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ORIGINAL RESEARCH
Risk Factors for All-Cause Hospital Readmission
Within 30 Days of Hospital Discharge
Robin L. Kruse, PhD, Harlen D. Hays, MPH, Richard W. Madsen, PhD, Matthew F. Emons, MD, MBA,
Douglas S. Wakefield, PhD, and David R. Mehr, MD, MS
ABSTRACT
• Objective: To develop a predictive model of 30-day
readmission using clinical and administrative data.
• Design: Retrospective cohort study. After dividing
data into developmental and validation sets, multivariable logistic regression was performed.
• Participants: Adults with data in Health Facts, a database composed of participating hospitals’ electronic
medical records. The index hospitalization was a
patient’s first qualifying hospital admission between
1 October 2008 and 31 August 2010. We excluded
observation stays, admissions with length of stay of 0
days, obstetric stays, and patients whose predominant
care setting was a psychiatric or rehabilitation unit.
• Measurements: Readmission within 30 days of live
discharge from the index hospitalization.
• Results: There were 463,351 index admissions to 91
hospitals, with 45,098 (9.7%) patients readmitted. In
multivariable modeling, factors associated with readmission included prior hospital admission, low hemoglobin, longer stays, and increasing Charlson index;
arthroplasty procedures were associated with lower
risk of readmission. Model discrimination was modest
in developmental data (c-statistic = 0.668) and slightly
lower (0.657) in validation data.
• Conclusions: Increased comorbidity and prior hospital
exposure are associated with unplanned readmission.
Despite the availability of many potentially relevant
clinical variables, model performance was modest
and few clinical variables were associated with readmission in a multivariable model. Focusing on specific
conditions with a narrower set of relevant variables
may facilitate identifying patients at particularly high
risk for readmission.
H
ospital readmission has gained increased attention both as a potential reflection of poor
health care quality and as a cost driver. The
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Medicare Payment Advisory Committee estimated that
readmissions resulted in $15 billion in Medicare expenditures annually [1]. The Patient Protection and Affordable
Care Act (ACA) includes payment reductions for hospitals with high readmission rates to help control Medicare
expenditures [2]. To improve care and avoid financial
penalties, hospitals and clinicians need to optimize discharge planning and care coordination. This is particularly salient as health care networks position themselves as
accountable care organizations [3]. Better understanding
risk factors for readmission can help achieve these aims.
Estimates of readmission rates vary widely, particularly
by diagnosis and patient age. Among Medicare enrollees,
30-day readmission often exceeds 20%. For example,
25.7% of Medicare fee-for-service enrollees with heart
failure were readmitted within 30 days [4] compared
with 19.6% of patients in a general Medicare fee-forservice sample [5]. Readmission rates for Medicare
fee-for-service patients with pneumonia, heart failure,
or myocardial infarction all exceeded 21%, with AfricanAmerican patients and patients in minority-serving hospitals readmitted more often [6]. Other populations also
experience high readmission. In a general population of
heart failure patients discharged from a single, urban
hospital, 24.2% were readmitted within 30 days [7].
Readmission following radical cystectomy for bladder
carcinoma was 19.7% in the first 30 days [8].
Given the magnitude and variation in readmission
rates, substantial effort has been directed at determining factors that are associated with readmission. A recent
review of 26 models [9] reported that most performed
poorly. Nine US studies of large population-based data-
From the University of Missouri School of Medicine,
Columbia, MO (Drs. Kruse, Madsen, Wakefield, and Mehr)
and Cerner Corporation, Kansas City, MO (Mr. Hays and
Dr. Emons).
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bases or multicenter studies, mostly involving Medicare
data or older patients, had c-statistics of 0.55 to 0.65
(the c-statistic varies from 0.5 to 1.0, where 1.0 indicates
perfect fit and 0.5 represents results no better than a coin
flip). Ross and colleagues [10] reviewed 117 studies of readmission following hospitalization for heart failure and
found few patient characteristics consistently associated
with readmission. Two models were reported, both with
poor discrimination (c-statistic 0.60 for both).
