Resuscitation Science
Regional Variation in the Incidence and Outcomes of
In-Hospital Cardiac Arrest in the United States
Dhaval Kolte, MD, PhD*; Sahil Khera, MD*; Wilbert S. Aronow, MD;
Chandrasekar Palaniswamy, MD; Marjan Mujib, MD, MPH; Chul Ahn, PhD; Sei Iwai, MD;
Diwakar Jain, MD; Sachin Sule, MD; Ali Ahmed, MD, MPH; Howard A. Cooper, MD;
William H. Frishman, MD; Deepak L. Bhatt, MD, MPH; Julio A. Panza, MD; Gregg C. Fonarow, MD
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Background—Regional variation in the incidence and outcomes of in-hospital cardiac arrest (IHCA) is not well studied and
may have important health and policy implications.
Methods and Results—We used the 2003 to 2011 Nationwide Inpatient Sample databases to identify patients ≥18 years
of age who underwent cardiopulmonary resuscitation (International Classification of Diseases, Ninth Edition, Clinical
Modification procedure codes 99.60 and 99.63) for IHCA. Regional differences in IHCA incidence, survival to hospital
discharge, and resource use (total hospital cost and discharge disposition among survivors) were analyzed. Of 838 465
patients with IHCA, 162 270 (19.4%) were in the Northeast, 159 581 (19.0%) were in the Midwest, 316 201 (37.7%) were
in the South, and 200 413 (23.9%) were in the West. Overall IHCA incidence in the United States was 2.85 per 1000
hospital admissions. IHCA incidence was lowest in the Midwest and highest in the West (2.33 and 3.73 per 1000 hospital
admissions, respectively). Compared with the Northeast, risk-adjusted survival to discharge was significantly higher in
the Midwest (odds ratio, 1.33; 95% confidence interval, 1.31–1.36), South (odds ratio, 1.21; 95% confidence interval,
1.19–1.23), and West (odds ratio, 1.25; 95% confidence interval, 1.23–1.27). IHCA survival increased significantly from
2003 to 2011 in the United States and in all regions (all Ptrend<0.001). Total hospital cost was highest in the West, whereas
discharge to skilled nursing facility and use of home health care among survivors was highest in the Northeast.
Conclusions—We observed significant regional variation in IHCA incidence, survival, and resource use in the United
States. This variation was explained only partially by differences in patient and hospital characteristics. Further studies
are needed to identify other potential factors responsible for these regional differences to improve outcomes after
IHCA. (Circulation. 2015;131:1415-1425. DOI: 10.1161/CIRCULATIONAHA.114.014542.)
Key Words: cardiopulmonary resuscitation ◼ costs and cost analysis ◼ heart arrest ◼ survival
E
ach year, ≈209 000 adult in-hospital cardiac arrests
(IHCAs) occur in the United States, with survival to hospital discharge rates of 18% to 20%.1–3 This is in comparison
with ≈326 200 adult out-of-hospital cardiac arrests (OHCAs)
annually in the United States, with overall survival rates of
10.6%.4 IHCA has not received the same level of focused
research as OHCA.5 Regional differences in early and late survival after OHCA have been well studied.6 However, to date,
potential regional differences in the incidence and outcomes
of IHCA have not been systematically described. Recent data
suggest that hospitals with higher survival rates have lower
IHCA incidence.7 Studies have also shown that significant
variability in IHCA survival exists across hospitals and that
this variation persists despite adjustment for measured patient
and hospital factors and for the duration of hospital participation in IHCA quality improvement programs.8,9 Similarly,
although IHCA survival has improved during the past decade,
the magnitude of improvement varies across hospitals.10,11
However, these studies have been limited largely to data from
hospitals participating in a voluntary IHCA quality improvement program and may not be representative of the entire
US population. Furthermore, it remains unknown whether
hospitals with lower survival rates or smaller improvements
Editorial see p 1377
Clinical Perspective on p 1425
Continuing medical education (CME) credit is available for this article. Go to http://cme.ahajournals.org to take the quiz.
Received November 26, 2014; accepted February 13, 2015.
From New York Medical College, Valhalla, NY (D.K., S.K., W.S.A., M.M., S.I., D.J., S.S., H.A.C., W.H.F., J.A.P.); Icahn School of Medicine at Mount
Sinai Hospital, New York, NY (C.P.); University of Texas Southwestern Medical Center, Dallas (C.A.); VA Medical Center, Washington, DC (A.A.);
Brigham and Women’s Hospital Heart & Vascular Center and Harvard Medical School, Boston, MA (D.L.B.); and David-Geffen School of Medicine,
University of California at Los Angeles (UCLA), Los Angeles (G.C.F.).
*Drs Kolte and Khera contributed equally.
Guest Editor for this article was Clyde W. Yancy, MD.
The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA.
114.014542/-/DC1.
Correspondence to Gregg C. Fonarow, MD, Department of Medicine, Division of Cardiology, University of California at Los Angeles, 10833 Le Conte
Ave, Los Angeles, CA 90095-1679. E-mail [email protected]
© 2015 American Heart Association, Inc.
Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.114.014542
1415
1416 Circulation April 21, 2015
in survival rates over time are clustered in specific states or
regions in the United States. If regional variation in the incidence, survival, and resource use for IHCA exists, these findings would provide a unique opportunity to identify reasons
for these differences and to develop targeted interventions to
enhance the quality of resuscitation and postresuscitation care
and to improve overall IHCA outcomes in the United States.
We used data from the 2003 to 2011 Nationwide Inpatient
Sample (NIS) databases to determine whether regional differences exist in the incidence, survival to hospital discharge,
and resource use (total hospital cost and discharge disposition among survivors) for IHCA in the United States and, if
so, to examine whether patient- and hospital-level factors can
explain these regional differences.
Methods
Data Source
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Data were obtained from the 2003 to 2011 NIS databases. The NIS,
sponsored by the Agency for Healthcare Research and Quality as a
part of the Healthcare Cost and Utilization Project, is the largest publicly available all-payer inpatient care database in the United States.
It contains discharge-level data from ≈8 million hospital stays from
≈1000 hospitals designed to approximate a 20% stratified sample of
all US community hospitals, representing >95% of the US population.
Criteria used for stratified sampling include hospital ownership, bed
size, teaching status, urban or rural location, and geographic region.
Inpatient stay records in the NIS include clinical and resource use
information available from discharge abstracts derived from statemandated hospital discharge reports. Discharge weights are provided
for each patient discharge record and can be used to obtain national
estimates. Discharge weights in NIS are calculated for each stratum
by first stratifying the NIS hospitals on the same variables used for
creating the sample and then dividing the number of universe discharges in that stratum by the number of NIS discharges in the stratum.12 In NIS, the universe is all inpatient discharges from community
hospitals in the United States. Weighted estimates are calculated by
uniformly applying stratum weights to the discharges according to
the stratum from which the discharge was drawn.
This study was deemed exempt by the New York Medical College
Institutional Review Board because the Healthcare Cost and
Utilization Project–NIS is a publicly available database that contains
deidentified patient information.
Study Population
We used the International Classification of Diseases, Ninth Edition,
Clinical Modification (ICD-9-CM) codes 99.60 or 99.63 to identify all patients ≥18 years of age who underwent cardiopulmonary
resuscitation (CPR) for IHCA (n=838 951). This approach has been
used in previous studies using administrative databases to identify
patients with IHCA.1,13,14 Patients who experienced multiple episodes
of IHCA and CPR during the same hospitalization were considered
a single IHCA case. We did not use ICD-9-CM code 427.5 (cardiac
arrest) to identify IHCA cases because this may lead to erroneous
estimates. Cardiac arrest (427.5) may be coded in patients who
present to the emergency department with OHCA and survive to be
admitted to the hospital. Indeed, previous studies have used ICD9-CM code 427.5 listed as the principal diagnosis to identify OHCA
cases.15,16 Similarly, cardiac arrest may also be coded in patients with
do-not-resuscitate (DNR) orders for whom no treatment is attempted.
