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Keywords:

  • Joint replacement surgery;
  • Complications;
  • Arthritis;
  • Patient safety

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Objective

To describe inpatient complications for primary total knee replacement (TKR) in a period of rapidly growing orthopedic surgery capacity, declining length of stay, and more frequent discharge to rehabilitation facilities.

Methods

Complication incidence according to published coding algorithms was estimated for 35,531 primary TKR admissions of northern Illinois residents to 65 Illinois hospitals. Complication odds were estimated as a function of patients' clinical and sociodemographic status, hospital volume, residency training, TKR length of stay, International Classification of Diseases, Ninth Revision (ICD-9) coding intensity, and discharges to skilled nursing or rehabilitation facilities.

Results

Primary TKR admissions increased 36% between 1993 and 1999, length of stay declined 43%, average ICD-9 code use increased 31%, and rehabilitation discharges increased 68%. Major complication rates declined 44% (12.4% to 6.9%; P < 0.0001) over this period, reflecting a 50% reduction in the adjusted odds of complication between 1993 and 1999. There was no association of procedure volume and outcome.

Conclusion

It is likely that the reduction in complications reflects true safety improvements as well as reduced length of stay.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

There are virtually no population-based data on outcomes for total knee replacement (TKR) surgery. Serious, disabling complications are not common and long-term functional and pain outcomes are nearly always achieved (1). The few studies that have attempted to document perioperative complications provide a wide range of estimates based on specific prostheses or multiple types of joint operations (2–4). More recent improvements in operative technique and patient safety may be masked by the simultaneous increase in use of rehabilitation facilities and decreases in average inpatient length of stay. The decline in hospital length of stay thus raises questions about the comparability of inpatient complication rates recorded in the 1980s and early 1990s to current results. These factors, combined with changes in the patient population undergoing surgery, have prevented any valid regional or national comparisons of TKR outcomes over the past decade.

This study provides an updated, regional analysis of the epidemiology of International Classification of Diseases, Ninth Revision (ICD-9) coded complications for TKR, based on data from a 7-year period of steadily growing orthopedic surgery capacity, more rigorous clinical pathways and guidelines, declining lengths of stay, and increased use of rehabilitation facilities. Data are presented for all primary TKR operations performed on residents of all ages within 9 counties in northern Illinois at 65 nonVeteran's Administration Illinois hospitals between 1993 and 1999. Changes in TKR inpatient complication rates over the 1990s are presented. In addition to risk adjustment for patients' clinical and sociodemographic characteristics, the study is among the first to examine the association of complication incidence with hospital-level measures of TKR volume, length of stay, rehabilitation discharge rate, and ICD-9 coding intensity.

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Patient and hospital sample

This study used Illinois Hospital and Health Systems Association COMPdata files. The files were searched for primary TKR admissions to any Illinois hospital during 1993–1999. Admissions had to be of residents of 9 counties in northern Illinois. Nine-county northern Illinois encompasses the greater Chicago metropolitan area with a 1995 estimated population of more than 7.73 million. State-mandated discharge data include age, sex, patient origin zip code, and ICD-9 codes for a principal and up to 8 secondary diagnoses. Attending surgeon data were incomplete and thus were not analyzed. The study was approved by the Northwestern University Institutional Review Board.

Data are presented for all discharges with a first-listed ICD-9 procedure code (81.54) for primary TKR operation. The sample thus included patients with bilateral knee procedures (diagnostic-related group [DRG] 471), but excluded admissions for knee replacement revision surgery (81.55), for which complications are often a principal cause of admission. Study admissions included 33,153 (93.3%) DRG 209 (lower limb reattachment), 2,279 (6.4%) DRG 471 (bilateral joint procedure), 64 (0.2%) DRG 468 (extensive operating room procedure), and 35 (0.1%) coded as other DRGs. Less than 12% of all admissions had more than 1 ICD-9 procedure code listed other than TKR. Because about 1.8% of all admissions of northern Illinois residents occurred at hospitals in outlying regions of the state, only data from regional hospitals with >70 admissions over the 7-year study period are presented. Also excluded are the approximate 5% of admissions at study hospitals for patients from other states, a rate that has changed little during the study period and would have little influence on changes over time. This produces a sample of 65 hospitals with more than 98% of all primary TKR discharges of northern Illinois residents.