Claims data have been extensively used to study
readmission; they typically include demographic characteristics, diagnoses and procedures, and insurance
information but lack other clinical data that might
identify patients during a hospital stay who are at high
risk of readmission. Factors such as the use of high-risk
medications, critical care exposure, laboratory abnormalities, organ dysfunction, and severity of illness indicators
might be more powerful predictors of 30-day readmission than diagnosis. Though often unavailable in claims
data, these variables are readily available in electronic
health records. We analyzed the Health Facts database
(Cerner Corporation, Kansas City, MO), electronic
health data aggregated from numerous health systems,
to provide insight into additional factors associated with
hospital readmissions. Our overall objective was to create
a predictive model for all causes of 30-day hospital readmission using variables pertaining to the index hospitalization available at the time of discharge. We considered
characteristics of the entire index admission because
our focus was risk of future readmission. By relying on
data from electronic health records, the model could be
refined and embedded in an electronic health record to
inform discharge planning.
METHODS
Using a retrospective cohort design, we identified adults
with an acute care hospitalization (index admission) and
determined which patients had an inpatient readmission within 30 days following discharge. We compared
patients who were and were not re-hospitalized to determine risk factors for readmission. The Health Sciences
Institutional Review Board at the University of Missouri
deemed the study exempt from review.
Health Facts Database
We used Health Facts, a database assembled from participating hospitals and health systems’ comprehensive clinical records. Health Facts has been used in several studies
204 JCOM May 2013 Vol. 20, No. 5
of acute myocardial infarction (AMI) outcomes [11–14]
as well as surveillance of meningococcal disease in children [15]. Billing and encounter data are integrated with
clinical information relating to drug order/dispensing
and the results of diagnostic testing. Data are submitted
from diverse hospitals and outpatient clinics throughout
the United States. Depending on the specific electronic
health record components implemented in each facility,
different data elements are contributed to Health Facts.
Cerner Corporation has established Health Insurance
Portability and Accountability Act (HIPAA)–compliant
policies and procedures that use statistical methods to deidentify data prior to inclusion in Health Facts. Because
patients are de-identified when hospitals contribute their
data, readmissions can only be tracked within the same
health system. However, in Medicare data, 78% of readmissions are to the same hospital [16].
Inclusion and Exclusion Criteria
We included inpatient admissions with at least 1 of each
of the following: diagnosis or procedure, medication
order, and laboratory order. The index hospitalization
was the first qualifying acute care hospital admission
for a patient between 1 October 2008 and 31 August
2010. We included patients who were at least 18 years
of age at admission and who were discharged alive. We
excluded the following: (1) admissions with a length
of stay of 0 days; (2) outpatient (observation) stays; (3)
patients with primary or secondary diagnosis of pregnancy or complications of pregnancy, childbirth and the
puerperium (International Classification of Diseases, 9th
Revision, Clinical Modification [ICD-9-CM] diagnosis
codes 630–679) or who had primary or secondary procedures that were obstetrical (ICD-9-CM procedure codes
72–75); and (4) patients whose predominant care setting
during the admission was a psychiatric or rehabilitation
unit. After reviewing distributions of medication orders,
we operationalized predominant care setting as a psychiatric or rehabilitation unit if more than 90% of the
medication orders originated from that type of unit. We
included patients with psychiatric conditions requiring
temporary acute care not excluded by the above criterion,
such as alcohol detoxification, drug detoxification, or
stabilization following a suicide attempt. We did not consider elective admissions following the index admission as
readmissions. Elective (planned) readmissions were those
so designated by the admitting physician except when
the patient was admitted through the emergency departwww.jcomjournal.com
ORIGINAL RESEARCH
ment. Admissions within 24 hours of discharge from the
index admission were combined with the index admission
and treated as 1 admission (most were within 3 hours
and not likely true discharges).
Analysis
We used SAS for Windows, version 9.2 (SAS Institute
Inc., Cary, NC) for all analyses. Potential risk factors
were selected based on the literature, availability in
Health Facts, and clinical judgment of the physicianinvestigators. Descriptive statistics including unadjusted
odds ratios were calculated; the chi-square statistic was
used to determine statistical association of each potential
risk factor with 30-day readmission. Because there were
nearly 400 candidate variables, they were grouped into
categories for initial modeling: patient and hospital characteristics, medications, laboratory results, microbiology
results, indicators of organ dysfunction, characteristics of
the index admission, treatment, and diagnoses. A complete list of variables is available from the authors.
