Abstract
Background
There remains uncertainty regarding the relative importance of patient factors such as comorbidity and provider factors such as hospital volume in predicting complication rates after total hip arthroplasty (THA).
Purpose
We therefore identified patient and provider factors predicting complications after THA.
Methods
We reviewed discharge data from 138,399 patients undergoing primary THA in California from 1995 to 2005. The rate of complications during the first 90 days postoperatively (mortality, infection, dislocation, revision, perioperative fracture, neurologic injury, and thromboembolic disease) was regressed against a variety of independent variables, including patient factors (age, gender, race/ethnicity, income, Charlson comorbidity score) and provider variables (hospital volume, teaching status, rural location).
Results
Compared with patients treated at high-volume hospitals (above the 20th percentile), patients treated at low-volume hospitals (below the 60th percentile) had a higher aggregate risk of having short-term complications (odds ratio, 2.00). A variety of patient factors also had associations with an increased risk of complications: increased Charlson comorbidity score, diabetes, rheumatoid arthritis, advanced age, male gender, and black race. Hispanic and Asian patients had lower risks of complications.
Conclusions
Patient and provider characteristics affected the risk of a short-term complication after THA. These results may be useful for educating patients and anticipating perioperative risks of THA in different patient populations.
Level of Evidence
Level II, prognostic study. See Guidelines for Authors for a complete description of levels of evidence.
Introduction
THA is effective for decreasing pain and improving the function of patients with arthritis refractory to nonoperative treatment with antiinflammatory medications, activity modification, and weight loss. Despite the efficacy of THA, complications can occur which result in poor functional outcomes for a subset of patients. Given hip arthroplasty is a common and costly procedure, documenting and improving the quality of care and outcomes after THA remains a priority. Identifying risk factors that predict postoperative complications and, more specifically, being able to predict those patients at higher risk before surgery is an important step in searching for strategies that might reduce short-term complication rates.
The most common major complications include mortality, infection, dislocation, revision, and pulmonary embolism [4–6]. The rates of complication have been reported in international registries [2, 3, 8]. In addition, several papers have used administrative databases to evaluate complications in Medicare patients, with emphasis on the relationship between hospital and surgeon volume to rates of mortality and complications during the first 90 days after THA [4, 10]. The California Patient Discharge Database similarly contains data on mortality and complications. The database has the advantage of capturing complication rates of patients in the population of a state comparable in size to those covered in international registries. In addition, the age range is not limited by Medicare coverage. In the absence of a domestic joint replacement registry, the database provides a large alternative source of information on the rates and predictors of complication rates in a large group of patients from the United Stated including all age groups.
To confirm reported risk factors noted in the literature, we therefore identified patient and provider factors predicting complications after THA using the California database.
Patients and Methods
We obtained data for all hospitalizations in California during the years 1995 through 2005 from California’s Office of Statewide Health Planning and Development (OSHPD). The OSHPD database is compiled annually and includes discharge abstracts from all licensed nonfederal hospitals in California [11, 12]. Each discharge abstract reports demographic information that includes age, gender, insurance type, and the race or ethnicity of the patient. In addition, International Classification of Diseases, 9th Revision (ICD-9) codes are entered into the record for each patient; the number of codes entered is not prespecified and the maximum allowed is up to 20 inpatient procedures and 24 diagnoses per hospitalization (Table 1). Hospital characteristics are also reported, including the teaching status and whether a hospital is classified as rural in location. The OSHPD state inpatient database was initiated as a component of the Healthcare Cost and Utilization Project (HCUP) and is collected through mandatory reporting by all nonfederal hospitals in the state of California. Institutional Review Board approval was obtained for this study.
Table 1.