Each patient's zip code of origin was matched to Claritas (San Diego, CA) zip code demographic data based on 1995 census update estimates. Patient race is not included in Illinois hospital data. However, area populations were defined by patients from zip codes with >50% African American population (1.18 million 1995 residents) or low income (<$30,000 median household income, 1.56 million 1995 residents).

Complication incidence

The overall incidence of perioperative complications was derived from ICD-9 coding algorithms published and distributed by the US Agency for Healthcare Research and Quality (AHRQ) (5, 6). Codes for wound infection; iatrogenic complications; pulmonary compromise; acute myocardial infarction; gastrointestinal hemorrhage or ulceration; venous thrombosis or pulmonary embolism; mechanical complication due to device, implant, or graft; or pneumonia after major surgery were combined with inpatient death to compute a single major complication incidence rate.

To assess complication severity, mean length of stay and the proportion of admissions with stays > 9 days (the longest 5% of TKR stays) were compared for complicated and uncomplicated admissions. To assess the sensitivity of findings to a wider range of moderate or less well-defined complications, complication incidence was estimated with and without inclusion of documented fluid and electrolyte disorders, urinary retention, and other postoperative symptoms, based on coding derived from Norton et al for TKR-specific, likely adverse events (7).

Clinical characteristics

Chronic disease comorbidity was defined by the presence of ICD-9 defined coronary artery disease or previous myocardial infarction (11%), heart failure (3.7%), cerebrovascular disease (1.6%), chronic obstructive pulmonary disease (7.9%), diabetes (12.3%), gastrointestinal ulcer (1.2%), peripheral vascular disease (0.8%), chronic liver or renal disease (0.2%), dementia or paraplegia (0.1%), or cancer other than skin (0.7%). These codes produced a 30.7% overall comorbidity prevalence. Use of the Deyo specification of the Charlson Comorbidity Index (8) or broader comorbidity-coding algorithms did not produce significantly different results (9, 10). Because of the protective association with complication rates previously found by Norton et al, ICD-9 coded obesity (which Norton et al found to be significantly undercoded) and rheumatoid arthritis were included in models as separate risk factors (7).

Measures of annual volume, length of stay, ICD-9 coding intensity, and rehabilitation discharge rate

Complication rates were compared for patients grouped into 5 hospital volume strata by their hospitals' average annual volume. Volume strata were defined by average annual TKR admission rates of <50, 51–85, 86–120, 121–180, and >180 per year. Each hospital strata reflects approximately one-fifth (>7,000) of all TKR admissions. Seven area hospitals were classified as having orthopedic residency training programs as indicated by the American Academy of Orthopedic Surgery Website (11).

Unlike some Medicare DRGs, TKR does not have a complications and comorbidities split in DRG reimbursement. However, it is more than likely that complication coding rates are influenced by systematic differences in hospital coding resources, medical records budgets, and information systems. A related problem is that hospitals with longer lengths of stay run the risk of being identified as having artificially high complication rates, simply because of the longer screening window for events to occur and be documented. Some complications, such as wound infection, wound dehiscence, and thromboembolic events, usually appear at the third to fifth day or later after surgery. Thus, proprietary internet report cards reward hospitals that avoid complications by swiftly discharging patients to skilled nursing or rehabilitation facilities.

To address this bias, measures of each hospitals' TKR average length of stay, mean ICD-9 coding use (mean number of diagnosis codes actually used out of 9 possible code positions), and the proportion of discharges to skilled nursing facility or rehabilitation facility versus home care were computed. These hospital-level factors shed light on the extent to which longer stays and more aggressive ICD-9 coding are likely to be driving higher hospital complication rates (12, 13).

Statistical analysis

Chi-square tests and linear correlations by year of discharge were used to determine the significance of trends in complication rates across patient age groups and years of admission. Chi-square tests and one-way analysis of variance were used to assess the significance of differences between patients who underwent TKR at hospitals grouped into the 5 hospital volume strata. Spearman's rank order correlations were used to test the association between the 65 hospitals' average complication rates and average length of stay, proportion of rehabilitation discharges, and ICD-9 coding intensity. A multilevel random effects logit model (xtlogit in Stata; Stata Corporation, College Station, TX) of complication incidence was used to estimate the simultaneous effect of hospital, patient, and temporal (1993–1998 compared with 1999) variables (14). This procedure takes into account the panel structure of the data to control for clustering of observations within hospitals.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

