Generalist Versus Specialist Care forAcute Myocardial Infarction

Ira S. Nash, MD, Robert R. Corrato, MD, Mark J. Dlutowski, BA, John P. O’Connor, PhD,and David B. Nash, MD, MBA

Early studies conflict regarding improved patient out-comes with cardiologist-directed care for acute myocar-dial infarction (AMI). We sought to assess the magnitudeand mechanism of the influence of physician specialtyon inpatient mortality for AMI. Using data from thePennsylvania Health Care Cost Containment Council andelsewhere, we developed age-stratified logistic regres-sion models of inpatient mortality, utilizing a split sam-ple strategy for model development and validation. Re-ferral bias and physician caseload were explicitlyaddressed. We analyzed 30,351 admissions for AMI. Inpatients <65 years old, the adjusted odds ratio (OR) formortality with cardiologist care was 0.89 (95% confi-dence interval [CI] 0.640 to 1.24, p 5 0.49) relative togeneralist care. In patients >65 years of age, the ad-justed OR was 0.86 (95% CI 0.72 to 1.03, p 5 0.10).

Caseload was significantly higher among cardiologistsand was inversely related to inpatient mortality. Mortal-ity models with caseload but not physician designationor physician designation without caseload found eachpredictor statistically significant in the absence of theother (OR for cardiologist care 0.82, 95% CI 0.71 to0.95, p 5 0.007; OR for patients with low volumephysicians relative to high volume 1.27, 95% CI 1.05 to1.51, p 5 0.014). Older patients of physicians withhigher case loads had a lower risk adjusted inpatientmortality for AMI. This probably explains the trend to-ward better outcomes among patients of cardiologistsrather than noncardiologists. Q1999 by ExcerptaMedica, Inc.

(Am J Cardiol 1999;83:650–654)

Jollis et al1 reported that Medicare patients withacute myocardial infarction (AMI) cared for by

cardiologists during their initial hospital stay had alower 1-year risk adjusted mortality than patients ofgeneral internists or family practitioners. Ayanian etal2 could not confirm an advantage to cardiologist-based care. The public report released by the Penn-sylvania Health Care Cost Containment Council(PHC4)3 concluded, in part, that patients with AMIwho were cared for by cardiologists had a lowerrisk-adjusted inpatient mortality than patients of inter-nists or family practitioners. We now present the find-ings of a new analysis of the PHC4 data, undertaken tomore completely explore this issue and determine thepossible mechanism behind it.

METHODSWe developed multivariate logistic regression

models of the likelihood of inhospital mortality basedon the data collected by each institution and compiledby the PHC4, as well as the other data obtained frompublic sources.

Database: The PHC4 is a state agency charged withcollecting and disseminating “useful informationabout the charges and patient outcomes for variousmedical and surgical procedures.”4 To elucidate thepatterns of care and outcomes among patients hospi-

talized with AMI, the PHC4 collected data on all AMIadmissions in Pennsylvania in 1993.

Cases were identified on the basis of InternationalClassification of Diseases, Ninth Revision, ClinicalModification (ICD) diagnosis codes. All cases in-cluded had AMI coded as the principal diagnosis. Thehospitalization also had to have been coded as theinitial episode of care for the infarction, to eliminatethose patients with a repeat hospitalization for a singleclinical event.

The state database contains demographic informa-tion, including patient age, gender, zip code, admis-sion source (emergency room, nursing home, etc.),and payor information. Data derived from local recordabstraction also includes up to 14 secondary diagnosesbased on ICD codes. Charge data were collected byeach hospital as well. Severity of illness data werecollected on each patient using the Atlas severitygrouping system (a proprietary rating system devel-oped by Mediqual Systems, Inc., Westborough, Mas-sachusetts), which is used by all Pennsylvania hospi-tals by state mandate. This severity of illness scoringsystem supersedes the previously developed Medis-Groups system. Each patient is assigned a diseasespecific “severity” index, called an admission severitygroup (ASG) of 0, 1, 2, 3, or 4, based on a variety ofclinically abstracted variables (lab values, radio-graphic findings, blood pressure, etc.) combined withelements of the medical history (e.g., a history ofstroke or angina) according to a proprietary logisticregression algorithm. Information regarding the selec-tion of these variables and the modeling procedureused is available on the MediQual web site of theInternet at http://www.MediQual.com. Higher scoresare assigned to patients with higher anticipated inpa-

From the Zena and Michael A. Wiener Cardiovascular Institute of theMount Sinai Medical Center, New York, New York; and Office ofHealth Policy and Clinical Outcomes, Thomas Jefferson University,Philadelphia, Pennsylvania. Manuscript received May 14, 1998;revised manuscript received and accepted October 8, 1998.