Missing Data
Missing data were common among laboratory results. In
general, we considered a missing value for a particular
laboratory test as indicating that the care team felt there
was no reason to order the test. Thus, missing values were
assumed to lie within a test’s normal range. For example,
129,061 patients had hematocrit values and 334,290 had
no hematocrit value. For the purposes of modeling, we
assume that the 334,290 patients had normal hematocrits. “Missing” medications and procedures were assumed not to have been ordered or performed. Hospital
exposure in the year prior to the index admission was
considered missing if Health Facts had no inpatient or
outpatient encounters for the patient during this time.
Modeling Process
We developed logistic regression models and accounted
for nesting of patients within hospitals with generalized
estimating equations using the GENMOD procedure in
SAS software. During model development and validation we considered effect size, clinical relevance, and
statistical significance. We used the c-statistic (area under
the receiver operator characteristic [ROC] curve) to assess model discrimination. To assess model calibration,
we divided the predicted probabilities into deciles and
compared each decile’s median value with the observed
proportion of 30-day readmission.
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The data were randomly divided into developmental
and validation data sets, setting aside 10% of the observations for validation. The developmental data set
was further divided into 20 random samples (without
replacement). Using chi-square tests for categorical variables and 2-sample t tests for continuous variables, we
determined the relationship of each potential predictor to
30-day hospital readmission. Variables within a category
that were significant at the 0.0001 level were included
in a logistic regression with backward elimination that
also used a 0.0001 level. We chose this level in view of
the large sample size (about 20,000 observations per
sample). Each laboratory result had up to 4 variables:
baseline, nadir, peak, and discharge results. As a preliminary step, these 4 variables were compared; the one
with the strongest association with readmission was used
in the pool of potential predictors. Modeling proceeded
on all 20 subsamples. Variables retained in models for at
least 10 of the 20 samples were eligible for inclusion in
the final model.
Within each developmental sample, the collection of
category “winners” was used as potential predictors in a
logistic regression model with backward elimination at
the 0.005 level. The most common “winners” from the
20 developmental sets were estimated in the full developmental data set. To make sure that the modeling process
did not exclude important variables, we tested whether
including other variables improved model performance.
We focused on categories of variables that were not represented in the final model (eg, indicators of impaired immune function and medications) and variables that were
dropped late in the process. Adding these other variables
back to the model resulted in minimal improvement in
the c-statistic (0.01), so we did not include variables beyond the 5 originally selected.
Model Validation
The variable coefficients from the final model were used
to calculate readmission risk for patients in the validation
sample. To visualize model discrimination and calibration, we plotted an ROC curve and a calibration plot,
respectively.
RESULTS
Characteristics of the included hospitals are shown in
Table 1. There were 91 hospitals included, ranging in
size from less than 5 to over 500 beds. All US census
regions were represented, with the majority of hospitals
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Table 1. Characteristics of Participating Hospitals
Characteristic
Hospitals, n (%)
n = 91
Patients, n (%)
n = 463,351
No. of beds
< 100
29 (31.9)
38,948 (8.4)
100–199
16 (17.6)
63,831 (13.8)
200–299
19 (20.9)
85,556 (18.5)
300–399
17 (18.7)
123,574 (26.7)
≥ 500
10 (11.0)
151,442 (32.7)
Northeast
36 (39.6)
198,177 (42.8)
Midwest
16 (17.6)
78,404 (16.9)
South
33 (36.3)
15,4813 (33.4)
West
6 (6.6)
31,957 (6.9)
Urban
89 (97.8)
462,849 (99.9)
Rural
2 (2.2)
Census region
Location
502 (0.1)
Teaching status
Teaching
42 (46.2)
329,871 (71.2)
Non-teaching
49 (53.8)
133,480 (28.8)
in the Northeast and South regions. Almost half (46.2%)
were academic health centers.
Derivation of the study cohort is depicted in Figure 1.
There were 463,351 index hospital stays, with 45,098
(9.7%) patients readmitted within 30 days of discharge
from the index hospitalization. Mean patient age was 61
years (95% CI 61.0–61.1), with 27.3% of the population
age 75 years or older. Over half (54.6%) were women.