Demographics of patient sample
| Characteristic | Description of sample |
|---|---|
| Number of patients | 138,399 |
| Mean age (standard deviation) | 66 years (+/− 13 yrs.) |
| Gender | |
| 1) Male | 1) 79,514 (57%) |
| 2) Female | 2) 58,885 (43%) |
| Race/Ethnicity | |
| 1) White | 1) 117,107 (85%) |
| 2) Black | 2) 6,051 (4%) |
| 3) Hispanic | 3) 9,368 (7%) |
| 4) Asian/Pacific Islander | 4) 3,006 (2%) |
| 5) Other | 5) 2,867 (2%) |
| Income < 20th percentile | 5,840 (4%) |
| Complicated diabetes | 743 (< 1%) |
| Peripheral vascular disease | 2,179 (2%) |
| Rheumatoid arthritis | 5,565 (4%) |
| Hospital volume | |
| 1) High | 1) 27,480 (20%) |
| 2) Intermediate | 2) 56,431 (41%) |
| 3) Low | 3) 54,488 (39%) |
| Teaching status | 18,455 (13%) |
| Rural location | 3,128 (2%) |
We identified 138,399 patients undergoing their first THA using the ICD-9 procedure code for primary THA (81.51) who met inclusion and exclusion criteria. A previously published coding algorithm was modified and used to exclude 20,291 patients with infection, pathologic fracture, or undergoing revision arthroplasty [4, 10] (Appendix 1). We also excluded 3,848 patients with a non-California zip code to decrease the probability of the patient having prior admissions meeting exclusion criteria or experiencing a subsequent complication treated outside of the state. The unit of analysis was hospital discharge for each patient. All patients had basic demographic data as mandated by the state reporting requirements so no patients were excluded for missing data. Baseline patient characteristics were recorded in the database and analyzed. The mean age of the patient sample was 66 years with 85% being white. The population was diverse with 4% being black, 7% Hispanic, and 2% Asian. Complicated diabetes is defined as diabetes associated with end-organ damage; uncomplicated diabetes was noted in 8%, whereas less than 1% of patients had complicated diabetes. A diagnosis of rheumatoid arthritis was noted in 4% of patients (Table 1).
We selected the primary patient-based predictors: the Charlson comorbidity index [1, 9], age, race, gender, and income using zip code as a proxy as reported in the OSHPD database crossreferenced to US Census data. The Charlson comorbidity index assesses 19 comorbid conditions and has been validated for use in administrative database studies [1, 9]. This study uses the approach of Deyo et al. that adapted the Charlson index by defining the 19 comorbid conditions using ICD-9-CM coding and subsequently determining if the relevant codes are included in a patient record [1, 9]. In addition to the Charlson score, individual comorbidities were included for separate analysis consisting of diabetes, peripheral vascular disease, and rheumatoid arthritis.
Hospitals characteristics included surgical volume of THA, rural location, and teaching status. Teaching status and rural location are self-reported by the participating hospitals. Surgical volume was defined as the average number of primary THAs performed yearly during the study period. Hospitals were classified by their annual average volume as high-, intermediate-, or low-volume hospitals. Hospitals were categorized as low-volume if they were in the lowest 40th percentile by annual volume among hospitals where THA was performed. Intermediate-volume hospitals were defined as the next 40th percentile; high-volume hospitals were defined as the highest 20th percentile.
The outcomes analyzed as the dependent variables were the aggregate rate of short-term complications as well as the separately analyzed rates of individual complications, including mortality or readmission for the specific complications of infection, dislocation, revision surgery, perioperative fracture, neurologic injury, and thromboembolic disease at 90 days postoperatively. Previously published algorithms [4, 5] were adapted to detect codes consistent with a complication. The coding algorithms use ICD-9 nomenclature to identify patients undergoing total hip replacement using the 81.51 procedure code. Additional associated diagnoses, exclusion criteria, and complications are defined based on ICD-9 procedure and diagnoses codes judged by the authors to be consistent with the diagnoses or complications of interest. These algorithms were modified to correct for coding changes made during the study period [7, 11] (Appendix 1). Mortality was identified by the linkage of the California State Death Statistical Master File to the OSHPD database. This allowed us to identify hospital deaths occurring after discharge and the time elapsed before death in patients undergoing primary THA. The DSMF is a database of death certificates for all individuals dying in California and of those California residents who die outside of California’s borders but within the United States [13].