Furthermore, health data coders may not include code 427.5, preferring to rank other diagnoses codes higher (eg, comorbidities), which
will skew the coded incidence of IHCA.5 We excluded records with
missing data on survival or discharge disposition (n=487). This gave
us a final study sample of 838 465 IHCA patients. Patients with ventricular tachycardia (VT) or ventricular fibrillation (VF) were identified by ICD-9-CM code 427.1 or 427.41 (n=176 153, 21.0%). Patient
records without either of these codes were considered to have pulseless electric activity (PEA) or asystole as the cardiac arrest rhythm
(n=662 312, 79.0%) because these diagnoses do not have unique ICD9-CM codes. Patients with IHCA were divided into 4 groups based on
the hospital location: the Northeast, Midwest, South, and West, corresponding to the census regions as defined by the US Census Bureau.17
For state-level analyses, states with <100 IHCA cases (Alaska and
North Dakota) were excluded.
Outcomes Measured
We compared incidence rates of IHCA among the 4 geographic
regions and across individual states. Our primary outcome of interest
for this study was survival to hospital discharge, defined as the proportion of IHCA patients who did not die during the hospitalization and
were discharged alive. We used total hospital cost and discharge disposition among survivors as secondary outcomes. The NIS contains
the data element TOTCHG, which represents the total charges for
each hospital record.18 This charge information represents the amount
that hospitals billed for the entire hospital stay but does not reflect
how much hospital services actually cost. Total hospital charges were
converted to costs by use of Healthcare Cost and Utilization Project
cost-to-charge ratios on the basis of the hospital accounting reports
collected by the Centers for Medicare & Medicaid Services.19 Cost
was calculated as total hospital charges multiplied by cost-to-charge
ratio. Costs were adjusted for inflation by use of the region-specific
Consumer Price Index provided by the US Bureau of Labor Statistics,
with 2014 as the index base.20 Thus, all costs were standardized over
the study period and are reported in 2014 US dollars. Data on cost
was missing for 60 866 records. Therefore, results of cost analysis are
based on a sample size of 777 599 IHCA patients.
Discharge disposition among survivors was classified as home
(self-care), short-term hospital, skilled nursing facility, home health
care, or other by use of the DISPUNIF variable in the NIS databases.18 All outcomes are reported for the overall cohort and separately according to the cardiac arrest rhythm.
Patient and Hospital Characteristics
Baseline patient characteristics included were demographics (age, sex,
race), primary expected payer, weekday versus weekend admission,
median household income for the patient’s ZIP code, 29 Elixhauser
comorbidities as defined by the Agency for Healthcare Research and
Quality, other clinically relevant comorbidities (smoking, dyslipidemia, known coronary artery disease, family history of coronary
artery disease, previous myocardial infarction, previous percutaneous
coronary angioplasty, previous coronary artery bypass grafting, previous cardiac arrest, family history of sudden cardiac death, carotid
artery disease, dementia, and atrial fibrillation), primary diagnosis
of acute myocardial infarction, and cardiac arrest rhythm.21,22 A list
of ICD-9-CM and Clinical Classifications Software codes used to
identify comorbidities is provided in Table I in the online-only Data
Supplement. Hospital characteristics such as hospital location (rural,
urban), bed size (small, medium, and large), and teaching status were
also included.
Statistical Analysis
Weighted estimates were calculated by applying discharge weight to
the unweighted discharge records. Weighted estimates were used for
all statistical analyses. Overall and region- and state-specific IHCA
incidence rates were calculated by dividing the number of IHCA cases
by the number of total hospitalizations (expressed as cases per 1000
hospital admissions) during the study period. Incidence rates were
compared among the 4 census regions by use of Poisson regression
for the number of IHCA cases, offset by the log of number of total
hospital admissions. The Northeast was used as the reference region.
For descriptive analyses, patient and hospital characteristics were
compared among the 4 regions with the Pearson χ2 test for categorical variables and 1-way ANOVA for continuous variables. In addition, we used standardized differences, calculated as the difference
in means or proportions divided by a pooled estimate of the standard
Kolte et al Regional Variation in IHCA 1417
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deviation, to compare patient and hospital characteristics across the
4 regions with the Northeast as the reference region. Compared with
traditional significance testing, standardized differences are not as
sensitive to sample size and are useful in identifying meaningful differences.23,24 Typically, an absolute standardized difference >10% is
considered clinically meaningful.
To examine differences in survival to hospital discharge among the
4 geographic regions, a multivariable logistic regression model was
constructed with the use of generalized estimating equations with
exchangeable working correlation matrix to account for clustering
of outcomes within hospitals. The Northeast was used as the reference region. Variables included in the regression model were age, sex,
race, primary expected payer, weekday versus weekend admission,
29 Elixhauser comorbidities, other clinically relevant comorbidities
(smoking, dyslipidemia, known coronary artery disease, family history of coronary artery disease, previous myocardial infarction, previous percutaneous coronary intervention, previous coronary artery
bypass grafting, previous cardiac arrest, family history of sudden
cardiac death, carotid artery disease, dementia, and atrial fibrillation),
primary diagnosis of acute myocardial infarction, initial cardiac arrest
rhythm (for the overall cohort), and hospital characteristics (location,
bed size, and teaching status). These covariates were selected a priori
and represent variables known to influence IHCA survival and overall
in-hospital mortality.21,25 We used a similar logistic regression model
to calculate risk-adjusted survival rates for individual states using
previously described methods.26 For analyzing temporal trends in
survival, we added year (continuous variable defined as 2003–2011)
as an independent variable to the above logistic regression model to
obtain the adjusted odds ratio (OR) per year. This approach has been
used in previous studies.10,27 For comparing total hospital cost among
the 4 regions, we used a multivariable linear regression model, adjusting for all the variables mentioned above. Because total hospital cost
had a positively skewed distribution, we used logarithmic transformation of cost as the dependent variable in the linear regression model.
We used ArcGIS Online and Esri Maps for Office (Esri, Redlands,
CA) to map IHCA incidence and survival to hospital discharge rates
in the 4 census regions and in the individual states. We examined the
correlation between IHCA incidence rate and survival to discharge at
the regional and state levels using linear regression.7
Data were complete for all covariates except race (15.7% missing),
median household income for patient’s ZIP code (2.6% missing), hospital characteristics (0.5% missing), Elixhauser comorbidities (0.3%
missing), primary expected payer (0.1% missing), and sex (<0.1%
missing). Additionally, different regions do not compare uniformly
for inclusion criteria for the “other” race category in NIS. Hence, the
“other” race (2.3%) category was also treated as missing. We performed multiple imputations to impute missing values using the fully
conditional specification method (an iterative Markov chain Monte
Carlo algorithm) in SPSS 20.0. Results with and without imputation
were not meaningfully different, so only the former are presented.
Statistical analysis was performed with IBM SPSS Statistics 20.0
(IBM Corp, Armonk, NY). All P values were 2 sided with a significance threshold of P<0.05. Categorical variables are expressed as
percentages and continuous variables as mean±SD or median (interquartile range) as appropriate. The OR and 95% confidence interval
(CI) are used to report the results of logistic regression analyses.
Results
IHCA Incidence
From 2003 to 2011, of 838 465 IHCAs included in our study,
162 270 (19.4%) were in the Northeast, 159 581 (19.0%) in the
Midwest, 316 201 (37.7%) in the South, and 200 413 (23.9%) in
the West. The total number of hospital admissions for patients
≥18 years of age during this period was 293 364 578, giving
an overall incidence of adult IHCA of 2.85 per 1000 hospital admissions in the United States. IHCA incidence varied
Figure 1. Regional variation in in-hospital cardiac arrest (IHCA) incidence and survival to hospital discharge rates. IHCA incidence (per
1000 hospital admissions) by region (A) and state (B) and survival to hospital discharge (%) among IHCA patients by region (C) and
state (D). States were divided into 4 groups according to quartiles (Q) of incidence and survival rates. Alabama, Delaware, Idaho, and
Washington, DC did not participate in the Nationwide Inpatient Sample during the study period. For state-level analyses, Alaska and
North Dakota were excluded because of low numbers of IHCA patients (<100 IHCA patients).