Most patients were Caucasian (78.8%) and 15.0% were
African American. Patients age 65 years or older with
other or unknown insurance were assumed to have
Medicare. Almost half (45.1%) of patients had Medicare, 14.2% had commercial insurance, and 4.9% had
Medicaid. Insurance status was unknown for 28.3% of
patients.
The unadjusted associations of individual risk factors
with readmission are shown in Table 2. Patients with
hospital exposure in the prior 12 months had higher
odds of readmission than those with no or unknown
hospital exposure. Readmission increased with age,
with 12.2% of those age 85 years and older readmitted. Compared with commercial insurance, Medicare
and Medicaid were also associated with more frequent
readmission, while self-pay patients were less likely to
206 JCOM May 2013 Vol. 20, No. 5
be readmitted. Relative to patients with a Charlson
index [17] of 0, patients with scores above 5 were much
more likely to be readmitted. Several diagnoses and
conditions were associated with readmission, including
cancer, end-stage renal disease, and major organ transplantation. Compared with those with lower values, the
odds of readmission were more than double for patients
with blood urea nitrogen ≥ 35 mg/dL or serum creatinine levels of ≥ 2 mg/dL. Readmission increased with
the number of medications ordered and dispensed during the index admission. In particular, high-dose oral
corticosteroids and chemotherapy agents were strongly
associated with readmission.
The multivariable model contains 5 independent
variables (Table 3). The adjusted odds of readmission
increased with length of stay, the Charlson index, prior
hospitalization, and low hemoglobin (nadir of all values).
In the model, which controls for comorbidities, patients
hospitalized for arthroplasty were about half as likely to
be readmitted as other patients. The c-statistic in the
developmental data was 0.67.
The model performed almost as well in the validation
data, with a c-statistic of 0.66 (Figure 2A). The calibration curve (Figure 2B) indicates reasonable performance
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ORIGINAL RESEARCH
981,581 admissions in Health Facts
• Submitted diagnosis and procedure information,
pharmacy, and laboratory data
• Discharged 10/01/08 through 9/30/10
202,074 admissions with patients < 18 years old at the time of admission
23,339 in-hospital deaths
2587 admissions with ≥ 90% of orders originating from a rehabilitation unit
3659 admissions with ≥ 90%
of orders originating from a
psychiatric unit
99,884 admissions with primary or
secondary diagnosis pregnancyrelated or obstetrical
666,519 qualifying admissions*
463,362 index (first) admissions
• 45,098 readmitted within 30 days
• 418,264 not readmitted within 30 days
Figure 1. Flow chart showing inclusion and exclusion of inpatient admissions in the analytic cohort. *More than 1 exclusion
reason can apply to an admission, therefore the sum of the individual exclusions exceeds the total admissions excluded.
across all levels of risk. The lowest and highest estimated
individual risk of readmission were 1.9% and 49%, respectively. In the validation data, 3.1% and 21.8% of the
patients in the lowest and highest deciles of risk were
readmitted, respectively.