We used multiple variable logistic regression models to determine the role of the patient and provider characteristics as independent variables in predicting the occurrence of the complications selected as dependent variables. This method allows us to report the odds ratio for each patient and provider independent variable adjusted for all of the other variables included in the model. The regression models included the patient characteristics of race/ethnicity, age, gender, income, specific comorbidities, and modified Charlson comorbidity index and the provider characteristics of hospital volume, rural location, and teaching status as independent variables. The strength of association between the risk of a complication and the patient and provider characteristics is reported as the odds ratio in relation to a reference group adjusted for all the other variables included in the model. P-values and 95% confidence intervals are reported with the odds ratios. All statistical analyses were conducted using Stata/SE 8.0 (Stata Corp, College Station, TX).
Results
Overall, the 90-day complication rate after primary THA was 3.8%. The most common complication identified was dislocation (1.4%). The mortality rate was 0.68%. The rates of infection, thromboembolic disease (including pulmonary embolism and deep venous thrombosis), neurovascular injury, perioperative fracture, and revision surgery were each below 1% (Table 2).
Table 2.
90-day complication rates following total hip arthroplasty
| Complication | Rate (# of cases) |
|---|---|
| Mortality | 0.68% (943) |
| Dislocation | 1.39% (1,930) |
| Infection | 0.70% (969) |
| Thromboembolic disease | 0.64% (883) |
| Perioperative fracture | 0.01% (14) |
| Revision surgery | 0.93% (1,289) |
| Neurovascular Injury | 0.05% (74) |
| Overall rate of any complication within 90-days | 3.81% (5,277) |
Increased age was associated with a higher risk of a short-term complication as was a higher Charlson comorbidity index (Table 3). One of the stronger predictors of an increased aggregate risk of a complication within 90 days was the presence of complicated diabetes (odds ratio [OR], 1.94; 95% confidence interval [CI], 1.49–2.53; p < 0.001) as a result of increased risks of mortality and infection. Relative to white patients, black patients had an increased risk of complications (OR, 1.19; 95% CI, 1.05–1.35; p = 0.007), whereas Hispanic (OR, 0.75; 95% CI, 0.67–0.85; p < 0.001) and Asian patients (OR, 0.54; 95% CI, 0.42–0.69; p < 0.001) had a lower risk. Patients’ quintile of income was not associated with the aggregate risk of a complication. Hospital volume was the strongest predictor of a complication with both low-volume (OR, 2.00; 95% CI, 1.82–2.20; p < 0.001) and intermediate-volume (OR, 1.33; 95% CI, 1.22–1.45; p < 0.001) hospitals having an increased OR in relation to high-volume hospitals (Table 3). Teaching status and rural location were not associated with increased risks for most complications (Table 4).
Table 3.