Address for reprints: Ira S. Nash, MD, The Zena and Michael A.Wiener Cardiovascular Institute, Mount Sinai Medical Center, Box1030, One Gustave L. Levy Place, New York, New York 10029.E-mail: ira.nash@mssm.edu.

650 ©1999 by Excerpta Medica, Inc. 0002-9149/99/$–see front matterAll rights reserved. PII S0002-9149(98)00961-8

tient mortality and scores are constructed to yieldstandardized probabilities of death.5

Attending physician identity and specialty desig-nation were supplied by each hospital. Identified phy-sicians were notified and given the opportunity toreview the accuracy of assignments. There was nostandard state-mandated process for identifying theresponsible physician. In cases where an attendingphysician was identified but no specialty was reported,we obtained physician specialty information frompublic sources, including the Masterfile of the Amer-ican Medical Association, which is accessible via theworld wide web of the Internet (http://www.ama-assn.org). We obtained supplemental information on eachhospital, including the staffing pattern of the emer-gency room and the presence or absence of an accred-ited training program in internal medicine, familypractice, or cardiology from the Hospital and Health-system Association of Pennsylvania.

The state database excluded patients who refusedtreatment or were,30 or .99 years old, becausepatients in these groups were believed likely to haveunusual clinical presentations or outcomes less closelylinked with the quality of inpatient care they re-ceived.6 Patients were also excluded, on similargrounds, if they met state-defined criteria for “clinicalcomplexity”—anoxic brain damage on admission, sig-nificant trauma, metastatic cancer, or heart transplan-tation. Determination of the appropriateness of thisdesignation required additional documentation fromhospitals in cases of anoxia and trauma and was de-rived from the standard data sets (ICD codes) forcancer and transplant patients.6

Candidate variables for mortality predictors in theregression model included demographic data such asage and gender, the ASG, and clinical conditionsderived from the secondary ICD codes. To maximizethe reliability of the data used, variables derived fromICD codes were included only if they were unambig-uously specified and were not part of a complex cod-ing scheme. For example, we did not include theanatomic site of the index infarction in the analysis,despite that the state report included it6 and it is wellknown that anterior infarctions carry a graver progno-sis than nonanterior infarctions.7 Such a determinationfrom this data set could only be made based on themodifiers (terminal digits) of the primary ICD codefor myocardial infarction (410.XX), which are as-signed on the basis of chart abstracters’ distinguishing“anterolateral wall” (410.0X), “other anterolateralwall” (410.1X), “other unspecified site” (410.8X),“unspecified” (410.9X), and “subendocardial” (410.7X)from one another. Because of the inherent overlap inthese categories and the absence of other confirmatorydata, we determined that it would be impossible toaccurately define anterior infarction from this dataset.Similar reasoning led us to reject other potential clin-ical variables, including cardiac conduction systemdisturbances and nonspecific rhythm disorders. Weretained other clinical variables derived from second-ary ICD codes where less overlap and potential am-biguity existed, such as dialysis dependence, presence

of congestive heart failure or diabetes mellitus, or theoccurrence of specific, well-defined arrhythmias suchas atrial fibrillation or ventricular fibrillation. Eachwas represented by a single dichotomous variable.

We included as candidate predictors dichotomousdummy variables coding for the presence or absenceof a cardiac surgery program at the admitting hospitaland for hospital teaching status, based on the presenceof a postgraduate training program in either internalmedicine, family practice, or cardiology, according toinformation from the Hospital and Health SystemAssociation of Pennsylvania. We designated cardiol-ogists as specialists and combined internists and fam-ily practitioners into generalists. Because the primaryintent of the analysis was to compare outcomes amongpatients of medical generalists and specialists, thesmall number of patients treated by cardiothoracicsurgeons were excluded from the analysis.

The outcomes of all initial hospitalizations forAMI were included regardless of whether the hospi-talization ended in discharge, transfer, or death. Weexcluded from the analysis the outcomes of hospital-izations at a second institution to which a patient mayhave been transferred for further care of the sameAMI. This avoided enriching the pool of survivorsamong the patients of cardiologists, because trans-ferred patients had a lower mortality rate and weremore likely to be cared for by a cardiologist at thereceiving institution.6 Including 2 hospitalizations un-der the care of 2 physicians would also have made itmore difficult to assign a single responsible physicianand adjust overall patient outcome for hospital char-acteristics.