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DISCUSSION
Our model identified increased comorbidity and length
of stay, prior hospital exposure, and low hemoglobin as
risk factors for all cause readmission in a general adult
inpatient population; having a hip or knee arthroplasty
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Table 2. Unadjusted Bivariable Association of Selected Risk Factors with 30-Day Readmission Following Hospital
Discharge
Patients Who Were Readmitted
No. of Patients
Number (%)
Odds Ratio (95% CI)
18–44
88,747
6548 (7.4)
Reference
45–54
75,158
6402 (8.5)
1.17 (1.13–1.21)
55–64
88,409
8213 (9.3)
1.29 (1.24–1.33)
65–74
84,584
8917 (10.5)
1.48 (1.43–1.53)
75–84
80,996
9481 (11.7)
1.66 (1.61–1.72)
85–90
45,457
5537 (12.2)
1.74 (1.68–1.81)
78,325
6046 (7.7)
Reference
Demographic characteristics
Age
Insurance
Commercial, other
Medicare*
208,949
23,845 (11.4)
1.54 (1.50–1.59)
Medicaid
22,756
2562 (11.3)
1.52 (1.44–1.59)
Self-pay
22,373
1567 (7.0)
0.90 (0.85–0.95)
130,948
11,078 (8.5)
1.10 (1.07–1.14)
Married/life partner
179,104
16,557 (9.2)
Reference
Divorced/separated
41,918
4496 (10.7)
1.18 (1.14–1.22)
Single
84,535
8022 (9.5)
1.03 (1.00–1.06)
Widowed
63,452
7588 (12.0)
1.33 (1.30–1.37)
Unknown
94,342
8435 (8.9)
0.96 (0.94–0.99)
7467 (10.8)
1.13 (1.10–1.16)
Unknown
Marital status
Race/ethnicity
African-American
Caucasian
69,381
365,039
35,243 (9.6)
Reference
Hispanic
10,620
932 (8.8)
0.90 (0.84–0.96)
Other known
13,795
1238 (9.0)
0.92 (0.87–0.98)
4516
218 (4.8)
0.48 (0.41–0.54)
Female
252,927
23,600 (9.3)
Reference
Male
210,365
21,496 (10.2)
1.11 (1.08–1.13)
338,271
34,887 (10.3)
1.29 (1.26–1.32)
Medical
286,997
30,386 (10.6)
1.28 (1.25–1.30)
Surgical
147,273
12,497 (8.5)
Unknown
27,981
2215 (7.6)
249,014
25,835 (10.4)
Reference
25,458
2342 (9.2)
0.88 (0.84–0.92)
Unknown
Sex (59 unknown)
Index hospitalization
Urgent or emergent admission
Type of admission
Reference
0.89 (0.85–0.93)
Admission source
Emergency department
Hospital, other facility
Skilled nursing facility, nursing home
Other
Unknown
208 JCOM May 2013 Vol. 20, No. 5
5527
797 (14.4)
1.46 (1.35–1.57)
166,396
14,709 (8.8)
0.84 (0.82–0.86)
16,956
1415 (8.4)
0.79 (0.74–0.83)
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ORIGINAL RESEARCH
Table 2. Unadjusted Bivariable Association of Selected Risk Factors with 30-Day Readmission Following Hospital
Discharge (continued)
Patients Who Were Readmitted
No. of Patients
Number (%)
≤ 2 days
91,800
5677 (6.2)
Reference
2.01–3 days
83,752
6077 (7.3)
1.19 (1.14–1.23)
3.01–4.25 days
99,038
8127 (8.2)
1.36 (1.31–1.40)
4.26–7 days
96,108
10,750 (11.2)
1.91 (1.85–1.98)
Over 7 days
92,653
14,467 (15.6)
2.81 (2.72–2.90)
Nursing home exposure prior 90 days
5054
1038 (20.5)
2.11 (1.98–2.27)
Left hospital against medical advice
prior 12 months
4428
707 (16.0)
1.54 (1.42–1.67)
231,399
21,885 (9.46)
Reference
Odds Ratio (95% CI)
Hospital length of stay
Prior health care utilization
Hospital exposure prior 12 months
No
Yes
67,492
11,087 (16.4)
1.88 (1.84–1.93)
164,460
12,126 (7.37)
0.76 (0.74–0.78)
0
188,254
11,939 (6.3)
Reference
1–5
251,160
28,310 (11.3)
1.88 (1.84–1.92)
23,584
4764 (20.2)
3.74 (3.60–3.88)
353
85 (24.1)
4.69 (3.67–5.99)
Unknown
Diagnoses and conditions
Charlson index
6–10
11–20
Organ system dysfunctions
0
389,745
1
58,236
7498 (12.9)
1.50 (1.46–1.54)
2–5
15,370
2541 (16.5)
2.00 (1.92–2.09)
9531
1608 (16.9)
1.91 (1.81–2.02)
38,321
6627 (17.3)
2.10 (2.04–2.16)
Blood dyscrasia
Cancer
Coronary artery disease
35,059 (9.0)
Reference
73,989
8704 (11.8)
1.29 (1.26–1.33)
113,997
13,383 (11.7)
1.33 (1.30–1.36)
10,553
2159 (20.5)
2.45 (2.34–2.58)
