European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
www.elsevier.com/locate/ejcts
Evaluating outcomes of hospital care following coronary artery bypass
surgery in Rome, Italyq
Nera Agabitia,*, Carla Anconab, Francesco Forastiereb, Massimo Arcàb, Carlo A. Peruccib
a
Agency for Public Health, Lazio Region, Via di S. Constanza 53, 00198 Rome, Italy
b
Department of Epidemiology, ASL RM/E, Rome, Italy
Received 21 June 2002; received in revised form 17 December 2002; accepted 20 December 2002
Abstract
Objective: Monitoring health outcomes across hospitals has become a growing interest as a potential means to promote quality of care, but
in Italy it is at the beginning stage. We aimed at comparing the performance of different cardiac surgery units and testing the utility of
routinely collected data in this respect. Methods: From the Lazio region hospital information system (HIS), we selected a cohort of 1603
individuals (84% males; mean age 63 years, SD 8) residing in Rome (2,685,890 inhabitants), who underwent isolated coronary artery bypass
surgery (CABG, ICD-9-CM code: 36.1) in the period 1996– 97 in seven major cardiac surgery units in the city. They were identified as A, B,
C (teaching), D and E (non-teaching) units. Information on vital status at 30 days after the CABG surgery was obtained through an automatic
record linkage with the Municipal Registry of Rome. The association between cardiac surgery units and outcome was evaluated through
logistic regression taking into account the following a priori risk factors in different models: gender, age, socio-economic status, type of
ischaemic heart disease and comorbidities. Results: The overall mortality was 5.4% (range 2.1– 11.4%). Statistically significant predictors of
outcome included: age (OR ¼ 7.5 for age $ 70 vs. 35 –49 years), acute myocardial infarction (OR ¼ 32.7 vs. acute – subacute
forms/angina), chronic myocardial ischaemia (OR ¼ 4.2 vs. acute– subacute forms/angina), other heart diseases (OR ¼ 4.8), chronic renal
disease (OR ¼ 16.0) and peripheral arterial disease (OR ¼ 2.9). Statistically significant variability in mortality was observed across
hospitals; taking hospital A as reference, hospital D showed the highest risk (OR ¼ 5.7, 95% CI ¼ 1.9– 17.3, in the fully adjusted model).
Conclusions: We suggest that a true variation in quality of care play a role in the observed differences across hospitals, although chance and
inaccurately measured risk factors cannot be excluded. Despite some limitations, the HIS is a valid tool for screening cardiac surgery units
with poor performance.
q 2003 Elsevier Science B.V. All rights reserved.
Keywords: Coronary artery bypass surgery; Discharge abstract data; Thirty-day mortality
1. Introduction
In the last few decades, quality assessment in health care
has become a growing interest for policymakers, administrators and clinicians, each having different perspectives [1].
As cardiac surgery is more costly than other specialities and
has a moderately high in-hospital mortality, special
attention has been devoted to the quality of care provided
by cardiac surgical centres. With respect to this, in-hospital
q
Part of the manuscript was presented at: International Symposium ‘Risk
Stratification in Cardiac Surgery for Quality Assurance and Accreditation’,
Turin (Italy), January 16, 1999 and Fourth International Conference
‘Strategic Issues in Health Care Management’, University of St. Andrews,
March 20–April 1, 2000.
* Corresponding author. Tel.: þ39-06-830-60476; fax: þ 39-06-83060463.
E-mail address: [email protected] (N. Agabiti).
mortality after coronary artery bypass surgery (CABG) has
been studied extensively, methods for profiling providers
have been developed, and several risk-adjustment models
have been proposed to take into account differences in
patient severity of disease [2,3]. In many countries, Cardiac
Surgery Registries have been developed since the 1980s to
monitor outcomes over time and across providers or
surgeons, and to evaluate the efficacy of programmes
aimed at improving the quality and appropriateness of
cardiac surgical procedures [4,5]. In the UK, where since
1977 cardiac surgeons voluntarily submit their annual
figures to an anonymous dataset mainly to assess their
own practice, the issue of disseminating outcome data in
cardiac surgical practice has been undertaken as a new
challenge by the National Health Service [6].