Odds ratios for a complication within 90-days according to patient and hospital characteristics
| Patient or hospital characteristic | Reference group | 90-day overall complication risk (Odds ratio, 95% confidence interval, p-value) |
|---|---|---|
| Patient characteristic | ||
| Age > 75 | Age > 65–75 | 1.39 (1.30–1.48, p < 0.001) |
| Age > 55–65 | Age > 65–75 | 0.89 (0.83–0.96, p = 0.005) |
| Age ≤ 55 | Age > 65–75 | 0.72 (0.65–0.81, p < 0.001) |
| Male gender | Female Gender | 1.10 (1.03–1.17, p = 0.02) |
| Black race | White Race | 1.19 (1.05–1.35, p = 0.007) |
| Hispanic ethnicity | White Race | 0.75 (0.67–0.85, p < 0.001) |
| Asian race | White Race | 0.54 (0.42–0.69, p < 0.001) |
| Income < 80th percentile | Income ≥ 20th percentile | 1.11 (0.97–1.27, p = 0.12) |
| Patient comorbidity | ||
| Charlson co-morbidity | Continuous variable | 1.21 (1.18–1.24, p < 0.001) |
| Uncomplicated diabetes | Patients without diabetes | 1.31 (1.19–1.44, p < 0.001) |
| Complicated diabetes | Patients without diabetes | 1.94 (1.49–2.53, p < 0.001) |
| Peripheral vascular disease | Patients without PVD | 1.66 (1.30–2.11, p < 0.001) |
| Rheumatoid disease | No rheumatoid disease | 1.53 (1.23–1.91, p < 0.001) |
| Hospital characteristics | ||
| Low-volume hospitals | High-volume hospitals | 2.00 (1.82–2.20, p < 0.001) |
| Intermediate volume hospitals | High-volume hospitals | 1.33 (1.22–1.45, p < 0.001) |
| Teaching status | Non-teaching status | 1.05 (0.96–1.15, p = 0.30) |
| Rural location | Non-rural location | 1.16 (0.97–1.38, p = 0.11) |
p < 0.05 are given in bold.
Table 4.
Odds ratios for specific complications at 90-days according to patient and hospital characteristics
| Patient or hospital characteristic | Reference group | 90-day mortality risk (Odds ratio, 95% confidence interval, p-value) | 90-day infection risk (Odds ratio, 95% confidence interval, p-value) | 90-day dislocation risk (Odds ratio, 95% confidence interval, p-value) | 90-day revision risk (odds ratio, 95% confidence interval, p-value) | 90-day thromboembolism risk (odds ratio, 95% confidence interval, p-value) |
|---|---|---|---|---|---|---|
| Patient characteristic | ||||||
| Age > 75 | Age > 65–75 | 2.60 (2.22–3.04, p < 0.001) | 1.28 (1.09–1.51, p = .003) | 1.25 (1.12–1.40, p < 0.001) | 1.12 (0.96–1.31, p = 0.16) | 1.12 (0.96–1.31, p = 0.16) |
| Age > 55–65 | Age > 65–75 | 0.61 (0.49–0.76, p < 0.001) | 1.10 (0.93–1.31, p = 0.26) | 0.91 (0.81–1.03, p = 0.14) | 0.72 (0.60–0.87, p < 0.001) | 0.72 (0.60–0.87, p < 0.001) |
| Age ≤ 55 | Age > 65–75 | 0.26 (0.17–0.38, p < 0.001 | 1.34 (1.05–1.72, p = 0.02) | 0.69 (0.58–0.83, p < 0.001) | 0.42 (0.30–0.57, p < 0.001) | 0.42 (0.30–0.57, p < 0.001) |
| Male gender | Female gender | 1.23 (1.08–1.41, p = 0.002) | 1.14 (0.99–1.30, p = 0.06) | 1.16 (1.06–1.28, p = 0.001) | 1.06 (0.93–1.22, p = 0.37) | 1.06 (0.93–1.22, p = 0.37) |
| Black race | White race | 1.21 (0.89–1.66, p = 0.23) | 1.34 (10.5–1.73, p = 0.02) | 0.98 (0.79–1.21, p = 0.83) | 1.89 (1.44–2.47, p < 0.001) | 1.89 (1.44–2.47, p < 0.001) |
| Hispanic ethnicity | White race | 0.84 (0.62–1.13, p = 0.25) | 0.95 (0.74–1.21, p = 0.67) | 0.67 (0.55–0.83, p < 0.001) | 0.73 (0.53–1.01, p = 0.06) | 0.73 (0.53–1.01, p = 0.61) |