100,259
12,556 (12.5)
1.45 (1.42–1.49)
55,420
8298 (15.0)
1.78 (1.73–2.06)
3855
650 (16.7)
1.89 (1.74–1.87)
Pneumonia
33,421
4145 (12.4)
1.34 (1.30–1.39)
Sepsis
17,652
2639 (15.0)
1.67 (1.60–1.74)
145,623
18,438 (12.7)
1.58 (1.55–1.61)
Blood urea nitrogen ≥ 35 mg/dL
64,069
10,670 (16.7)
2.12 (2.07–2.17)
Estimated glomerular filtration rate
70,782
11,231 (15.9)
2.00 (1.95–2.04)
Diabetes
End stage renal disease
Fluid or electrolyte imbalance
Heart failure
Major organ transplantation
Laboratory studies
Absolute lymphocyte count < 800/µL
< 40 mL/min/1.73m2
Hemoglobin (nadir)†
< 8 g/dL
34,776
5667 (16.3)
2.34 (2.27–2.42)
8–11 g/dL
161,479
18,924 (11.7)
1.60 (1.56–1.63)
> 11 g/dL
267,096
20,507 (7.7)
Reference
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Table 2. Unadjusted Bivariable Association of Selected Risk Factors with 30-Day Readmission Following Hospital
Discharge (continued)
Patients Who Were Readmitted
No. of Patients
Number (%)
Odds Ratio (95% CI)
Platelet count < 100,000/µL
27,786
4560 (16.4)
1.91 (1.85–1.98)
Serum creatinine ≥ 2 mg/dL
46,920
7961 (17.0)
2.09 (2.03–2.14)
101,657
15,129 (14.9)
1.93 (1.90–1.98)
Serum potassium > 5.5 mmol/L
19,661
3302 (16.8)
1.94 (1.87–2.02)
Total bilirubin ≥ 2 mg/dL
19,541
2944 (15.1)
1.69 (1.62–1.76)
White blood cells < 4,000/µL
27,595
4143(15.0)
1.70 (1.65–1.76)
Serum albumin < 3 g/dL
Treatments and procedures
No. of medications dispensed
0
190,255
17,259 (9.1)
1–10
98,186
8243 (8.4)
0.92 (0.89–0.94)
Reference
11–20
112,771
11,192 (9.9)
1.10 (1.08–1.13)
21–30
44,761
5585 (12.5)
1.43 (1.38–1.48)
31 or more
17,378
2819 (16.2)
1.94 (1.86–2.03)
Amiodarone
9407
1478 (15.7)
1.75 (1.66–1.86)
Arthroplasty
26,083
1160 (4.4)
0.42 (0.39–0.44)
Beers criteria, any agent (age > 74)
22,480
2813 (12.5)
1.08 (1.03–1.12)
Blood transfusion
8190
1304 (15.9)
1.78 (1.67–1.89)
Chemotherapy agent
2535
700 (27.6)
3.58 (3.28–3.91)
Hemodialysis
10,760
2148 (20.0)
2.38 (2.27–2.50)
High-dose oral corticosteroid
14,228
2295 (16.1)
1.83 (1.74–1.91)
Note: To protect confidentiality, the age of patients over 90 years old was reset to 90.
*Includes patients age 65 or older with other or unknown insurance.
†Patients
with unknown hemoglobin (3.5%) were assumed to have values over 11 g/dL.
was associated with lower risk of readmission. Model
performance was modest (c-statistic = 0.67).
Our model performed as well or better than most
models based on claims data. In fact, it performed as
well or better than most models that studied a single
condition. A recent systematic review of readmission
risk models [9] found 26 distinct models. Nine models
from large, multicenter US studies reported c-statistics of
0.55–0.65. Of the 6 models with a reported c-statistic of
0.7 or above, 4 were based on European or Australian
data and 1 US study was based on heart failure patients
at a single center. Coleman’s [18] model using Medicare
Current Beneficiary Survey data had good discrimination (c = 0.77) when prior utilization and diagnoses were
included; adding self-reported survey items on functional
status and vision improved model performance (c = 0.83).
210 JCOM May 2013 Vol. 20, No. 5
Stronger models can undoubtedly be created by including additional clinical and social factors or by restricting
the population to more narrowly defined constellations
of conditions. We were able to identify 25% of our sample
with less than a 6% risk of readmission. In the validation
data, 21.8% and 15.1% of the patients in the highest two
risk deciles were readmitted. Although far from perfect,
such data can help target interventions to prevent readmission to higher risk individuals.
