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P OSTER
A BSTRACTS
C98
Effect of level of education on regular physical activity in an elderly
population within a Military Health System.
H. Kumar, T. Combest, S. George. Internal Medicine, Walter Reed
National Military Medical Center, Bethesda, MD.
Supported By: No funding to disclose
Background:Previous research has shown that those with higher
levels of education are also more physically active.The effect of level
of education and participation in regular physical activity is currently
unknown among patients aged 60 and older in a military health system.We
conducted a descriptive study utilizing survey data from participants
of a fall prevention program to determine the effect of level
of education on physical activity.
Methods:The study involved surveys of 135 community dwelling
older adults aged 60 and older who were falls clinic participants. The
surveys were conducted over three years.Physical activity and exercise(Regular
physical activity, consisting of 3 days a week for 30 minutes
and at least 2 days with muscular conditioning) was self reported
in the initial screening of all participants in the falls clinic.Demographic
information was analyzed using means and simple frequencies.
Univariate analysis was performed using chi square and the
Fisher Exact test where appropriate, with the primary outcome being
reported exercise.Groups were stratified according to level of education
(high school or less, college education, and graduate level education).Results:135
surveys were collected over the three year study period.
There were almost equal proportions of males (43%) and
females (57%). The average age was 80 years. About one third of the
participants (33%, n=46) reported that were exercising. Of those surveyed,
47% (n=63) reported a high school education, 39% (n=53) a
college education, and 14% (n=19) a masters degree or above.
Among those that reported a high school education, 25% (n=16)
were exercising; those with a college education, 45% (n=24) were exercising;
and those with a masters or higher, 37% (n=7) were exercising.
Univariate analysis did not show a significant difference among
those that reported exercising based on gender (p=0.92). This held
true when analyzed according to exercise and gender among those
with a high school education (p=1.0), college education (p=1.0), and
masters education (p=0.47). Univariate analysis was significant
among groups stratified by education (p=0.03).
Conclusion: Level of Physical activity was positively associated
with the level of education amongst those over sixty in this military
medical system. Lowest level of physical activity was associated with
the high school and lower level education from the three groups.
C99
Can a real-time checklist, automatically generated by the electronic
medical record, predict 30-day readmissions in hospitalized elderly?
A. Khan, 1,2 K. Padua, 1 M. Malone, 1,2 M. Volbrecht, 1 P. Pagel. 1 1.
Aurora Health Care, Milwuakee, WI; 2. University Of Wisconsin
School of Medicine and Public Health, Madison, WI.
Background
Approximately one-fifth of Medicare beneficiaries are readmitted
within 30 days. A software called “ACE Tracker” enables the
health care team to quickly view risk factors that may be correlated
with readmission at the bedside and identify vulnerable seniors in the
hospital.
Research Question
Can a real-time “ACE Tracker” tool embedded in the electronic
medical record (EMR) predict 30-day readmissions?
Description of ACE Tracker and readmission risk tool:
The Acute Care for Elders (ACE) Tracker is a real-time report
that captures relevant data from the electronic medical record of
older patients including: readmission risk score, number of medications,
Morse fall score, urinary catheter usage, functional status,
Braden and pain score.
The readmission risk score is generated from the electronic
medical record ranging from 0-20 based on presence of following risk
factors.
1) Admitting diagnoses: congestive heart failure (CHF), psychosis,
other vascular surgeries, chronic obstructive pulmonary disease
(COPD), pneumonia, gastrointestinal problems 2) Chronic diseases:
CHF, COPD, diabetes mellitus, shortness of breath, skin ulcers,
cirrhosis, leukemia, peripheral vascular disease, stroke, metastatic
cancer, malnutrition, acute respiratory failure, rheumatoid arthritis,
hypertension. 3) Demographics: hospital admission in prior 6 months,
length of stay. 4) Social factors: functional status, medicaid, living situation
and educational barriers.
Validation of readmission risk tool
The readmit risk score was determined for 227 patients at four
hospital on one day and those patients were followed for thirty days
afterwards.
Forty one percent had a value score of 7 or more. Using a cutoff
value of 7, sensitivity was 61%, specificity= 22%, positive predictive
value=12%, negative predictive value= 77%. The positive and negative
likelihood ratios were 0.8 and 1.8.
Univariate and multivariate analyses were performed on variable
predictors available on “ACE Tracker”. The risk of readmission
was correlated with number of medications (p=0.03).
Conclusion
The number of medications variable on “ACE Tracker” correlates
with readmission. The readmission risk score is better in identifying
those who are not at risk for readmission.
C100
Evaluation of a computerized physician order entry (CPOE) alert
program on prescribing of selected drugs among the elderly.
L. E. Rios Rojas, 1,2 I. H. Gomolin, 1,2 P. Lester. 1. Geriatric Medicine,
Winthrop University Hospital, Mineola, NY; 2. Medicine, State
University of New York, Stony Brook, NY.
Background:
Emphasis is placed on reducing adverse drug reactions
(ADR)among elderly. Knowledge of predisposition to ADRs or poor
efficacy may improve prescribing. Diphenhydramine is commonly
prescribed to decrease transfusion reactions or as a hypnotic. Studies
show no efficacy for these indications and use predisposes to ADRs 1 .
Antipsychotics are commonly prescribed in the hospital with potential
for continued treatment despite increased risk of death associated
with their use.
Methods:
Winthrop Hospital is a community teaching institution. CPOE
became operational by 2009. In January 2011, a series of alerts were
built into CPOE for selected medications and include links to relevant
papers. We evaluated the effect of alerts on prescribing frequency
by comparing the number of patients for whom these medications
were prescribed during second quarters of 2009 and 2010 to
the second quarter of 2011. Alerts were created for diphenhydramine,
metoclopramide and all antipsychotics. Prescribing patterns
were evaluated by ascertaining the pharmacy database which contained
all medication orders since the introduction of CPOE. Frequency
was adjusted for total admissions among those over 65 years
during each quarter of interest. Chi square was used to compare pre
and post frequencies.
Results:
Diphenhydramine prescriptions were reduced when comparing
2011 vs. 2010 (38% reduction, RR 0.63, 95% CI = 0.58 to 0.67) p