1418 Circulation April 21, 2015
Table 1. Regional Differences in Baseline Characteristics of Patients With IHCA
Absolute Standardized Difference
Overall
Northeast
(n=162 270)
Midwest
(n=159 581)
South
(n=316 201)
West
(n=200 413)
P Value
Midwest vs
Northeast
South vs
Northeast
West vs
Northeast
Age, mean±SD, y
67.2±16.1
69.2±16.0
67.1±15.8
66.6±16.1
66.3±16.4
<0.001
13.3
16.2
18.3
Female, %
45.4
45.9
45.5
46.0
43.9
<0.001
1.0
0.1
4.2
Race/ethnicity, %
<0.001
White
66.7
70.5
77.4
63.4
60.3
15.8
15.0
21.4
Black
20.3
20.3
19.4
27.2
10.2
2.1
16.4
28.3
Hispanic
9.4
7.4
2.0
7.7
19.5
26.1
1.1
35.9
Asian/Pacific Islander
3.0
1.6
0.7
1.0
9.2
8.4
5.2
34.0
Native American
0.5
0.2
0.5
0.6
0.7
4.9
6.6
8.2
Primary expected
payer, %
<0.001
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Medicare
64.3
67.7
65.8
65.5
58.6
4.0
4.7
19.0
Medicaid
10.1
9.6
9.0
8.5
13.7
1.9
4.0
12.9
Private insurance
18.2
17.4
19.5
16.9
19.9
5.3
1.5
6.2
5.2
4.1
4.0
6.8
4.6
1.0
11.5
2.2
5.1
10.3
14.7
0.6
1.0
0.2
Uninsured
Other
Weekend admission, %
2.2
1.1
1.6
2.4
3.1
23.7
23.9
23.7
23.5
24.0
Median household
income, %
<0.001
<0.001
0–25th percentile
31.3
25.1
27.9
41.6
22.9
6.3
35.5
5.1
26th–50th percentile
25.8
21.8
31.0
26.2
24.2
21.1
10.2
5.8
51st–75th percentile
23.3
23.6
25.7
19.4
27.4
4.9
10.2
8.7
76th–100 percentile
19.6
29.6
15.4
12.9
25.5
34.5
41.6
9.0
9.9
10.7
10.9
8.9
10.2
0.6
6.2
1.7
Hospital characteristics, %
Bed size
Small
<0.001
Medium
24.6
30.8
19.9
23.5
25.0
25.5
16.6
13.1
Large
65.5
58.4
69.2
67.7
64.8
22.6
19.2
13.1
Urban location
90.6
95.2
88.8
85.8
95.9
<0.001
23.9
32.6
3.1
Teaching hospital
44.3
58.4
55.1
39.1
32.4
<0.001
6.6
39.5
54.2
19.6
Comorbidities, %*
Smoking
13.9
9.5
15.1
14.2
16.0
<0.001
17.1
14.7
Dyslipidemia
17.6
15.2
19.6
17.3
18.5
<0.001
11.6
5.7
8.7
CAD
27.1
26.4
30.3
25.8
27.1
<0.001
8.8
1.3
1.7
Family history of CAD
0.8
0.6
0.9
0.8
0.8
<0.001
3.2
2.3
2.3
Previous myocardial
infarction
5.4
4.8
6.1
5.0
6.0
<0.001
5.6
0.5
5.2
Previous PCI
3.4
2.9
3.7
3.2
3.8
<0.001
4.7
1.8
5.2
Previous CABG
5.5
5.2
5.8
5.3
5.7
<0.001
2.5
0.6
2.2
Previous cardiac
arrest
0.3
0.1
0.2
0.3
0.3
<0.001
2.4
4.0
3.3
Atrial fibrillation
23.0
23.8
23.5
21.8
23.8
<0.001
0.7
4.8
0.1
5.3
4.3
4.9
5.1
6.8
<0.001
2.6
3.7
10.6
Congestive heart
failure
35.5
35.6
36.7
35.4
34.8
<0.001
2.2
0.5
1.7
Chronic pulmonary
disease
25.6
23.9
27.4
25.8
25.4
<0.001
7.9
4.4
3.5
Alcohol abuse
Depression
Diabetes mellitus
(uncomplicated)
5.5
4.5
6.2
5.2
6.0
<0.001
7.9
3.6
7.0
22.2
20.6
22.1
22.2
23.4
<0.001
3.7
4.0
6.7
(Continued)
Kolte et al Regional Variation in IHCA 1419
Table 1. Continued
Absolute Standardized Difference
Overall
Diabetes mellitus
(complicated)
7.5
Northeast
(n=162 270)
6.3
Midwest
(n=159 581)
7.3
South
(n=316 201)
6.6
West
(n=200 413)
9.9
P Value
<0.001
Midwest vs
Northeast
4.0
South vs
Northeast
West vs
Northeast
1.6
13.5
Hypertension
50.2
46.5
50.9
50.5
52.1
<0.001
8.8
8.1
11.2
Fluid and electrolyte
disorder
48.3
44.0
47.8
49.4
50.3
<0.001
7.6
10.8
12.6
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Obesity
8.0
5.4
8.5
8.2
9.5
<0.001
12.3
10.9
15.6
Peripheral vascular
disease
9.7
7.6
10.6
9.8
10.6
<0.001
10.4
7.6
10.2
Pulmonary circulation
disorders
4.8
4.1
5.1
4.9
5.0
<0.001
4.6
3.7
4.3
Renal failure
(chronic)
23.8
21.4
23.6
24.4
24.8
<0.001
5.3
7.1
8.0
VT/VF, %
21.0
18.7
23.3
20.1
22.5
<0.001
11.3
3.7
9.4
9.9
9.4
10.4
9.6
10.5
<0.001
3.2
0.7
3.7
Primary diagnosis of
AMI, %
AMI indicates acute myocardial infarction; CABG, coronary artery bypass grafting; CAD, coronary artery disease; IHCA, in-hospital cardiac arrest; PCI, percutaneous
coronary intervention; VF, ventricular fibrillation; and VT, ventricular tachycardia.
*See Table II in the online-only Data Supplement for a complete list of comorbidities.
across the 4 geographic regions and was 2.75, 2.33, 2.81, and
3.73 per 1000 admissions in the Northeast, Midwest, South,
and West, respectively (P<0.001; Figure 1A). Significant variation in IHCA incidence rates was also seen among individual
states (range, 0.86–6.31 per 1000 admissions; Figure 1B).
Baseline Patient and Hospital Characteristics
The mean±SD age of the overall cohort was 67.2±16.1 years.
Compared with the Northeast, patients in the Midwest, South,
and West were younger (Table 1). Male predominance was
seen in all 4 regions. Compared with the Northeast, the
Midwest had a higher proportion of whites, the South had a
higher proportion of blacks, and the West had a higher proportion of Hispanics and Asian/Pacific Islanders (Table 1).
Patients in the West were less likely to have Medicare and
more likely to have Medicaid as the primary expected payer
compared with those in Northeast. The South had the highest proportion of patients with a median household income in
the lowest quartile. Overall, patients were admitted to large,
urban, nonteaching hospitals. The Northeast and Midwest had
a higher proportion of teaching hospitals than the South and
West (Table 1).
The prevalence of most comorbidities was similar across
the 4 regions except for smoking and obesity, which were
more prevalent in the Midwest, South, and West compared
with the Northeast (Table 1 and Table II in the online-only
Data Supplement). Patients in the South and West also had
a higher prevalence of deficiency anemias and fluid/electrolyte disorders compared with those in Northeast. In the overall cohort, VT/VF was the cardiac arrest rhythm in 21.0%
patients. IHCA patients in the Northeast were less likely to
have VT/VF as the cardiac arrest rhythm compared with those
in other regions; however, this difference was most significant
between the Northeast and Midwest (18.7% versus 23.3%;
P<0.001; absolute standardized difference=11.3; Table 1).
