Factors Associated with Heart Failure Readmissions from Skilled Nursing Facilities
Background: Despite guideline-driven pharmacological therapies and careful transitional care, the rates of preventable hospital re-admission
of heart failure patients and associated costs remain unacceptably high in the SNF populations. Transfer to SNF is one strategy
to limit hospitalizations. As such, 25% of patients are still symptomatic at time of discharge.
Purpose: The objective of this study is to identify patient factors affecting re-admissions of HF patients residing in SNF within 30-days.
Methods: A retrospective electronic chart review was completed on patients >65 years with HF who were admitted into large medical
center between 2012 and 2014. Descriptive statistics and univariate analyses using the chi-square test or Fisher's exact test for categorical
variables and the Mann-Whitney test for continuous data was used to compare patients readmitted within 30 days vs. those who were not readmitted within 30 days. Significant factors associated with readmission in the univariate analysis (p<0.10) were included
for a multivariate logistic regression model. A receiver operating characteristic (ROC) curve was constructed to look at the final model's
ability to predict the outcome. A numerical measure of the accuracy of the model was obtained from the area under the curve (AUC),
where an area of 1.0 signifies near perfect accuracy. The analysis of LOS was accomplished by applying standard methods of survival
analysis, i.e., computing the Kaplan-Meier product limit curves, where the data were stratified by readmission within 30 days (Yes vs.
No). No data were considered 'censored'. The groups were compared using the log-rank test. The median rates for each group were
obtained from the Kaplan-Meier/Product-Limit Estimates and their corresponding 95% confidence intervals were computed, using
Greenwood's formula to calculate the standard error. Unless otherwise specified, a result was considered statistically significant at the
p<0.05 level of significance.
Results: Fifteen variables: creatinine, weight difference, CKD, Angina, Arrhythmia, VHD, Tobacco, ADL, independent in bathing, independent
in the toilet, S3 Heart sounds present, HJR, AF, Nitrates, and Hydralazine, were identified for the multivariate logistic regression
as potential risk factors associated with "readmission within 30 days". Based on 23 readmissions within 30 days, our final model
included only 2 predictor variables. Creatinine and ADLs were included in the final model as this subset of predictors was found to be
the best for prediction of "readmission within 30 days". Creatinine (p<0.0087) and ADLs (p<0.0077) were both significantly associated
with readmission within 30 days in the final logistic regression model. Every 1-unit increase in creatinine is associated with an 87%
increase in the odds of being readmitted within 30 days (OR = 1.87). Those patients who require assistance with ADLs are over 9 times
more likely to be readmitted within 30 days (OR=9.25) as compared to patients who are independent.
Heart Failure, Re-admissions, Skilled Nursing Facilities (SNF), Nursing Homes (NH), Factors
Background and Significance:
Heart failure (HF) affects more than 5 million patients in the United
States and by 2030, >8 million people in the United States (1
in every 33) will have HF (Jencks, Williams, & Coleman, 2009).
Annually, more than 1 million patients are hospitalized with a primary
diagnosis of heart failure, accounting for a total Medicare
expenditure exceeding $17 billion with > 50% patients readmitted
to hospital within 6months of discharge. Annual expenditures for
both primary and secondary diagnosis of heart failure have been
estimated to be as high as $38 billion of which $23 billion is for
hospital stay. Between 2012 and 2030, total direct medical costs
of HF are projected to increase from $21 billion to $53 billion.
(Heidenreich et al., 2013). The rates of readmissions to the hospital
for HF are high and expected to increase with an estimated
yearly incidence of 550,000 readmissions and 658,000 annual ED encounters (Yancy et al., 2013).