Studies of readmission vary widely in terms of case
finding and the time period studied. Vest et al [19] conducted a systematic review of preventable readmissions;
indicators of complexity or general ill health (eg, Charlson index), increasing length of stay, and Medicare or
Medicaid status were the most commonly identified risk
factors. In a large, prospective cohort study of all-cause
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Table 3. Multivariable (Adjusted) Association of Independent Risk Factors with All-Cause 30-Day Readmission
Following an Acute Hospitalization
Risk Factor
Charlson index
Coefficient
Odds Ratio (95% CI)
0.1335
1.14 (1.14–1.15)
Length of stay
≤ 2 days
Reference
2–3 days
0.1151
1.12 (1.08–1.17)
3–4.25 days
0.2631
1.30 (1.25–1.35)
4.25–7 days
0.4790
1.62 (1.56 –1.67)
> 7 days
0.7305
2.08 (2.00–2.15)
–0.8014
0.45 (0.42–0.48)
0.1970
1.22 (1.18–1.26)
Arthroplasty procedure
Hemoglobin (nadir)
< 8 g/dL
8–11 g/dL
Reference
> 11 g/dL
–0.2247
0.80 (0.78–0.82)
Hospital exposure prior 12 months
No
Reference
Yes
0.3919
1.48 (1.44–1.52)
Unknown
–0.2252
0.80 (0.78–0.82)
C-statistic
0.668
readmission in 11 Ontario hospitals, van Walraven [20]
found that length of stay, acuity (emergent admission),
comorbidity [17], and emergency department use in the
prior 6 months were associated with death or readmission. Prior hospital exposure is a consistent risk factor for
readmission [5,7,18,21,22]. Other common risk factors
include poor physical functioning [5,21–23] and social
factors such as social instability and living alone [7,21–
26]. While a large number of risk factors in our analysis
had high odds ratios when considered in bivariable analyses, many occurred infrequently, making them less useful
for patient-level prediction in a multivariable model. For
example, the odds ratio for readmission was 3.58 among
patients who received chemotherapy agents, but only
2535 patients (0.55%) received them. This suggests that
analyzing a more homogeneous group of patients (eg,
patients with cancer or heart failure) might help identify
a more predictive set of risk factors.
Readmission rates in large patient samples are often 15%
or higher, depending on the population and the definition
of readmission [4,5,7,23,27,28]. Patients in our sample had
a lower readmission rate than these studies, although van
Walraven [20] found a similar rate among general patients
in 11 Ontario hospitals (8.0% combined outcome of death
www.jcomjournal.com
or readmission within 30 days). There are likely several
reasons for our relatively low rate. First, in Medicare data,
about a fifth of readmissions are to a different hospital
[16,29]. In our data, we could only track readmissions
to the same hospital system. Second, we restricted index
admissions to the first hospitalization during a period.
Because the first admission is less likely to have been preceded by a prior admission than subsequent stays, and
prior hospital utilization was associated with readmission,
this likely reduced our rate. Consistent with this suggestion, individuals with prior hospital exposure had a much
higher risk of readmission than the overall sample. We
excluded elective admissions from the readmission count;
many studies with high readmission rates included elective readmissions [4,5,21,29]. Facilities that contributed
data to Health Facts might be early adopters of electronic
medical records, which could have affected care delivery.
Finally, our data included adults in Medicare managed care
plans who are typically excluded from analyses of Medicare
claims because hospital claims for those individuals are
not submitted. Because patients in managed care are often
healthier, their readmission rate could be lower.
Although our c-statistic is modest, our model points
the way towards several paths to clinically useful point-
Vol. 20, No. 5 May 2013 JCOM 211
RISK FOR READMISSION
25
1.0
A
0.8
Sensitivity
Actual readmission, %
20
B
15
0.6
10
0.4
5
0.2
0
0
0
5
10
15
20
25
Predicted readmission,%
0
0.2
0.4
0.6
0.8
1.0
1 - Specificity
Figure 2. Calibration (Panel A) and receiver operator characteristic (Panel B) plots for logistic model of all-cause 30-day readmissions in the validation data set.
of-service rules. For example, other important variables
such as functional data, support systems, and health
literacy could be collected by hospital personnel during discharge planning to estimate readmission risk.