Similarly, although patients in the Northeast appeared less
likely to have acute myocardial infarction as the primary diagnosis compared with those in Midwest and West, this observed
difference was not considered meaningful (absolute standardized difference <10).
Survival to Hospital Discharge
In the overall study cohort, survival to hospital discharge
was 24.7% (95% CI, 24.6–24.8). Compared with the
Northeast, survival to hospital discharge was higher in the
Midwest, South, and West (20.7% in the Northeast versus
27.7% in the Midwest, 24.3% in the South, and 26.2% in
the West; P<0.001; absolute standardized difference, 16.4,
8.7, and 12.9 for the Midwest, South, and West, respectively, compared with the Northeast; Figure 1C). When
adjusted for demographics, comorbidities, hospital characteristics, primary diagnosis of acute myocardial infarction, and initial cardiac arrest rhythm, risk-adjusted survival
remained significantly higher in the Midwest (OR, 1.33;
95% CI, 1.31–1.36; P<0.001), South (OR, 1.21; 95% CI,
1.19–1.23; P<0.001), and West (OR, 1.25; 95% CI, 1.23–
1.27; P<0.001) compared with the Northeast (Table 2).
Trend analysis revealed a significant increase in IHCA survival from 2003 to 2011 in the United States (adjusted OR
[per year], 1.05; 95% CI, 1.04–1.05; Ptrend<0.001) and in
each of the 4 census regions (adjusted OR [per year]: for
Northeast, 1.06 [95% CI, 1.06–1.07]; Midwest, 1.04 [95%
CI, 1.03–1.04]; South, 1.05 [95% CI, 1.04–1.05]; and West,
1.04 [95% CI, 1.03–1.04]; Ptrend<0.001 for all; Figure 2).
Significant variation in survival to discharge was also seen
across individual states (range, 18.1%–37.9%; Figure 1D).
1420 Circulation April 21, 2015
Table 2. Regional Differences in Outcomes of Patients With IHCA
Northeast
Midwest
South
West
Overall
Survival to discharge, %
20.7
27.7
24.3
26.2
Unadjusted OR
Reference
1.47 (1.44–1.49)
1.23 (1.21–1.25)
1.36 (1.34–1.38)
Adjusted OR
Reference
1.33 (1.31–1.36)
1.21 (1.19–1.23)
1.25 (1.23–1.27)
Total hospital cost, US $*
34 544±48 430
29 640±38 504
27 328±36 327
44 947±58 520
Unadjusted parameter estimate
Reference
0.93 (0.92–0.93)
0.85 (0.85–0.86)
1.34 (1.33–1.35)
Adjusted parameter estimate
Reference
0.88 (0.88–0.89)
0.85 (0.84–0.85)
1.27 (1.26–1.28)
Asystole and PEA
Survival to discharge, %
Unadjusted OR
Adjusted OR
18.3
25.4
22.4
23.8
Reference
1.51 (1.49–1.54)
1.29 (1.27–1.31)
1.39 (1.37–1.42)
Reference
1.41 (1.38–1.44)
1.27 (1.25–1.30)
1.31 (1.28–1.33)
33 825±47 476
28 382±37 512
26 678±36 046
44 156±32 407
Unadjusted parameter estimate
Reference
0.90 (0.89–0.91)
0.84 (0.83–0.85)
1.32 (1.31–1.33)
Adjusted parameter estimate
Reference
0.87 (0.86–0.87)
0.84 (0.84–0.85)
1.26 (1.25–1.27)
30.9
35.3
31.8
34.2
Unadjusted OR
Reference
1.22 (1.18–1.26)
1.04 (1.01–1.07)
1.16 (1.13–1.20)
Adjusted OR
Reference
1.14 (1.11–1.18)
1.05 (1.01–1.08)
1.08 (1.04–1.11)
37 704±52 302
33 794±41 342
29 898±37 308
47 676±57 077
Unadjusted parameter estimate
Reference
0.99 (0.98–1.01)
0.88 (0.86–0.89)
1.38 (1.35–1.40)
Adjusted parameter estimate
Reference
0.95 (0.94–0.97)
0.89 (0.87–0.90)
1.32 (1.30–1.35)
Total hospital cost, US $*
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VF and pulseless VT
Survival to discharge, %
Total hospital cost, US $*
Numbers in parenthesis represent 95% confidence interval. IHCA indicates in-hospital cardiac arrest; OR, odds ratio; PEA, pulseless
electric activity; VF, ventricular fibrillation; and VT, ventricular tachycardia.
*Total hospital cost (inflation adjusted to 2014 US dollars) is expressed as mean±SD. Unadjusted and adjusted parameter estimates
reported for total hospital cost are the antilog of the β coefficients [exp(β)] obtained from the log-transformed linear regression models.
Risk-adjusted survival rate was lowest in New York (20.4%)
and highest in Wyoming (40.2%; Table III in the online-only
Data Supplement).
When stratified according to the cardiac arrest rhythm, overall survival to discharge was 22.5% and 33.0% in patients with
PEA/asystole and VT/VF, respectively. In patients with PEA/
asystole as the cardiac arrest rhythm, survival to discharge
was higher in the Midwest, South, and West compared with
the Northeast (Table 2). Similar results were seen in patients
with VT/VF. However, regional differences in survival were
smaller in magnitude in patients with VT/VF compared with
those with PEA/asystole (Table 2).
Table IV in the online-only Data Supplement shows differences in hospital and patient characteristics among IHCA
survivors and nonsurvivors. In addition to geographic region,
several other baseline characteristics were independently
associated with increased or decreased risk of survival to hospital discharge among IHCA patients (Table V in the onlineonly Data Supplement).
Association Between IHCA Incidence and Survival
Among Regions and States
Although the Midwest had the lowest IHCA incidence and
highest survival rate, there was no association overall between
IHCA incidence and survival rate at the regional level (r=0.024,
P=0.98; Figure 3A). However, at the state level, there was a
significant negative correlation between IHCA incidence and
survival (r=−0.50, P=0.001); that is, states with higher survival also had a lower IHCA incidence rate (Figure 3B).
Total Hospital Cost
The median inflation-adjusted hospital cost for the overall
study cohort was US $15 673 (interquartile range, $7164–
$35 016). Total hospital cost incurred was lowest in the South
and highest in the West (Figure 4A). When adjusted for potential confounding variables, compared with the Northeast, hospital cost was significantly lower in the Midwest (adjusted
parameter estimate, 0.88; 95% CI, 0.88–0.89; P<0.001) and
South (adjusted parameter estimate, 0.85; 95% CI, 0.84–0.85;
P<0.001) and higher in the West (adjusted parameter estimate, 1.27; 95% CI, 1.26–1.28; P<0.001). Similar regional
differences in total hospital cost were seen when patients were
stratified according to the cardiac arrest rhythm (Table 2 and
Figure I in the online-only Data Supplement).
Discharge Disposition Among Survivors
Overall, among IHCA patients who survived to hospital discharge, the discharge disposition was as follows: home (selfcare), 30.2%; short-term hospital, 14.5%; skilled nursing
facility, 40.4%; home health care, 13.7%; and other, 1.2%.
Compared with the Northeast, survivors of IHCA in the
Midwest, South, and West were more likely to be discharged
home (self-care) and less likely to require home health care
or transfer to skilled nursing facility (P<0.001; Figure 4B).
Kolte et al Regional Variation in IHCA 1421
Figure 2. Temporal trends in in-hospital cardiac
arrest (IHCA) survival. Temporal trends (2003–2011)
in IHCA survival in the United States (A) and in 4
census regions (B). Ptrend<0.001 for all.
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Similar results were seen in patients with PEA/asystole and
VT/VF (Figure II in the online-only Data Supplement).