Several studies in many countries have documented wide
inter-surgeon, inter-hospital and inter-regional variations in
1010-7940/03/$ - see front matter q 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S1010-7940(02)00866-7
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N. Agabiti et al. / European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
patient mortality after CABG that persist despite statistical
adjustment for differences in patient case-mix, i.e. demographic and clinical characteristics [7,8]. However, even if
adjustment for patients’ illness severity is well performed, it
is not clearly established whether high mortality rates mean
poor quality. It has also been suggested that hospital league
tables for mortality following heart surgery can be of limited
value because of the possible year to year random variations
in death rate even when case-mix does not change [9], and
mortality rates have been criticised as a valid tool in
assessing quality of health care [10].
The Italian National Health System (NHS) provides
universal coverage for all 57 million citizens. As in many
European countries, health policy has been dealing with
important choices aimed at improving health care quality
and containing costs. Within a national research framework
on the themes of effectiveness and equity in hospital care,
we evaluated mortality at 30 days following CABG in five
public hospitals in Rome, taking into account individual
characteristics as reported in the regional hospital information system (HIS) data set, and using an empirical
method to evaluate patients’ illness severity.
2. Materials and methods
2.1. Subjects
From the Lazio HIS, we defined a cohort of 1603 patients
(age $35 years) residing in Rome (2,685,890 inhabitants),
who underwent CABG (ICD-9-CM code: 36.1) during
1996 – 1997 in the five specialised hospitals in the city. We
previously excluded patients who underwent major operations on heart and/or arteries other than CABG during the
same admission to analyse isolated CABG patients.
Personal identification number, gender, age, residential
address, up to four diagnoses and up to four surgical
procedures (ICD-9 codes), admission and discharge date
were available from the HIS database. Information on
census tracts (CTs) of residence and vital status 30 days
after CABG surgery of each subject was obtained through
an automatic record linkage with the Municipal Registry of
Rome. For each CT (average population: 480 inhabitants) a
small area socio-economic index (SEI; four classes, I being
the lowest social class and IV the highest) was derived from
selected census variables including educational level,
occupation, dwelling ownership, family size, people/room
density [11]. Patients were then attributed the SEI value
relevant to the CT of residence. Hospitals were identified as
A, B, C (teaching), D and E (non-teaching).
1. Type of ischaemic heart disease: acute – subacute forms/angina pectoris, chronic forms/old myocardial infarction, acute myocardial infarction, mixed forms (defined
when more than one type was reported).
2. Comorbidities in the index admission: defined as chronic
conditions other than ischaemic heart disease existing
before the patient’s admission to the hospital – other
heart diseases, diabetes, hypertension, chronic obstructive respiratory disease (COPD), chronic renal diseases,
peripheral arterial diseases (including cerebrovascular
diseases). Comorbidities were defined adapting the ICD9 codes assigned by Deyo et al. to the Charlson’s
comorbidity index [12] to obtain an illness severity
definition more appropriate to the perioperative setting
[13]. We also prepared a list of death-related conditions
(e.g. cardiac arrest), and complications (acute conditions
that probably arose after the CABG operation, distinguished in local and systemic), and excluded them when
defining illness severity.
3. Comorbidities both in the index and in the previous
hospital admissions: we also examined the comorbidities
reported in previous hospital admissions (up to 12
months before CABG surgery) obtained by an automatic
record linkage procedure within the HIS database to
provide a better definition of the patient’s illness severity.
A patient was defined as having a comorbidity when this
was reported in the index hospital admission and/or in a
previous hospital admission.
Operative definition of the variables is listed in the
Appendix A.
2.3. Data analysis
Risk factors distribution and their association with the
outcome were first explored by univariate analysis. Logistic
regression analysis was then performed in order to evaluate
the relationship between hospital of care and risk of 30-day
mortality, after adjusting for potential confounders (ORs
and 95% CI). Backward stepwise regression was used to
discard the set of independent variables that did not add to
the performance of the model ðP . 0:20Þ [14]. We applied
different models to estimate better the effect of hospitals on
mortality taking into account various potential confounders.
The following variables were included in the models:
Model 1 (restricted model): demographic variables
(gender, age, SEI);
2.2. Risk factors
Model 2: demographic variables, type of ischaemic heart
disease, comorbidities (type);
We analysed the diagnoses reported in the discharge
abstracts (coded according to ICD-9) to define the following
variables as a priori risk factors:
Model 3 (full model): demographic variables, type of
ischaemic heart disease, comorbidities (both in the index
and in the previous admissions).
