Evolution of the Volume-Outcome Relation for Hospitals Performing Coronary Angioplasty
Background—Hospitals performing more surgical procedures tend to yield better outcomes. This study examines the evolution of this volume-outcome relation over time.
Methods and Results—The relation between the number of percutaneous transluminal coronary angioplasty (PTCA) procedures performed at hospitals (volume) and in-hospital bypass surgery and death for 353 488 patients treated in California between 1984 and 1996 was examined. Descriptive statistics and logistic regression were used to compare outcomes for 3 periods: 1984 to 1987, 1988 to 1992, and 1993 to 1996. The in-hospital mortality rate was 2.5% for hospitals performing <200 PTCA procedures per year but only 1.3% for hospitals performing >400 procedures per year in 1984 to 1987. By 1993 to 1996, mortality rates in these 2 volume categories narrowed to 1.7% and 1.3%, respectively. Bypass surgery rates also narrowed and fell in low-volume (<200 procedures) versus high-volume (>400 procedures) hospitals from 12.4% versus 6.9% in 1984 to 1987 to 4.6% versus 3.3% in 1993 to 1996. In a logistic regression, PTCA procedures significantly predicted in-hospital mortality and bypass surgery rates in all 3 time periods. However, coefficient estimates indicate that improvements over time in outcomes for hospitals performing <200 procedures were comparable to the predicted benefits of increasing volume above 400 procedures within time periods.
Conclusions—Over time, the disparity in outcomes between low- and high-volume hospitals has narrowed, and outcomes have improved significantly for all hospitals. Given these improvements, lower minimum volume standards may be advisable in less populated areas, where the alternative is no angioplasty at all.
Studies examining the relation between the number of patients undergoing surgery at a hospital and their postsurgical outcomes indicate that larger-volume hospitals tend to yield better outcomes.1 This correlation has been found for patients undergoing CABG, PTCA, and hip fracture surgery.2 3 4 5 6 7 8 9 10 These studies have been used to recommend minimum annual procedure volumes for hospitals and physicians as well as regionalization of care at larger facilities.7 11 12
Most studies of the volume-outcome relation used data spanning 3 to 4 years of procedures at most. In addition, the analyses have been cross-sectional; that is, changes in the volume-outcome relation over time have not been considered. Yet, such changes have important implications for decisions regarding minimum volume standards and regionalization of innovative technologies. This study examined the evolution of the relation between angioplasty procedure volume and patient outcomes in California between 1984 and 1996.
Patient data were obtained from the OSHPD discharge data, Version A, from 1984 to 1996. The OSHPD database provides standardized information from hospital discharge abstracts for all patients admitted to California hospitals.
The OSHPD files contained 355 673 admissions between 1984 and 1996 in which a procedure code for coronary angioplasty (ICD-9-CM codes 36.0, 36.00, 36.01, 36.02, and 36.05) was recorded in the discharge abstract. To avoid double counting, only the first angioplasty was examined for patients who received >1 angioplasty during a hospitalization. If a patient received an angioplasty during >1 admission, each admission was included in the sample. Only patients 20 to 100 years old were included in the analysis.
Hospital Volume and Outcomes
The annual PTCA procedure volume for each hospital for each year was calculated as the number of discharges with an ICD-9-CM code for coronary angioplasty during the calendar year of admission. Hospitals that never exceeded an annual volume of 15 PTCA procedures in 1984 to 1996 were excluded from the analysis.
The primary outcomes of the study were in-hospital bypass surgery or death. Information on the relative timing of angioplasty versus bypass surgery during hospitalization was unavailable for the years 1984 to 1989 in the copy of the OSHPD data obtained for this study. However, over the period 1990 to 1996, only 172 patients (1.7% of all patients who underwent CABG in 1990 to 1996) received bypass surgery before angioplasty. Thus, bypass surgery performed during the same admission as the angioplasty was counted as surgery after unsuccessful angioplasty.
Other Study Variables
Age, sex, AMI, and multivessel PTCA were included as explanatory variables in logistic regressions. Patients were identified as having an AMI during admission if the ICD-9-CM code 410 was recorded for the primary diagnosis. Admissions for the subsequent care of an AMI (410.x2) were excluded from this classification. Patients were categorized as receiving multivessel PTCA if their coronary angioplasty procedure was recorded as ICD-9-CM code 36.05.