Discussion
In this large, all-payer, nationwide database of hospitalized
patients, we observed significant variations in IHCA incidence, survival, and resource use across geographic regions
within the United States. The Midwest had the lowest IHCA
incidence and highest survival to hospital discharge rate. Total
hospital cost was highest in the West, whereas discharge to
skilled nursing facility and use of home health care were highest in the Northeast. This variation in survival and resource
use was explained only partially by differences in patient case
mix or hospital characteristics.
Previous studies have reported large variations in IHCA
incidence ranging from 2.5 to 13.1 per 1000 admissions.1,3,28,29
In our study, the incidence of in-hospital CPR for IHCA
was 2.85 per 1000 admissions, which is consistent with that
reported by Ehlenbach et al1 and Kazaure et al.3 IHCA incidence was lowest in the Midwest and highest in the West.
IHCA incidence is a function of both the patient’s severity
of illness and the institutional response and process of care
for treating acutely and chronically ill patients and preventing IHCA. The prevalence of most comorbidities was similar
across all regions among patients who experienced an IHCA,
suggesting that the burden of comorbidities or illness may
contribute little to the regional differences in IHCA rates. In
a single-center study, Wallmuller et al30 showed that IHCA is
attributable to cardiac causes in 63% of cases with either VF
or PEA/asystole as the cardiac arrest rhythm. Regional differences in receipt of guideline-recommended therapies for
patients admitted with cardiovascular diseases have been well
described and may contribute to some of the regional differences in IHCA, particularly those events related to a cardiac
origin.31–33
Merchant et al34 previously reported that hospital characteristics such as small size, urban location, and high proportion of black patients are independently associated with higher
IHCA event rates. In our study, IHCA rates were higher in
regions with a greater proportion of nonwhite patients, which
may reflect racial disparities in the quality of inpatient care, as
reported in several studies.35,36 Furthermore, a higher proportion of hospitals in the West were medium sized and in urban
locations compared with the Midwest (Table 1). Hospital size
may be an indirect marker of available resources, with larger
hospitals having more resources in place for the early recognition of clinical deterioration and prevention of IHCA. Finally,
regional variation in IHCA rates could also be attributable to
differences in ICD-9-CM coding, completeness of case ascertainment, and potential for unreported or unrecognized cases.
Previous studies have shown hospital variation in IHCA survival and survival trends.8,11 However, these studies included
only 10% of all hospitals in the United States participating in
a voluntary IHCA quality improvement program and may not
be representative of the entire population. Furthermore, it is
unclear whether hospitals with lower survival rates or smaller
improvements in survival rates over time are clustered in specific states or regions in the United States. We observed substantial regional variation in IHCA survival, which was lowest
in the Northeast and highest in the Midwest. Although not
systematically described, similar regional variation in survival
has been observed in previous studies.10 However, the reasons
for these large variations in outcomes across states and regions
are less clear. An inverse relationship between a hospital’s
1422 Circulation April 21, 2015
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Figure 3. Correlation between in-hospital cardiac arrest (IHCA)
incidence and survival. Correlation between crude IHCA
incidence rate (per 1000 hospital admissions) and unadjusted
survival to discharge rate (%) by region (A) and state (B).
IHCA incidence and survival rates has been described.7 We
found a similar relationship at the state and, to some extent, at
the regional level, suggesting that states/regions with higher
IHCA survival rates may also be good at preventing IHCA.
In our cohort, 21% of IHCA patients had VT/VF as the
cardiac arrest rhythm. Although this proportion is lower
than that reported by Merchant et al8 (2 of 5 [40%] with
VT/VF) and Wallmuller et al30 (39% with VF), our findings
correlate well with several other studies using the Get With
The Guidelines–Resuscitation registry that showed VT/VF
as the cardiac arrest rhythm in 19.2% to 25.8% of IHCA
patients.7–10,24,30,37 Overall survival to discharge in our study
was higher than in previous reports, particularly in patients
with PEA/asystole.9,10,38 Patient who did not have ICD-9-CM
codes for VT or VF were presumed to have PEA/asystole
in our study. These data should therefore be interpreted
with caution because it is possible that this group may have
included patients who received CPR when not in PEA/asystole (eg, patients with bradycardia). This may also explain the
higher survival rate among patients with IHCA that was not
attributable to VT/VF and hence the overall cohort compared
with similar patients in previous studies.12 Regional variation
in outcomes was smaller in magnitude in patients with VT/
VF compared with those with asystole/PEA as the cardiac
arrest rhythm, suggesting that regional differences in resuscitation and postresuscitation care may be variable, depending
on the cardiac arrest rhythm.
Other potential reasons for regional variations in IHCA
survival may include differences in IHCA preparedness (eg,
availability and allocation of resources for preventing and
managing IHCA, rapid response or code teams, dedicated
nursing staff, routine resuscitation simulations), quality of
CPR and postresuscitation care, institutional culture (eg,
leadership, participation in quality improvement programs,
duration of resuscitation attempt, implementation of DNR
orders), and regulatory requirements (eg, mandatory reporting of IHCA incidence and outcomes).5,37 Despite substantial
regional variation in survival, we found a significant temporal
improvement in IHCA survival nationally and in all 4 regions
from 2003 to 2011. Our results are consistent with a previous
smaller study from a volunteer registry involving 374 US hospitals that showed a trend for improved IHCA survival from
2000 to 2009.10
Another important finding of our study is the regional variation in resource use in IHCA patients. Total hospital cost
was highest in the West. On the other hand, the use of post–
acute care services (skilled nursing facility and home health
care) among survivors was lowest in the West and highest in
the Northeast. A recent report from the Institute of Medicine
concluded that regional variation in total Medicare spending is driven largely by variation in the use of post–acute
care services and, to a lesser extent, by variation in the use
of acute care services.39 Data from the Dartmouth Atlas of
Health Care have shown that patients treated in regions with
higher spending intensity do not have better quality of care
or outcomes.40,41 Thus, it is possible that regional variation
in hospital cost and use of post–acute care services by IHCA
patients may reflect differences in expenditure of resources
with higher spending for acute care (partly as a result of a
higher IHCA event rate) and relatively lower spending for
post–acute care in the West and vice versa in the Northeast.
Other factors that can contribute to regional variation in
resource use include differences in primary payer status
and advance directives specifying limitations in end-of-life
care.42 Alternatively, regional differences in discharge disposition may be attributable to differences in the availability of
resources (eg, post–acute care services) or level of disability
among survivors of IHCA.
Limitations
Our study has important limitations. First, although we
adjusted for multiple confounding variables, the possibility
of residual measured and unmeasured confounders affecting
IHCA outcomes cannot be completely eliminated. Second,
because the NIS is an administrative database, the accuracy
and consistency of the data depend heavily on the training
and expertise of the coders and the coding practices and
capabilities of individual hospitals. We may have underestimated the incidence of IHCA because of inconsistent coding
of CPR and lack of information on the use of defibrillators.
Kolte et al Regional Variation in IHCA 1423
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Figure 4. Regional variation in resource use for in-hospital cardiac arrest. Regional differences in inflation-adjusted total hospital cost (A)
and discharge disposition among survivors (B).
Patients who received open-chest CPR (ICD-9-CM 37.91)
and those who may have experienced an IHCA but whose
records do not include a code for CPR were not included
in our study. It is difficult to validate individual ICD-9-CM
codes or to identify IHCA patients for whom CPR was not
coded from medical records because the NIS contains deidentified data. Third, the lack of complete information on
cardiac arrest rhythm is an important limitation of our study.
Because PEA and asystole do not have unique ICD-9-CM
codes, the outcome data in this subgroup need to be interpreted with caution. Fourth, there may be variation in survival to discharge with favorable neurological status, which
may be a more important outcome than survival to discharge
alone. However, data on cerebral performance category on
admission and at discharge are not available in the NIS;
hence, we were unable to determine the proportion of survivors with good neurological status in the present study.
