Article Info :
By: Jacques Donzé, MD,
References:
Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially
avoidable 30-day hospital readmissions in medical patients: derivation and
validation of a prediction model. JAMA Intern Med.
Predicting
Avoidable 30-Day Readmissions
For medical patients, a simple prediction model
appears to identify the risk of potentially avoidable early readmissions before
discharge. The model can help clinicians seek out patients who may need more
intensive transitional care interventions.
Throughout the United States, readmission rates are
increasingly being used for benchmarking across hospitals. Some hospital
readmissions may be avoidable, which in turn has led to the levying of
financial penalties on hospitals with high risk-adjusted rates. Recent studies
have estimated that the 30-day readmission rate for Medicare beneficiaries is
almost 20%, and these occurrences cost the U.S. healthcare system as much as
$17 billion annually.
In June 2009, CMS began publishing 30-day
readmission data for select medical diseases, resulting in hospital
readmissions becoming an important metric for measuring the quality of patient
care. The changing regulations issued by CMS means that hospital reimbursements
can be reduced based on an adjustment factor determined by a hospital’s
expected and observed 30-day readmission rates. These changes have also raised
the bar for decreasing unnecessary surgical readmissions. In addition to the
financial implications, unplanned hospital readmissions further limit hospital
resources. For each patient readmitted, there is an opportunity lost to treat
another patient who needs care.
New Prediction Model for 30-Day Readmission In JAMA
Internal Medicine, Dr. Donzé and colleagues had a study published that derived
and validated a prediction model for potentially avoidable 30-day hospital
readmissions in medical patients. The model used administrative and clinical
data that was readily available prior to discharge. “Our purpose was to help
clinicians target transitional care interventions most efficiently,” Dr. Donzé
says. “The goal was to develop a score to predict potentially avoidable
readmissions. In other words, we wanted to predict which patients may be most
likely to benefit from intensive interventions.” The HOSPITAL score is able to
indicate readmission risk before a patient is discharged. This allows
clinicians to target a timely transitional care intervention. In their
retrospective analysis, Dr. Donzé and colleagues analyzed potentially avoidable
30-day readmissions at three hospitals using a validated computerized
algorithm. Several factors were identified as being independently associated
with the risk of potentially avoidable readmission.
These factors were then used to build a prediction
score—referred to as the HOSPITAL score—for early readmission: hemoglobin at
discharge; discharge from an oncology service; sodium level at discharge,
procedure during the index admission; index type of admission; number of
admissions during the last 12 months; and length of stay (Table 1). “The
strength of the HOSPITAL score is its simplicity,” says Dr. Donzé.
“Physicians
can easily review these seven variables at a patient’s bedside prior to
discharge. The more of these risk factors a patient has, the greater the risk
of readmission. If a patient is deemed high risk for readmission, a return trip
to the hospital could be prevented by offering additional interventions, such
as a home visit from nurses, patient coaching, or a pharmacist consultation.”
Validating Results on the HOSPITAL Score Using the HOSPITAL score, Dr. Donzé
and colleagues stratified the risk of potentially avoidable readmission into
three categories: low, intermediate, and high. Low-risk patients, who scored
between 0 and 4 points, had a 5.2% estimated risk of potentially avoidable
readmission.
The observed proportion in the derivation set was
5.4%. High-risk patients, who scored 7 or higher on the HOSPITAL score, had an
estimated probability of potentially avoidable readmission of 18.3% and an
observed probability of 18.7% (Table 2). When compared with other prediction
models, Dr. Donzé says the HOSPITAL score had fair discriminatory power and
good calibration in identifying the risk of potentially avoidable readmission.
“The HOSPITAL score was developed to identify
readmissions that might be prevented and are therefore potentially actionable,”
he says. “It can be used for all medical patients regardless of their primary
cause of admission. Perhaps most important is the fact that the HOSPITAL score
is able to indicate readmission risk before a patient is discharged. This
allows clinicians to target a timely transitional care intervention.” The
HOSPITAL score can be a valuable tool in the national effort to reduce
healthcare costs and improve the quality of care, according to Dr. Donzé.
“Identifying patients who at least have the potential to benefit from more
intensive transitional interventions is an important first step in reducing
hospital readmissions. All patients should receive high-quality transitional
care that meets certain standards. We’re not recommending that low- and
average-risk patients be deprived of effective transitional interventions. That
said, certain interventions that have been shown to be successful are resource
intensive. One way to make the best use of limited resources is to reserve them
for those who are most likely to benefit from them.” Validating Readmissions
Prediction Model Although the HOSPITAL model is promising, no prediction model
will be a perfect indicator of preventable readmissions. Because the model was
created and validated at one hospital, a multicenter international validation
of the model is currently underway. “Predicting potentially avoidable
readmissions is only a proxy for identifying who might benefit from specific
interventions,” says Dr. Donzé. “Studies of interventions that target this
patient group are needed to definitively prove the usefulness of the HOSPITAL
score.
Patients at Highest Risks for Readmission Based on
analysis of ICD-9 coding data, Dr. Sweeney and colleagues found that
gastrointestinal complications carried a high risk of readmission (27.6%),
while surgical infections reached 22.1%. These top two reasons accounted for
nearly half of all readmissions, according to study findings. Complex
gastrointestinal procedures, such as pancreatectomy, colectomy, and liver
resection, likely had higher complication rates because of the complexity of
these surgeries. The top surgical complications identified in the study were
wound infections, pulmonary complications, and urinary tract infections (UTIs).
Patients with postoperative sepsis or UTIs were about five times more likely to
be readmitted than patients without these complications. Postoperative wound
infections and postoperative pulmonary complications were associated with a
3.5-fold increase in readmission rates. Additionally, several comorbidities
substantially affected readmission risk, most notably cancer, open wounds, and
dyspnea. Patients who were immunosuppressed, poor wound healers, and with
baseline pulmonary disease were also vulnerable to complications that raise the
likelihood of postoperative readmission.
