Context Acute myocardial infarction (AMI) is a common condition that is treated
by physicians with varying levels of clinical experience, but whether the
level of experience affects outcome remains uncertain.
Objective To evaluate the relationship between the average annual volume of cases
treated by admitting physicians and mortality after AMI.
Design, Setting, and Patients Retrospective cohort study using linked administrative databases containing
patient admission information for 98 194 patients treated by 5374 physicians
between April 1, 1992, and March 31, 1998, in Ontario, Canada.
Main Outcome Measures Mortality risk rates for 30 days and 1 year post-AMI, adjusted by physician
volume and patient, physician, and hospital characteristics.
Results The 30-day mortality rate was 13.5% and the 1-year mortality rate was
21.8%. A strong inverse relationship between the average annual volume of
AMI cases treated by the admitting physician and mortality after an AMI was
observed. The 30-day risk-adjusted mortality rate was 15.3% for physicians
who treated 5 or fewer AMI cases per year (lowest quartile) compared with
11.8% for physicians who treated more than 24 AMI cases annually (highest
quartile; P<.001). The 1-year risk-adjusted mortality
rate was 24.2% for physicians who treated 5 or fewer AMI cases per year (lowest
quartile) compared with 19.6% for physicians who treated more than 24 AMI
cases annually (highest quartile; P<.001).
Conclusion Patients with AMI who are treated by high-volume admitting physicians
are more likely to survive at 30 days and 1 year.
Physician volume is a well-established determinant of outcomes after
invasive cardiac procedures. Previous studies have demonstrated an inverse
relationship between annual surgeon volume of coronary artery bypass graft
(CABG) procedures and in-hospital mortality and between annual cardiologist
volume of percutaneous coronary interventions (PCIs) procedures and complication
rates after the procedure.1-3
These associations have led to development of American Heart Association/American
College of Cardiology guidelines that recommend a minimum annual volume of
procedures that should be performed by cardiac surgeons and invasive cardiologists.4,5
The contribution of physician experience to survival after an acute
myocardial infarction (AMI) is less well understood. Treatment of AMI is complex
and requires considerable acumen and clinical skills. Patients with AMI can
present with a wide variety of clinical manifestations and may develop a number
of complications that require immediate recognition and treatment. Optimal
treatment for AMI patients has undergone rapid change during the past 2 decades,
with new advances occurring frequently.5 Whether
physicians who only occasionally treat AMI patients can provide care comparable
with those who treat AMI patients more frequently remains uncertain. Therefore,
we conducted a population-based study to evaluate the relationship between
annual volume of AMI patients treated by admitting physicians and mortality
after an AMI.
We obtained information from the Ontario Myocardial Infarction Database
(OMID), which links together information from several health care administrative
databases in Ontario, as described previously.6
All 139 397 patients admitted to Ontario hospitals with an AMI between
April 1, 1992, and March 31, 1998, were identified based on a "most responsible
diagnosis" of AMI, (International Classification of Diseases,
9th Revision code 410) in the Canadian Institute for Health Information
(CIHI) hospital discharge database.7 Patients
were excluded from the study if they were younger than 20 years or older than
105 years, were not Ontario residents or had an invalid Ontario health card
number, were admitted as transfers from another acute care institution or
to a noncardiac surgical service, had an AMI coded as an in-hospital complication,
were discharged alive with a length of stay of less than 3 days, or were admitted
with an AMI in the year before the index admission. A total of 108 308
patients met these criteria. The rationale for these inclusion and exclusion
criteria are described in detail elsewhere.6
Previous multicenter chart audits have demonstrated a high accuracy rate of
AMI coding in this cohort.6,8
The CIHI database was, in turn, linked to the Ontario Registered Persons Database,
which contains information on the vital status of all Ontario residents.