Fifth, the NIS lacks information on certain preresuscitation
(eg, location of IHCA, interventions already in place before
IHCA such as mechanical ventilation and use of intravenous vasopressors, and presence of a rapid response system)
and resuscitation (eg, medication use, time delay between
the onset of IHCA and CPR, quality of CPR, and time to
defibrillation) variables, which may vary among hospitals
and influence IHCA survival.43,44 Finally, data on patient and
family preferences concerning end-of-life care, DNR status,
and timing of DNR are not collected in the NIS. Therefore,
some of the observed variation in IHCA outcomes may be
attributable to systematic regional and institutional differences in initiation of DNR orders.45
Conclusions
In this large, all-payer, nationwide database of hospitalized
patients, we observed a significant variation in IHCA incidence, survival, and resource use across geographic regions
within the United States. This variation was explained only
partially by differences in patient case mix or hospital characteristics. We also found significant improvement in IHCA
survival nationally and in all regions from 2003 to 2011. A
national surveillance program to monitor and report incidence, processes of care, and outcomes at the state, regional,
and national levels could help identify additional patient- and
hospital-level factors responsible for the observed geographic
differences to develop targeted interventions to enhance the
overall quality of resuscitation and postresuscitation care and
to improve IHCA outcomes.
Disclosures
Dr Bhatt discloses the following relationships: advisory board:
Elsevier Practice Update Cardiology, Medscape Cardiology, and
Regado Biosciences; board of directors: Boston VA Research
Institute and Society of Cardiovascular Patient Care; chair:
American Heart Association Get With The Guidelines Steering
Committee; Data Monitoring committees: Duke Clinical Research
Institute, Harvard Clinical Research Institute, Mayo Clinic, and
Population Health Research Institute; honoraria: American College
of Cardiology (senior associate editor, Clinical Trials and News,
1424 Circulation April 21, 2015
http://www.ACC.org), Belvoir Publications (editor in chief, Harvard
Heart Letter), Duke Clinical Research Institute (clinical trial steering committees), Harvard Clinical Research Institute (clinical
trial steering committee), HMP Communications (editor in chief,
Journal of Invasive Cardiology), Journal of the American College
of Cardiology (associate editor; section editor, Pharmacology),
Population Health Research Institute (Clinical Trial Steering
Committee), Slack Publications (chief medical editor, Cardiology
Today’s Intervention), and WebMD (CME steering committees);
other: Clinical Cardiology (deputy editor); research funding:
Amarin, AstraZeneca, Bristol-Myers Squibb, Eisai, Ethicon, Forest
Laboratories, Ischemix, Medtronic, Pfizer, Roche, Sanofi Aventis,
and The Medicines Company; and unfunded research: FlowCo,
PLx Pharma, and Takeda. Dr Iwai discloses the following relationships: honoraria: Medtronic, Inc, Biotronik, and St. Jude Medical;
and speaker’s bureau: Biosense-Webster. The other authors report
no conflicts.
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1712. doi: 10.1001/archinte.165.15.1705.
Clinical Perspective
Each year, ≈209 000 adult in-hospital cardiac arrests (IHCAs) occur in the United States, with survival to hospital discharge
rates of 18% to 20%. IHCA has not received the same level of focused research as out-of-hospital cardiac arrest. We analyzed data on 838 465 IHCA patients included in the 2003 to 2011 Nationwide Inpatient Sample to examine regional differences in IHCA incidence, survival to discharge, and resource use. Overall IHCA incidence in the United States was 2.85
per 1000 hospital admissions. IHCA incidence was lowest in the Midwest and highest in the West (2.33 and 3.73 per 1000
hospital admissions, respectively). Compared with the Northeast, risk-adjusted survival to discharge was significantly higher
in the Midwest (odds ratio, 1.33; 95% confidence interval, 1.31–1.36), South (odds ratio, 1.21; 95% confidence interval,
1.19–1.23), and West (odds ratio, 1.25; 95% confidence interval,1.23–1.27). Risk-adjusted survival was lowest in New York
(20.4%) and highest in Wyoming (40.2%). IHCA survival increased significantly from 2003 to 2011 in the United States and
in all regions (all Ptrend<0.001). Total hospital cost was highest in the West, whereas discharge to skilled nursing facility and
use of home health care among survivors was highest in the Northeast. Regional variation in IHCA outcomes was explained
only partially by differences in patient and hospital characteristics. A national surveillance program to monitor and report
IHCA incidence, processes of care, and outcomes at the state, regional, and national levels could help identify additional
patient- and hospital-level factors responsible for the observed geographic differences to develop targeted interventions to
enhance the quality of resuscitation and postresuscitation care and to improve IHCA outcomes.
Go to http://cme.ahajournals.org to take the CME quiz for this article.
Regional Variation in the Incidence and Outcomes of In-Hospital Cardiac Arrest in the
United States
Dhaval Kolte, Sahil Khera, Wilbert S. Aronow, Chandrasekar Palaniswamy, Marjan Mujib,
Chul Ahn, Sei Iwai, Diwakar Jain, Sachin Sule, Ali Ahmed, Howard A. Cooper, William H.
Frishman, Deepak L. Bhatt, Julio A. Panza and Gregg C. Fonarow
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Circulation. 2015;131:1415-1425; originally published online March 19, 2015;
doi: 10.1161/CIRCULATIONAHA.114.014542
Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2015 American Heart Association, Inc. All rights reserved.
Print ISSN: 0009-7322. Online ISSN: 1524-4539
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SUPPLEMENTAL MATERIAL
1
Supplemental Tables
Table 1. International Classification of Diseases, Ninth Edition, Clinical Modification and Clinical Classifications Software
Codes used to identify co-morbidities
Co-morbidities
Source
Code(s)
Dyslipidemia
Coronary artery disease
CCS
ICD-9-CM
53
414.00 – 414.07
Family history of coronary artery disease
Prior myocardial infarction
Prior percutaneous coronary intervention
Prior coronary artery bypass grafting
ICD-9-CM
ICD-9-CM
ICD-9-CM
ICD-9-CM
V17.3
412
V45.82
V45.81
Prior cardiac arrest
Family history of sudden cardiac death
Carotid artery disease
ICD-9-CM
ICD-9-CM
ICD-9-CM
V12.53
V17.41
433.10
Dementia
ICD-9-CM
290.xx, 294.1x, 294.2x, 294.8, 331.0-331.12, 331.82, 797
Atrial fibrillation
Acute myocardial infarction
ICD-9-CM
ICD-9-CM
427.31
410.1x – 410.9x
CCS – Clinical Classification Software; ICD-9-CM – International Classification of Diseases, Ninth Edition, Clinical Modification
2
Table 2. Regional Differences in Comorbidities of Patients with In-Hospital Cardiac Arrest
