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Effects of Provider Patient Volume and Comorbidity on Clinical and Economic Outcomes for Total Knee Arthroplasty
The Journal of Arthroplasty Vol. 25 No. 6 2010
Effects of Provider Patient Volume and Comorbidity on Clinical and Economic Outcomes for Total Knee Arthroplasty A Population-Based Study Min-Hsiung Wei, MD, MHA,* Yi-Ling Lin, MHA,y Hon-Yi Shi, DrPH,y and Herng-Chia Chiu, PhDy
Abstract: Our study examined how provider patient volume, postoperative infection rate, and perioperative complication affect length of stay, hospitalization charges, and adverse outcomes for patients undergoing total knee arthroplasty (TKA). The study sample included patients who had undergone total knee arthroplasty at all acute care hospitals in Taiwan between 2000 and 2003. Two economic indicators revealed linear associations with surgeon's patient volume, hospital's patient volume, and comorbidity score. Patients who developed postoperative infections remained hospitalized an average of 8.49 days longer than did patients with no infection. Postoperative infection was associated with surgeon experience. Our findings indicate that a surgeon's patient volume has a more significant effect than a hospital's patient volume on clinical outcomes. However, patient volumes for both surgeon and hospital are equally important in economic outcomes. Keywords: clinical outcome, economic outcome, complications, total knee arthroplasty. © 2010 Elsevier Inc. All rights reserved.
Total knee arthroplasty (TKA) is now a routine treatment for relieving knee pain and improving knee function [1,2] as well as overall health-related quality of life [3]. The incidence of arthroplastic knee procedures is increasing in aging Western populations such as that in the United States, where more than 430 000 such procedures were performed in 2003 [4]. In Taiwan, the annual number of such procedures nearly doubled in a 6-year span, from 6500 in 1998 to 12 088 in 2004 [5]. The efficient use of limited public health resources requires identifying efficient and highly skilled health care providers. Studies from the United States [6,7], Canada [7], and England [8] have explored the relationship between patient volume and clinical outcome by analyzing health care claims data. Others have addressed the association From the *Department of Orthopedics, St. Joseph's Hospital, Huwei Township; and yGraduate Institute of Healthcare Administration, Kaohsiung Medical University, Kaohsiung City, Taiwan. Supplementary material available at www.arthroplastyjournal.org. Submitted December 5, 2008; accepted June 28, 2009. No benefits or funds were received in support of the study. Reprint requests: Herng-Chia Chiu, PhD, Graduate Institute of Healthcare Administration, Kaohsiung Medical University, 100 ShihChuan 1st Road, San Ming District, Kaohsiung City, Taiwan 807, ROC. © 2010 Elsevier Inc. All rights reserved. 0883-5403/2506-0012$36.00/0 doi:10.1016/j.arth.2009.06.033
between the volume of patients treated by a provider (surgeon or hospital) and cost [9]. The answer to the question of which has the greater effect on economic efficiency in the health care industry—the hospital or the individual surgeon—is still unclear [10,11]. Very few studies have simultaneously examined the relationships between patient volume, clinical outcome, and economic outcome [10-13]. Reports examining the volume and clinical outcome of TKA in countries other than the United States or Canada have analyzed relatively small numbers of patients and providers [14]. Other than provider patient volume, there are few factors associated with resource use and surgical outcomes. Comorbidity was consistently and significantly associated with the costs of treating TKA patients [7,11]. Several studies found that TKA patients who developed postoperative infections consumed 3 to 4 times more health resources than those who had no infections [1517]. Others studies also demonstrated that comorbidity was associated with postoperative complications [7,18]. We examined the associations of volume of TKA patients treated per surgeon and per hospital in Taiwan with economic and clinical outcomes. We sought to answer the following questions:
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1. Are a surgeon's patient volume and a hospital's patient volume equally important for reducing
Clinical and Economic Outcomes for TKA Wei et al
health care costs while maintaining adequate quality of care? 2. Is patient volume linearly associated with resource consumption, and how is patient volume related to clinical outcome? 3. To what extent do perioperative complications, postoperative infection, and comorbidity in TKA patients affect resource use? 4. How do predictors for postoperative hospital index infections differ from those for perioperative hospital complications?
Materials and Methods Data Sources The National Health Insurance (NHI) inpatient claims databases for years 2000 through 2003 were analyzed. The NHI requires hospitals in Taiwan to apply for reimbursement using a standard claims format stating provider information (both physician and hospital), patient demographic data (age, sex), International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes, and discharge status. In Taiwan, 99% of residents have enrolled in the NHI program since the national universal compulsory program was implemented in 1995. All hospitals providing acute inpatient care must agree with the NHI to provide care for their patients. Therefore, the NHI database is the largest and most complete health care data set in Taiwan. The use of such data sets in clinical research has some limitations but has been validated by many studies [19,20]. Sample Selection and Exclusion Criteria Data for patients with ICD-9-CM procedure code 81.54 (knee arthroplasty) were extracted from the NHI database. That is, knee arthroplasties were analyzed in the overall population. To minimize selection bias and data errors, serial exclusion criteria were observed (Fig. 1). Exclusion criteria were as follows: primary diagnosis other than osteoarthritis (ICD-9-CM codes 715 and 716), rheumatoid arthritis (ICD-9-CM code714), or gouty arthritis (ICD-9-CM code 2740); history of bilateral TKA (n = 3297) or unicompartmental knee arthroplasty (n = 722); TKA with minor surgeries or procedures (n = 1687); treatment charges lower than NHI standard fees (n = 69); length of stay less than 3 days (n = 95); unavailable data for sex or age (n = 39). Thus, 6157 cases (16.30%) were excluded from the original sample of 37 775. Outcome Measures The outcome variables in our study were both economic and clinical. Length of hospital stay and inflation-adjusted total hospitalization charges were analyzed to assess economic outcomes, whereas adverse outcomes were measured by incidence of postoperative infections and perioperative complications. The mortal-
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ity rate was only 0.1% and thus was excluded as an outcome variable. Length of hospital stay and total hospitalization charges were both considered continuous variables, whereas adverse outcome was considered a categorical variable. Postoperative infection was defined to include both implant infection (ICD-9-CM code 996.66) and wound infection (ICD-9-CM code 998.5), as in other studies [11]. Indicators of perioperative complications were in accordance with those in other studies: deep venous thrombosis [12,21], pulmonary embolism [12,21], acute myocardial infarction [12,21], pneumonia [21], urinary tract infection [10], and upper gastrointestinal bleeding. Patients with any of those complications were noted as having perioperative complications, and the variable was recorded as a dichotomous variable. The 2 variables were analyzed as explained variables in the economic models and were treated as dependent variables in the clinical outcome model (logistic regression model). Provider Patient Volume Measurement and Comorbidity Score Surgeons' and hospitals' patient volumes were analyzed by quartiles because of the skewed distribution of the data. Because of the very small numbers of patients
NHI inpatient data claim data (2000- 2003)
Inpatients undergoing knee arthroplasty (ICD-9-CM procedure code 81.54): 37,775 patients
Primary diagnosis code (ICD-9CM procedure codes 714, 715, 716, and 2740): 37,527 patients
Excluded bilateral TKA: 3,297 patients Excluded unicompartmental knee arthroplasty: 722 patients
Excluded TKA with minor procedures: 1,687 patients Excluded physician fees lower than NHI payment standards: 69 patients Excluded length of stay <3 days: 95 patients Excluded cases without sex and age data: 95 patients
Included in final study sample: 31,618 patients (83%)
Fig. 1. Flowchart depicting the process of determining study sample inclusion and exclusion.