Analytic plan: To control for possible systematicdifferences in the kinds of patients treated by cardiol-ogists and generalists, which would bias the compar-ison of mortality rates of their respective patients, wefirst developed a logistic regression model with spe-cialist status of the attending physician as the depen-dent variable.8 This “propensity” model9 included allthe available patient-specific demographic and clinicaldata detailed above, as well as the following variablesnot included in the mortality models: insurance (pay-or) information, location of the patient before admis-sion (e.g., nursing home, rehabilitation facility, home),the state region where the patient was hospitalized,and the staffing pattern of the emergency roomthrough which he or she was admitted. The propensitymodel was developed using the entire population, andyielded for each patient the estimated probability ofbeing cared for by a cardiologist, expressed as a con-tinuous variable between 0 and 1. It can be shown thatpatients with the same propensity score have the samedistribution of the patient-specific variables, regard-less of whether they were treated by a cardiologist ora generalist.9,10 (i.e., the propensity score is a balanc-ing score that allows control for referral bias due to theobserved variables). This score was then included inthe mortality models as an additional independentvariable to explicitly adjust for referral bias.

We hypothesized that physician caseload, definedas the number of hospitalizations for AMI managed by

CORONARY ARTERY DISEASE/MYOCARDIAL INFARCTION CARE 651

a particular physician in Pennsylvania in 1993, wouldhave an impact on patient outcomes. We thereforecharacterized patients according to the quartile of an-nual case volume managed by their attending physi-cian. To avoid an assumption of a linear interquartileeffect, we represented each quartile of physician ex-perience by a dummy variable, rather than treatingcaseload as a single variable with 4 possible values.The highest volume quartile of physician caseload wasused as a reference.

Because most prior studies of myocardial infarc-tion outcomes have been performed using an elderlypatient population drawn from Medicare records,1,2

and advanced age is an important risk factor for mor-tality associated with AMI,7 we split our mortalityanalysis into 2 strata, defined by age,65 years andage $65 years. In each stratum, a separate logisticregression mortality model was developed.

The propensity score and patient age were includedas continuous variables. All other variables were di-chotomous, including dummy variables coding forgender, ASG score, and quartiles of physician case-load. Scores of 0 and 1 on the ASG scale werecombined because very few deaths were expected ineach group, and used as the reference for dummyvariables coding for ASG scores of 2, 3 or 4. Using across-validation strategy in each stratum, the patientsample was first split into 2 equal populations, witheach physician’s cases divided evenly and randomly,so that1⁄2 could be used for model development andthe other for validation. In each age stratum, a logisticregression mortality model utilizing all the candidateclinical variables was constructed using the develop-ment set. The list of independent variables was thentrimmed based on the calculated odds ratio (OR),retaining for testing in the validation set all thosevariables that had an OR.1.2 or,0.83. By trimmingthe list of variables by effect size, as opposed toretaining all variables or retaining all of those withstatistical significance, we were assured of includingonly those variables that had a meaningful clinicalimpact on the outcome. Patient age, gender, and thepropensity score were also retained in the final mod-els, along with the research variable of interest codingfor generalist versus specialist care. The trimmed setof predictor variables was then used to generate asecond mortality logistic regression model in the val-idation set of each age stratum. We report the outputof these final models. The impact of the caseload onthe statistical significance of the physician specialtyvariable was assessed by running the final model withthe variables coding for caseload removed. Likewise,the models were also run with the physician specialtyvariable removed to evaluate the effect of caseloadalone.

Each regression model was evaluated for its abilityto accurately discriminate between individuals whosurvived and those who did not, as measured by thecstatistic and for its ability to predict outcomes atvarying levels of risk (calibration) by the Hosmer-Lemeshow statistic.11 All programming was per-formed in SAS.

RESULTSThere were 40,684 hospitalizations for AMI in

Pennsylvania in 1993 identified by the PHC4. Of thistotal, 9,766 (24%) were excluded from the analysis.The bulk of these (8,049) were excluded because theyrepresented secondary admissions resulting from ahospital to hospital transfer. Another 567 patients(1.4%) were cared for by cardiac surgeons and ex-cluded. The remaining 30,351 formed the basis of ouranalysis.