The financial implications of readmission are significant. Advanced
heart failure is characterized by progressive clinical deterioration reflected in frequent hospital admissions. In order to
decrease costs, the National Quality Forum has endorsed hospital
risk-standardized readmission rates (RSRRs) as performance measures
(Yancy et al., 2013). The patient protection Affordable Care
Act 2010 created incentives to reduce readmission, with penalties
for hospitals including no reimbursement for services provided
and/or loss of revenue if the HF patient is readmitted within 30
days (Walsh et al., 2012). Hospitals with high readmission rates
can lose ~3% of their Medicare reimbursement by 2015 (Yancy et al., 2013).
In order to reduce readmissions, patients may be referred to skilled
nursing facilities (SNF) and Nursing Homes (NH) for recuperative
care and maximizing pharmacological therapies after hospitalization.
This has increased to serve as a potential strategy for reducing
readmissions (Desai & Stevenson, 2012). However, in spite of
the pharmacological and transitional care from hospital to SNFs,
the rates of preventable re-admission remain unacceptably high.
Investigators evaluated 557 heart failure patients that were readmitted
within six months after hospitalization and reported a 40%
readmission rate (Hamner & Ellison, 2005). Similar findings were
reported: readmission or death rates of 40% in 114 patients studied,
with these events occurring within six months of discharge
(Logeart et al., 2004).
Reducing HF hospital readmission rates from SNF and NH has
become a national priority because approximately 20% of these
patients are Medicare beneficiaries and are readmitted within 30
days (Walsh et al., 2012). The Readmissions are multifactorial
and problematic in this unique population because of high rate of
incidences, cost, and disruptions in care, disease progression, and
increased mortality. HF readmissions are a complex problem and
a single solution has not been sufficient in decreasing the readmission
risk. Identification of heart failure predictors in patients from
SNFs and NH could lead to an improved referral pattern and an
improved 30 day outcome (Ouslander et al., 2009; Unroe, Greiner,
Colon-Emeric, Peterson, & Curtis, 2012).
The objective of this study is to identify patient specific factors
affecting readmissions of HF patients from SNFs within 30-days.
Almost one quarter of older hospitalized adults with HF are discharged
to SNFs (Jung, Yeh, & Pressler, 2012). Nearly half of
the 164,672 patients with HF that were discharged to SNFs, were
readmitted to a hospital within 90 days after discharge, and 30%
died within 90days (Unroe et al., 2012). Heart Failure is a heterogeneous
condition, with many causes. Accurate classification of
patient factors that affect readmissions may have positive impact
on outcomes of patients that resides in SNFs.
There are many factors that may contribute to readmission of HF
patients. These factors include comorbidities such as diabetes and
decreased renal function at discharge. (Krumholz et al., 2000).
Other factors influencing readmission in HF patients include dyspnea,
increasing age, renal problems, fluid status/weight, anemia,
LOS, ADLs, BNP, hypotension, and comorbidities. The
proportion of patient factors responsible for avoidable readmissions
varied extensively between studies reviewed.
Numerous studies have identified dyspnea as the strongest predictor
of readmission (Allen et al., 2011; Altice & Madigan, 2012;
Anderson, 2014; Fonarow & Committee, 2003; Mentz et al., 2013;
Mentz et al., 2014; Mentz et al., 2015). Up to 77% of patients who
were admitted for acute episodes of heart failure initially presented
to the emergency department with dyspnea (Fonarow & Committee,
2003). A majority of patients who require re-admission for HF
have shortness of breath. Patients are dyspneic with exertion and
at rest. Fatigue is also present in this group of patients. However,
dyspnea is the primary factor for seeking care by patients and
for referral of patients to hospitals for admission by health care
providers (Fonarow & Committee, 2003). Ninety three percent of
the SNFs population studied reports dyspnea as breathing related
problems that predicts re-admission (Allen et al., 2011).