Comorbidity
Smoking
Dyslipidemia
CAD
Family history of CAD
Prior myocardial infarction
Prior PCI
Prior CABG
Prior cardiac arrest
Family h/o sudden cardiac death
Carotid artery disease
Dementia
Atrial fibrillation
AIDS
Alcohol abuse
Deficiency anemia
RA/collagen vascular diseases
Chronic blood loss anemia
Congestive heart failure
Chronic pulmonary disease
Coagulopathy
Depression
Diabetes mellitus (uncomplicated)
Diabetes mellitus (complicated)
Drug abuse
Hypertension
Hypothyroidism
Liver disease
Lymphoma
Overall
NE
MW
S
W
13.9%
17.6%
27.1%
0.8%
5.4%
3.4%
5.5%
0.3%
<0.1%
0.9%
6.1%
23.0%
0.4%
5.3%
20.6%
2.3%
2.0%
35.5%
25.6%
13.2%
5.5%
22.2%
7.5%
2.9%
50.2%
8.5%
4.3%
1.3%
(n=162,270)
9.5%
15.2%
26.4%
0.6%
4.8%
2.9%
5.2%
0.1%
<0.1%
0.7%
6.9%
23.8%
0.6%
4.3%
15.3%
1.9%
1.7%
35.6%
23.9%
11.2%
4.5%
20.6%
6.3%
2.6%
46.5%
7.3%
3.9%
1.4%
(n=159,581)
15.1%
19.6%
30.3%
0.9%
6.1%
3.7%
5.8%
0.2%
<0.1%
1.0%
5.4%
23.5%
0.2%
4.9%
19.3%
2.5%
2.1%
36.7%
27.4%
12.2%
6.2%
22.1%
7.3%
2.3%
50.9%
8.9%
3.5%
1.3%
(n=316,201)
14.2%
17.3%
25.8%
0.8%
5.0%
3.2%
5.3%
0.3%
<0.1%
0.9%
6.1%
21.8%
0.5%
5.1%
20.3%
2.5%
2.0%
35.4%
25.8%
13.0%
5.2%
22.2%
6.6%
2.8%
50.5%
7.9%
3.9%
1.3%
(n=200,413)
16.0%
18.5%
27.1%
0.8%
6.0%
3.8%
5.7%
0.3%
<0.1%
0.8%
6.1%
23.8%
0.2%
6.8%
26.5%
2.4%
2.2%
34.8%
25.4%
16.0%
6.0%
23.4%
9.9%
3.8%
52.1%
10.1%
5.9%
1.3%
3
P Value
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.005
Absolute Standardized Difference
MW vs NE
17.1
11.6
8.8
3.2
5.6
4.7
2.5
2.4
0.4
3.9
6.0
0.7
6.4
2.6
10.6
3.7
2.7
2.2
7.9
3.1
7.9
3.7
4.0
1.9
8.8
5.9
2.3
0.5
S vs NE
14.7
5.7
1.3
2.3
0.5
1.8
0.6
4.0
0.5
2.6
3.0
4.8
1.5
3.7
13.3
3.7
2.1
0.5
4.4
5.6
3.6
4.0
1.6
1.2
8.1
2.4
0.1
1.1
W vs NE
19.6
8.7
1.7
2.3
5.2
5.2
2.2
3.3
1.4
1.7
3.3
0.1
5.5
10.6
27.9
3.4
3.3
1.7
3.5
14.0
7.0
6.7
13.5
7.1
11.2
9.9
9.1
0.6
Fluid and electrolyte disorder
Metastatic cancer
Other neurological disorders
Obesity
Paralysis
Peripheral vascular disease
Psychoses
Pulmonary circulation disorders
Renal failure (chronic)
Solid tumor without metastasis
Peptic ulcer (non-bleeding)
Valvular disease
Weight loss
48.3%
3.9%
11.2%
8.0%
3.5%
9.7%
3.3%
4.8%
23.8%
3.1%
<0.1%
5.8%
9.9%
44.0%
4.4%
10.6%
5.4%
3.3%
7.6%
3.0%
4.1%
21.4%
3.4%
<0.1%
5.6%
7.1%
47.8%
3.7%
11.0%
8.5%
3.2%
10.6%
3.5%
5.1%
23.6%
3.0%
<0.1%
5.6%
10.6%
49.4%
3.8%
11.1%
8.2%
3.2%
9.8%
3.0%
4.9%
24.4%
3.1%
<0.1%
5.9%
10.7%
50.3%
4.0%
12.1%
9.5%
4.4%
10.6%
3.9%
5.0%
24.8%
3.0%
0.1%
5.9%
10.5%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
7.6
3.9
1.3
12.3
0.4
10.4
2.8
4.6
5.3
1.9
0.7
0.1
12.3
10.8
3.3
1.7
10.9
0.5
7.6
0.1
3.7
7.1
1.3
1.3
1.4
12.4
NE – Northeast; MW – Midwest; S – South; W – West; CAD – coronary artery disease; PCI – percutaneous coronary intervention; CABG –
coronary artery bypass grafting; AIDS – acquired immunodeficiency syndrome; RA – rheumatoid arthritis; VT – ventricular tachycardia; VF –
ventricular fibrillation; AMI – acute myocardial infarction
4
12.6
2.2
4.7
15.6
5.8
10.2
5.0
4.3
8.0
2.0
1.8
1.3
12.0
Table 3. Risk-Adjusted Survival to Hospital Discharge Rates for IHCA Patients by State
State
Risk-Adjusted
Survival (%)
Arkansas
23.8
Arizona
32.8
California
25.1
Colorado
31.5
Connecticut
25.0
Florida
26.2
Georgia
24.9
Hawaii
23.6
Iowa
33.4
Illinois
26.0
Indiana
28.7
Kansas
25.4
Kentucky
25.4
Louisiana
32.3
Massachusetts
29.9
Maryland
25.8
Maine
30.9
Michigan
27.7
Minnesota
32.2
Missouri
24.1
Mississippi
30.2
Montana
31.6
North Carolina
25.1
Nebraska
30.7
New Hampshire
31.3
New Jersey
21.3
New Mexico
28.4
Nevada
22.8
New York
20.4
Ohio
29.0
Oklahoma
26.6
Oregon
28.3
5
Pennsylvania
28.3
Rhode Island
22.7
South Carolina
25.9
South Dakota
34.5
Tennessee
24.1
Texas
26.9
Utah
29.5
Virginia
26.3
Vermont
26.0
Washington
34.7
Wisconsin
31.5
West Virginia
28.6
Wyoming
40.2
6
Table 4. Baseline Characteristics of IHCA Patients Stratified by Survival to Hospital Discharge
Survival to Hospital Discharge
Age (years), mean ± SD
Female
Race/Ethnicity
Caucasian
African-American
Hispanic
Asian/Pacific Islander
Native American
Primary expected payer
Medicare
Medicaid
Private insurance
Uninsured
Other
Weekend admission
Median household income
0-25th percentile
26th-50th percentile
51st-75th percentile
76th-100th percentile
Hospital characteristics
Bed size
Small
Medium
Large
Urban location
No
(n=631,396)
67.8 ± 16.1
44.8%
Yes
(n=207,069)
65.1 ± 15.9
47.1%
65.7%
21.2%
9.5%
3.1%
0.5%
69.8%
17.7%
9.1%
2.8%
0.6%
65.5%
9.9%
16.9%
5.5%
2.1%
24.4%
60.9%
10.4%
22.0%
4.3%
2.3%
21.8%
31.9%
25.6%
23.1%
19.4%
29.4%
26.2%
24.0%
20.4%
P Value
<0.001
<0.001
<0.001
Absolute Standardized
Difference
16.9
4.6
8.7
8.8
1.4
1.6
1.3
<0.001
<0.001
<0.001
9.5
1.7
12.9
5.8
1.4
6.1
5.6
1.3
2.3
2.6
<0.001
9.8%
24.7%
65.5%
90.8%
10.2%
24.2%
65.6%
90.1%
7
<0.001
1.2
1.1
0.2
2.2
Teaching hospital
Hospital region
Northeast
Midwest
South
West
Comorbidities
Smoking
Dyslipidemia
CAD
Family history of CAD
Prior myocardial infarction
Prior PCI
Prior CABG
Prior cardiac arrest
Family h/o sudden cardiac death
Carotid artery disease
Dementia
Atrial fibrillation
AIDS
Alcohol abuse
Deficiency anemia
RA/collagen vascular diseases
Chronic blood loss anemia
Congestive heart failure
Chronic pulmonary disease
Coagulopathy
Depression
Diabetes mellitus (uncomplicated)
Diabetes mellitus (complicated)
Drug abuse
Hypertension
44.2%
44.4%
20.4%
18.3%
37.9%
23.4%
16.2%
21.3%
37.1%
25.3%
13.2%
16.2%
25.6%
0.6%
5.2%
3.3%
5.6%
0.2%
<0.1%
0.7%
6.5%
22.5%
0.4%
5.2%
19.8%
2.4%
1.8%
34.1%
25.2%
13.8%
5.1%
22.2%
6.9%
2.7%
49.2%
15.9%
22.1%
31.5%
1.4%
5.9%
3.8%
5.0%
0.3%
<0.1%
1.3%
4.8%
24.5%
0.3%
5.7%
23.1%
2.1%
2.4%
39.8%
27.0%
11.5%
6.6%
22.0%
9.1%
3.5%
53.3%
8
0.144
<0.001
0.4
10.8
7.7
1.6
4.4
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.004
0.452
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.073
<0.001
<0.001
<0.001
7.5
15.0
13.1
8.3
2.6
3.2
2.9
0.7
0.2
5.3
7.4
4.8
0.9
2.5
8.2
2.4
4.0
11.7
4.3
7.0
6.6
0.5
8.0
4.7
8.2
Hypothyroidism
Liver disease
Lymphoma
Fluid and electrolyte disorder
Metastatic cancer
Other neurological disorders
Obesity
Paralysis
Peripheral vascular disease
Psychoses
Pulmonary circulation disorders
Renal failure (chronic)
Solid tumor without metastasis
Peptic ulcer (non-bleeding)
Valvular disease
Weight loss
VT/VF
Primary diagnosis of AMI
8.3%
4.7%
1.5%
48.8%
4.4%
10.8%
7.3%
3.3%
9.7%
3.0%
4.9%
23.7%
3.4%
<0.1%
5.6%
9.4%
18.7%
9.1%
9.1%
3.2%
0.9%
46.6%
2.4%
12.5%
10.2%
4.1%
9.6%
4.2%
4.6%
24.0%
2.2%
<0.1%
6.2%
11.6%
28.1%
12.3%
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.070
<0.001
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
2.8
7.4
5.2
4.4
11.4
5.5
10.3
4.4
0.5
6.5
1.4
0.8
7.0
1.1
2.5
7.2
22.3
10.2
CAD – coronary artery disease; PCI – percutaneous coronary intervention; CABG – coronary artery bypass grafting; AIDS – acquired
immunodeficiency syndrome; RA – rheumatoid arthritis; VT – ventricular tachycardia; VF – ventricular fibrillation; AMI – acute
myocardial infarction.