There were 35,531 admissions of northern Illinois residents at 65 area hospitals with >70 TKR admissions in 1993–1999. Two-thirds of TKR patients were women; mean age was 69 years, with only 16 patients <19 years old and 144 patients 90–97 years old. Just 4.6% of admissions had a diagnosis of rheumatoid arthritis; only 4.4% had obesity codes; 6.4% were coded as having bilateral procedures; and 1% were emergently admitted, transferred from another hospital, or had trauma codes. Patients' primary discharge destinations were routine home care (31.4%), skilled nursing facilities (26.4%), rehabilitation facilities (21.8%), and home health care (17.8%). Approximately 10% of admissions were from low-income zip codes (which accounted for 20% of the area population) and 9% from zip codes with 50% or greater African American population (which accounted for 15% of the total area population).

Complication incidence

The overall complication rate was 9.2%, which includes 81 inpatient deaths (0.3%). The lowest complication rate was for patients discharged to routine home care (7.7%) followed by home health nursing (8.7%), rehabilitation hospital (9.4%), and skilled nursing facility (10.4%). As compared with the 5.1-day overall mean length of stay for all TKR admissions, mean length of stay was 7.4 days for patients with a complication (P < 0.0001). Among patients with the 5% longest stays (>9 days), 39.0% were identified as having an AHRQ ICD-9 complication code.

Among the 65 hospitals, overall complication rates varied from 3% to 24%, length of stay varied from 3.8 to 8.8 days, mean ICD-9 codes used varied from 2.3 to 5.4 per TKR discharge, and the proportion of patients discharged to rehabilitation settings varied from 9% to 92%. At the aggregate hospital level, there was virtually no correlation between hospital complication rate and TKR average length of stay (r = 0.01, P = 0.93), nor was there a significant correlation between complication rate and the proportion of patients discharged to rehabilitation or skilled nursing facilities (r = −0.02, P = 0.87). These hospital-level findings imply that the length of the screening interval was actually a very minor factor in the observed decline in complication rates. However, there was a moderate but highly significant correlation between hospitals' greater mean ICD code use for TKR patients and higher hospital complication rates (r = 0.48, P < 0.0001).

Table 1 provides data on specific complications found to have >0.5% incidence, stratified by age and sex. Overall complication rates varied from 6.7% for younger patients (27.9% of sample admissions) to 12.4% for the oldest age group (12.5% of sample admissions) (P < 0.0001), and men had significantly more complications documented than women (11.4% versus 8.1%; P < 0.0001). Table 1 presents additional complication incidence estimates attributable to codes for fluid and electrolyte disorders (4.3%), urinary retention (4.0%), and general symptoms (4.0%). Because inclusion of these more ambiguous codes (which provide a 19.9% overall complication rate) did not appreciably change any of the results of this analysis, only results based on published AHRQ complication indicators (which do not include these conditions) are presented below.

Table 1. Selected complication rates by patient age and sex*
 Age < 65 (n = 9,915)Age 65–79 (n = 21,176)Age >80 (n = 4,440)Male (n = 12,143)Female (n = 23,338)Total (n = 35,531)
  • *

    Derived from the Agency for Healthcare Research and Quality International Classification of Diseases, Ninth Revision coding algorithm with prevalence > 0.5%. Additional complications derived from Norton et al (7) total knee replacement-specific coding algorithm.

  • P < 0.01 for sex comparison, P < 0.01 for age group comparison.

  • P < 0.01 for age group comparison.

  • §

    P < 0.01 for sex comparison. P < 0.01 for age group comparison. Data for acute myocardial infarction (0.3%), wound infection (0.3%), pulmonary compromise (0.4%), gastrointestinal hemorrhage or ulceration (0.2%), pulmonary compromise (0.4%), central nervous system complication (0.1%), and inpatient death (0.3%) not shown.

  • P < 0.01 for sex comparison. P < 0.01 for age group comparison. Symptoms include alteration of consciousness, drowsiness, hallucinations, syncope and collapse, convulsions, dizziness and giddiness, sleep disturbances, fever and chills, hyperhidrosis, amnesia (retrograde), generalized pain, or hypothermia.