908 The Journal of Arthroplasty Vol. 25 No. 6 September 2010 in quartiles 1 and 2 for both surgeons and hospitals, the 2 groups were merged. Therefore, 3 groups were defined: low volume (quartiles 1 and 2), moderate volume (quartile 3), and high volume (quartile 4). The quartile or percentile method has been used in other studies in analyzing administrative data sets [7,22]. Using the quartile method, we found that the average number of TKA procedures for low-volume surgeons and hospitals in our study was comparable with the mean number of procedures for the low-volume group reported in 1997 by Kreder et al [10]. The Charlson comorbidity index (CCI), as modified by Deyo et al [23], was used to calculate comorbidity scores based on admissions data. The CCI has been validated as a proxy of illness severity in other TKA outcome studies [7,18]. All other variables were analyzed in 3 strata to improve group distribution. Covariates Patient- and hospital-related covariates were controlled. Patient-specific variables included age, sex, and primary diagnosis (rheumatoid arthritis and osteoarthritis). Hospital-specific covariates were ownership (public, private, or nonprofit) and hospital accreditation status (medical center, regional hospital, or district hospital). Statistics Analysis of variance was used to evaluate the 3 volume groups for statistical differences in continuous variables (age, length of hospital stay), whereas χ2 tests were used for categorical variables. Multiple linear regression was used to examine relationships between patient volume and economic outcome after adjusting for patient demographics and hospital attributes, and multiple logistic regression was used to examine predictors of postoperative infection and perioperative complications. Study subjects with complications were given a code of 1, and those without complications were given a code of 0. The SPSS software package (version 12.0, SPSS Inc, Chicago, Ill) was used for statistical analyses.
Results Provider Volume Distribution Between 2000 and 2003, 823 surgeons in 295 hospitals performed TKAs in 31 618 patients (Tables 1 and 2). Patient volume data were skewed for both surgeons and hospitals; approximately one quarter of surgeons and hospitals performed more than 80% of all TKAs. Stratification of patient volume by surgeon revealed a mean annual volume of 2.15 (range, 1-3), 5.68 (range, 49), and 31.94 patients (range, 10-463) among low-, moderate-, and high-volume surgeons, respectively. Stratification of hospital volume revealed a mean annual volume of 3.35 (range, 1-6), 13.75 (range, 7-23), and 94.19 patients (range, 24-592) among low-, moderate-, and high-volume hospitals, respectively.
The average age of patients across the 3 groups was 74 years. Compared with moderate- and low-volume surgeons, high-volume surgeons treated a higher percentage of female patients (P b .001). The highvolume hospitals provided care for more patients with rheumatoid arthritis than did medium- and lowvolume hospitals (P b .001). Individual surgeons and hospitals significantly differed in average length of hospital stay and inflation-adjusted total charge (P b .001). Patients who underwent TKA performed by low-volume surgeons and at low-volume hospitals had longer hospital stays and higher hospitalization costs than did patients in the 2 other volume groups. A statistically significant difference (P b .001) was noted in surgeon volume strata (Table 2; available online at www.arthroplastyjournal.org) but not in hospital volume strata. Model of Health Resources Use The effect of provider volume on average length of hospital stay and total hospitalization charges was examined by multivariate linear regression analyses. Except for sex and diagnosis, all variables (surgeons' patient volume, hospitals' patient volume, CCI score, postoperative infections, perioperative complications, and age) were significantly and consistently related to duration of hospitalization and inflation-adjusted total medical costs. Three variables (surgeon volume, hospital volume, and CCI score) were linearly associated with length of hospital stay and total medical costs. Hospital stays of patients treated by low- and moderate-volume surgeons were longer by 1.19 (P b .001) and 0.59 days (P b .001), respectively, than those treated by high-volume surgeons after controlling for hospital volume and other factors (Table 3; available online at www.arthroplastyjournal.org). The effects of hospital volume on both economic outcomes were similarly associated. After controlling for surgeon volume, severity of illness, and other covariates, medical charges (in new Taiwan dollars [NT$]) to patients who underwent surgery at low- and moderate-volume hospitals were NT$2770 and NT$2015 higher, respectively, than charges to those treated at high-volume hospitals. Increased severity of comorbid diseases in TKA patients was positively associated with length of hospital stay and total medical charges (Table 3; available online at www.arthroplastyjournal.org). Patients with CCI scores of 0 or 1 had shorter hospital stays and lower medical charges than those with CCI scores of 2 or higher. Postoperative infection and other in-hospital complications had the greatest effect on both economic variables. Patients with postoperative infections required 8.49 more days of hospitalization than those without infections. The total-charges model indicated that patients with postoperative infections
Clinical and Economic Outcomes for TKA Wei et al
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Table 1. Comparison of Patient and Provider Characteristics Among 3 Hospital Volume Groups Characteristic No. of patients (%) No. of hospitals (%) Average annual volume of patients undergoing TKA per surgeon Annual volume of patients undergoing TKA per hospital (mean ± SD) Age of patients in years (mean ± SD) No. of females (%) No. of patients by diagnosis (%) Osteoarthritis (avascular necrosis) Rheumatoid arthritis No. of CCI scores (%) 0 1 ≥2 No. of patients by hospital level (%) Medical center Regional hospital District hospital No. of patients by hospital ownership structure (%) Public Nonprofit For profit Average length of hospital stay (mean ± SD) 95% CI Total charges in NT$ (mean ± SD) Inflation-adjusted No. of postoperative infections (%) No. of perioperative complications (%)
Low Volume: Quartiles 1 and 2
Medium Volume: Quartile 3
High Volume: Quartile 4
1114 (3.52) 149 (50.51) 1-6
3755 (11.88) 74 (25.09) 7-23
26 749 (84.60) 72 (24.41) 24-592
3.35 ± 2.73
13.75 ± 8.12
94.19 ± 112.46
b.001
73.93 ± 8.69 753 (67.59)
74.43 ± 8.05 2627 (69.96)
73.88 ± 7.85 20 078 (75.06)
b.001 b.001
1096 (98.38) 18 (1.62)
3685 (98.14) 70 (1.86)
26 044 (97.36) 705 (2.64)
b.01
956 (85.81) 123 (11.04) 35 (3.14)
3228 (85.96) 415 (11.05) 112 (2.98)
22 123 (82.70) 3828 (14.31) 798 (2.98)
b.001
0 (0.00) 104 (9.34) 1010 (90.66)
63 (1.68) 1602 (42.66) 2090 (55.66)
13 380 (50.02) 6231 (23.29) 7138 (26.69)
b.001
222 (19.93) 117 (10.50) 775 (69.57) 10.79 ± 3.74 10.57-11.01
1474 (39.25) 387 (10.31) 1894 (50.44) 10.22 ± 3.36 10.12-10.33
8007 (29.93) 9862 (36.87) 8880 (33.20) 8.66 ± 2.92 8.62-8.69
b.001
128 822 ± 10 582 11 (0.99) 26 (2.33)
128 483 ± 10 076 24 (0.64) 82 (2.18)
127 137 ± 13 101 144 (0.54) 650 (2.43)
P
b.001 b.001 .120 .646
Postoperative infection: infection cases in each group/all procedures in each group.
had used NT$27 798 more in health care services than did patients without infections.