There were 3,591 inhospital deaths, for an overallinpatient mortality rate of 11.8%. There were signifi-cant differences in mortality and other characteristicsbetween the 2 age strata, as shown in Table I. Cardi-ologists had, on average, a higher volume of casesthan the noncardiologists, and there were significantdifferences in patient characteristics between the 2groups (Table II). The median caseload was 12, andthe quartiles were defined as 1 to 6 cases, 7 to 12cases, 13 to 23 cases, and$24 cases.

The outcome of the logistic regression model pre-dicting care by a cardiologist (propensity score) had ac statistic of 0.68, indicating good discrimination be-tween patients managed by cardiologists and general-ists.

In the younger age stratum, comprised of patients,65 years old, there were 4,549 patients cared for bycardiologists and 5,817 by noncardiologists. The un-adjusted mortality rate for the patients of the cardiol-ogists was 4.1%. Patients of the noncardiologists hadan unadjusted mortality rate of 4.3%. The multivariatepredictors from this final regression model for inpa-tient mortality in this age stratum are shown in TableIII. There was no significant effect of physician spe-cialty on inpatient mortality seen in the validation set,with a calculated OR of 1.05 (95% confidence inter-vals [CI] 0.69 to 1.59, p5 0.81), where values,1indicated a favorable impact of cardiologist care onmortality. Thec statistic for this final model was 0.86,

TABLE I Profile of the Two Age Strata

Stratum 1(age ,65 yrs)

Stratum 2(age $65 yrs) p Value

No. of patients 10,366 19,985Mean age (yrs) 54.1 76.1Mortality rate (%) 4.2 15.8 0.001Mean ASG 0.733 1.547 0.0001Cardiologist care (%) 43.9 32.2 0.001

TABLE II Differences in Populations Treated by Cardiologistsand Noncardiologists

Cardiologists Generalists

No. of patients treated 10,976 19,942Average no. of patients

treated per physician 20.2 5.7Mean patient age (yrs) 66.2 70.0Mean patient ASG score 1.173 1.324Unadjusted mortality (%) 9.7 13.0

p 5 0.0001 for all comparisons.

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demonstrating excellent discrimination between thosepatients who did or did not die during their hospital-ization. The Hosmer-Lemeshow statistic showed nosignificant differences between observed and pre-dicted mortality across the deciles of risk (Table IV).

In the older age stratum, the unadjusted mortalityrate for cardiologists’ patients was 13.7%; for thenoncardiologists’ patients, it was 16.8%. The multi-variate predictors from this final regression model areshown in Table V. There was a trend toward betteroutcomes for cardiologists’ patients with and adjustedOR of 0.86 (95% CI 0.72 to 1.03, p5 0.10). Thecstatistic for this model was 0.82; the Hosmer-Leme-show statistic showed good calibration across decilesof predicted mortality (Table VI). When the caseloadvariable were removed, the adjusted OR for cardiol-ogist care decreased to 0.82 (95% CI 0.71 to 0.95, p50.007), indicating a highly significant effect of physi-cian specialty on inpatient mortality. Thec statistic forthe model remained unchanged and the calibration

was also similar (goodness-of-fit statistic5 6.996,p 5 0.54 with volume variables included; goodness-of-fit statistic5 7.873, p5 0.45 without them).

In the full model in the older age stratum, there wasa trend toward better outcomes with increasing case-load of the attending physician. When physician spe-cialty was dropped from the model, this trend wasstrengthened and there was a statistically significantdifference in outcome between the lowest and highestquartile of physician caseload, with an OR of 1.27(95% CI 1.05 to 1.51, p5 0.014). Once again, thecstatistic remained unchanged at 0.82 and the calibra-tion of the model remained good.

DISCUSSIONThe current analysis suggests that patients with

AMI have better outcomes if their physician has ahigher caseload. When physician case load is droppedfrom the model in the older age stratum, but physicianspecialty is retained, the “specialist effect” becomeshighly statistically significant. Likewise, when spe-cialty designation is dropped, the caseload achievedstatistical significance. The discriminatory power and

TABLE III Multivariate Predictors of Inpatient Mortality inPatients ,65 Years Old