| Asian race | White race | 1.27 (0.82–1.97, p = 0.29) | 0.87 (0.55–1.36, p = 0.54) | 0.41 (0.26–0.63, p < 0.001) | 0.33 (0.15–0.73, p = 0.006) | 1.17 (0.75–1.83, p = 0.49) |
| Income < 80th percentile | Income ≥ 20th percentile | 1.09 (0.79–1.51, p = 0.58) | 1.62 (1.26–2.09, p < 0.001) | 1.18, (0.96–1.32, p = 0.12) | 0.68 (0.46–0.99, p = 0.047) | 0.68 (0.46–0.99, p = 0.047) |
| Patient comorbidity | ||||||
| Charlson co-morbidity | Continuous variable | 1.51 (1.45–1.58, p < 0.001) | 1.22 (1.15–1.28, p < 0.001) | 1.10 (1.05–1.15, p < 0.001) | 1.11 (1.04–1.19, p = 0.003) | 1.11 (1.04–1.19, p = 0.003) |
| Uncomplicated diabetes | Patients without diabetes | 1.45 (1.18–1.77, p < 0.001) | 1.72 (1.42–2.08, p < 0.001) | 1.45 (1.25–1.67, p < 0.001) | 0.86 (0.67–1.11, p = 0.26) | 0.86 (0.67–1.11, p = 0.26) |
| Complicated diabetes | Patients without diabetes | 2.65 (1.67–4.22, p < 0.001) | 3.70 (2.39–5.74, p < 0.001) | 1.42 (0.86–2.34, p = 0.17) | 1.04 (0.46–2.33, p = 0.93) | 1.04 (0.46–2.33, p = 0.93) |
| Peripheral vascular disease | Patients without PVD | 2.00 (1.49–2.69, p < 0.001) | 1.31 (0.87–1.96, p = 0.20) | 1.12 (0.81–1.53, p = 0.49) | 1.10 (0.69–1.77, p = 0.69) | 1.10 (0.69–1.77, p = 0.69) |
| Rheumatoid disease | No rheumatoid disease | 1.88 (1.17–3.03, p = 0.01) | 1.47 (0.90–2.41, p = 0.12) | 1.50 (1.05–2.15, p = 0.26 | 1.46 (0.82–2.61, p = 0.20) | 1.46 (0.82–2.61, p = 0.20) |
| Hospital characteristics | ||||||
| Low-volume hospitals | High-volume hospitals | 1.82 (1.44–2.30, p < 0.001) | 2.35 (1.87–2.94, p < 0.001) | 2.43 (2.08–2.84, p < 0.001) | 1.78 (1.42–2.22, p < 0.001) | 1.78 (1.42–2.22, p < 0.001) |
| Intermediate volume hospitals | High-volume hospitals | 1.45 (1.17–1.79, p = 0.001) | 1.48 (1.20–1.83, p < 0.001) | 1.40 (1.21–1.62, p < 0.001) | 1.22 (1.00–1.49, p = 0.05) | 1.22 (1.00–1.49, p = 0.046) |
| Teaching status | Non-teaching status | 0.93 (0.74–1.17 p = 0.53) | 1.04 (0.85–1.28, p = 0.70) | 1.15 (0.99–1.33, p = 0.06) | 1.11 (0.90–1.36, p = 0.34) | 1.11 (0.90–1.36, p = 0.34) |
| Rural location | Non-rural location | 0.97 (0.66–1.43, p = 0;88) | 1.42 (0.96–2.08, p = 0.08) | 0.90 (0.66–1.23, p = 0.52) | 1.77 (1.22–2.57, p = 0.003) | 1.77 (1.22–2.57, p = 0.003) |
p < 0.05 are given in bold.
Discussion
Many reports from various registries and individual papers report risk factors predicting complication rates after total hip arthroplasty (THA). However, the findings vary and there remains uncertainty regarding the relative importance of patient factors such as comorbidity and provider factors such as hospital volume in predicting complications. The California Office of Statewide Health Planning and Development (OSHPD) database provides a large alternate source of information. To confirm information in the literature, we therefore identified patient and provider factors predicting complications after THA using this alternate database. We specifically report the role of a variety of patient and hospital characteristics in predicting rates of mortality, infection, revision, dislocation, and thromboembolic disease after THA.