Admitting Physician Characteristics
The admitting physician for each AMI patient was determined by linking
the OMID cohort to the Ontario Health Insurance Plan (OHIP) database, which
contains information on physician claims for all fee-for-service billings
in Ontario. Ninety-five percent of Ontario physicians bill OHIP for their
services, while the remainder have an alternate form of payment (eg, salary).
The latter are either specialists who typically do not manage AMI cases (eg,
psychiatrists or laboratory physicians) or family physicians whose office
practice is remunerated by salary or capitation but whose inpatient practice
is usually fee-for-service and, hence, captured in this study.
Physician billing codes for each patient were analyzed and the admitting
physician was identified as the physician who submitted a claim for services
rendered on the admission date. Billing codes for emergency department physicians
were not included in determining the admitting physician. In the event that
2 or more physicians submitted claims on the admission date, the admitting
physician was defined as the physician who submitted the most claims for follow-up
care during that hospitalization. In the event that no claims for service
were submitted for the day of admission, this process was repeated for the
day following admission. A unique admitting physician was identified for 98 194
patients, representing the final study cohort. Once the admitting physician
for each patient was identified, additional characteristics of that physician
were identified by linkage to the Corporate Providers Database of the Ontario
Ministry of Health and the Southam Medical Database. These data sources provided
information on the self-reported specialty, medical school of graduation,
age, and sex of the physician. The total annual billings (in Canadian dollars)
and the percentage of claims billed for in-hospital compared with outpatient
work were also calculated for each physician. All patient health card numbers
and physician billing numbers were scrambled to maintain patient and physician
confidentiality.
Specialty Classification of Physicians
All physicians in the study were classified into 1 of 4 groups: cardiologists,
general internists, family physicians, or other specialists. Cardiologists
in Canada undergo 2 to 3 years of specialist training after completing 3 years
of core internal medicine training. General internists in Canada are required
to undertake 4 years of general internal medicine training. Family physicians
(including general practitioners) in Canada complete either 1 or 2 years of
postgraduate training after completing medical school. Physicians in the other
specialty group were predominantly physicians in other specialties of internal
medicine (eg, pulmonology, nephrology, hematology). In Ontario, most AMI care
in teaching and large community hospitals is provided by cardiologists and
internists, whereas in most smaller community hospitals, family physicians
are usually the attending physicians because of a relative lack of specialists.
Family physicians often cover inpatient wards on a rotating basis or admit
their own patients to the hospital. There were 195 acute care hospitals in
Ontario in 1996, serving a population of 11.1 million residents.
Patient Severity Adjustment
To control for variations in patient severity at admission, we used
the Ontario AMI mortality prediction rules. These rules are based on logistic
regression models that predict 30-day and 1-year mortality after an AMI. International Classification of Diseases, 9th Revision
codes were used to identify the prevalence of 9 clinical risk factors in the
15 secondary diagnostic fields of the CIHI database in addition to the age
and sex of the patient. These variables included severity of cardiac disease
(eg, congestive heart failure, cardiogenic shock, and arrhythmia) and comorbid
conditions (eg, cancer, diabetes mellitus, renal failure). The predictive
models showed good predictive power, with areas under the receiver operating
characteristic curve of 0.78 for 30-day mortality and 0.79 for 1-year mortality.
Their development and validation are described in detail elsewhere.9
We also adjusted for socioeconomic status by linking the first 3 digits
of the postal code (Forward Sortation Area) to 1996 census data from Statistics
Canada, a central database that contained information on the median annual
personal income of the neighborhood where each patient resided, as described
previously.10
Use of Cardiac Procedures and Secondary Prevention Medications
Information on rates of invasive cardiac procedure use (eg, cardiac
catheterization, PCI, and CABG surgery) within 1 year of the index AMI were
obtained by linkage to the OHIP physician services and CIHI hospital discharge
databases.6 Data on use of various secondary
prevention medications in 48 383 elderly AMI survivors were obtained
by linkage to the Ontario Drug Benefit (ODB) program database.11
The ODB program is a government-funded drug benefit program that covers outpatient
drug costs for all Ontario residents aged 65 years or older. Some patients
may have elected to purchase aspirin over the counter rather than receive
it through the ODB program.