N. Agabiti et al. / European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
The area under the receiver operator characteristic
(ROC) curve was estimated as a measure of the overall
predictive ability of each model [15].
All statistical analyses were conducted using the
statistical program STATA 5.0.
3. Results
601
11.4%, with hospitals D, E and C showing values
significantly higher than hospital A (OR ¼ 6.1, 95%
CI ¼ 2.5 – 14.8; OR ¼ 3.6%, CI ¼ 1.5 – 8.6; and
OR ¼ 2.8, 95% CI ¼ 1.2– 6.5, for hospitals D, E and C,
respectively). While the high outlier position of hospital D
was confirmed independently on the model used for riskadjustment, the inclusion of clinical factors (models 2 and 3)
reduced the excess risk observed in hospitals E and C.
3.1. Characteristics of the study subjects
4. Discussion
Most of the patients (84%) were males; mean age was
63.2 years (SD ¼ 8.5). A higher proportion of lower social
class patients were treated in public non-teaching hospitals
(hospitals D 27%, E 25%) compared to other hospitals. A
similar pattern was observed for females and older patients.
Overall, the most frequent type of ischaemic disease was
chronic forms (64%), while acute myocardial infarction was
reported in 2.3% of the cases. A total of 1227 subjects (65%)
had at least one episode of care during the previous 12
months. Prevalence of reported comorbidities increased
when including previous admissions (40% for the index
admissions, 63% for index plus previous admissions).
Hospital D showed a high rate of comorbidities (60%,
Table 1) and the highest value of mean length of stay (43
days, Table 2). Complication rates also varied across
hospitals (range: from 0.9 in Hospital A to 9.1% in Hospital
C).
3.2. Determinants of 30-day mortality
Table 3 shows the univariate association between risk
factors and 30-day mortality (overall, 5.4%). Statistically
significant predictors of outcome included: age $ 70 years
(OR ¼ 7.5 vs. 35 –49 years old), acute myocardial infarction (OR ¼ 32.7 vs. acute –subacute forms/angina), chronic
myocardial ischaemia (OR ¼ 4.2 vs. acute– subacute forms/
angina). Considering comorbidities reported in the index
admission, other heart disease (OR ¼ 4.8), chronic renal
disease (OR ¼ 16.0) and peripheral arterial disease
(OR ¼ 2.9) were significant risk factors for 30-day
mortality whereas diabetes and hypertension showed a
protective role (OR ¼ 0.7 and 0.5, respectively). This
protective effect substantially decreased when we included
comorbidities reported in the previous admissions, whereas
the positive association of other heart disease, chronic renal
disease and peripheral arterial disease slightly decreased but
maintained statistical significance.
3.3. Differences in 30-day mortality across the hospitals
Thirty-day mortality across the seven hospitals are
compared in Table 4, where the three described adjustment
models are considered. All models showed an excellent
predictive ability (the best model being model 2: area under
the ROC ¼ 0.85). Crude death rates ranged from 2.1 to
This is one of the first attempts to evaluate mortality rates
after CABG in Italy using data from HIS. We observed
differences in 30-day mortality across different hospitals,
also after adjusting for the patients’ illness severity. These
findings could be related to various factors: chance,
inappropriate choice of the considered outcome, inaccurately
measured risk factors and true differences in quality of care.
Random variation is one of the critical points when
examining differences in mortality data. It has been
observed that the validity of mortality rates depend on the
availability of sufficiently large numbers, to minimise the
potential effects of chance [9]. Studies on the impact of
random variation on hospital mortality rates have led to
conflicting results that may reflect differences in diagnosis
that were studied and in sampling size [10]. Time periods
greater than 1 year have been proposed as a better tool when
comparing hospital mortality data [16]. In addition, under
the assumption of a perfect risk-adjustment in simulated
analysis, it has been shown that 56 –82% of the observed
mortality difference between high and low outlier hospitals
could be attributed to random variation [17]. In general,
making quantitative comparisons between institutions is a
complex problem on the statistic point of view and caution
is needed when screening and interpreting small differences
in hospital performances [18]. In our study, as far as hospital
D is considered, random variation seems to play a minor
role, as shown by the high statistical significance of the
crude and adjusted ORs.