Timeliness in reporting of symptoms of dyspnea has been associated
with decreased risk of re-admission. Researchers found a
correlation between relieving of dyspnea and decreased risk of 30
day re-admission in 45% HF patients (Mentz et al., 2013) Breathing
related problems reported by 93% of HF patients led to them
seeking care early and thereby preventing re-admission (Altice &
Madigan, 2012). A delay in reporting symptoms and seeking care,
which can range from 6 hours to 3 days, appears to correlate with
deterioration and re-admission (Friedman & Quinn, 2008). Studies
of symptom monitoring and response training appeared to have
an early but not sustained benefit resulting in no difference in 90-
day event-free survival (Jurgens, Lee, Reitano, & Riegel, 2013).
Consistently, researchers report increasing age as one of the many
factors that accounts for avoidable re-admissions. This non-modifiable
risk factor is often associated with cognitive deficits. Older
age, disability status and increased LOS were associated with
re-admission (Muus et al., 2010). Social and environmental factors
may influence the severity and adaptability of aging process in older
HF patients. Advanced age has been implicated as predictor of
re-admissions (Boxer et al., 2012; Dolansky et al., 2010; Hammill
et al., 2011). In 161 elderly patients that had four or more HF admissions,
it was found that one of the predictors of readmission
was increasing age (Vinson, Rich, Sperry, Shah, & McNamara,
1990). Advanced age has been identified as a cause of frequent
re-admissions in the SNFs population. The re-admission rate was
increased from 23% to 27% within 30days (Allen et al., 2011). In
addition, advanced age was identified as a significant contributor
to re-admission among US veterans residing at SNFs (Muus et al.,
2010). Likewise, there were high correlation between increasing
age and readmission within 30 days and 90days (Schrager et al., 2013).
Worsening renal function which is shown as an increase in BUN/
Creatinine is a strong predictor of re-admission (Allen et al.,
2011; Allen, Smoyer Tomic, Smith, Wilson, & Agodoa, 2012; Fonorow,
2005; Hutt, Frederickson, Ecord, & Kramer, 2003; Lazzarini,
Mentz, Fiuzat, Metra, & O'Connor, 2013; Mentz et al., 2013;
Tamhane, Voytas, Aboufakher, & Maddens, 2008; Wang, Lin, Lee,
& Wu, 2011; Y. Wang et al., 2011) Improved assessment of BUN/
Cr, BP, and heart rate monitoring may lower hospital re-admission
rates. However, the management of HF may precipitate decreased
renal function because of the use of ACE inhibitors and diuretics
that are often prescribed for HF patients. Guideline-driven therapies
include ACEi optimization and neuro-hormonal antagonists,
which may further worsen the BUN/Cr and lower the blood pressure. This can lead to hypotension and worsening of the renal
function, and diuretics that are often administered for symptoms
relief of dyspnea may leads to hyponatremia (Desai & Stevenson,2012).
Fluid volume status or weight gain is usually associated with and
serves as hallmark of congestion during acute HF exacerbation.
Treatment guidelines include the recommendation to optimize
euvolemic status. Congestion encompasses indicators of volume
overload: orthopnea, jugular venous distention, congested on chest
X-ray, a gain of greater than or equal to two pounds in the previous
week, edema, and the need to increase diuretic dosing at a visit.
Patients that were not congested had an 87% survival, compared
41% of patients with major congestion. As such, this leads to higher
re-admission rate (Lucas et al., 2000).
On the contrary, in a retrospective analysis of the randomized
clinical trial -Diuretic Optimization Strategy Evaluation in Acute
Heart Failure (DOSE-AHF) studied markers of decongestion at 72
hours: weight loss and net fluid loss level and found no correlation
between either weight gain or re-admission. Thus, fluid retention
has not been reliable as a sole entity in detecting early decompensation.
Increased appetite may result in weight gain and can mimic
fluid retention (Kociol, Liang, et al., 2013).