Physician and Hospital AMI Volume
The average annual volume of AMI cases treated was determined for each
admitting physician by dividing the total number of AMI cases treated during
the 6-year study period by the number of years the physician actually treated
1 or more AMI patients. The average annual volume of AMI cases treated was
also calculated for each hospital in Ontario. Hospitals were classified as
low (≤33 AMI cases per year), medium (34-99 cases per year), or high (≥100
cases per year) volume.
The final cohort of 98 194 patients was divided into 4 approximately
equally sized physician volume quartiles, with approximately 25 000 patients
in each quartile. This corresponded to average annual physician volumes of
1 to 5, 6 to 13, 14 to 24, and more than 24 AMI cases treated per year. Univariate
analyses of patient and physician characteristics were conducted within each
of these physician volume quartiles. Risk-adjusted mortality rates by physician
volume were determined by dividing the observed mortality rates by the expected
mortality rates predicted from the Ontario AMI mortality prediction rules
and multiplying this ratio by the average mortality rate during the 6-year
study period. The risk-adjusted mortality rate can be interpreted as the mortality
rate that would be expected if the case mix were identical in different physician
volume groups. To determine if there was a threshold effect for physician
volume, 30-day and 1-year risk-adjusted mortality rates with 95% confidence
intervals (CIs) were also calculated by decile of physician volume.
Multivariable analyses of the physician volume effect were also conducted
using random effects hierarchical logistic regression models that adjusted
for patient characteristics (eg, age, sex, predicted 30-day mortality, socioeconomic
status), other physician characteristics (eg, specialty, age, sex), and hospital
characteristics (eg, hospital volume and teaching status, availability of
on-site revascularization facilities). These multilevel models were fit using
the software package MLwiN12 and took into
account the hierarchical nature of the data, with patients clustered within
physicians and physicians clustered within hospitals. Backward stepwise regression
analyses were conducted to identify the significant independent predictors
of 30-day mortality at the P<.05 level. Prespecified
interaction terms between each physician volume quartile and specialty were
included in these models. The discrimination and calibration of these models
was determined.13,14
A propensity score analysis was also conducted in which care by a low-volume
physician was the exposure.15 A logistic regression
model was used to determine the probability of having been treated by a low-volume
physician, using the variables from the Ontario AMI mortality prediction rules
as covariates. Patients were stratified into quintiles according to their
propensity of having been treated by a low-volume physician. The effect of
having been treated by a low-volume physician as opposed to a non–low-volume
physician was estimated across quintiles. All traditional statistical analyses
were conducted using SAS version 8 software (SAS Institute Inc, Cary, NC).
The final cohort of 98 194 patients was divided into 4 quartiles
of physician AMI volume (Table 1).
The characteristics of patients were very similar across the 4 quartiles with
the exception of low-volume physicians (≤5 AMI cases per year), whose patients
were slightly older on average than those in the other quartiles. Patients
of low-volume physicians were also more likely to be admitted to low- or medium-volume
hospitals, although almost half (46.4%) of these patients were admitted to
high-volume hospitals. In contrast, patients of high-volume physicians (>24
AMI cases per year) were predominantly admitted to high-volume hospitals.
The characteristics of admitting physicians by physician volume quartile
are shown in Table 2. The median
age of the physicians was similar across the quartiles, although the proportion
of high-volume physicians was lower at the extremes of age (ie, <35 and ≥65
years). The vast majority of physicians (n = 4455) were in the low-volume
category. They were more likely to be family physicians (76.5%), with the
highest relative proportion of female physicians (17.0%) in the low-volume
category. High-volume physicians were predominantly cardiologists. The total
amount of clinical work (measured by total billings) was similar across volume
quartiles, although the proportion of in-hospital work was greatest among
higher-volume physicians.