Although mortality data have been extensively used in
assessing quality of care, a number of limitations have been
noted. When a hospital’s observed mortality rate is so much
greater than expected, the hospital is considered as a high
outlier and is presumed to be delivering poor quality care.
However, the validity of this assumption has not been
clarified [17]. Previous studies have found weak relationships between risk-adjusted hospital mortality rates and other
independent measures of hospital quality, and it has been
proposed that optimum measurement of quality of care
should take into account several outcomes and process of
care indicators [19]. However, this criticism is generally
based on the possible use of mortality data to compare
hospital performance for different medical conditions or
general surgery [17], while cardiac surgery, in particular
CABG surgery, represents an exception. We used 30-day
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N. Agabiti et al. / European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
Table 1
Characteristics of patients who underwent coronary artery bypass graft surgery by hospital, Rome, 1996–97
Hospital
A (340)
%
B (359)
%
C (438)
%
D (167)
%
E (299)
%
Total (1603)
%
Gender
Males
Females
84.4
15.6
88.0
12.0
87.7
12.3
76.6
23.3
79.9
20.1
84.5
15.5
Age (years)
35–49
50–69
$ 70
6.8
66.2
27.1
7.8
74.1
18.1
9.8
65.5
24.7
4.2
66.5
29.3
8.4
65.9
25.7
7.9
67.7
24.4
Socio-economic indexa
I
II
III
IV
19.7
28.2
32.3
19.7
20.9
32.3
29.8
17.0
17.8
36.3
29.2
16.7
10.2
32.9
29.3
27.5
12.0
30.1
32.1
25.7
17.0
32.2
30.6
20.2
Type of ischaemic heart disease
Acute/subacute forms angina pectoris
Chronic forms (including previous AMI)
Acute myocardial infarction
Mixed
58.5
35.6
2.3
3.5
5.3
78.
4.5
11.4
–
99.3
0.2
0.5
4.2
15.0
3.6
77.2
0.3
54.5
1.7
43.5
14.1
64.1
2.2
19.6
Comorbidities
No
Yes
51.8
48.2
36.8
63.2
84.2
15.8
40.1
59.9
71.2
28.8
59.7
40.3
2.3
21.2
26.2
6.2
1.2
4.1
6.1
22.3
36.5
0.8
0.8
7.0
4.8
2.5
3.9
0.2
0.7
3.4
10.2
21.0
33.5
6.6
2.4
7.8
5.7
11.7
8.7
1.7
2.0
5.3
5.3
14.5
19.9
2.6
1.2
5.2
32.6
67.3
27.0
72.9
47.0
52.9
25.7
74.2
46.5
53.5
37.2
62.8
15.0
26.2
40.3
8.2
2.6
9.7
16.7
24.8
44.6
2.5
3.9
10.9
18.3
12.8
26.0
2.7
2.7
10.5
19.8
29.3
41.9
12.6
3.6
14.9
19.7
16.0
24.4
5.3
2.7
11.4
17.6
20.6
34.6
5.4
0.1
11.0
Other heart disease
Diabetes
Hypertension
COPDb
Chronic renal disease
Peripheral arterial diseasec
Comorbidities both in the index and in the previous admissions
No
Yes
Other heart disease
Diabetes
Hypertension
COPDb
Chronic renal disease
Peripheral arterial diseasec
a
b
c
Socio-economic index: I highest social class, IV lowest social class.
Chronic obstructive pulmonary disease.
Including cerebrovascular disease.