Anemia can be prevalent in the SNFs population due to dental
problems, decreased appetite and poor nutrition. Anemia makes
HF worse and has been shown to impact readmission rates. Readmission
of patients with heart failure and anemia as secondary
diagnoses, were significant (p<0.001) in SNFs population. Patients
with heart failure and anemia had increased LOS (7.3 versus 5.1
days) and unplanned readmission (p<0.001). In a retrospective
study of 127 elderly SNF population with HF, anemia (71%) led to
higher readmission (Tamhane et al., 2008). This suggests reduced
hemoglobin may merely be a marker for the epiphenomena of
advanced heart failure. Furthermore, anemia was an independent
predictor of re-admission and mortality in heart failure patients
with reduced left ventricular dysfunction (Al-Ahmad et al., 2001).
Length of stay (LOS) and number of ED visits have been shown
to provide additional information regarding re-admission. A significant
proportion of patients are discharged to SNFs (Hutt, Elder,
Fish, & Min, 2011). In a retrospective study of more than 10,000
patients admitted with HF, 30% were discharged to SNFs (Hutt et
al., 2011). This may be in an attempt to decrease length of stay in
the hospital. In a simulated study of the relationship between LOS
and readmission within 7 days and 30 days, it was reported that
if there was a 1 day increase in LOS, reductions in readmission
rates could be estimated in the 1% to 8% range for HF patients
(Carey, 2014). Therefore increasing LOS for some patients may be
a means of decreasing readmission and improving quality of care.
However, other investigators found no associations between LOS
and readmission (Kaboli et al., 2012). Patients and hospitals with
longer length of stay showed reduced readmission rate in HF patients
(Eapen et al., 2013). Alternatively, individuals hospitalized
for heart failure, had no increase in 30-day re-hospitalization when
LOS was decreased by a day (Unruh, Trivedi, Grabowski, & Mor,2013).
Functional status/ADL. Frailty, mobility, disability, and impaired
ADLs status are associated with readmissions. According to Heart
Failure Society of America (2013), functional capacity is defined
in terms of ambulation. Patients who present with exacerbation of
heart failure are typically dyspneic and fatigued on presentation.
Consequently, patients will often limit their physical activity in
order to compensate for their worsening heart failure symptoms.
Anderson (2014) found that individuals with HF who require assistance
with ADLs were significantly more likely to be readmitted
for heart failure within 60days (Anderson, 2014).
Elevated Serum biomarkers, such as Brain Natriuretic peptide
(BNP) has been associated with increasing rate of readmissions
(Mentz et al., 2013). Reduction in NT-proBNP was significantly
associated with symptom relief (r=0.13, P = 0.04) in a retrospective
analysis of 308 elderly HF subjects, suggesting that positive
relationship exist between commonly used markers of decongestion
and patient reported symptom relief and less chance of readmission.
(Kociol, McNulty, et al., 2013). Similarly, early dyspnea
relief in 2984 patients studied was associated with lower BNP value
with resultant reduced readmissions (Mentz et al., 2013).
Hypotension is associated with readmission and patients that are
hypotensive, had previous SNF's stay, or decompensated during
the night, and tend to have higher re-admissions (Hutt et al., 2003).
Systolic BP of less than 115 was associated with 15% risk of readmission
and death (Fonorow, 2005). The hypotensive episodes
in the SNFs population may be attributable to administration of
Guideline Directed Medical Therapy, such as the ACEi and ARBs
that often prescribed lowers the blood pressure. Fifty three percent
cases of HF following MI, and HF following uncontrolled hypertension
were deemed avoidable (Vinson et al., 1990) This findings
led to the move toward further research into identification of high
risk patients and avoidable readmission.
Comorbidites increase the risk of re-admissions in older HF patients.