Stent placement was not included as an explanatory variable in the regressions because information on stent insertion was available only for 1995 to 1996. Emergency CABG can result from failure to place a stent, so this variable may serve more as a marker than a predictor of outcomes. The component variables of the Charlson comorbidity index, which measures illness severity among patients, were included as explanatory variables. The variables were coded by the use of a methodology developed specifically for administrative data.13
Length of stay was also included in the regression explaining inpatient death, because one is more likely to observe in-hospital death for patients who have longer hospitalizations. Length of stay was not included in the regression for CABG because longer hospitalizations were likely to be a consequence of bypass surgery as opposed to a predictor of its occurrence. Altogether, 2185 admissions were dropped from the analysis because of the sample exclusions described above or missing data on length of stay or age.
The relation between volume and outcomes was analyzed over 3 time periods: 1984 to 1987, 1988 to 1992, and 1993 to 1996. Tables comparing outcomes were generated for patients treated in hospitals with low volume (<200 procedures per year), intermediate volume (200 to 400 procedures per year), and high volume (>400 procedures per year). Patient characteristics by hospital volume and period were also generated. Results are also presented for 3 components of the comorbidity index that are highly prevalent among patients with coronary disease or strongly associated with outcomes (diabetes, renal insufficiency, and peripheral vascular disease).
A logistic regression model was used to examine the relation between hospital volume and outcomes after adjustment for patient-specific characteristics. After previous studies of the volume-outcome relation for PTCA, the logarithmic transformation of hospital volume was used as the explanatory variable of interest in the logistic regressions.5 7 Both prior studies and this analysis show that log(volume) appears to be linearly associated with the logit of in-hospital mortality rates.
Two additional dummy variables were included in the regressions to compare outcomes for admissions that occurred during 1988 to 1992 and 1993 to 1996 versus 1984 to 1987. Interactions of these dummy variables with the logarithmic transformation of hospital volume were included. Coefficients on the dummy variables indicate whether outcomes have improved or worsened over time. Coefficients on the interaction terms indicate whether the magnitude of the relation between hospital volume and patient outcomes became larger or smaller over time. All standard errors for regression estimates were derived by the use of a robust estimate of variance.14 15 16
The logistic regression estimates were used to compare differences in patient outcomes within time periods that were caused by differences in procedure volume. The estimates were also used to compare changes in outcomes for low-volume hospitals across time periods. Logistic predictions were derived under 2 scenarios for the sample of low-volume hospital patients in each time period. In the first case, the mean procedure volume for all low-volume hospitals was substituted for actual patient volume when forming predicted outcomes. In the second case, the mean procedure volume for all high-volume hospitals was used in place of actual patient volume in predictions for each patient. Mean predicted outcomes under these scenarios provide an estimate of the expected difference in outcomes for a representative sample of patients if they were treated in a typical low-volume versus high-volume hospital.
Predicted outcomes for patients in low-volume hospitals in the initial period (1984 to 1987) if they instead had received angioplasty in low-volume hospitals in future periods were also derived. These predictions were derived by substituting the dummy variables and hospital volume interaction terms for future time periods (1988 to 1992 or 1993 to 1996) into logistic predictions for the sample of low-volume hospital patients in the initial time period. These predictions were also calculated with the use of the mean procedure volume for all low-volume hospitals in 1984 to 1996. The use of mean procedure volume for each hospital size category ensures that changes in outcomes observed in the predictions are not due to increases in procedure volume that have occurred over time within the set of low-volume (<200 procedures per year) and high-volume (>400 procedures per year) hospitals.
The analysis was based on 353 488 admissions for patients who received PTCA in California between 1984 and 1996. The mean age was 63.2 years. Thirty-one percent were women. Overall, 22.7% of patients treated with PTCA had an acute myocardial infarction, and 11.3% of patients had multivessel angioplasty. The mean of the Charlson comorbidity index was 0.38.
During the study period, 129 California hospitals performed angioplasty. Of these, 94 (73%) hospitals performed PTCAs in 1984. Seven (5%) hospitals began performing PTCA in 1985. An additional 22 (17%) hospitals began offering PTCA in 1986 to 1989. Only 6 (5%) hospitals began performing PTCA in 1990 or afterward. Of those hospitals performing PTCA during the sample period, 8 (6%) had discontinued this procedure by 1996.