(95% CI, 5.41-8.57) in patients with a CCI score of 0 than in patients with CCI score of 2 or higher (Table 4).
Adverse-Outcome Models Of all study subjects, 139 (0.57%) had postoperative infections during their hospital stay and 758 (2.4%) had perioperative complications. Multivariate-adjusted logistic regression for infection models revealed a significantly higher infection risk in TKA patients treated by low- or moderate-volume surgeons than those treated by high-volume surgeons (Fig. 2). Stratification of hospital patient volume, however, revealed that patients treated in low-volume institutions had a significantly higher risk of having infections than those treated in high-volume institutions (odds ratio, 2.33; 95% confidence interval [CI], 1.08-5.05). Patient demographic factors (age, sex) and primary diagnosis were not significant predictors. Interestingly, all patient demographic and disease burden indicators that were not predictors of postoperative infection were significant predictors in the perioperative-complication model. Conversely, provider patient volume significantly affected the incidence of postoperative infections but was not a significant predictor in the perioperative-complication model. The likelihood of in-hospital complications was 6.81 times higher
Impact of Patient Volume on Economic and Clinical Outcomes Our study indicated that patient volume, disease severity, and complication rate significantly affected hospital resource use. When controlling for all covariates, we found that patient volume was negatively associated with length of hospital stay and health care costs. The evidence suggests that both higher-volume hospitals and higher-volume surgeons are equally important in minimizing health care costs. However, individual surgeon experience had a greater effect on cost savings. Until now, very few studies have comprehensively examined how surgeon and hospital volume affect use of health care services [11,12]. These findings demonstrate the important economic effects of patient volume. The incidence of postoperative infections was inversely and linearly associated with surgeon patient volume after stratification by both crude and adjusted logistic regression models. Hospital patient volume stratification revealed an association only between low- and highvolume groups. Although the incidence of postoperative
Discussion
910 The Journal of Arthroplasty Vol. 25 No. 6 September 2010
Fig. 2. Risk of infection for 3 prediction variables (surgeon's patient volume, hospital's patient volume, and CCI), adjusted for covariates. Each trail is represented by a square and a horizontal line. The square represents the corresponding risk odds ratio for infection, and the line represents the 95% CI for the 3 prediction variables.
infection was higher in the moderate-volume hospitals than in the high-volume hospitals, the difference did not reach statistical significance. Overall, incidences of postoperative infections were highest for the low- and moderate-volume surgeons and hospitals. These statistical results demonstrate for the first time that individual surgeons have a larger effect on surgical outcomes than hospitals do. Provider (surgeon or hospital) patient volume revealed no association with incidence of perioperative complications. However, baseline patient characteristics were correlated with incidence of perioperative complications. Our findings suggest that postoperative infections and perioperative complications should be considered as individual independent variables when conducting clinical-outcomes or economic-outcomes
studies. The linear relationship between volume and economic outcome and between volume and clinical outcome demonstrates the important relationship between volume and outcome. In the economic-regression model, stratification of both individual surgeon patient volume and hospital patient volume revealed a linear trend in adjusted β value for length of hospital stay and health care charges (Table 3; available online at www. arthroplastyjournal.org). The increasing odd ratios for the adverse outcomes indicated that a linear association exists between patient volume and postoperative infection rate (Fig. 2). This linear relationship has not been demonstrated until now [12,21]. The correlations between volume and outcome that we observed further validate the “practice makes perfect” theory proposed by Luft et al [24].
Table 4. Predictors of Perioperative Complication Crude Predictor Surgeon patient volume High (reference) Moderate Low Hospital patient volume High (reference) Moderate Low CCI scores 0 (reference) 1 ≥2 Age Sex Male (reference) Female Diagnosis Osteoarthritis (avascular necrosis) (reference) Rheumatoid arthritis
Adjusted
% (No.)
Odds Ratio
P
Odds Ratio
95% CI
P
2.44 (627) 2.05 (87) 2.61 (44)
0.83 1.07
.117 .666
0.90 1.17
0.705-1.154 0.823-1.659
.411 .412 .385
2.43 (650) 2.18 (82) 2.33 (26)
0.96 0.90
.838 .356
0.83 0.65
0.640-1.072 0.411-1.035
.105 .152 .070
1.46 5.82 1.02
b.001 b.001 b.001
1.74 6.81 1.03
1.416-2.126 5.406-8.573 1.017-1.038
b.001 b.001 b.001 b.001
1.54 (126) 2.69 (632)
1.77
b.001
1.85
1.523-2.258
b.001
2.41 (744) 1.77 (14)
0.73
.241
0.49
0.279-0.850
b.01
2.01 (530) 2.90 (127) 10.69 (101)
Perioperative complication as event controlled for hospital ownership (public, nonprofit and for-profit), hospital accreditation level (medical center, regional hospital and district hospital).
Clinical and Economic Outcomes for TKA Wei et al
Effect of Infection and Complications on Health Care Use In addition to provider patient volume, postoperative infections and complications also predicted consumption of health care dollars. Any incidence of infection increased hospital stay by 8.49 days and total medical charges by NT$27 798. These findings demonstrate the magnitude of increase in health care dollars required to treat infection. Several studies also indicate that the health care resources required for infected TKA patients consumed 3- to 4-fold more health resources as compared with those for patients without infection [15,17], but infection severity was not adjusted for in those studies. Minimizing postoperative infection would substantially reduce treatment costs for TKA patients. Perioperative complications are also associated with increased use of health care resources. The effects of infections and complications are rarely considered in economic-prediction models as was done in our study. Our findings demonstrate the varying magnitude of health care resource consumption resulting from different complications of TKA. Impact of Illness Severity on Economic and Clinical Outcomes Disease burden (CCI score) was consistently and significantly associated with the economic burden of treating TKA patients, which confirmed the findings of earlier studies [7,11]. However, the linear nature of the association was a novel finding. Another notable finding was that perioperative complications were 6.81 times more likely in patients with CCI scores of 2 or higher than in patients with CCI scores of less than 1. Conversely, however, postoperative infection was not associated with CCI score. A study by Kreder et al [7] reported that patients with CCI scores of 2 or higher were twice as likely to have in-hospital complications than those with CCI scores of less than 1. A clinical investigation by Soohoo et al [18] revealed that patients with CCI scores of higher than 2 had a 2-fold higher 90-day risk of postoperative infection than patients with scores of zero. These findings are further evidence that TKA patients should be examined carefully by surgeons before admission. Our results have the following implications: • First, the average numbers of TKA procedures performed by low-volume surgeons and hospitals analyzed in this study were comparable with those reported in 1997 by Kreder et al [10] but much smaller than those described in more recent studies [6,12,21]. Because our results suggest that low-volume providers are more costly, the NHI system should develop a mechanism such as pay for performance to encourage high-volume
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physicians and hospitals to provide service to more TKA patients. • Second, after controlling for disease severity and other covariates, we found that patients treated by low- to moderate-volume surgeons or hospitals had higher infection rates than those treated by highvolume surgeons or hospitals. We propose 2 policies. (1) Surgical outcomes should be made publicly available. Patients have the right to know which providers achieve the best surgical outcomes. For example, the NHI could consider adopting the report-card system currently used in the United States to periodically disclose the infection rates for specific surgical procedures. (2) A mechanism for providing more education and training opportunities for low-volume surgeons and hospitals should be put into place to improve outcome. The Taiwan health care system, unlike that of the United States or of European nations, is a closed staff system requiring physicians to practice in only one hospital or clinic. Therefore, opportunities to practice or learn from other health care facilities are limited. Further studies may examine whether the varying infection rates that we observed were due to differences in surgical skill or simply due to differences in hygiene and sterilization procedures. • Third, when the association between patient volume and outcome is examined, infection and other complications should be analyzed as different independent variables. A separate analysis would clarify the extent and magnitude of the effects of these factors on the use of health care services. When postoperative infection and perioperative complication were treated as dependent variables, the explained variables differed between models. Therefore, postoperative infection and perioperative complications should be analyzed separately in future studies. Several limitations of this study are noted. First, the claims data evaluated in this study were not originally compiled for the purpose of analyzing clinical outcomes. Some information analyzed in studies elsewhere [6,7,21], such as functional outcome and readmission due to in-hospital complications, were unavailable in this study. Second, perioperative complications were derived from ICD-9-CM codes, and these complications might have existed before surgery. However, this confounding factor is expected when using a claims data set. Therefore, the in-hospital incidence of complications should be weighted accordingly. Third, because no volume thresholds have been suggested for TKA patients, the volume cutoff points were based on quartile thresholds. Although most outcome studies use either percentiles or quartiles as thresholds, the limitations of this method are acknowledged.