Associated diagnosis of anemia, hypertension, and diabetes
confer higher risk and are likely indicator of disease progression
and are predictors of re-admissions and mortality (Allen et al.,
2011; Chen et al., 2012; Hutt et al., 2003; Ouslander et al., 2009;
Tamhane et al., 2008). Comorbidity has been shown as a predictor
of unplanned readmission (Hallerbach et al., 2008). In addition,
comorbidities were associated with increasing re-admission in
>80year old within 30 days with comorbidities odds ratio = 2.6;
95% CI, 1.5-4.7 (Marcantonio et al., 1999). Comorbidities were
found to be frequently associated with 7 & 30 day readmission of
SNF patients (Ouslander & Berenson, 2011). Older HF patients
from SNFs residents who had acute episode leading to readmission
had higher level of comorbidities with acute HF (Hutt et al., 2003).
This study identified patient factors affecting re-admission of
elderly patients with Heart failure within 30 days who were discharged
to SNFs. The identified factors may predict patient related
mediators of early re-admissions and may assist with the development
of interventions that may reduce 30 days re-admissions.
A retrospective chart review was conducted to examine patterns
and factors affecting readmission of SNF patients with HF. Eligible
patient records were identified from a tertiary institution's
Electronic Medical Record as unplanned or emergent, general or direct re-admissions as from 2012 to 2014 until the 128 subjects obtained.
Medical records of 128 consecutive elderly patients, admitted to
an 800-bed tertiary institution were reviewed. Inclusion criteria included
the following: Patients with a heart failure diagnosis (DRG
428) and subsequently discharged to NH or SNF. All patients had
to be insured by Medicare or Medicaid to increase generalizability.
SNF patients admitted through the ED, admitted electively or from
the outpatient settings were included. SNF patients transferred
from other hospitals were excluded from the chart review.
Medical records of eligible patients were reviewed to confirm diagnosis
of HF using Framingham criteria and DRG coding. Data
collection was performed using a standardized data extraction tool.
Follow-up data includes information on subsequent hospital readmission,
survival status, heart transplantation, and visits to the
emergency department using the electronic medical record. All patients'
data were reviewed for 180-days following discharge and
to account for 30-day readmissions and readmission patterns over
Sample size including power analysis:
Approximately 128 patients that met the inclusion criteria as described
above were included in the study. A power analysis indicated
that a sample size of 128 patients was sufficient (power .80)
with an alpha of .05 to detect statistically significant results. Oversampling
of patients was done to allow for exploratory statistical
analysis. One hundred and nine (n-109) patients with 15% (n=19)
oversampling included for a total N=128. Multiple regression
analysis included eight independent variables of interest identified
from the theoretical framework and previous studies.
Operational definition of Independent variables and dependent variables
Patient associated factors such as dyspnea, increasing age, renal
problems, fluid status/weight, anemia, LOS, ADLs, BNP, hypotension,
and comorbidities age were identified as predictors and
were continuous variables. The dependent variables were clinical
events, such as re-admission within 30 days or ED admissions.
Data collection procedures:
Eligible patients were identified using the inclusion criteria as
described above. Data collection tool was used to obtain relevant
information. Data was collected on Heart Failure patients, 65
years or older, that were discharged from a tertiary institution in
the Northeastern US to Nursing Homes and Skilled Nursing facilities
from 2012 to 2014 until the target subject number of 128 was
reached. Data about patient's health from the electronic medical record were obtained.
• Demographic data: age, sex, race etc. will be obtained
• Symptoms: Dyspnea, wt/fluid status, swelling/edema, fatigue,
• Diagnosis: Systolic or diastolic Heart failure, NYHA classification,
• Vital signs: Blood pressure, HR, height, weight
• Medications: ACEi, BB, Mineralocorticoids
• Blood tests results: BUN/Cr, BNP, sodium, hematocrit, HBa1c
• Number of re-admissions
• Length of Stay
Baseline patient characteristics were expressed as mean, standard
deviation (SD) for continuous variables and as proportions for
categorical variables. Subgroup comparisons were done with nonpaired
t-test for continuous variables or chi-square test for categorical
variables. A survival curve was constructed according to the
method of Kaplan and Meir 13. The effect of relevant covariates
on cause specific readmissions was evaluated by Cox proportional
hazardous regression models. Quantitative Data Analysis was
performed and Chi-square used. Multivariable logistic regression
model was used to assess which of the patient specific variables
were independently associated with 30-day re-admission by adjusting
for variables known to impact re-admission. Statistical
Significance (SD) was calculated using Statistical Package for Social
Sciences (SPSS). Alpha level of .05 was considered statistically
significant for all analyses. The critical value of 3.841 or greater
was considered statistically significant.