Use of various secondary preventive medications in elderly AMI survivors
is shown in Table 3 by physician
volume quartile. Elderly patients of low-volume physicians were least likely
to receive aspirin, β-blockers, or statins within 90 days of hospital
discharge, compared with those of high-volume physicians (P<.001 for all comparisons). Patients of low-volume physicians were
also the least likely to receive invasive cardiac procedures, including cardiac
catheterization, PCI, and CABG surgery, within 1 year of their AMI (P<.001).
The overall 30-day mortality rate in the study cohort was 13.5% and
the 1-year mortality rate was 21.8%. An inverse relationship was found between
admitting physician AMI volume and mortality after an AMI (Table 3 and Figure 1).
The 30-day risk-adjusted mortality rate ranged from a high of 15.3% among
low-volume physicians to a low of 11.8% among high-volume physicians (P<.001), while the 1-year risk-adjusted mortality rate
was 24.2% among low-volume physicians compared with 19.6% among high-volume
physicians (P<.001). Analyses of the physician
volume–mortality relationship by decile of physician volume are shown
in Figure 1. A definitive volume
threshold where mortality rates plateaued did not appear to exist, with mortality
differences greatest at the extremes of physician volume. The physician volume
effect existed not only at 30 days but also through 1 year of follow-up.
Based on the 11 variables listed in Table 1, a logistic regression model was used to generate propensity
scores describing the probability that any given patient would be cared for
by a low-volume physician. The c statistic of the
model was 0.56. After adjusting for the propensity score among these patients,
patients cared for by lower-volume physicians had a higher rate of death at
30 days (15.2% vs 12.6%; 95% CI for difference, 2.2%-3.0%; P<.001) and at 1 year (24.3% vs 20.6%; 95% CI for difference, 3.2%-4.2%; P<.001).
Multivariable analyses of physician volume and specialty effects on
30-day mortality are shown in Table 4.
Because of a significant interaction between physician volume and specialty,
the overall effect of both factors could not be expressed as a single odds
ratio (OR). Table 4 demonstrates
a consistent volume-outcome gradient within each physician specialty after
adjustment for possible confounders. It also shows that physician specialty
was not an independent predictor of 30-day mortality in most subgroups after
adjusting for physician volume. The discrimination of this model was good
(c statistic = 0.80), while the calibration was poor
(Hosmer-Lemeshow χ28 = 170; P<.001), likely secondary to the large sample size.16
Low-volume physicians in other specialties had the highest adjusted OR (2.49;
95% CI, 1.92-3.24) for 30-day mortality. Low hospital volume was not a significant
independent predictor of 30-day mortality, with an OR of 0.96 (95% CI, 0.86-1.08)
after adjustment for physician volume and other factors. Admission to a revascularization
hospital was also not a predictor of 30-day mortality (OR, 1.12; 95% CI, 0.94-1.35),
but admission to a teaching hospital was predictive of lower 30-day mortality
(OR, 0.79; 95% CI, 0.68-0.92).
We also examined the association between physician volume and outcome
during different points of the study. Among patients cared for in 1992, the
adjusted 30-day mortality rates for patients in the lowest and highest quartiles
of physician volume were 15.8% and 12.3%. Similarly, in 1996, the corresponding
death rates were 15.2% and 10.5%.
This study demonstrates a strong inverse association between average
annual volume of AMI cases treated by admitting physicians and patient mortality
after an AMI. Physicians who treated the most patients on an annual basis
had the lowest 30-day and 1-year patient mortality rates, even after adjusting
for potential confounders. The impact of physician volume on outcomes of AMI
patients was comparable with the impact of physician volume on outcomes of
invasive cardiac procedures.1-3
Our study suggests that "practice makes perfect" in treating AMI, a common
condition treated by physicians with widely varying levels of clinical experience.
The association between physician volume and mortality was robust and existed
across physician specialties.