912 The Journal of Arthroplasty Vol. 25 No. 6 September 2010 Nonetheless, the volume strata in this study were comparable to those of Kreder et al [10]. Our study of a TKA population identified associations between provider patient volume and economic and clinical outcome. Patient volumes of both individual surgeons and hospitals were positively associated with economic savings and quality of care. A strong linear association was noted between health care use and surgeon volume, hospital volume, and comorbidity score. Postoperative infections correlated with the experience level of individual surgeons as well as that of hospital surgical teams, whereas perioperative complications were associated with patient demographics and severity of illness rather than with provider experience. Compared to hospital providers, individual surgeons supplied better overall quality of care for TKA patients. Clinicians, hospitals, and third-party payers should consider how all of these factors affect the cost and quality of treatment for TKA patients.
10.
11.
12.
13.
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15.
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Table 2. Patient and Provider Characteristics for 3 Surgeon Volume Groups Characteristic No. of patients (%) No. of surgeons (%) Average volume range of patients undergoing TKA per year Annual volume of patients undergoing TKA per surgeon (mean ± SD) Age of patients in years (mean ± SD) No. of females (%) No. of patients by diagnosis (%) Osteoarthrosis (avascular necrosis) Rheumatoid arthritis CCI scores (%) 0 1 ≥2 No. of patients by hospital level (%) Medical center Regional hospital District hospital No. patients by hospital ownership structure (%) Public Nonprofit For profit Average length of hospital stay (mean ± SD) 95% CI Total charges in NT$ (mean ± SD) Inflation-adjusted No. of postoperative infections (%) No. of perioperative complications (%)
Low Volume: Quartiles 1 and 2
Medium Volume: Quartile 3
High Volume: Quartile 4
1686 (5.33) 412 (50.06) 1-3 2.15 ± 1.48
4253 (13.45) 208 (25.28) 4-9 5.68 ± 3.57
25 679 (81.21) 203 (24.67) 10-463 31.94 ± 43.53
74.24 ± 8.71 1153 (68.39)
73.71 ± 8.38 3092 (72.70)
73.96 ± 7.77 19 213 (74.82)
b.05 b.001
1647 (97.69) 39 (2.31)
4140 (97.34) 113 (2.66)
25 038 (97.50) 641 (2.50)
.718
1366 (81.02) 250 (14.82) 70 (4.15)
3538 (83.18) 574 (13.49) 141 (3.31)
21 403 (83.34) 3542 (13.79) 734 (2.85)
b.01
379 (22.48) 584 (34.64) 723 (42.88)
1067 (25.09) 1693 (39.81) 1493 (35.10)
11 997 (46.72) 5660 (22.04) 8022 (31.24)
b.001
560 (33.21) 462 (27.40) 664 (39.38) 10.66 ± 4.41 10.45-10.87
1350 (31.74) 1218 (28.64) 1685 (39.62) 9.86 ± 3.37 9.76-9.96
7793 (30.35) 8686 (33.83) 9200 (35.83) 8.65 ± 2.83 8.61-8.68
b.001
131 487 ± 20 491 23 (1.36) 44 (2.61)
129 305 ± 11 704 36 (0.85) 87 (2.05)
126 762 ± 12 103 120 (0.47) 627 (2.44)
P
b.001
b.001 .248
Postoperative infection: infection cases in each group/all procedures in each group.
Table 3. Predictors of Average Length of Stay and Total Charge Average Length of Stay Predictor Surgeon volume High (reference) Moderate Low Hospital volume High (reference) Moderate Low CCI scores 0 (reference) 1 ≥2 Complication (yes) Infection (yes) Age Sex Male (reference) Female Diagnosis Osteoarthritis (avascular necrosis) (reference) Rheumatoid arthritis
Total Charge
β Value
Standardized β Value
95% CI
P
β Value
Standardized β Value
0.59 1.19
0.07 0.09
0.491 to 0.689 1.036 to 1.348
b.001 b.001
1649 3554
0.04 0.06
1231 to 2068 2896 to 4215
b.001 b.001
0.74 1.01
0.08 0.06
0.634 to 0.850 0.810 to 1.205
b.001 b.001
2015 2770
0.05 0.04
1561 to 2474 1940 to 3609
b.001 b.001
0.30 1.14 1.29 8.49 0.02
0.03 0.06 0.06 0.21 0.05
0.196 to 0.391 0.961 to 1.334 1.075 to 1.487 8.076 to 8.911 0.014 to 0.022
b.001 b.001 b.001 b.001 b.001
2051 8283 8473 27 798 42
0.06 0.11 0.10 0.16 0.03
1630 to 2452 7468 to 9042 7530 to 9271 26 041 to 29 564 24 to 59
b.001 b.001 b.001 b.001 b.001
−0.01
−0.00
−0.085 to 0.060
.737
−648
−0.02
−955 to −343
b.001
−0.09
−0.01 −0.311 to 0.125 Adjusted R2 = 14.6%
.405
289
0.00 −631 to 1212 Adjusted R2 = 11.2%
.539
95% CI
P
Predictors of average length of hospital stay and total hospitalization charge controlled for hospital ownership (public, nonprofit, and for profit) and hospital accreditation level (medical center, regional hospital, and district hospital).
Effects of Provider Patient Volume and Comorbidity on Clinical and Economic Outcomes for Total Knee Arthroplasty A Population-Based Study Min-Hsiung Wei, MD, MHA,* Yi-Ling Lin, MHA,y Hon-Yi Shi, DrPH,y and Herng-Chia Chiu, PhDy
Abstract: Our study examined how provider patient volume, postoperative infection rate, and perioperative complication affect length of stay, hospitalization charges, and adverse outcomes for patients undergoing total knee arthroplasty (TKA). The study sample included patients who had undergone total knee arthroplasty at all acute care hospitals in Taiwan between 2000 and 2003. Two economic indicators revealed linear associations with surgeon's patient volume, hospital's patient volume, and comorbidity score. Patients who developed postoperative infections remained hospitalized an average of 8.49 days longer than did patients with no infection. Postoperative infection was associated with surgeon experience. Our findings indicate that a surgeon's patient volume has a more significant effect than a hospital's patient volume on clinical outcomes. However, patient volumes for both surgeon and hospital are equally important in economic outcomes. Keywords: clinical outcome, economic outcome, complications, total knee arthroplasty. © 2010 Elsevier Inc. All rights reserved.