The data collected and analyzed in this study included socio-demographic
information, medical conditions, symptoms, guideline
directed medical therapy (GDMT), functional capacity, and significant
test and clinical findings upon assessment. The sample size
of participants in the study was one twenty eight, (n=128) elderly
subjects with heart failure (HF) readmission from SNF. The dependent
variable was hospital readmission. All patients studied
were patients discharged to SNFs, of age 65 years or older, with
primary or secondary diagnosis of CHF, and experiencing multiple
chronic conditions. Hospital readmission was defined as any
readmission within 30 days after hospital discharge to SNF's. The
independent variables were patient factors such as renal functional
status, anemia, functional status, medical conditions, to mention a
few. The categorical data was defined as yes and no. The guideline
directed medications were measured by yes=taking the medication
and no=not taking the medication. Age was defined as the elderly
person's chronological years of life.
Descriptive statistics (frequencies and percentages for categorical
data; mean ± standard deviation and median for continuous data)
and univariate analyses using the chi-square test or Fisher's exact
test, as deemed appropriate, for categorical variables and the
Mann-Whitney test for continuous data was used to compare patients
readmitted within 30 days vs. those who were not readmitted
within 30 days. Those factors that appeared to be associated with
readmission in the univariate analysis (p<0.10) were included in
the selection process for a multivariate logistic regression model.
Best subsets selection was used as a screening method to identify
the best set of predictor variables for the logistic regression model.
A receiver operating characteristic (ROC) curve was constructed
to look at the model's ability to predict the outcome of HF readmission.
A numerical measure of the accuracy of the model was
obtained from the area under the curve (AUC), where an area of
1.0 signifies near perfect accuracy, while an area of less than 0.5
indicates that the model is worse than just flipping a coin. The
following was used as a guide for AUC: 0.9-1.0 Excellent, 0.8-0.9
Very good, 0.7-0.8 Good, 0.6-0.7 Average, 0.5-0.6 Poor.
The analysis of LOS was accomplished by applying standard
methods of survival analysis, i.e., computing the Kaplan-Meier
product limit curves, where the data were stratified by readmission
within 30 days (Yes vs. No). No data were considered 'censored'.
The groups were compared using the log-rank test. The median
rates for each group were obtained from the Kaplan-Meier/Product-Limit
Estimates and their corresponding 95% confidence intervals
were computed, using Greenwood's formula to calculate the
standard error. Unless otherwise specified, a result was considered
statistically significant at the p<0.05 level of significance.
Sample characteristics/Results (See table 1):
The datasets included patients with HF with mean age 83.46 years.
Twenty three (18%) of the patients had at least one re-admission.
The baseline social-demographic distribution N (%) showed the
patients were distributed as 60 (46.9.9%) males and 68 (53.1%)
females with the majority (56 (43.8%)) in the age category greater
than 85 years (Table 1). Given the location of the study hospital,
122 patients (95.3%) were Caucasian, 2 (1.6%) were black/African
American, and 2 (1.6%) were Asian. Fifty two (40.6%) of the
patientswere widowed and 50 (39.1%) were married. Subjects that
were studied had a mean heart rate of 79.18, mean systolic blood
pressure of 131.5 mmHg and mean diastolic blood pressure of 68.6
mmHg. The mean brain natriuretic protein (BNP) was 1174.89,
mean BUN was 40.12 with mean creatinine of 1.60. The average
hematocrit was 33.99, Left ventricular ejection fraction (LVEF)
was 47.22, and differences in weight measurements was 9.91. The
mean length of days (LOS) for the first admission was 9.22 days
while the LOS for the readmission was 10.91days.