9
Table 5. Independent Predictors of Survival to Hospital Discharge in Patients with IHCA
Variable
Odds Ratio
95% Confidence Interval
P Value
Age (>70 vs. ≤70 years)
0.69
0.68 – 0.70
<0.001
Sex (Female vs. Male)
1.17
1.15 – 1.18
<0.001
Caucasian (Ref.)
1.00
—
—
African-American
0.81
0.80 – 0.82
<0.001
Hispanic
0.95
0.94 – 0.97
<0.001
Asian/Pacific Islander
0.88
0.85 – 0.91
<0.001
Native American
1.08
1.01 – 1.16
0.019
Medicare
0.81
0.80 – 0.82
<0.001
Medicaid
0.84
0.82 – 0.85
<0.001
Private insurance (Ref.)
1.00
—
—
Uninsured
0.62
0.60 – 0.64
<0.001
Other
0.86
0.83 – 0.89
<0.001
0.87
0.86 – 0.88
<0.001
0-25th percentile (Ref.)
1.00
—
—
26th-50th percentile
1.07
1.05 – 1.08
<0.001
51st-75th percentile
1.09
1.07 – 1.10
<0.001
76th-100th percentile
1.14
1.12 – 1.16
<0.001
Small
1.07
1.05 – 1.09
<0.001
Medium
1.02
1.01 – 1.03
0.001
Large (Ref.)
1.00
—
—
Hospital location (Urban vs. Rural)
0.85
0.84 – 0.87
<0.001
Teaching vs. Non-teaching hospital
1.06
1.05 – 1.07
<0.001
Race/Ethnicity
Primary expected payer
Weekend vs. Weekday admission
Median household income
Hospital size
Hospital region
10
Northeast (Ref.)
1.00
—
—
Midwest
1.33
1.31 – 1.36
<0.001
South
1.21
1.19 – 1.23
<0.001
West
1.25
1.23 – 1.27
<0.001
Smoking
1.06
1.04 – 1.07
<0.001
Dyslipidemia
1.28
1.26 – 1.30
<0.001
CAD
1.22
1.21 – 1.24
<0.001
Family history of CAD
1.73
1.65 – 1.82
<0.001
Prior myocardial infarction
0.92
0.90 – 0.94
<0.001
Prior PCI
0.92
0.90 – 0.95
<0.001
Prior CABG
0.74
0.72 – 0.76
<0.001
Prior cardiac arrest
0.97
0.88 – 1.08
0.613
Family h/o sudden cardiac death
0.71
0.43 – 1.16
0.174
Carotid artery disease
1.57
1.49 – 1.65
<0.001
Dementia
0.78
0.76 – 0.80
<0.001
Atrial fibrillation
1.12
1.10 – 1.13
<0.001
AIDS
0.97
0.89 – 1.06
0.467
Alcohol abuse
1.17
1.14 – 1.20
<0.001
Deficiency anemia
1.24
1.22 – 1.25
<0.001
RA/collagen vascular diseases
0.76
0.73 – 0.79
<0.001
Chronic blood loss anemia
1.46
1.41 – 1.51
<0.001
Congestive heart failure
1.25
1.23 – 1.26
<0.001
Chronic pulmonary disease
1.05
1.04 – 1.07
<0.001
Coagulopathy
0.82
0.81 – 0.84
<0.001
Depression
1.14
1.12 – 1.17
<0.001
Diabetes mellitus (uncomplicated)
0.92
0.90 – 0.93
<0.001
Diabetes mellitus (complicated)
1.18
1.16 – 1.21
<0.001
Drug abuse
1.30
1.26 – 1.34
<0.001
Hypertension
1.11
1.10 – 1.13
<0.001
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Hypothyroidism
1.01
0.99 – 1.02
0.528
Liver disease
0.66
0.64 – 0.68
<0.001
Lymphoma
0.65
0.62 – 0.68
<0.001
Fluid and electrolyte disorder
0.90
0.89 – 0.91
<0.001
Metastatic cancer
0.55
0.54 – 0.57
<0.001
Other neurological disorders
1.22
1.20 – 1.24
<0.001
Obesity
1.14
1.12 – 1.16
<0.001
Paralysis
1.28
1.24 – 1.31
<0.001
Peripheral vascular disease
0.86
0.84 – 0.87
<0.001
Psychoses
1.34
1.31 – 1.38
<0.001
Pulmonary circulation disorders
0.87
0.85 – 0.89
<0.001
Renal failure (chronic)
0.93
0.92 – 0.94
<0.001
Solid tumor without metastasis
0.69
0.67 – 0.72
<0.001
Peptic ulcer (non-bleeding)
0.58
0.44 – 0.78
<0.001
Valvular disease
1.12
1.10 – 1.15
<0.001
Weight loss
1.42
1.39 – 1.44
<0.001
Cardiac arrest rhythm (VT/VF vs. PEA/asystole)
1.56
1.54 – 1.58
<0.001
Primary diagnosis (AMI vs. Other)
1.11
1.10 – 1.13
<0.001
CAD – coronary artery disease; PCI – percutaneous coronary intervention; CABG – coronary artery
bypass grafting; AIDS – acquired immunodeficiency syndrome; RA – rheumatoid arthritis; VT –
ventricular tachycardia; VF – ventricular fibrillation; AMI – acute myocardial infarction.
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Supplemental Figures and Figure Legends
Figure 1. Regional Variation in Total Hospital Cost for IHCA by Cardiac Arrest Rhythm
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Figure 2. Regional Variation in Discharge Disposition Among Survivors of IHCA by Cardiac
Arrest Rhythm
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Figure Legends:
Figure 1. Regional Variation in Total Hospital Cost for IHCA by Cardiac Arrest Rhythm
Regional differences in inflation-adjusted total hospital cost for IHCA patients with
asystole/PEA (A) and VT/VF (B) as the initial cardiac arrest rhythm.
PEA – pulseless electrical activity; VT – ventricular tachycardia; VF – ventricular fibrillation.
Figure 2. Regional Variation in Discharge Disposition Among Survivors of IHCA by Cardiac
Arrest Rhythm
Regional differences in discharge disposition among survivors of IHCA with asystole/PEA (A)
and VT/VF (B) as the initial cardiac arrest rhythm.
PEA – pulseless electrical activity; VT – ventricular tachycardia; VF – ventricular fibrillation.
15