The number of procedures performed annually in each hospital ranged from 1 to 1247. The median annual PTCA volume for 1984 to 1987 was 89 procedures per year. Through 1988 to 1992, the median volume rose to 200 procedures per year, and this figure increased to 272 PTCAs per year for 1993 to 1996. A total of 93% of hospitals open in 1984 to 1987 performed <200 procedures per year in ≥1 of these years; by 1993 to 1996, 44% of hospitals remained in this same situation. For 1984 to 1987, 36.2% of patients were treated in a hospital performing <200 PTCA procedures per year. By 1993 to 1996, 11% of patients were still being treated in hospitals performing <200 PTCA procedures annually.
The overall in-hospital mortality rate in 1984 to 1996 for patients who underwent PTCA was 1.5%, whereas the rate of bypass surgery for patients who received PTCA was 5.4%. Table 1⇓ lists information on mortality rates, CABG rates, and patient characteristics by size category and time period. The in-hospital mortality rate was 2.5% for hospitals performing <200 PTCA procedures per year but only 1.3% for hospitals performing >400 procedures per year in 1984 to 1987. By 1993 to 1996, mortality rates in these two volume categories narrowed to 1.7% and 1.3%, respectively. Hospitals performing <200 PTCA procedures also had higher rates of bypass surgery in 1984 to 1987 (12.4%) versus hospitals performing >400 procedures (6.9%). By 1993 to 1996, bypass surgery rates in these two categories narrowed and fell to 4.6% and 3.3%, respectively.
Table 1⇑ indicates that characteristics of patients who received PTCA differed across time periods and by hospital volume. During later time periods, patients were on average older, more likely to be women, more likely to have an acute myocardial infarction (AMI), more likely to receive multivessel angioplasty, and more frail according to the Charlson comorbidity index. In addition, patients who were treated in hospitals providing <200 PTCA procedures per year were slightly younger, more likely to have an AMI, and less likely to receive multivessel PTCA.
Table 2⇓ contains results from logistic regression models used to examine the relation between hospital volume and outcomes after adjustment for patient characteristics. The odds ratios for the natural logarithm of hospital volume indicate that there was a significant negative association between higher procedure volume and inpatient mortality rates in 1984 to 1987 (OR 0.84, P<0.001), 1988 to 1992 (OR 0.85, P<0.001), and 1993 to 1996 (OR 0.91, P=0.004). The results also indicate that inpatient mortality rates were lower in later periods. Patients admitted in 1988 to 1992 were 0.55 times as likely to die in-hospital relative to patients admitted in 1984 to 1987 (P=0.025). Patients treated during 1993 to 1996 were 0.34 times as likely to die in-hospital compared with patients admitted in 1984 to 1987 (P<0.001).
The odds ratios in Table 2⇑ also indicate that there is a negative relation between increasing hospital PTCA volume and bypass surgery in 1984 to 1987 (OR 0.80, P<0.001), 1988 to 1992 (OR 0.81, P<0.001), and 1993 to 1996 (OR 0.79, P<0.001). CABG rates declined in later periods. Patients admitted in 1988 to 1992 were 0.45 times as likely to undergo bypass surgery relative to patients admitted in 1984 to 1987 (P<0.001). Patients treated during 1993 to 1996 were 0.40 times as likely to undergo bypass surgery compared with patients admitted in 1984 to 1987 (P<0.001).
Estimates from the logistic model were used to predict the magnitude of differences in outcomes between low- and high-volume hospitals within time periods. The predicted improvements that low-volume hospitals had in 1988 to 1992 and 1993 to 1996 relative to 1984 to 1987 were also derived and appear in Table 3⇓. On the basis of the logistic model estimates, if all patients treated in low-volume hospitals in 1984 to 1987 were treated in a mean low-volume hospital (performing 90 PTCA procedures per year), they would have a mean inpatient mortality rate of 2.5%. If these low-volume patients had instead been treated during 1984 to 1987 in a hospital performing 600 procedures per year (the approximate mean volume for high-volume hospitals during 1984 to 1996), their predicted mortality rate falls to 1.9%. By 1993 to 1996, the predicted mortality rate of patients treated in a typical low-volume hospital (1.8%) would still have been lower if they were instead treated in a facility conducting 600 procedures per year (predicted mortality rate 1.5%). However, the estimated differential of 0.3 percentage points in the later period is smaller than the 0.6–percentage point decline in mortality rate predicted for 1984 to 1987.