Total knee arthroplasty (TKA) is now a routine treatment for relieving knee pain and improving knee function [1,2] as well as overall health-related quality of life [3]. The incidence of arthroplastic knee procedures is increasing in aging Western populations such as that in the United States, where more than 430 000 such procedures were performed in 2003 [4]. In Taiwan, the annual number of such procedures nearly doubled in a 6-year span, from 6500 in 1998 to 12 088 in 2004 [5]. The efficient use of limited public health resources requires identifying efficient and highly skilled health care providers. Studies from the United States [6,7], Canada [7], and England [8] have explored the relationship between patient volume and clinical outcome by analyzing health care claims data. Others have addressed the association From the *Department of Orthopedics, St. Joseph's Hospital, Huwei Township; and yGraduate Institute of Healthcare Administration, Kaohsiung Medical University, Kaohsiung City, Taiwan. Supplementary material available at www.arthroplastyjournal.org. Submitted December 5, 2008; accepted June 28, 2009. No benefits or funds were received in support of the study. Reprint requests: Herng-Chia Chiu, PhD, Graduate Institute of Healthcare Administration, Kaohsiung Medical University, 100 ShihChuan 1st Road, San Ming District, Kaohsiung City, Taiwan 807, ROC. © 2010 Elsevier Inc. All rights reserved. 0883-5403/2506-0012$36.00/0 doi:10.1016/j.arth.2009.06.033
between the volume of patients treated by a provider (surgeon or hospital) and cost [9]. The answer to the question of which has the greater effect on economic efficiency in the health care industry—the hospital or the individual surgeon—is still unclear [10,11]. Very few studies have simultaneously examined the relationships between patient volume, clinical outcome, and economic outcome [10-13]. Reports examining the volume and clinical outcome of TKA in countries other than the United States or Canada have analyzed relatively small numbers of patients and providers [14]. Other than provider patient volume, there are few factors associated with resource use and surgical outcomes. Comorbidity was consistently and significantly associated with the costs of treating TKA patients [7,11]. Several studies found that TKA patients who developed postoperative infections consumed 3 to 4 times more health resources than those who had no infections [1517]. Others studies also demonstrated that comorbidity was associated with postoperative complications [7,18]. We examined the associations of volume of TKA patients treated per surgeon and per hospital in Taiwan with economic and clinical outcomes. We sought to answer the following questions:
906
1. Are a surgeon's patient volume and a hospital's patient volume equally important for reducing
Clinical and Economic Outcomes for TKA Wei et al
health care costs while maintaining adequate quality of care? 2. Is patient volume linearly associated with resource consumption, and how is patient volume related to clinical outcome? 3. To what extent do perioperative complications, postoperative infection, and comorbidity in TKA patients affect resource use? 4. How do predictors for postoperative hospital index infections differ from those for perioperative hospital complications?
Materials and Methods Data Sources The National Health Insurance (NHI) inpatient claims databases for years 2000 through 2003 were analyzed. The NHI requires hospitals in Taiwan to apply for reimbursement using a standard claims format stating provider information (both physician and hospital), patient demographic data (age, sex), International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes, and discharge status. In Taiwan, 99% of residents have enrolled in the NHI program since the national universal compulsory program was implemented in 1995. All hospitals providing acute inpatient care must agree with the NHI to provide care for their patients. Therefore, the NHI database is the largest and most complete health care data set in Taiwan. The use of such data sets in clinical research has some limitations but has been validated by many studies [19,20]. Sample Selection and Exclusion Criteria Data for patients with ICD-9-CM procedure code 81.54 (knee arthroplasty) were extracted from the NHI database. That is, knee arthroplasties were analyzed in the overall population. To minimize selection bias and data errors, serial exclusion criteria were observed (Fig. 1). Exclusion criteria were as follows: primary diagnosis other than osteoarthritis (ICD-9-CM codes 715 and 716), rheumatoid arthritis (ICD-9-CM code714), or gouty arthritis (ICD-9-CM code 2740); history of bilateral TKA (n = 3297) or unicompartmental knee arthroplasty (n = 722); TKA with minor surgeries or procedures (n = 1687); treatment charges lower than NHI standard fees (n = 69); length of stay less than 3 days (n = 95); unavailable data for sex or age (n = 39). Thus, 6157 cases (16.30%) were excluded from the original sample of 37 775. Outcome Measures The outcome variables in our study were both economic and clinical. Length of hospital stay and inflation-adjusted total hospitalization charges were analyzed to assess economic outcomes, whereas adverse outcomes were measured by incidence of postoperative infections and perioperative complications. The mortal-
907
ity rate was only 0.1% and thus was excluded as an outcome variable. Length of hospital stay and total hospitalization charges were both considered continuous variables, whereas adverse outcome was considered a categorical variable. Postoperative infection was defined to include both implant infection (ICD-9-CM code 996.66) and wound infection (ICD-9-CM code 998.5), as in other studies [11]. Indicators of perioperative complications were in accordance with those in other studies: deep venous thrombosis [12,21], pulmonary embolism [12,21], acute myocardial infarction [12,21], pneumonia [21], urinary tract infection [10], and upper gastrointestinal bleeding. Patients with any of those complications were noted as having perioperative complications, and the variable was recorded as a dichotomous variable. The 2 variables were analyzed as explained variables in the economic models and were treated as dependent variables in the clinical outcome model (logistic regression model). Provider Patient Volume Measurement and Comorbidity Score Surgeons' and hospitals' patient volumes were analyzed by quartiles because of the skewed distribution of the data. Because of the very small numbers of patients
NHI inpatient data claim data (2000- 2003)
Inpatients undergoing knee arthroplasty (ICD-9-CM procedure code 81.54): 37,775 patients
Primary diagnosis code (ICD-9CM procedure codes 714, 715, 716, and 2740): 37,527 patients
Excluded bilateral TKA: 3,297 patients Excluded unicompartmental knee arthroplasty: 722 patients
Excluded TKA with minor procedures: 1,687 patients Excluded physician fees lower than NHI payment standards: 69 patients Excluded length of stay <3 days: 95 patients Excluded cases without sex and age data: 95 patients
Included in final study sample: 31,618 patients (83%)
Fig. 1. Flowchart depicting the process of determining study sample inclusion and exclusion.