There are several limitations of studies examining administrative databases. First, this study was performed using a database of all patients in California over an 11-year period; this population may be less prone to selection bias than those studies looking at isolated Medicare populations. However, one potential bias in this population stems from patients having had surgery in California and sustaining a complication elsewhere, which would go unrecorded. More research is needed to determine if there is substantial bias in groups moving or receiving care outside of California. Another potential source of bias comes from relying on administrative registries. There can be substantial discrepancies between administrative data and audited and validated clinical data [10]. Second, the use of readmission and death records may underestimate morbidity and mortality if complications are not coded properly or do not require hospitalization. Third, the OSHPD statewide database does not include information on long-term functional outcomes. As a result, we could not evaluate the relationship of the predictor variables to functional outcome. Fourth, we were limited in our ability to identify confounding variables such as surgeon volume and training. Information on surgeon volume was not available and could not be evaluated separately from hospital volume. The studies by Katz et al. suggest both surgeon volume and hospital volume are independently associated with complication rates after THA [4]. Fifth, the California database includes hospital identifier but not surgeon identifiers, so we could not identify information on the relative importance of hospital and surgeon volume. Despite these limitations, the California discharge database has the advantage of being mandated by the state to include all admissions [13]. In addition, California is a large state with a diverse population allowing for the analysis of large numbers of patients from a variety of socioeconomic categories. In the absence of a formal domestic registry, the complication rates reported in this study provide an initial estimate of complication rates using population-based data on a large group of patients in the United States of all groups.
The overall 90-day complication rate of 0.68% for mortality, 0.64% for pulmonary embolus, and 1.39% for hip dislocation was lower than previously reported rates in the Medicare population of 1.0%, 0.9%, and 3.1%, respectively [6]. The Swedish Registry reported a similar 90-day mortality rate of 0.76% while the readmission rate was 3.9% within 30 days [3] (Table 5). The Australian and Finnish registries annual reports do not detail complication rates over periods shorter than 1-year so direct comparison to our study is not available [2, 8]. The higher rates of complication in Medicare analyses may demonstrate the selection bias in the Medicare population toward older and potentially sicker patients. Interestingly, our population had a higher wound infection rate of 0.9% than that previously reported in the Medicare population of 0.2% [6]. Further research is needed to elucidate the potential causes for this with respect to potential differences in the prevalence of diabetes, nosocomial infections, regional variations in pathogens, or intrinsic differences in our California population. Our dislocation rate of 1.39% was similar to previously published data in the Medicare population for those treated by surgeons who performed more than 50 THAs per year, 1.5%; however, this is notably different from the dislocation rate in those treated by surgeons who performed five or fewer per year, which has been reported as 4.2% [6]. Our study demonstrated similar increased risks of dislocation at lower-volume hospitals after adjusting for patient and provider characteristics. These observations may be useful for targeting interventions with a goal to decrease dislocation and complication rates at lower-volume centers.
Table 5.
Short-term complication rates compared to Swedish Registry and Medicare Database analyses
| Complication | 90-day mortality | 90-day dislocation | 90-day thromboembolic disease | 90-day infection | 30-day readmission rate | Overall rate of any complication within 90-days |
|---|---|---|---|---|---|---|
| Katz et al. [4] | 1.00% | 3.10% | 0.90% | 0.20% | Not reported | Not reported |
| Swedish Registry [3] | 0.76% | Not reported | Not reported | Not reported | 3.90% | Not reported |
| SooHoo et al. [current study] | 0.68% | 1.39% | 0.64% | 0.90% | Not reported | 3.81% |
Age, comorbidity, and race/ethnicity had an effect on the risk of short-term complications similar in magnitude to that of hospital volume. These findings are similar to those reported by Katz et al. who found age, gender, comorbidity, race, and income were associated with a higher risk of complications in the Medicare population [4]. Confirmation of these observations suggests the need for further study on the relative importance and underlying causes of these differences among populations. Future studies of these predictive factors would benefit from enriched data sources that include functional outcomes. Identifying these differing risks may be useful in counseling patients regarding the risks of surgery. The causes of these differences between populations warrant additional study to determine if they should play a role in patient selection or result in different approaches to perioperative care in patients at increased risk of complications.