Table 2
Events related to the hospital admissions for patients who underwent coronary artery bypass graft surgery by hospital, Rome, 1996–97
Hospital
A (340)
%
B (359)
%
C (438)
%
D (167)
%
E (299)
%
Total (1603)
%
Length of stay, days mean (SD)
17.7 (10.9)
26.7 (18.1)
11.9 (6.1)
43.1 (24.0)
22.7 (10.7)
21.9 (17.8)
Complications
No
Yes
Local
Systemic
99.1
0.9
–
0.9
95.0
5.0
1.1
4.5
90.9
9.1
0.7
8.7
93.4
6.6
1.2
5.4
93.6
6.4
1.3
5.0
94.3
5.7
0.8
5.0
N. Agabiti et al. / European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
603
Table 3
Thirty-day mortality by demographic characteristics and clinical conditions
N
Death (%)
OR
95% CI
Gender
Males
Females
1354
249
5.2
6.4
1
1.3
0.7– 2.2
Age (years)
35–49
50–69
$ 70
126
1086
391
1.6
3.9
10.7
1
2.5
7.5
0.6– 10.4
1.8– 31.3
273
516
490
324
4.4
6.8
3.3
7.1
1
1.6
0.7
1.7
0.8– 3.1
0.3– 1.6
0.8– 3.4
226
1027
36
314
1.3
5.4
30.6
5.4
1
4.2
32.7
4.2
957
646
4.7
6.3
1
1.4
0.9– 2.1
85
233
319
41
20
83
18.8
3.9
3.1
7.3
45.0
13.2
4.8
0.7
0.5
1.4
16.0
2.9
2.6– 8.7
0.3– 1.4
0.3– 1.0
0.4– 4.6
6.4– 39.8
1.5– 5.8
569
1007
4.5
5.9
1
1.3
0.8– 2.1
283
331
554
86
49
177
9.5
5.7
4.7
8.1
20.4
11.3
2.2
1.1
0.8
1.6
4.9
2.6
4– 3.6
0.6– 1.8
0.5– 1.3
0.7– 3.6
2.4– 10.4
1.5– 4.4
Socio-economic indexa
I
II
III
IV
Type of ischaemic heart disease
Acute/subacute forms angina pectoris
Chronic forms (including previous AMI)
Acute myocardial infarction
Mixed
Comorbidities in the index admission
No
Yes
Other heart diseases
Diabetes
Hypertension
COPDb
Chronic renal diseases
Peripheral arterial diseasesc
Comorbidities both in the index admission and in the previous admissions
No
Yes
Other heart diseases
Diabetes
Hypertension
COPDb
Chronic renal disease
Peripheral arterial diseasec
a
b
c
1.3– 13.6
8.5– 125.1
1.2– 14.7
Univariate analysis (OR, 95% CI). Rome, 1996–97.
Socio-economic index: I highest social class, IV lowest social class.
Chronic obstructive pulmonary disease.
Including cerebrovascular disease.
mortality after CABG as the outcome measure, which is
considered to fairly reflect the institutional habits concerning
surgical treatment and postoperative patient care and has
been widely used as a valid tool to judge quality of surgical
centres [4,5].
The quantification of illness severity represents a major
problem in our case as in many studies comparing health
outcomes, and the validity of administrative data in this
respect has been questioned [20]. We derived information
on illness severity from the ICD-9 diagnosis and procedure
codes and we cannot completely exclude different patterns
of accuracy and completeness of coding across hospitals.
HIS in Lazio region has been implemented in 1994 and
specific training programs for personnel (physicians and
administrators) are periodically realised in each hospital to
improve quality of coding on the basis of regional ICD-9CM guidelines [21]. The regional health authority routinely
checks data quality from all hospitals in the region and, in
general, data registration has improved along years. A ‘reabstract study’ of a random sample of 395 medical records
for patients who had bypass operations in 1997 – 98 was
conducted as part of a larger validation study to discharge
abstract data in our region1. Accuracy of CABG procedure
1
La valutazione della qualità della compilazione e codifica della scheda
di dimissione ospedaliera nel Lazio. http://www.asplazio.it/asp_online/
att_ospedaliera/sio/sio_altre_pubblicazioni.php.
604
N. Agabiti et al. / European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
Table 4
Thirty-day mortality after coronary artery bypass surgery by hospitala
Hospital
A (340)
B (359)
C (438)
D (167)
E (299)
Death (%)
Crude OR 95% CI
2.1
1
4.2
2.1 (0.8–5.1)
5.5
2.8 (1.2–6.5)
11.4
6.1 (2.5–14.8)
7.0
3.6 (1.5–8.6)
Model 1 (ROC ¼ 0.75)
OR 95% CI
1
2.2 (0.9–5.6)
2.7 (1.1–6.5)
5.4 (2.2–13.3)
3.4 (1.4–8.3)
Model 2 (ROC ¼ 0.85)
OR 95% CI
1
1.6 (0.6–4.8)
2.4 (0.8–6.9)
5.9 (1.9–18.5)
2.9 (0.99–8.2)
Model 3 (ROC ¼ 0.83)
OR 95% CI
1
1.4 (0.5–4.1)
2.0 (0.7–5.8)
5.7 (1.9–17.3)
2.7 (0.97–7.8)
a
Results from different logistic regression models, Rome, 1996–97. The following variables were included in the models: Model 1: gender, age, socioeconomic status; Model 2: gender, age, socio-economic status, type of ischaemic disease, comorbidities; Model 3: gender, age, socio-economic status, type of
ischaemic disease, comorbidities both in the index and in the previous admissions.