If patients treated in a representative low-volume hospital during 1984 to 1987 were instead treated in a low-volume hospital during 1988 to 1992, the regression estimates indicate that their predicted mortality rate falls from 2.5% to 1.5%. If these same patients were treated in a representative low-volume hospital during 1993 to 1996, their predicted mortality rate falls even further to 1.3%. Thus, holding patient characteristics constant, low-volume hospitals markedly reduced inpatient mortality rates in subsequent time periods. Moreover, the improvements in inpatient mortality rates that low-volume hospitals had over time were comparable in magnitude to the predicted gains in outcomes that would have come from moving their patients to a high-volume hospital in the same time period.
Similar analyses for CABG rates are presented in Table 3⇑. Representative patients treated in a hospital performing 90 PTCA procedures per year in 1984 to 1987 were predicted to have a mean bypass surgery rate of 12.9%. If these patients had instead been treated in the mean high-volume hospital, their predicted CABG rate would have fallen to 8.8%. By 1993 to 1996, the predicted CABG rate for patients treated in a typical low-volume hospital (5.6%) would still have been better if they were instead treated in a facility conducting 600 procedures per year (CABG rate 3.7%). However, the estimated differential of 1.9 percentage points in the later period is smaller than the 4.1–percentage point decline in bypass surgery predicted for 1984 to 1987.
If patients treated in a typical low-volume hospital during 1984 to 1987 were instead treated in a low-volume hospital performing 90 PTCA procedures per year during 1988 to 1992, the regression estimates indicate that their predicted bypass surgery rate falls from 12.9% to 6.6%. If these same patients were treated in a low-volume hospital performing 90 procedures per year during 1993 to 1996, their predicted bypass surgery rate falls to 5.5%. Thus, the reductions in CABG rates that low-volume hospitals had over time were larger in magnitude than the predicted gains in outcomes that would have come from moving their patients to high-volume hospitals within the same time period.
Although the volume-outcome relation persists over time, in-hospital mortality and CABG rates have decreased for all hospital sizes. Moreover, the relative performance advantage of high- versus low-volume hospitals has decreased over time. Both descriptive statistics and logistic regressions that control for differences in patient characteristics across hospital volume categories support this conclusion. Predictions based on the logistic estimates indicate that the improvements that low-volume hospitals have achieved over time are substantial. In fact, they are comparable in magnitude to the relative outcomes advantage of high-volume hospitals observed in the time period 1984 to 1987.
Implications for Minimum Volume Standards
These results have important implications for decisions regarding minimum volume standards and regionalization of innovative technologies. The relative benefit of more favorable outcomes at larger facilities must be weighed against the potential decline in access resulting from minimum volume standards or regionalization. For instance, lack of access to angioplasty as the result of regionalization may have negative health consequences for patients in less populated areas requiring emergency angioplasty.7 Acknowledgment of this tradeoff is implicit in current recommended guidelines. Past studies have identified significantly lower in-hospital mortality, bypass surgery, or complication rates for PTCA at thresholds as high as 400 or 600 procedures per year.6 7 8 However, the American College of Cardiology/American Heart Association guidelines have minimum volume standards that remain at 200 procedures annually.17 The results in this study suggest that even lower minimum volume standards may be justifiable in less populated areas, where the alternative is no access to angioplasty at all. Regionalization may ensure better outcomes in the early stages of a new medical intervention. However, if access to care is affected by timing and distance, then less centralization may be preferred as technologies improve.
Increases in Size of Low-Volume Hospitals
Improvements in outcomes for hospitals performing <200 procedures per year are not merely due to gradual increases in procedure volume in this size category. The logistic regression estimates explicitly controlled for actual (vs categoric) procedure volume and still demonstrated outcome improvements over time. In addition, the predictions in Table 3⇑ held procedure volume constant at 90 procedures per year for low-volume hospitals and demonstrated sizable outcome improvements over time for low-volume hospitals. A substantial number of hospitals are likely to continue operating below the recommended minimum volume of 200 PTCA procedures per year, given that 26% of all hospitals remained in this situation in 1996; 18 out of 121 California hospitals performed <100 procedures in 1996. Thus, it is important to continually reassess outcomes in low-volume facilities. Given that California contains a higher concentration of low-volume hospitals than has been found in nationally representative samples,7 it is also important to determine whether these results hold in data from other states.