The results of our study are consistent with a previous study conducted
using Pennsylvania hospital discharge data from 1993 that demonstrated an
inverse association between physician volume and in-hospital mortality after
an AMI. That study demonstrated that patients of low-volume physicians who
treated 1 to 6 AMI patients per year had a 43% higher in-hospital mortality
rate than high-volume physicians, defined as those who treated 24 or more
AMI patients per year.17 Using a larger population-based
sample of 98 194 patients and 5374 physicians during a 6-year period,
we observed a similar effect on 30-day and 1-year risk-adjusted mortality,
suggesting that the physician volume effect is a universal phenomenon that
exists in different countries and is independent of the health care system.
Physician volume was a stronger predictor of 30-day AMI mortality than was
physician specialty in both our study and the Pennsylvania analysis.17 Our analyses suggest that physician volume may explain
the AMI survival benefit with cardiological care found in other studies.18
Our data do not allow us to precisely define a minimum annual volume
of AMI cases that should be treated to optimize patient outcomes. There did
not appear to be any volume threshold beyond which mortality rates leveled
off (Figure 1). Nevertheless, our
results do suggest that significant reductions in AMI mortality could be achieved
by shifting the primary responsibility for treating more AMI patients to a
smaller number of high-volume physicians. Hospitals that have low-volume physicians
could consider designating a few physicians to handle all of their AMI cases
or they could mandate that low-volume physicians work with high-volume physicians
when treating AMI patients. This strategy may be more difficult to implement
in small, rural hospitals, where only a few physicians are available to provide
AMI care. Alternatively, educational strategies could be developed toward
improving the knowledge and clinical expertise of low-volume physicians. For
example, use of standardized care maps and admission orders might improve
compliance with recommended treatment protocols (eg, aspirin, β-blockers)
among low-volume physicians. One recent study from the United States has also
suggested that referring patients from lower-volume to higher-volume hospitals
might lower AMI mortality, although low hospital volume was not a significant
predictor of 30-day mortality in our analysis.19
Our study has certain limitations. First, because our study relied on
linked administrative databases, we were unable to adjust for all possible
clinical factors that influence mortality after an AMI. Nevertheless, we did
adjust for patient age, sex, comorbidities, socioeconomic status, and physician
specialty as well as other physician and hospital characteristics, and we
still found a consistent physician volume–outcome relationship. Second,
there may have been undercoding of comorbid conditions in our administrative
databases, which may have reduced the ability of our statistical regression
models to adjust for factors that may affect the physician volume–outcome
relationship.20 Third, we did not have information
on in-hospital use of various therapies such as thrombolytics, aspirin, and β-blockers,
which could partially explain the volume-outcome relationship. One recent
study from Minnesota showed that low-volume physicians were the least likely
to treat their AMI patients with aspirin and thrombolytics.21
High-volume physicians may also be better at recognizing an AMI and interpreting
difficult electrocardiograms. They may be faster at making decisions regarding
thrombolytics, choose more appropriate risk stratification tests, make more
appropriate referral decisions, and be more skilled at treating complications
such as cardiogenic shock and arrhythmia. These possible explanations will
need to be investigated in future studies.
In summary, we found a significant inverse relationship between the
average annual volume of patients treated by the admitting physician and mortality
after an AMI. Increases in the annual volume of cases treated were associated
with significant reductions in 30-day mortality that were sustained at 1 year
of follow-up. These results have important policy implications for optimizing
the quality of care for AMI patients. Although the exact mechanisms contributing
to this complex phenomenon remain to be elucidated, our data suggest that
shifting the care of more AMI patients to a smaller number of high-volume
physicians could potentially result in a significant decrease in the number
of AMI deaths that occur each year. Developing strategies to improve the clinical
expertise of low-volume physicians might also lead to better patient outcomes.
1.Hannan EL, Siu AL, Kumar D, Kilburn Jr H, Chassin MR. The decline in coronary artery bypass graft surgery mortality in New
York State: the role of surgeon volume.
