scholar
Koval KJ, Skovron L, Aharonoff GB, Zuckerman JD. Predictors of functional recovery after hip fracture in the elderly. Clin Orthop.1998;348:22-28.Google Scholar
Jette AM, Harris BA, Cleary PD, Campion EW. Functional recovery after hip fracture. Arch Phys Med Rehabil.1987;68:735-740.Google Scholar
Ceder L, Thorngren K-G, Wallden B. Prognostic indicators and early home rehabilitation in elderly patients
with hip fractures. Clin Orthop.1980;152:173-184.Google Scholar
Diamond TH, Thornley SW, Sekel R, Smerdely P. Hip fracture in elderly men: prognostic factors and outcomes. Med J Aust.1997;167:412-415.Google Scholar
Lyons AR. Clinical outcomes and treatment of hip fractures. Am J Med.1997;103(2A):51S-64S.Google Scholar
Cobey JC, Cobey JH, Conant L.
et al. Indicators of recovery from fractures of the hip. Clin Orthop.1976;117:258-262.Google Scholar
Koval KJ, Skovron ML, Aharonoff GB.
et al. Ambulatory ability after hip fracture. Clin Orthop.1995;310:150-159.Google Scholar
Svensson O, Stromberg L, Ohlen G, Lindgren U. Prediction of the outcome after hip fracture in elderly patients. J Bone Joint Surg Br.1996;78:115-118.Google Scholar
Koval KJ, Skovron ML, Aharonoff GB.
et al. Ambulatory ability after hip fracture: a prospective study in geriatric
patients. Clin Orthop.1995;310:150-159.Google Scholar
Koval KJ, Skovron ML, Polatsch D.
et al. Dependency after hip fracture in geriatric patients: a study of predictive
factors. J Orthop Trauma.1996;10:531-535.Google Scholar
Weatherall M. One year follow up of patients with fracture of the proximal femur. N Z Med J.1994;107:308-309.Google Scholar
Magaziner J, Simonsick EM, Kashner M.
et al. Survival experience of aged hip fracture patients. Am J Public Health.1989;79:274-278.Google Scholar
Mullen JO, Mullen NL. Hip fracture mortality: a prospective, multifactorial study to predict
and minimize death risk. Clin Orthop.1992;280:214-222.Google Scholar
Nettleman MD, Alsip J, Schrader M, Schulte M. Predictors of mortality after acute hip fracture. J Gen Intern Med.1996;11:765-767.Google Scholar
Magaziner J, Lydick E, Hawkes W.
et al. Excess mortality attributable to hip fracture in white women age 70
years and older. Am J Public Health.1997;87:1630-1636.Google Scholar
Jensen JS. Determining factors for the mortality following hip fractures. Injury.1984;15:411-414.Google Scholar
Kenzora JE, McCarthy RE, Lowell JD.
et al. Hip fracture mortality: relation to age, treatment, preoperative illness,
time of surgery, and complications. Clin Orthop.1984;186:45-46.Google Scholar
Marottoli RA, Berkman LF, Leo-Summers L, Cooney LM. Predictors of mortality and institutionalization after hip fracture:
the New Haven EPESE Cohort. Am J Public Health.1994;84:1807-1812.Google Scholar
Lu-Yao GL, Baron JA, Barrett JA, Fisher ES. Treatment and survival among elderly Americans with hip fractures. Am J Public Health.1994;84:1287-1291.Google Scholar
Todd CJ, Freeman CJ, Camilleri-Ferrante C.
et al. Differences in mortality after fracture of hip: the East Anglian audit. BMJ.1995;310:904-908.Google Scholar
Finson V, Borset M, Rossvoll I. Mobility, survival, and nursing-home requirements after hip fracture. Ann Chir Gynaecol.1995;84:291-294.Google Scholar
Turcotte R, Godin C, Duchesne R, Jodoin A. Hip fractures and Parkinson's disease: a clinical review of 94 fractures
treated surgically. Clin Orthop.1990;256:132-136.Google Scholar
Pitto RP. The mortality and social prognosis of hip fractures. Int Orthop.1994;18:109-113.Google Scholar
Kyo T, Takaoka K, Ono K. Femoral neck fracture: factors related to ambulation and prognosis. Clin Orthop.1993;292:215-222.Google Scholar
Dolk T. Influence of treatment factors on the outcome after hip fractures. Ups J Med Sci.1989;94:209-221.Google Scholar
Magaziner J, Simonsick E, Kashner TM, Hebel JR. Patient-proxy response comparability on measures of patient health
and functional status. J Clin Epidemiol.1988;41:1065-1074.Google Scholar
Kane RL, Chen Q, Finch M.
et al. Functional outcomes of posthospital care for stroke and hip fracture
patients under Medicare. J Am Geriatr Soc.1998;46:1525-1533.Google Scholar
Magaziner J, Bassett SS, Hebel JR, Gruber-Baldini A. Use of proxies to measure health and functional status in epidemiologic
studies of community-dwelling women aged 65 years and older. Am J Epidemiol.1996;143:283-292.Google Scholar
Magaziner J, Zimmerman SI, Gruber-Baldini A.
et al. Proxy reporting in five areas of functional status: comparison with
self-reports and observations of performance. Am J Epidemiol.1997;146:418-428.Google Scholar
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic
(ROC) curve. Radiology.1982;143:29-36.Google Scholar
Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons Inc; 1989.
Carson JL, Duff A, Berlin JA.
et al. Perioperative blood transfusion and postoperative mortality. JAMA.1998;279:199-205.Google Scholar
Holt EM, Evans RA, Hindley CJ, Metcalf JW. 1,000 femoral neck fractures: the effect of pre-injury mobility and
surgical experience on outcomes. Injury.1994;25:91-95.Google Scholar
Morrison SR, Chassin MR, Siu AL. The medical consultant's role in caring for patients with hip fracture. Ann Intern Med.1998;128:1010-1020.Google Scholar
Applegate WB, Miller ST, Graney MJ.
et al. A randomized controlled trial of a geriatric assessment unit in a community
rehabilitation hospital. N Engl J Med.1990;322:1572-1578.Google Scholar
Kennie DC, Reid J, Richardson IR.
et al. Effectiveness of geriatric rehabilitative care after fractures of the
proximal femur in elderly women: a randomised clinical trial. BMJ.1988;297:1083-1086.Google Scholar
Kane RL, Finch M, Blewett L.
et al. Use of post-hospital care by Medicare patients. J Am Geriatr Soc.1996;44:242-250.Google Scholar