Iatrogenic complications, %4.57.09.08.35.66.6
 Cardiac complications, %0.30.61.00.70.50.5
 Peripheral vascular complications, %0.40.60.60.50.50.5
 Respiratory complications, %1.31.92.32.01.71.8
 Digestive complications, %0.71.31.61.70.91.2
 Urinary complications, %0.91.41.70.92.11.3
Venous thrombosis or pulmonary embolism, %1.41.71.61.51.51.5
Mechanical complications due to device, implant, or graft, %0.90.70.80.90.70.8
Pneumonia, %0.40.71.20.90.50.7
Any major complication or death, %§6.79.712.411.48.19.2
Moderate and less well-defined complications      
 Fluid and electrolyte disorders, %2.84.56.73.84.64.3
 Urinary retention, %2.74.35.42.54.74.0
 General postoperative symptoms, %3.94.04.14.23.94.0

Annual trends in the 1990s

As presented in Table 2, there was a 36% overall growth in primary TKR admissions of area residents over this period, occurring across all age categories, including a huge 52% increase for patients age 80 or over. Average length of stay declined continuously from 7.5 days in 1993 to 4.3 days in 1999, a 43% reduction. Although length of stay declined, mean ICD-9 code use actually increased 31% over the period, from 3.2 codes per 9 positions in 1993 to 4.2 codes in 1999. This increase in ICD-9 coding is consistent (whether cause or effect) with the growth of chronic disease comorbidity prevalence from 26% in 1993 to 32% in 1999. Rehabilitation discharges grew 68%, from 34% in 1993 to 57% of all TKR admissions in 1999.

Table 2. Annual trends in primary total knee replacement (TKR) admissions*
 1993199419951996199719981999Total
  • *

    All variables P < 0.001 test for linear trend except age group (P = 0.02). ICD-9 = International Classification of Diseases, Ninth Revision.

Primary TKR admissions, n4,2114,3874,7465,4095,5285,5035,74735,531
Age < 65, %25.927.426.527.427.728.730.927.9
Age 66–79, %62.861.261.559.559.357.956.559.6
Age > 80, %11.311.412.113.113.013.412.612.5
Chronic comorbidity, %26.328.929.330.831.734.432.121.0
Discharged to skilled nursing or rehabilitation hospital, %34.136.643.648.654.257.156.648.2
ICD-9 codes used, mean ± SD3.2 ± 1.53.6 ± 2.03.7 ± 2.13.9 ± 2.14.1 ± 2.14.2 ± 2.24.2 ± 2.23.8 ± 2.1
Length of stay, mean ± SD7.5 ± 3.36.4 ± 2.75.5 ± 2.54.8 ± 2.34.4 ± 2.04.3 ± 2.04.3 ± 1.95.2 ± 2.6
Overall complication rate, %12.410.110.49.68.97.56.99.2

Although the thromboembolism codes and deaths showed no linear trends, other complication rates declined rapidly and substantially over the period. From 1993 to 1999, there was a 44% reduction in complications (12.4% to 6.9%) with an equivalent reduction across all the additional complication codes presented in Table 1. All trends were highly significant (P < 0.0001) except for the annual changes in age groups (P = 0.02).

Hospital TKR volume findings

Table 3 presents complication rates aggregated across 5 hospital volume categories based on annual primary TKR hospital admission rates of <50, 51–85, 86–120, 121–180, and >180. The number of hospitals represented in each quintile varied from 26 in the lowest volume group to only 5 hospitals in the very highest TKR volume group. Unlike typical surgical volume-outcome effects, the highest volume hospitals for TKR admissions had higher than average complication rates, fewer ICD-9 diagnosis codes, shorter lengths of stay, and less comorbid illness than lower volume hospitals. There clearly was no linear trend between complication rates and hospital volume in this sample. The highest complication rate (12.3%) was found for the 10 middle-quintile hospitals.

Table 3. Differences across 5 primary total knee replacement (TKR) hospital volume strata*
 Annual primary TKR volumeTotal
< 50 (n = 26)51–85 (n = 16)86–120 (n = 10)121–180 (n = 8)> 180 (n = 5)
  • *

    All variables P < 0.001 for chi-square tests and analysis of variance. ICD-9 = International Classification of Diseases, Ninth Revision.