908 The Journal of Arthroplasty Vol. 25 No. 6 September 2010 in quartiles 1 and 2 for both surgeons and hospitals, the 2 groups were merged. Therefore, 3 groups were defined: low volume (quartiles 1 and 2), moderate volume (quartile 3), and high volume (quartile 4). The quartile or percentile method has been used in other studies in analyzing administrative data sets [7,22]. Using the quartile method, we found that the average number of TKA procedures for low-volume surgeons and hospitals in our study was comparable with the mean number of procedures for the low-volume group reported in 1997 by Kreder et al [10]. The Charlson comorbidity index (CCI), as modified by Deyo et al [23], was used to calculate comorbidity scores based on admissions data. The CCI has been validated as a proxy of illness severity in other TKA outcome studies [7,18]. All other variables were analyzed in 3 strata to improve group distribution. Covariates Patient- and hospital-related covariates were controlled. Patient-specific variables included age, sex, and primary diagnosis (rheumatoid arthritis and osteoarthritis). Hospital-specific covariates were ownership (public, private, or nonprofit) and hospital accreditation status (medical center, regional hospital, or district hospital). Statistics Analysis of variance was used to evaluate the 3 volume groups for statistical differences in continuous variables (age, length of hospital stay), whereas χ2 tests were used for categorical variables. Multiple linear regression was used to examine relationships between patient volume and economic outcome after adjusting for patient demographics and hospital attributes, and multiple logistic regression was used to examine predictors of postoperative infection and perioperative complications. Study subjects with complications were given a code of 1, and those without complications were given a code of 0. The SPSS software package (version 12.0, SPSS Inc, Chicago, Ill) was used for statistical analyses.
Results Provider Volume Distribution Between 2000 and 2003, 823 surgeons in 295 hospitals performed TKAs in 31 618 patients (Tables 1 and 2). Patient volume data were skewed for both surgeons and hospitals; approximately one quarter of surgeons and hospitals performed more than 80% of all TKAs. Stratification of patient volume by surgeon revealed a mean annual volume of 2.15 (range, 1-3), 5.68 (range, 49), and 31.94 patients (range, 10-463) among low-, moderate-, and high-volume surgeons, respectively. Stratification of hospital volume revealed a mean annual volume of 3.35 (range, 1-6), 13.75 (range, 7-23), and 94.19 patients (range, 24-592) among low-, moderate-, and high-volume hospitals, respectively.
The average age of patients across the 3 groups was 74 years. Compared with moderate- and low-volume surgeons, high-volume surgeons treated a higher percentage of female patients (P b .001). The highvolume hospitals provided care for more patients with rheumatoid arthritis than did medium- and lowvolume hospitals (P b .001). Individual surgeons and hospitals significantly differed in average length of hospital stay and inflation-adjusted total charge (P b .001). Patients who underwent TKA performed by low-volume surgeons and at low-volume hospitals had longer hospital stays and higher hospitalization costs than did patients in the 2 other volume groups. A statistically significant difference (P b .001) was noted in surgeon volume strata (Table 2; available online at www.arthroplastyjournal.org) but not in hospital volume strata. Model of Health Resources Use The effect of provider volume on average length of hospital stay and total hospitalization charges was examined by multivariate linear regression analyses. Except for sex and diagnosis, all variables (surgeons' patient volume, hospitals' patient volume, CCI score, postoperative infections, perioperative complications, and age) were significantly and consistently related to duration of hospitalization and inflation-adjusted total medical costs. Three variables (surgeon volume, hospital volume, and CCI score) were linearly associated with length of hospital stay and total medical costs. Hospital stays of patients treated by low- and moderate-volume surgeons were longer by 1.19 (P b .001) and 0.59 days (P b .001), respectively, than those treated by high-volume surgeons after controlling for hospital volume and other factors (Table 3; available online at www.arthroplastyjournal.org). The effects of hospital volume on both economic outcomes were similarly associated. After controlling for surgeon volume, severity of illness, and other covariates, medical charges (in new Taiwan dollars [NT$]) to patients who underwent surgery at low- and moderate-volume hospitals were NT$2770 and NT$2015 higher, respectively, than charges to those treated at high-volume hospitals. Increased severity of comorbid diseases in TKA patients was positively associated with length of hospital stay and total medical charges (Table 3; available online at www.arthroplastyjournal.org). Patients with CCI scores of 0 or 1 had shorter hospital stays and lower medical charges than those with CCI scores of 2 or higher. Postoperative infection and other in-hospital complications had the greatest effect on both economic variables. Patients with postoperative infections required 8.49 more days of hospitalization than those without infections. The total-charges model indicated that patients with postoperative infections
Clinical and Economic Outcomes for TKA Wei et al
909
Table 1. Comparison of Patient and Provider Characteristics Among 3 Hospital Volume Groups Characteristic No. of patients (%) No. of hospitals (%) Average annual volume of patients undergoing TKA per surgeon Annual volume of patients undergoing TKA per hospital (mean ± SD) Age of patients in years (mean ± SD) No. of females (%) No. of patients by diagnosis (%) Osteoarthritis (avascular necrosis) Rheumatoid arthritis No. of CCI scores (%) 0 1 ≥2 No. of patients by hospital level (%) Medical center Regional hospital District hospital No. of patients by hospital ownership structure (%) Public Nonprofit For profit Average length of hospital stay (mean ± SD) 95% CI Total charges in NT$ (mean ± SD) Inflation-adjusted No. of postoperative infections (%) No. of perioperative complications (%)
Low Volume: Quartiles 1 and 2
Medium Volume: Quartile 3
High Volume: Quartile 4
1114 (3.52) 149 (50.51) 1-6
3755 (11.88) 74 (25.09) 7-23
26 749 (84.60) 72 (24.41) 24-592
3.35 ± 2.73
13.75 ± 8.12
94.19 ± 112.46
b.001
73.93 ± 8.69 753 (67.59)
74.43 ± 8.05 2627 (69.96)
73.88 ± 7.85 20 078 (75.06)
b.001 b.001
1096 (98.38) 18 (1.62)
3685 (98.14) 70 (1.86)
26 044 (97.36) 705 (2.64)
b.01
956 (85.81) 123 (11.04) 35 (3.14)
3228 (85.96) 415 (11.05) 112 (2.98)
22 123 (82.70) 3828 (14.31) 798 (2.98)
b.001
0 (0.00) 104 (9.34) 1010 (90.66)
63 (1.68) 1602 (42.66) 2090 (55.66)
13 380 (50.02) 6231 (23.29) 7138 (26.69)
b.001
222 (19.93) 117 (10.50) 775 (69.57) 10.79 ± 3.74 10.57-11.01
1474 (39.25) 387 (10.31) 1894 (50.44) 10.22 ± 3.36 10.12-10.33
8007 (29.93) 9862 (36.87) 8880 (33.20) 8.66 ± 2.92 8.62-8.69
b.001
128 822 ± 10 582 11 (0.99) 26 (2.33)
128 483 ± 10 076 24 (0.64) 82 (2.18)
127 137 ± 13 101 144 (0.54) 650 (2.43)
P
b.001 b.001 .120 .646
Postoperative infection: infection cases in each group/all procedures in each group.
had used NT$27 798 more in health care services than did patients without infections.
(95% CI, 5.41-8.57) in patients with a CCI score of 0 than in patients with CCI score of 2 or higher (Table 4).