Electronic systems could alert clinicians when patients
had developed increased risk due to changes in laboratory results or medication orders. A recent meta-analysis
found that individualized discharge plans reduce readmissions over routine discharge care [30]. One example
is the Re-Engineered Discharge (RED) program [31], an
11-component intervention that included patient education, organizing postdischarge appointments and services, confirming medications, a written discharge plan,
and a postdischarge phone call. In a trial of 749 patients,
RED reduced subsequent hospital utilization by 30%.
An intervention that provided patients with communication tools, encouraged them to take a more active role in
their care, and provided visits and calls from a “transition coach” reduced 30- and 90-day readmissions [32].
With better risk models, graded interventions would be
possible, reserving proven but resource-intensive strategies for the highest risk individuals. Thus, our model
may serve as a basis for developing more refined models
that include social and functional information, allowing hospitals to focus resources on patients at highest
risk.
212 JCOM May 2013 Vol. 20, No. 5
Strengths and Limitations
We analyzed a large, cross-sectional sample of hospitalized adults from 91 hospitals. In addition to administrative variables, we included clinical information on laboratory results, medications and treatments, and severity of
illness. Using data that can be accessed in real time can
support predicting readmission at the time of a patient’s
discharge. Because we had data on patients regardless
of insurance status, our sample included patients in
Medicare managed care who are typically absent from
Medicare claims data. Ideally, we would want to include
information on functional limitations, social support,
substance abuse, socioeconomic status, and social instability such as number of address changes [7,22–24,26].
We were unable to track readmissions to hospitals in different health systems or hospitals not included in Health
Facts, which undoubtedly reduced the readmission rate
in our data. Patients admitted to a different hospital have
some differences from those admitted to the same hospital [16]. Nonetheless, because only about 20% of patients
are admitted to a different hospital, that is unlikely to
have a major effect on our findings. Participating institutions may not be representative of US hospitals in general.
In particular, rural hospitals are underrepresented in our
study. We were unable to identify discharge medications,
which could be an important factor in readmissions.
www.jcomjournal.com
ORIGINAL RESEARCH
CONCLUSION
Comorbidity and prior utilization are strong risk factors for readmission in a general population of patients
discharged from the hospital. Despite the availability of
a large number of potentially relevant clinical variables,
model performance was modest. Considering that the
proportion of readmissions that are potentially preventable is likely under 25% [28,33], it will be difficult for
hospitals to successfully identify and intervene with patients with a high likelihood of readmission. Including
information on social support and functional status will
likely improve model performance. Specific laboratory
abnormalities might be more strongly associated with
readmission in relevant subpopulations of patients (eg,
infection indicators in pneumonia patients). Using clinical data to predict readmission in more homogeneous
groups of patients is worthy of further study.
Acknowledgements: The authors would like to acknowledge
Jane Griffin, RPh, and Jeffrey Binkley, PharmD, for their
facilitation of this project. Preliminary results from this study
were presented at the Cerner Health Conference on 10
October 2011.
Corresponding author: Robin L. Kruse, PhD, MA306 Medical Sciences Building, University of Missouri School of Medicine, Columbia, MO 65212, [email protected].
Funding/support: This study was partially supported by the
Tiger Institute Research Group, a collaborative research effort
between the University of Missouri and the Cerner Corporation. The Tiger Institute, wholly owned by the University of
Missouri, had no role in conduct of the study; management,
analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript.
Financial disclosures: Mr. Hays and Dr. Emons are employed
by the Cerner Corporation.
Author contributions: conception and design, RLK, HDH,
MFE, DSW, DRM; analysis and interpretation of data, RLK,
HDH, RWM, MFE, DRM; drafting of article, RLK, MFE;
critical revision of the article, RLK, RWM, MFE, DSW,
DRM; statistical expertise, RWM; administrative or technical support, DRM; collection and assembly of data, RLK,
HDH, MFE.
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Copyright 2013 by Turner White Communications Inc., Wayne, PA. All rights reserved.
214 JCOM May 2013 Vol. 20, No. 5
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