code (ICD-9-CM 36.1) was high (confirmation rate: 98%);
comorbidities showed high levels of specificity (i.e. diabetes
99%, hypertension 97%, COPD 99%) and of positive
predictive value (i.e. diabetes 96%, hypertension 93%,
COPD 85%) but lower levels of sensitivity (i.e. diabetes
50%, hypertension 33%, COPD 31%); similar results were
found for surgical complications (specificity 98%, positive
predictive value 58%, sensitivity 26%). Quality of coding
tended to vary across hospitals leading to a possible
undifferential misclassification of comorbidity status.
Many studies have suggested the critical value of chronic
comorbidities in predicting health care outcomes, and an
underreporting of comorbidities in administrative data
compared to clinical data, has been reported, which differs
across conditions and type of comorbidities [22]. Confirming other studies based on claims data, in our dataset,
chronic conditions such as diabetes, hypertension and
COPD tend to have a paradoxical protective role [20]. We
tried to reduce this bias linking records under study with
previous admissions to take more information about
comorbidities, and the paradoxical protective effect vanished. Linking data sets have the advantage to reduce
differences in coding patterns across hospitals, but it can
also lower the impact on outcome of the current comorbidity
status given the observed small decrease of the positive
associations of some comorbidities when using both past
and present information. In addition, little is known about
the impact of comorbidities underreporting. In our study, the
inclusion of a more detailed definition of comorbidities had
no important impact on hospital ranking. Similarly,
information available from previous hospital admissions
yielded only small improvement in the performance of
models in a recent study aimed at comparing two
comorbidity risk-adjustment models [23].
Risk-adjustment is an essential tool when making
comparisons among providers. Several risk-adjustment
methods have been developed in the area of CABG surgery
based both on clinical data and discharge abstracts and a
debate about their reliability and application of different
methodologies has recently started [22]. Discharge abstract
data are commonly used to evaluate hospital quality for
various surgical procedures and medical conditions and
have important advantages, as they are readily available,
inexpensive to acquire, computer-readable and typically
encompass large populations. However, because of the
limited insight into the patients’ clinical conditions, they
have often been judged inadequate, compared to clinical
data sets in capturing illness severity. We had no
information about clinical variables potentially related to
outcomes after CABG, however, this problem could of
limited importance in our contest of hospital profiling where
we had to balance the importance of taking into account all
potential confounders and the need of running sufficiently
parsimonious models. The situation is different in the
clinical setting where exhaustive information of physiological and clinical variables is essential to generate a
prognostic score and to predict an individual patient
outcome. Although in our study a possible residual
confounding from unmeasured risk factors cannot be
excluded, it is unlikely that it completely explains the
magnitude of the observed differences. Moreover, it has
been observed that different methods, either data sources –
clinical or discharge abstract – produce generally comparable hospital rankings for CABG surgery, and severityadjustment alone cannot be considered sufficient to fully
interpret quality differences across hospitals [22]. More
recently, the impact on provider profiling after CABG of
different risk-adjustment models has been evaluated, and it
has been found that the hospital risk-adjusted bypass
surgery mortality rating is consistent regardless of the
risk-adjustment model applied [24]. In our study, we did not
apply commercial software to attribute individual severity
indices, but empirically attempted to distinguish comorbidities from complications or death-related conditions, as
suggested by other investigators, to improve the clinical
validity of our models and control for potential confounders
N. Agabiti et al. / European Journal of Cardio-thoracic Surgery 23 (2003) 599–608
[25]. Old age, female gender, presence of comorbidities and
concomitant surgery were risk factors for early mortality
after CABG surgery, confirming previous findings [3].