JAMA.1995;273:209-213.Google Scholar 2.Hannan EL, Racz M, Ryan TJ.
et al. Coronary angioplasty volume-outcome relationships for hospitals and
cardiologists.
JAMA.1997;277:892-898.Google Scholar 3.Jollis JG, Peterson ED, Nelson CL.
et al. Relationship between physician and hospital coronary angioplasty volume
and outcome in elderly patients.
Circulation.1997;95:2485-2491.Google Scholar 4. ACC/AHA guidelines and indications for coronary artery bypass graft
surgery: a report of the American College of Cardiology/American Heart Association
Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures
(Subcommittee on Coronary Artery Bypass Graft Surgery).
Circulation.1991;83:1125-1173.Google Scholar 5.Ryan TJ, Anderson JL, Antman EM.
et al. ACC/AHA guidelines for the management of patients with acute myocardial
infarction: a report of the American College of Cardiology/American Heart
Association Task Force on Practice Guidelines (Committee on Management of
Acute Myocardial Infarction).
J Am Coll Cardiol.1996;28:1328-1428.Google Scholar 6.Tu JV, Naylor CD, Austin P. Temporal changes in the outcomes of acute myocardial infarction in
Ontario: 1992 to 1996.
CMAJ.1999;161:1257-1261.Google Scholar 7. International Classification of Diseases, 9th Revision . Ann Arbor, Mich: Commission on Professional and Hospital Activities;
1992.
8.Tu JV, Austin P, Naylor CD, Iron K, Zhang H. Acute myocardial infarction outcomes in Ontario. In: Naylor CD, Slaughter P, eds. Cardiovascular
Health and Services in Ontario: An ICES Atlas. Toronto, Ontario: Institute
for Clinical Evaluative Sciences; 1999:83-110.
9.Tu JV, Austin PC, Walld R, Roos L, Agras J, McDonald KM. Development and validation of the Ontario acute myocardial infarction
mortality prediction rules.
J Am Coll Cardiol.2001;37:992-997.Google Scholar 10.Alter DA, Naylor CD, Austin P, Tu JV. The effects of socioeconomic status on access to invasive cardiac procedures
and mortality following acute myocardial infarction.
N Engl J Med.1999;341:1359-1367.Google Scholar 11.Tu JV, Austin P, Rochon PA, Zhang H. Secondary prevention after acute myocardial infarction, congestive
heart failure and coronary artery bypass graft surgery in Ontario. In: Naylor CD, Slaughter P, eds. Cardiovascular
Health and Services in Ontario: An ICES Atlas. Toronto, Ontario: Institute
for Clinical Evaluative Sciences; 1999:199-238.
12.Goldstein H, Rasbash J, Plewis I.
et al. A User's Guide to MLwiN. London, England: Institute of Education, University of London; 1998.
13.Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic
(ROC) curve.
Radiology.1982;143:29-36.Google Scholar 14.Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 1989.
15.Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the
propensity score.
J Am Stat Assoc.1984;79:516-524.Google Scholar 17.Nash IS, Corrato RR, Dlutowski MJ, O'Connor JP, Nash DB. Generalist versus specialist care for acute myocardial infarction.
Am J Cardiol.1999;83:650-654.Google Scholar 18.Jollis JG, DeLong ER, Peterson ED.
et al. Outcome of acute myocardial infarction according to the specialty of
the admitting physician.
N Engl J Med.1996;335:1880-1887.Google Scholar 19.Thiemann DR, Coresh J, Oetgen WJ, Powe NR. The association between hospital volume and survival after acute myocardial
infarction in elderly patients.
N Engl J Med.1999;340:1640-1648.Google Scholar 20.Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB. Discordance of databases designed for claims payment versus clinical
information systems: implications for outcomes research.
Ann Intern Med.1993;119:844-850.Google Scholar 21.Willison DJ, Soumerai SB, Palmer RH. Association of physician and hospital volume with use of aspirin and
reperfusion therapy in acute myocardial infarction.
Med Care.2000;38:1092-1102.Google Scholar