The associated medical conditions and co-morbidities showed
70(54.7%) patients had history of renal disease and 89 (69.5%)
patients had history of hypertension. Most of the patients had no
history of CAD 67(52.3%), no history of angina 120 (93.8%) and
no history of MI 108 (84.4%). In spite of patients residing at SNF,
only 4 (3.1%) had additional meds administered prior to admission
to relieve symptoms.
The subjects experienced high level of functional limitations.
About 107 (83.6%) required assistance with toileting, 123 (96.1%)
required assistance with bathing and 121 (94.5%) required assistance
with ADLs. Despite the high level of limitations, only 38
(29.7%) used assistive devices.
Fifteen candidate variables, creatinine, weight difference, CKD,
Angina, Arrhythmia, VHD, Tobacco, ADL, independent in bathing,
independent in the toilet, S3 Heart sounds present, HJR, AF,
Nitrates, and Hydralazine, were included in the selection process
for the multivariate logistic regression as they were potential risk
factors associated with "readmission within 30 days". Since there
were only 23 readmissions within 30 days, based on the rule-ofthumb
that for every one predictor variable included in a multivariate
model there needs to be 10 "events", our final model included
only 2 predictor variables. Creatinine and ADLs were included in
the final model as this subset of predictors was found to be the best
for prediction of "readmission within 30 days".
Creatinine (p<0.0087) and ADLs (p<0.0077) were both significantly
associated with readmission within 30 days in the final logistic
regression model. Every 1-unit increase in creatinine was
associated with an 87% increase in the odds of being readmitted
within 30 days (OR = 1.87). Those patients who required assistance
with ADLs were over 9 times more likely to be readmitted
within 30 days (OR=9.25) as compared to patients who are independent.
Receiver Operating Characteristic (ROC) curve:
A receiver operating characteristic (ROC) curve was constructed
to look at the final model's ability to predict the outcome. A numerical
measure of the accuracy of the model was obtained from
the area under the curve (AUC), where an area of 1.0 signifies near
There were fifteen variables that were significant to readmissions
within 30days in the SNFs population studied. However, there
were only 23 readmissions within 30 days, thus, our final model
included only 2 predictor variables. Creatinine and ADLs were
included in the final model as this subset of predictors was found to
be the best for prediction of "readmission within 30 days".
Increased creatinine revealed that many of the patients who were
readmitted into the hospital had decreased renal functioning. Although
this has been shown as a predictor of readmissions and
GDMT has been shown to improve morbidity, a significant proportion
of the studied population were not on therapy. About 72%
of the populations studied were not on Angiotensin Converting
enzyme inhibitor (ACEi), 86% were not on angiotensin receptor
blocker (ARBs), 30% were not on betablocker, and 88% were not
on mineralocorticoids. These are medications that can help improve
HF condition and has been shown to decrease mortality and
reduce re-admissions yet most of the patients were not placed on
However, 60 percent of the patients studied were receiving symptom
relief using diuretics. Diuretics have not been shown to reduce
readmissions, decrease mortality and are not evidence based
because research did not show that diuretics reduce morbidity,
mortality, and they do not change the outcome (Yancy et al.,
2013). The lack of use of the evidence based medications such as
betablockers and Angiotensin Receptor Inhibitors (ACEi) in the
population studied might be a contributing factor to re-admission.
The evidence based therapies were not prescribed for 60% of the
Findings of decreased renal function are significant in the SNFs
population. Worsening renal function was reported as the strongest
indicator for readmissions in the SNFs population. (Allen et al.,
2011; Allen et al., 2012; Fonorow, 2005; Hutt et al., 2003; Lazzarini
et al., 2013; Mentz et al., 2013; Tamhane et al., 2008; S. P. Wang
et al., 2011; Y. Wang et al., 2011) As such, assessment of intolerance
of neurohormonal antagonists and its antecedent hypotension
with worsening renal function is of utmost importance. Patients
with chronic kidney disease represented a unique sub-population
in this cohort. They constitute challenges to maintenance of fluid
volume status since the management goal is focused on preservation
of the remaining renal function.