Admissions6,3097,2036,8157,6647,54035,531
Mean overall complication rate, %7.48.212.38.79.59.2
ICD-9 codes used, mean ± SD3.8 ± 2.13.9 ± 2.14.3 ± 2.23.6 ± 2.03.7 ± 2.03.9 ± 2.1
Length of stay, mean ± SD5.4 ± 2.95.2 ± 2.75.1 ± 2.75.3 ± 2.55.0 ± 2.15.2 ± 2.6
Proportion of rehabilitation discharges, %52.553.342.942.750.248.2
Patient age, years, mean ± SD68.9 ± 0.169.1 ± 9.969.4 ± 9.769.7 ± 9.668.1 ± 10.069.0 ± 9.9
Admissions with comorbidity, %33.432.333.128.826.930.7

Complication risk model results

Table 4 presents data on the likelihood of complication as a function of each of the patient and hospital characteristics described above. Risk-adjusted odds ratios (OR) with 95% confidence intervals are presented, reflecting the change in the likelihood of patients in each risk factor category suffering a documented complication. Despite adjustment for a number of hospital- and patient-level effects, the rapid and linear decline in complication rates remains notable, as can be seen in the 50% difference in inpatient complication risk between 1993 and 1999.

Table 4. Effects of patient and hospital risk factors on perioperative complications after primary total knee replacement (TKR): results of random effects multiple logistic regression
 Odds ratio (95% confidence interval)P
  • *

    Reference category is age < 65 years.

  • Reference category is low volume, < 50 annual admissions, n = 26 hospitals.

  • ICD-9 = International Classification of Diseases, Ninth Revision.

  • §

    Reference category is 1999.

Male1.4 (1.3–1.5)>0.001
Age > 80*1.9 (1.7–2.2)>0.001
Age 65–79*1.4 (1.3–1.6)>0.001
Comorbidity1.3 (1.2–1.4)>0.001
Obesity0.6 (0.4–0.7)>0.001
Rheumatoid arthritis0.7 (0.6–0.9)0.004
Emergent admission, interhospital transfer, or trauma2.3 (1.8–3.1)>0.001
Bilateral operation1.6 (1.4–1.8)>0.001
Greater than 50% African American population zip code1.0 (0.9–1.1)0.92
Low-income zip code*0.9 (0.8–1.1)0.43
Hospital TKR annual volume 51–85 (14 hospitals)1.1 (0.9–1.4)0.63
Hospital TKR annual volume 86–120 (10 hospitals)1.6 (1.3–2.0)>0.001
Hospital TKR annual volume 121–180 (8 hospitals)1.3 (1.0–1.8)0.07
Hospital TKR annual volume > 180 (5 hospitals)1.2 (0.9–1.6)0.14
Orthopedic surgery residency program (7 hospitals)1.2 (0.9–1.6)0.10
Mean hospital ICD-9 code use for TKR1.4 (1.2–1.7)>0.001
Mean hospital length of stay for TKR1.1 (0.9–1.3)0.37
Hospital proportion of patients discharged to skilled nursing or rehabilitation facility1.5 (1.1–2.2)0.02
1993§2.0 (1.8–2.3)>0.001
1994§1.6 (1.4–1.8)>0.001
1995§1.6 (1.4–1.8)>0.001
1996§1.4 (1.3–1.7)>0.001
1997§1.3 (1.1–1.5)>0.001
1998§1.1 (0.9–1.2)0.28

Patient clinical and sociodemographic risk factors

As compared with younger patients, the likelihood of a complication was higher for patients age 65–79 years (OR = 1.4) and age 80 years and older (OR = 1.9, P < 0.0001). Patients with any chronic disease comorbidity code had higher complication likelihoods (OR = 1.3, P < 0.0001). Men were significantly more likely than women to have an AHRQ-defined complication (OR = 1.4, P < 0.0001). As expected, the likelihood of complication was lower for patients with documented rheumatoid arthritis (OR = 0.7, P = 0.004) or obesity (OR = 0.6, P < 0.0001), and significantly higher for patients with emergent admission, interhospital transfer or trauma (OR = 2.3, P < 0.0001), or bilateral procedures (OR = 1.6, P < 0.0001). Patients from low-income zip codes or high African American population zip codes did not have significantly different complication odds.

Hospital characteristics

Patients admitted to hospitals with orthopedic residency training programs (7 hospitals, not all of which were high volume, accounting for 15.7% of admissions) had complication outcomes not significantly different from nonteaching institutions. Although mean length of stay for TKR was not a predictor of complication, and the percentage of patients with rehabilitation discharges was only marginally associated with higher complication incidence, mean ICD-9 coding intensity was associated with complication rate (OR = 1.4, P < 0.0001 for an additional code used).