Adverse-Outcome Models Of all study subjects, 139 (0.57%) had postoperative infections during their hospital stay and 758 (2.4%) had perioperative complications. Multivariate-adjusted logistic regression for infection models revealed a significantly higher infection risk in TKA patients treated by low- or moderate-volume surgeons than those treated by high-volume surgeons (Fig. 2). Stratification of hospital patient volume, however, revealed that patients treated in low-volume institutions had a significantly higher risk of having infections than those treated in high-volume institutions (odds ratio, 2.33; 95% confidence interval [CI], 1.08-5.05). Patient demographic factors (age, sex) and primary diagnosis were not significant predictors. Interestingly, all patient demographic and disease burden indicators that were not predictors of postoperative infection were significant predictors in the perioperative-complication model. Conversely, provider patient volume significantly affected the incidence of postoperative infections but was not a significant predictor in the perioperative-complication model. The likelihood of in-hospital complications was 6.81 times higher
Impact of Patient Volume on Economic and Clinical Outcomes Our study indicated that patient volume, disease severity, and complication rate significantly affected hospital resource use. When controlling for all covariates, we found that patient volume was negatively associated with length of hospital stay and health care costs. The evidence suggests that both higher-volume hospitals and higher-volume surgeons are equally important in minimizing health care costs. However, individual surgeon experience had a greater effect on cost savings. Until now, very few studies have comprehensively examined how surgeon and hospital volume affect use of health care services [11,12]. These findings demonstrate the important economic effects of patient volume. The incidence of postoperative infections was inversely and linearly associated with surgeon patient volume after stratification by both crude and adjusted logistic regression models. Hospital patient volume stratification revealed an association only between low- and highvolume groups. Although the incidence of postoperative
Discussion
910 The Journal of Arthroplasty Vol. 25 No. 6 September 2010
Fig. 2. Risk of infection for 3 prediction variables (surgeon's patient volume, hospital's patient volume, and CCI), adjusted for covariates. Each trail is represented by a square and a horizontal line. The square represents the corresponding risk odds ratio for infection, and the line represents the 95% CI for the 3 prediction variables.
infection was higher in the moderate-volume hospitals than in the high-volume hospitals, the difference did not reach statistical significance. Overall, incidences of postoperative infections were highest for the low- and moderate-volume surgeons and hospitals. These statistical results demonstrate for the first time that individual surgeons have a larger effect on surgical outcomes than hospitals do. Provider (surgeon or hospital) patient volume revealed no association with incidence of perioperative complications. However, baseline patient characteristics were correlated with incidence of perioperative complications. Our findings suggest that postoperative infections and perioperative complications should be considered as individual independent variables when conducting clinical-outcomes or economic-outcomes
studies. The linear relationship between volume and economic outcome and between volume and clinical outcome demonstrates the important relationship between volume and outcome. In the economic-regression model, stratification of both individual surgeon patient volume and hospital patient volume revealed a linear trend in adjusted β value for length of hospital stay and health care charges (Table 3; available online at www. arthroplastyjournal.org). The increasing odd ratios for the adverse outcomes indicated that a linear association exists between patient volume and postoperative infection rate (Fig. 2). This linear relationship has not been demonstrated until now [12,21]. The correlations between volume and outcome that we observed further validate the “practice makes perfect” theory proposed by Luft et al [24].
Table 4. Predictors of Perioperative Complication Crude Predictor Surgeon patient volume High (reference) Moderate Low Hospital patient volume High (reference) Moderate Low CCI scores 0 (reference) 1 ≥2 Age Sex Male (reference) Female Diagnosis Osteoarthritis (avascular necrosis) (reference) Rheumatoid arthritis
Adjusted
% (No.)
Odds Ratio
P
Odds Ratio
95% CI
P
2.44 (627) 2.05 (87) 2.61 (44)
0.83 1.07
.117 .666
0.90 1.17
0.705-1.154 0.823-1.659
.411 .412 .385
2.43 (650) 2.18 (82) 2.33 (26)
0.96 0.90
.838 .356
0.83 0.65
0.640-1.072 0.411-1.035
.105 .152 .070
1.46 5.82 1.02
b.001 b.001 b.001
1.74 6.81 1.03
1.416-2.126 5.406-8.573 1.017-1.038
b.001 b.001 b.001 b.001
1.54 (126) 2.69 (632)
1.77
b.001
1.85
1.523-2.258
b.001
2.41 (744) 1.77 (14)
0.73
.241
0.49
0.279-0.850
b.01
2.01 (530) 2.90 (127) 10.69 (101)
Perioperative complication as event controlled for hospital ownership (public, nonprofit and for-profit), hospital accreditation level (medical center, regional hospital and district hospital).
Clinical and Economic Outcomes for TKA Wei et al
Effect of Infection and Complications on Health Care Use In addition to provider patient volume, postoperative infections and complications also predicted consumption of health care dollars. Any incidence of infection increased hospital stay by 8.49 days and total medical charges by NT$27 798. These findings demonstrate the magnitude of increase in health care dollars required to treat infection. Several studies also indicate that the health care resources required for infected TKA patients consumed 3- to 4-fold more health resources as compared with those for patients without infection [15,17], but infection severity was not adjusted for in those studies. Minimizing postoperative infection would substantially reduce treatment costs for TKA patients. Perioperative complications are also associated with increased use of health care resources. The effects of infections and complications are rarely considered in economic-prediction models as was done in our study. Our findings demonstrate the varying magnitude of health care resource consumption resulting from different complications of TKA. Impact of Illness Severity on Economic and Clinical Outcomes Disease burden (CCI score) was consistently and significantly associated with the economic burden of treating TKA patients, which confirmed the findings of earlier studies [7,11]. However, the linear nature of the association was a novel finding. Another notable finding was that perioperative complications were 6.81 times more likely in patients with CCI scores of 2 or higher than in patients with CCI scores of less than 1. Conversely, however, postoperative infection was not associated with CCI score. A study by Kreder et al [7] reported that patients with CCI scores of 2 or higher were twice as likely to have in-hospital complications than those with CCI scores of less than 1. A clinical investigation by Soohoo et al [18] revealed that patients with CCI scores of higher than 2 had a 2-fold higher 90-day risk of postoperative infection than patients with scores of zero. These findings are further evidence that TKA patients should be examined carefully by surgeons before admission. Our results have the following implications: • First, the average numbers of TKA procedures performed by low-volume surgeons and hospitals analyzed in this study were comparable with those reported in 1997 by Kreder et al [10] but much smaller than those described in more recent studies [6,12,21]. Because our results suggest that low-volume providers are more costly, the NHI system should develop a mechanism such as pay for performance to encourage high-volume
911
physicians and hospitals to provide service to more TKA patients. • Second, after controlling for disease severity and other covariates, we found that patients treated by low- to moderate-volume surgeons or hospitals had higher infection rates than those treated by highvolume surgeons or hospitals. We propose 2 policies. (1) Surgical outcomes should be made publicly available. Patients have the right to know which providers achieve the best surgical outcomes. For example, the NHI could consider adopting the report-card system currently used in the United States to periodically disclose the infection rates for specific surgical procedures. (2) A mechanism for providing more education and training opportunities for low-volume surgeons and hospitals should be put into place to improve outcome. The Taiwan health care system, unlike that of the United States or of European nations, is a closed staff system requiring physicians to practice in only one hospital or clinic. Therefore, opportunities to practice or learn from other health care facilities are limited. Further studies may examine whether the varying infection rates that we observed were due to differences in surgical skill or simply due to differences in hygiene and sterilization procedures. • Third, when the association between patient volume and outcome is examined, infection and other complications should be analyzed as different independent variables. A separate analysis would clarify the extent and magnitude of the effects of these factors on the use of health care services. When postoperative infection and perioperative complication were treated as dependent variables, the explained variables differed between models. Therefore, postoperative infection and perioperative complications should be analyzed separately in future studies. Several limitations of this study are noted. First, the claims data evaluated in this study were not originally compiled for the purpose of analyzing clinical outcomes. Some information analyzed in studies elsewhere [6,7,21], such as functional outcome and readmission due to in-hospital complications, were unavailable in this study. Second, perioperative complications were derived from ICD-9-CM codes, and these complications might have existed before surgery. However, this confounding factor is expected when using a claims data set. Therefore, the in-hospital incidence of complications should be weighted accordingly. Third, because no volume thresholds have been suggested for TKA patients, the volume cutoff points were based on quartile thresholds. Although most outcome studies use either percentiles or quartiles as thresholds, the limitations of this method are acknowledged.