Variable OR 95% CI

Care by cardiologist 1.051 0.639–1.593Propensity score 0.729 0.193–2.697Age 1.027 1.001–1.054Caseload (1–6 cases) 1.427 0.844–2.420Caseload (7–12 cases) 1.194 0.714–1.996Caseload (13–23 cases) 1.008 0.642–1.580CABG Hospital 0.543 0.361–0.805ASG score 2 2.460 1.501–4.190ASG score 3 9.473 5.678–16.409ASG score 4 32.912 16.040–68.314Atrial fibrillation or flutter 1.202 0.645–2.111Ventricular fibrillation 1.569 0.811–2.886Cardiogenic shock 12.499 7.912–19.780Cardiomyopathy 2.062 0.744–4.857Diabetes mellitus 0.930 0.650–1.318Dialysis 1.534 0.525–4.160Women 1.338 0.954–1.866Heart failure 1.121 0.782–1.591History of CABG 2.717 1.605–4.462Chronic renal failure 2.124 0.790–5.152Acute renal failure 1.899 0.841–4.067Malignant neoplasm 1.102 0.232–3.718

CABG 5 coronary artery bypass grafting.

TABLE IV Calibration of Regression Model for Patients ,65Years Old

Decile No. Observed Mortality Predicted Mortality

1 508 1 2.22 531 2 3.63 503 4 4.44 523 0 5.75 517 10 7.46 526 6 10.17 521 13 12.88 522 20 17.19 520 35 31.9

10 518 134 129.7

Hosmer-Lemeshow goodness-of-fit statistic 5 10.78 with 8 degrees of free-dom (p 5 0.2145).

TABLE V Multivariate Predictors of Inpatient Mortality inPatients $65 Years Old

Variable OR 95% CI

Care by cardiologist 0.862 0.721–1.030Propensity score 0.567 0.326–0.976Age 1.038 1.028–1.047Caseload (1–6 cases) 1.135 0.912–1.413Caseload (7–12 cases) 1.047 0.846–1.296Caseload (13–23 cases) 1.023 0.839–1.247ASG score 2 1.555 0.919–2.856ASG score 3 4.147 2.464–7.591ASG score 4 18.772 10.894–34.977Ventricular fibrillation 5.025 3.674–6.861Cardiogenic shock 13.832 10.958–17.553Dialysis 1.262 0.718–2.168Women 0.901 0.791–1.026Hypertension 0.763 0.579–0.994History of CABG 1.063 0.785–1.421Chronic renal failure 1.080 0.752–1.527Acute renal failure 3.254 2.540–4.165Neoplasm 0.870 0.572–1.286

Abbreviations as in Table III.

TABLE VI Calibration of Regression Model for Patients $65Years Old

Decile No. Observed Mortality Predicted Mortality

1 998 25 31.72 997 40 42.33 1,002 44 50.04 1,001 50 59.65 1,001 77 79.86 998 116 112.67 997 142 141.48 999 187 173.19 1,000 266 269.4

10 994 680 666.8

Hosmer-Lemeshow goodness-of-fit statistic 5 6.4314 with 8 degrees offreedom (p 5 0.5990).

CORONARY ARTERY DISEASE/MYOCARDIAL INFARCTION CARE 653

calibration of the model did not change appreciably witheither change, and when specialty and caseload wereboth included, neither variable maintained statistical sig-nificance. These findings are consistent with the ob-served high correlation between physician specialty andcaseload (Figure 1). Thus, physician specialty, in addi-tion to being a measure of formal training in a field, isalso a proxy for clinical experience. Work in other fieldshas documented the importance of physician experi-ence—independent of formal training—as an importantpredictor of clinical outcomes.12 It has also been wellestablished that procedural experience such as surgicalcaseloads has a significant effect on outcomes.13,14 Ourwork is the first of which we are aware to explicitlyaddress physician caseload as an important factor inassessing outcomes of patients with myocardial infarc-tion by physician specialty.

Our analytic approach was significantly differentfrom that used by the PHC4 in preparing the publicreport, in the following ways. (1) The populationanalyzed was not identical. We limited our analysis tothe mortality associated with the initial hospitalizationfor AMI, whereas the PHC4 combined results of 2models of inpatient mortality, encompassing both pa-tients who were cared for at a single institution andthose who were transferred from 1 hospital to another.(2) We included physician specialty as a researchvariable of interest in the analysis; the PHC4 did not.Instead, they used the analysis to predict mortality andapplied the same mortality model to patients of bothgroups. We identified the specialty of 277 physicianswho were “unlabeled” in the PHC4 database; weexcluded from analysis 245 patients whose physi-cians’ specialties we could not identify; and we ex-cluded a small number of patients under the care ofcardiac surgeons. (3) We attempted to control for thenonrandomized assignment of patients to cardiologistsversus specialists (referral bias) through the use of a

secondary logistic regression analy-sis, which yielded the estimatedprobability of cardiologist-basedcare, or propensity score. (4) We lim-ited our analysis to a smaller set ofpotential mortality predictors, basedon the inherent limitation of the abil-ity of administrative data sets to cap-ture clinical information.15,16