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

The findings of this study provide strong evidence that the observed 1990s trend toward fewer perioperative complications is probably real, and has persisted in spite of the large increase in operations for older patients. This study provides strong evidence that the reduction in complication rates is independent of artificial reductions that may have been related to changes in patient clinical or sociodemographic characteristics, or hospital-level variations in length of stay, rehabilitation facility use, or coding practices. The reductions in adverse events were generally not sensitive to the types of complications coded, whether based on explicit ICD-9 iatrogenesis codes (996–999) or more ambiguous codes for less well-defined symptoms.

The finding of no hospital volume effects is not surprising given that virtually all the TKR operations performed in the greater Chicago region are done at relatively high volume centers. Only a tiny proportion (<2%) of TKR procedures for area residents were performed at centers with <70 procedures over the 7-year study period. The study by Norton et al, which reported an overall 19% “likely” complication rate for Medicare patients age 65 and older hospitalized in 1985–1990 (for primary or revision surgery), found a lower complication threshold for hospitals after about 40 Medicare TKR operations annually, but no further decline in complication rates for higher volume (n > 80) institutions (7). The 1-year Medicare data on hip replacement surgery analyzed by Katz et al (15) indicate that differences became significant only between hospitals with >100 annual procedures and those with ≤10, as well as for surgeons doing fewer than 5 operations annually versus those doing 50 or more.

There remains an inherent signal-to-noise ratio in these or any administrative data when used to monitor adverse events (16–19). Indeed, complication rates have been found to be higher for hospitals that are better at keeping patients with complications alive after high-risk cardiovascular surgery (20, 21). Even the most detailed medical record review studies find wide disagreement over interpretation of physician notes and documentation about the etiology and severity of complications; quality judgments about antecedent medical care often disagree (3, 22, 23); and there is a retrospective outcome bias in ICD-9 coding (17, 24). Coders tend to document only the most serious conditions, and like the findings for obesity in this study, confer false protective effects on lesser diagnoses when more serious conditions are absent.

However, this general lack of sensitivity and specificity of ICD-9 coding is not as great a problem for documentation of surgical complications related to elective surgical procedures. Recently, administrative data have been evaluated in several large chart review studies, conducted by the Complications Screening Program (CSP), which is supported by the AHRQ and the Medicare Professional Review Organization (18, 25–27). The CSP study findings support the cautious use of administrative data for screening surgical (although not medical) complications. These findings lend weight to the use of complication coding as a relatively efficient and generalizable method of evaluating the safety of major elective surgery (28–30).

In the 1980s and early 1990s, joint replacement surgery required meticulous techniques based on eyeballing and a profound understanding of the physiology and kinesiology of the knee. Early techniques were replaced throughout the 1990s by instruments for placement of templates and directing bone cuts, as well as centering, angling, and sizing the prosthetic components, resulting in less tissue handling and traction. Experienced surgeons do more operations in a shorter time and less experienced surgeons use better techniques to perform the operation. The significant reduction in TKR complications reported here probably reflects these process improvements, as well as the adoption of evidence-based clinical paths and protocols. Such improvements offer evidence that primary TKR surgery has become an even safer option for patients facing severe joint pain and functional decline.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