The second finding, ambulatory status, showed that patients with a
decreased functional status were more likely to be readmitted. The
study findings suggest that patients requiring assistance with ADLs
were more likely to be re-admitted. The older the HF patients, the
more likely the need for assistance, and the higher re-admission
rate. Patients with limitations in their day to day functioning as
evidenced by impairment in their ability to perform activities of
daily living are more likely to be re-admitted. This finding supports
the recent study that shows patients with HF who require
assistance with ADLs were significantly more likely to be readmitted
for heart failure within 60days (Anderson, 2014). The SNFs
may need to invest more in the improvement and maintenance of functionality of the patients.
The heterogeneity of the patient factors examined may be attributable
to demographic changes. However, subtle changes in status
need to be recognized in view of the multiple co-morbidities in the
elderly population with HF. Investigators may find better discrimination
for predicting re-admission in patients that are discharged
after heart failure hospitalizations by including cognitive impairment
and care-seeking behaviors. It may be interesting to find out
if certain predictors are paramount to gender or race. There is
clearly a need for better discrimination of which predictors of readmission
is of significance in certain cohorts of HF patients.
There is no consensus as to what the predictors of HF are at the
present time. Complex interaction of many patient factors may
lead to readmission. Further investigation and refinement of the
patient factors affecting readmissions may lead to consensus.
Study population was limited to retrospective study using electronic
medical record (EMR) from a large sub urban teaching hospital.
This may limit the generalizability of the results. The study sample
was small, it was racially and ethnically limited to primarily
caucasian and representative of the population in the community.
The distribution of participants on some variables was uneven and
would have led to biased findings. From a statistical perspective,
and in order to build a more complex multivariate model, a larger
sample size would be necessary. As a result, future studies should
be conducted with larger and more diverse samples to prevent biased
EMR may exclude patient features that were unmeasurable that
could affect the results of the research findings. It is worth noting
that the lack of significant findings on blood pressure and lipid
control as factors affecting CHF patient's readmission may be related
to the EMR. If EMRs are to fulfill their promise as an effective
tool, it should be possible to measure factors differently when
distributed across all groups studied.
Collection of data from an EMR for research purpose is not novel.
However, for the most part, it is using EMR to answer broader
questions of science. The quality of the data collected from the
EMR may vary due to the knowledge and expertise of the staff. I
was unable to capture everything when compared with experimental
In spite of the limitations in this study, the results demonstrated
methodological strengths as several confounding key variables were explored.
Implication and Conclusion:
The study findings provide insight into the patient factors that affects
readmission rates in the population of older adult from SNFs
in a sub-urban region in the North Eastern part of USA. Findings
from this study adds stregnth to previous findings that poor renal
function and ADLwere among several factors that affect readmission
rates. Since the study finding suggest that worsening renal
function contributes to readmission, close monitoring of renal
function at discharge and post-discharge period may be useful in
reducing risk of hospital readmission. Health care professionals
should emphasize close monitoring of renal function. Effects of
limitations in the performance of ADL on readmissions should be
studied to find out if improvement in functional abilities of HF
patients in SNF may reduce readmissions. Future study with larger
samples is indicated to verify the current findings for generalization.
Table 1: Baseline Distribution of the social-demographic information
of the Study Population.
Table 2: Activity of Daily Living (ADL)
Table 3: Guideline Directed Medical Therapy (GDMT)
Table 4: Findings of subjective and objective assessment
Table 5: Guideline Directed Medical Therapy (GDMT) and device
Figure 1: Renal disease
Figure 2: ADLs
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