Evolution of Volume-Outcome Relation
Luft et al1 proposed reasons for why the volume-outcome relation exists, and Jollis et al5 discussed these hypotheses for angioplasty patients. The positive volume-outcome relation may be due to a learning effect among providers−“practice makes perfect”−or to selective referral; facilities with better outcomes may attract more patients. As with past studies, this analysis cannot identify the source of the volume-outcome effect.
In fact, this study raises the additional question of why outcomes improved over time and why they improved more for low- versus high-volume providers. Over the period 1984 to 1996, there were continual advances in catheters, guide wires, radiographic equipment, stents, and drug therapy for angioplasty patients.18 Low-volume hospitals may have been slower to adopt such technologies initially. Further investigation is required to determine how much improved outcomes are attributable to these advances in technology versus improved skills among providers.
Physician Volume-Outcome Relation
The California Office of Statewide Health Planning and Development (OSHPD) database does not record physician identifiers for inpatient procedures. Therefore, the extent to which the hospital volume-outcome relation is attributable to an underlying physician-volume effect could not be examined. However, a past study of Medicare patients in 1992 determined that the hospital volume-outcome relation persisted even after controlling for physician procedure volume.7 The combined end point of in-hospital bypass surgery or death in that study was greatest for patients treated by low-volume physicians in low-volume hospitals and lowest for patients treated by high-volume physicians in high-volume hospitals. Analysis of PTCA registry data from New York also concluded that a hospital volume-outcome relation persists after accounting for cardiologist procedure volume.8
Advantages and Disadvantages of Administrative Data
There are several limitations and benefits of using administrative data to analyze volume-outcome relations.10 Administrative data provide limited information to distinguish between complications and comorbidities.19 For instance, secondary diagnoses of AMI in the OSHPD data may represent AMI at the time of admission, AMI that occurred as a complication of PTCA, or other AMI events that occurred during hospitalization. Thus, secondary diagnosis of AMI was not used as an outcome measure.
The OSHPD data also lack information on preprocedural risk factors such as ventricular function, time since previous AMI, and use of preoperative intra-aortic balloon pumps, which have predicted inpatient mortality rates for PTCA patients.8 Even with the breadth of ICD-9-CM codes, hospitals may have failed to record all relevant conditions in the OSHPD data. A previous comparison of administrative claims versus clinical data for cardiac catheterization found that the former failed to identify more than half of patients with a prognostically important condition identified by the clinical information system.20 This same study also suggested that trends identified in administrative data may be confounded by coding “creep”: increased coding of comorbidities over time to raise reimbursement.20
Despite these shortcomings, administrative data are the only source of information by which the volume-outcome relation may be examined for a large sample of hospitals over a lengthy time period. The OSHPD data involve the universe of procedures in California and are good at identifying important variables such as age, sex, expensive procedures, and in-hospital death.10 For this reason, most volume-outcome studies rely on administrative data.2 4 5 7 9 10 Those studies using more detailed clinical data have reached similar conclusions regarding the presence of worse outcomes in low-volume hospitals.3 6 8 Further studies are necessary to determine whether the findings of this study are consistent and generalizable to other medical technologies.
As healthcare costs continue to rise, healthcare providers and policy makers will continue to be concerned with rational dissemination of new medical technologies. Early assessment of new technologies provides useful information for decisions regarding the initial distribution of scarce resources. However, continual assessment of progress in the evolution of medical care is essential for the determination of definitive guidelines. Further research is also required to identify the determinants of medical progress.
This research was supported by a Faculty Fellowship from the John M. Olin Foundation. The author thanks Laurence Baker and Jim Reynolds for providing the 1984 to 1989 OSHPD patient discharge data.
- Received September 17, 1999.
- Revision received November 2, 1999.
- Accepted November 29, 1999.
- Copyright © 2000 by American Heart Association
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