We are grateful to Molly Bazzani from Northwestern Memorial Hospital for her assistance with data acquisition.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES
  • 1
    Hawker G, Wright J, Coyte P, Paul J, Dittus R, Croxford R, et al. Health related quality of life after knee replacement: results of the knee replacement patient outcomes research team. J Bone Joint Surg 1998; 58: 16373.
  • 2
    Callahan CM, Drake BG, Heck DA, Dittus RS. Patient outcomes following tricompartmental total knee replacement. JAMA 1994; 271: 134957.
  • 3
    Gawande AA, Rhomas EJ, Zinner MJ, Brennan TA. The incidence and nature of surgical adverse events in Colorado and Utah in 1992. Surgery 1999; 126: 6675.
  • 4
    Heck DA, Melfi CA, Mamlin LA, Katz BP, Arthur DS, Dittus RS, et al. Revision rates after knee replacement in the United States. Med Care 1998; 36: 6619.
  • 5
    Johantgen M, Elixhauser A, Ball JK, Goldfarb M, Harris DR. Quality indicators using hospital discharge data: state and national applications. J Qual Improve 1998; 24: 88105.
  • 6
    Ball JK, Elixhauser A, Johantgen M, Harris DR, Goldfarb M. HCUP quality indicators, methods, software user's guide, version 1. 1: outcome, utilization, and access measures for quality improvement. US Agency for Health Care Research and Quality; 1998. AHRQ Publication No. 98-0036.
  • 7
    Norton EC, Garfinkel SA, McQuay LJ, Heck DA, Wright JG, Dittus R, et al. The effect of hospital volume on the in-hospital complication rate in knee replacement patients. Health Serv Res 1998; 33: 1191210.
  • 8
    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992; 45: 6139.
  • 9
    Elixhauser A, Steiner C, Harris R, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36: 827.
  • 10
    DesHarnais SI, Forthman MT, Homa-Lowry JM, Wooster LD. Risk-adjusted clinical quality indicators: indices for measuring and monitoring rates of mortality, complications, and readmissions. Qual Manage Health Care 2000; 9: 1422.
  • 11
    American Academy of Orthopedic Surgeons. URL: http://www.aaos.org/.
  • 12
    Silber JH, Rosenbaum PR, Ross RN. Comparing the contributions of groups of predictors: which outcomes vary with hospital rather than patient characteristics? J Am Stat Assoc 1995; 90: 718.
  • 13
    Brennan TA, Hebert LE, Laird NM, Lawthers A, Thorpe KE, Leape LL, et al. Hospital characteristics associated with adverse events and substandard care. JAMA 1991; 265: 32659.
  • 14
    STATA reference manual, release 7, volume 4. College Station (TX): Su-Z Stata Press; 2001. p. 37785.
  • 15
    Katz JN, Losina E, Barrett J, et al. Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States Medicare population. J Bone Joint Surg 2001; 83-A: 16229.
  • 16
    Iezzoni LI, Foley SM, Daley J, Hughes J, Fisher ES, Heeren T. Comorbidities, complications, and coding bias: does the number of diagnosis codes matter in predicting in-hospital mortality. JAMA 1992; 267: 2197203.
  • 17
    Iezzoni LI, Ash AS, Shwartz M, Daley J, Hughes JS, Mackieman YD. Judging hospitals by severity-adjusted mortality rates: the influence of the severity adjustment method. Am J Public Health 1996; 86: 137987.
  • 18
    Iezzoni LI, Daley J, Heeren T, Foley SM, Fisher ES, Duncan C, et al. Identifying complications of care using administrative data. Med Care 1994; 32: 70015.
  • 19
    Iezzoni LI, Daley J, Heeren T, Foley SM, Huges JS, Fisher ES, et al. Using administrative data to screen hospital for high complications rates. Inquiry 1994; 31: 4055.
  • 20
    Silber JH, Rosenbaum PR, Schwartz JS, Ross RN, Williams SV. Evaluation of the complication rate as a measure of quality of care in coronary graft bypass graft surgery. JAMA 1995; 274: 31723.
  • 21
    Pearce WH, Parker MA, Feinglass J, Ujiki M, Manheim LM. The importance of surgeon volume and training in outcomes for vascular surgical procedures. J Vasc Surg 1999; 29: 76878.
  • 22
    Brennan TA, Localio RJ, Laird NL. Reliability and validity of judgments concerning adverse events suffered by hospitalized patients. Med Care 1989; 27: 114858.
  • 23
    O'Neil AC, Peterson LA, Cook EF, Bates DW, Lee TH, Brennan TA. Physician reporting compared with medical-record review to identify adverse medical events. Ann Intern Med 1993; 119: 3706.
  • 24
    Caplan RA, Posner KL, Cheney FW. Effect of outcome on physician judgments of appropriateness of care. JAMA 1991; 265: 195760.
  • 25
    Weingart SN, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, et al. Use of administrative data to find substandard care. Med Care 2000; 38: 796806.
  • 26
    Lawthers AG, McCarthy EP, Davis RB, Peterson LE, Palmer H, Iezzoni LI. Identification of in-hospital complications from claims data: is it valid? Med Care 2000; 38: 78595.
  • 27
    McCarthy EP, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB,et al. Does clinical evidence support ICD-9-CM diagnosis coding of complications? Med Care 2000; 38: 86876.
  • 28
    Rosen AK, Geraci JM, Ash AS, McNiff KJ, Moskowitz MA. Postoperative adverse events of common surgical procedures in the Medicare population. Med Care 1992; 30: 75365.
  • 29
    Black C, Roos NP. Administrative data: baby or bathwater? Med Care 1998; 36: 35.
  • 30
    Brailer DJ, Kroch E, Pauly MN, Huang J. Comorbidity-adjusted complication risk: a new outcome quality measure. Med Care 1996; 34: 490505.