Our methods were also differentfrom those of Jollis et al,1 althoughboth studies examined the effect ofphysicians’ specialty on mortalityfollowing AMI. They reported risk-adjusted mortality in a Medicare co-hort at 1 year, utilizing clinical vari-ables previously abstracted from pa-tient records for the Health CareFinancing Administration Coopera-tive Cardiovascular Project.17 Theyalso made no adjustments for referralbias, nor did they report risk-adjustedinhospital mortality. Ayanian et al2

studied 30-day mortality, an endpoint not available to us, in a smaller, older cohortfrom Texas.

1. Jollis JG, DeLong ER, Peterson ED, Muhlbaier LH, Fortin DF, Califf RM,Mark DB. Outcome of acute myocardial infarction according to the specialty ofthe admitting physician.N Engl J Med1996;335:1880–1887.2. Ayanian JZ, Guadagnoli E, McNeil BJ, Cleary PD. Treatment and outcomes ofacute myocardial infarction among patients of cardiologists and generalist phy-sicians.Arch Intern Med1997;157:2570–2576.3. Nash IS, Nash DB, Fuster V. Do cardiologists do it better?J Am Coll Cardiol1997;29:475–478.4. Pennsylvania Health Care Cost Containment Council. Focus on heart attack insoutheastern Pennsylvania. A 1993 summary report for health benefits purchas-ers, health care providers, policy-makers, and consumers. Harrisburg, PA, 1996.5. Atlas scoring. A technical white paper. MediQual Systems, Inc., 1996.6. Pennsylvania Health Care Cost Containment Council. Focus on heart attack inPennsylvania; the technical report—1993. Parts A and B. Harrisburg, PA, 1996.7. Lee KL, Woodlief LH, Topol EJ, Weaver WD, Betriu A, Col J, Simoons M,Aylward P, Van de Werf F, Califf RM. Predictors of 30-day mortality in the eraof reperfusion for acute myocardial infarction: results from an international trialof 41,021 patients.Circulation 1995;91:1659–1668.8. Connors AF Jr, Speroff T, Dawson NV, Thomas C, Harrell FE Jr, Wagner D,Desbiens N, Goldman L, Wu AW, Califf RM, et al. The effectiveness of right heartcatheterization in the initial care of critically ill patients.JAMA1996;276:889–897.9. Rosenbaum PR, Rubin DB. The central role of the propensity score inobservational studies for causal effects.Biometrika1983;70:41–55.10. Rosenbaum PR, Rubin DB. Reducing bias in observational studies usingsubclassification on the propensity score.J Am Stat Assoc1984;79:516–554.11. Lemeshow S, Hosmer DW. A review of goodness of fit statistics for use in thedevelopment of logistic regression models.Am J Epidemiol1982;115:92–106.12. Kitahata MM, Koepsell TD, Deyo RA, Maxwell CL, Dodge WT, WagnerEH. Physicians’ experience with the acquired immunodeficiency syndrome as afactor in patients’ survival.N Engl J Med1996;334:701–706.13. Hannan EL, Kilburn H, Bernard H, O’Donnell JF, Lukacik G, Shields EP.Coronary artery bypass graft surgery: the relationship between in hospital mor-tality rate and surgical volume after controlling for clinical risk factors.Med Care1991;29:1094–1107.14. Kimmel SE, Berlin JA, Laskey WK. The relationship between coronary angio-plasty procedure volume and major complications.JAMA1995;274:1137–1142.15. Hannan EL, Kilburn H, Lindsey ML, Lewis R. Clinical versus administrativedata bases for CABG surgery: does it matter?Med Care1992;30:892–907.16. Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB.Discordance of databases designed for claims payment versus clinical informa-tion systems: implications or outcomes research.Ann Intern Med1993;119:844–850.17. Ellerbeck EF, Jencks SF, Radford MJ, Kresowik TF, Craig AS, Gold JA,Krumholz HM, Vogel RA. Quality of care for Medicare patients with acutemyocardial infarction: a four state pilot study from the cooperative cardiovascularproject.JAMA 1995;273:1509–1514.

FIGURE 1. Attending physician by quartiles of caseload. Among the patients whosephysicians treated fewer cases of AMI, generalist care predominates. Cardiologistspredominate as attending physicians in the highest volume quartile.

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