912 The Journal of Arthroplasty Vol. 25 No. 6 September 2010 Nonetheless, the volume strata in this study were comparable to those of Kreder et al [10]. Our study of a TKA population identified associations between provider patient volume and economic and clinical outcome. Patient volumes of both individual surgeons and hospitals were positively associated with economic savings and quality of care. A strong linear association was noted between health care use and surgeon volume, hospital volume, and comorbidity score. Postoperative infections correlated with the experience level of individual surgeons as well as that of hospital surgical teams, whereas perioperative complications were associated with patient demographics and severity of illness rather than with provider experience. Compared to hospital providers, individual surgeons supplied better overall quality of care for TKA patients. Clinicians, hospitals, and third-party payers should consider how all of these factors affect the cost and quality of treatment for TKA patients.
10.
11.
12.
13.
14.
15.
References 1. Callation CM, Drake BG, Heck DA, et al. Patient outcomes following tricompartmental total knee replacement. A meta-analysis. JAMA 1994;271:1349. 2. Hawker G, Wright J, Coyte P, et al. Health-related quality of life after knee replacement. J Bone Joint Surg Am 1998; 80:163. 3. Rand JA, Trousdale RT, Ilstrup DM, et al. Factors affecting the durability of primary total knee prostheses. J Bone Joint Surg Am 2003;85:259. 4. Merrill CT, ALixhauser N. Procedures in U.S. Hospitals, 2003. HCUP Fact Book No. 7, publication no. 06-0039. Rockville (Md): Agency for Healthcare Research and Quality; 2006. http://www.ahrq.gov/data/hcup/factbk7/ factbk7.pdf. Accessed June 13, 2009. 5. Department of Health, Executive Yuan, Taiwan. http:// www.doh.gov.tw/cht2006/index_populace.aspx. 6. Soohoo NF, Lieberman JR, Ko CY, et al. Primary total knee arthroplasty in California 1991 to 2001: does hospital volume affect outcomes? J Arthroplasty 2006;21:199. 7. Kreder HT, Grosso P, William JI. Provider volume and other predictors of outcome after total knee arthroplasty: a population study in Ontario. Can J Surg 2003;46:15. 8. Judge A, Chard J, Learmonth I, et al. The effects of surgical volumes and training centre status on outcomes following total joint replacement: analysis of the Hospital Episode Statistics for England. J Public Health (Oxf) 2006;28:116. 9. Martineau P, Filion KB, Huk OL, et al. Primary hip arthroplasty costs are greater in low-volume than in high-
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912.e1
Table 2. Patient and Provider Characteristics for 3 Surgeon Volume Groups Characteristic No. of patients (%) No. of surgeons (%) Average volume range of patients undergoing TKA per year Annual volume of patients undergoing TKA per surgeon (mean ± SD) Age of patients in years (mean ± SD) No. of females (%) No. of patients by diagnosis (%) Osteoarthrosis (avascular necrosis) Rheumatoid arthritis CCI scores (%) 0 1 ≥2 No. of patients by hospital level (%) Medical center Regional hospital District hospital No. patients by hospital ownership structure (%) Public Nonprofit For profit Average length of hospital stay (mean ± SD) 95% CI Total charges in NT$ (mean ± SD) Inflation-adjusted No. of postoperative infections (%) No. of perioperative complications (%)
Low Volume: Quartiles 1 and 2
Medium Volume: Quartile 3
High Volume: Quartile 4
1686 (5.33) 412 (50.06) 1-3 2.15 ± 1.48
4253 (13.45) 208 (25.28) 4-9 5.68 ± 3.57
25 679 (81.21) 203 (24.67) 10-463 31.94 ± 43.53
74.24 ± 8.71 1153 (68.39)
73.71 ± 8.38 3092 (72.70)
73.96 ± 7.77 19 213 (74.82)
b.05 b.001
1647 (97.69) 39 (2.31)
4140 (97.34) 113 (2.66)
25 038 (97.50) 641 (2.50)
.718
1366 (81.02) 250 (14.82) 70 (4.15)
3538 (83.18) 574 (13.49) 141 (3.31)
21 403 (83.34) 3542 (13.79) 734 (2.85)
b.01
379 (22.48) 584 (34.64) 723 (42.88)
1067 (25.09) 1693 (39.81) 1493 (35.10)
11 997 (46.72) 5660 (22.04) 8022 (31.24)
b.001
560 (33.21) 462 (27.40) 664 (39.38) 10.66 ± 4.41 10.45-10.87
1350 (31.74) 1218 (28.64) 1685 (39.62) 9.86 ± 3.37 9.76-9.96
7793 (30.35) 8686 (33.83) 9200 (35.83) 8.65 ± 2.83 8.61-8.68
b.001
131 487 ± 20 491 23 (1.36) 44 (2.61)
129 305 ± 11 704 36 (0.85) 87 (2.05)
126 762 ± 12 103 120 (0.47) 627 (2.44)
P
b.001
b.001 .248
Postoperative infection: infection cases in each group/all procedures in each group.
Table 3. Predictors of Average Length of Stay and Total Charge Average Length of Stay Predictor Surgeon volume High (reference) Moderate Low Hospital volume High (reference) Moderate Low CCI scores 0 (reference) 1 ≥2 Complication (yes) Infection (yes) Age Sex Male (reference) Female Diagnosis Osteoarthritis (avascular necrosis) (reference) Rheumatoid arthritis
Total Charge
β Value
Standardized β Value
95% CI
P
β Value
Standardized β Value
0.59 1.19
0.07 0.09
0.491 to 0.689 1.036 to 1.348
b.001 b.001
1649 3554
0.04 0.06
1231 to 2068 2896 to 4215
b.001 b.001
0.74 1.01
0.08 0.06
0.634 to 0.850 0.810 to 1.205
b.001 b.001
2015 2770
0.05 0.04
1561 to 2474 1940 to 3609
b.001 b.001
0.30 1.14 1.29 8.49 0.02
0.03 0.06 0.06 0.21 0.05
0.196 to 0.391 0.961 to 1.334 1.075 to 1.487 8.076 to 8.911 0.014 to 0.022
b.001 b.001 b.001 b.001 b.001
2051 8283 8473 27 798 42
0.06 0.11 0.10 0.16 0.03
1630 to 2452 7468 to 9042 7530 to 9271 26 041 to 29 564 24 to 59
b.001 b.001 b.001 b.001 b.001
−0.01
−0.00
−0.085 to 0.060
.737
−648
−0.02
−955 to −343
b.001
−0.09
−0.01 −0.311 to 0.125 Adjusted R2 = 14.6%
.405
289
0.00 −631 to 1212 Adjusted R2 = 11.2%
.539
95% CI
P
Predictors of average length of hospital stay and total hospitalization charge controlled for hospital ownership (public, nonprofit, and for profit) and hospital accreditation level (medical center, regional hospital, and district hospital).