A known side effect of risk-adjustment is to reduce the
precision of comparisons. Our results suggest that the excess
in crude mortality observed in hospitals C and E is mainly
due to their dealing with more severe patients than hospital
A. Although the residual excess in risk observed after
adjustment could deserve further investigations, we hypothesise differences in unknown aspects of current care across
the five hospitals in Rome. Our use of administrative data
did not allow to examine all the technical factors which may
have contributed to the different outcomes, however,
differences in complication rates across hospitals made us
confident in the hypothesis of different quality of care.
In conclusion, measuring quality of care is a priority in
Italy and further research is needed in order to define the
data required for valid hospital profiling and to diffuse this
information efficiently. In this respect, discharge abstract
data seem a valid tool for screening hospital performance as
long as completely and accurately recorded. Public health
researchers and clinicians should collaborate in developing
new methods to better understand the causes that result in
inter-institutional variations of health outcomes.
Acknowledgements
This work has been partially funded by the Italian
National Health Service – Progetto ‘Efficacia ed equità
dell’assistenza ospedaliera: pubblicizzazione ed informazione ai cittadini’ (Ministero della Sanità-Dipartimento
della Programmazione ‘Programmi speciali’ – Art. 12,
comma 2, lett b), del D.Lgs. 502/92).
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Appendix A
List of diagnoses and procedures with their ICD-9 codes presented in Table A1.
Table A1
List of diagnoses and procedures with their ICD-9 codes
Diagnoses and procedures
ICD-9 codes
Ischaemic heart diseases
Acute myocardial infarction
Acute and subacute forms
Old myocardial infarction
Angina pectoris
Chronic forms
410
411
412
413
414
Comorbidities
Other heart disease
Diabetes
Hypertension
Chronic obstructive pulmonary disease
Chronic renal disease
Malignant neoplasm
Peripheric arterial disease
Heart surgery
Bypass anastomosis for heart
Revascularisation
Percutaneous transluminal
Coronary angioplasty
Operations on valves
Other operations
Operations on arteries
Aorta
Arteries of head and neck
Peripheral vascular shunt or bypass
Complications
Local
Systemic
393 – 398
416 – 417
420 – 429
745 – 746, 759.3
250 – 250.9
401 – 405
490 – 494, 496
585 – 586
592.0,593.0, 593.9
140 – 208
430 – 438
440 – 442
444
447
Chronic rheumatic heart disease
Chronic pulmonary heart disease
Other forms of heart disease (included conduction disorders and heart failure)
Congenital anomalies of heart (included situs inversus)
Diabetes mellitus
Hypertension (essential and secondary)
Chronic bronchitis, emphysema, asthma, bronchiectasis and other chronic airway obstruction
Chronic renal failure and unspecified renal failure
Calculus of kidney, nephroptosis, unspecified disorders of kidney and ureter
Cerebrovascular disease
Atherosclerosis, aneurysm
Arterial embolism and thrombosis
Other disorders of arteries and arterioles
36.1 (36.10 – 36.19)
36.0 (36.01 – 36.09)
35.1 – 35.2
35.3 – 35.9
36.2 – 36.9
37.0 – 37.1
Other heart structures
Operations on vessels of heart
Other operations on heart and pericardium
38.34, 39.54
38.02, 38.12
39.25, 39.29
34.03
34.71
998.3
998.5
429.4, 429.8
997.1
415
997.5
584
997.0
518.5
997.2
485, 486
997.3
996.0, 996.7
998.1
038.1, 038.9
998.0
Reopening of recent thoracotomy site
Suture of laceration of chest wall
Disruption of operation wound
Postoperative infection
Functional disturbances following cardiac surgery
Cardiac complications
Acute pulmonary heart disease
Urinary complications
Acute renal failure
Nervous system complications
Pulmonary insufficiency following trauma and surgery
Respiratory complications
Bronchopneumonia, pneumonia
Digestive system complications
Mechanical complications of cardiac device, implant and graft
Haemorrhage or haematoma complicating a procedure
Septicaemia
Postoperative shock
427.5
799.1
780.0
785.5, 785.50
798.1
Cardiac arrest
Respiratory arrest
Alteration of consciousness
Shock
Instantaneous death
Death-related conditions