This study reports short-term complication rates following total hip arthroplasty and the role of some patient and provider factors in predicting the occurrence of complications. The elucidation of these factors is useful in patient education and discussion of the perioperative risks of THA in different patient population.
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Appendix 1
Inclusion Diagnosis codes -- to be flagged
- 715
degenerative disease
- 7150
degenerative disease
- 71500
degenerative disease
- 71509
degenerative disease
- 7151
degenerative disease
- 71510
degenerative disease
- 71515
degenerative disease
- 7152
degenerative disease
- 71520
degenerative disease
- 71525
degenerative disease
- 7153
degenerative disease
- 71530
degenerative disease
- 71535
degenerative disease
- 718
degenerative disease
- 71580
degenerative disease
- 71585
degenerative disease
- 71589
degenerative disease
- 7159
degenerative disease
- 71590
degenerative disease
- 71595
degenerative disease
- 714
rheumatoid arthritis, JRA, and RA with systemic involvement
- 7140
rheumatoid arthritis, JRA, and RA with systemic involvement
- 7143
rheumatoid arthritis, JRA, and RA with systemic involvement
- 71430
rheumatoid arthritis, JRA, and RA with systemic involvement
- 71431
rheumatoid arthritis, JRA, and RA with systemic involvement
- 71432
rheumatoid arthritis, JRA, and RA with systemic involvement
- 71433
rheumatoid arthritis, JRA, and RA with systemic involvement
- 7334
AVN
- 73340
AVN
- 73342
AVN
- 7310
Pagets
- 73300
osteoporosis
- 73301
osteoporosis
- 73302
osteoporosis
- 73303
osteoporosis
- 73309
osteoporosis
- 27800
obesity - NOS
- 27801
obesity - morbid
- 27802
obesity - overweight
- V850
obesity - BMI<19
- V851
obesity - BMI 19-24
- V8521
obesity - BMI 25-30
- V8522
obesity - BMI 25-30
- V8523
obesity - BMI 25-30
- V8524
obesity - BMI 25-30
- V8525
obesity - BMI 25-30
- V8530
obesity - BMI 30-40
- V8531
obesity - BMI 30-40
- V8532
obesity - BMI 30-40
- V8533
obesity - BMI 30-40
- V8534
obesity - BMI 30-40
- V8535
obesity - BMI 30-40
- V8536
obesity - BMI 30-40
- V8537
obesity - BMI 30-40
- V8538
obesity - BMI 30-40
- V8539
obesity - BMI 30-40
- V854
obesity - BMI>40
Inclusion Procedure codes
- 8151
total hip replacement
Exclusion Codes --
Procedures
- 7905
fracture - femur
- 7915
fracture - femur
- 7925
fracture - femur
- 7935
fracture - femur
- 8153
revision hip replacement
- 786
removal of implanted device
- 7860
removal of implanted device
- 7865
removal of implanted device
- 800
arthrotomy for removal of prosthesis
- 8000
arthrotomy for removal of prosthesis
- 8005
arthrotomy for removal of prosthesis
- 8153
Diagnosis
- 820
fracture of neck, shaft, or unspecified - femur
- 8200
fracture of neck, shaft, or unspecified - femur
- 8200
fracture of neck, shaft, or unspecified - femur
- 82001
fracture of neck, shaft, or unspecified - femur
- 82001
fracture of neck, shaft, or unspecified - femur
- 82003
fracture of neck, shaft, or unspecified - femur
- 82009
fracture of neck, shaft, or unspecified - femur
- 8201
fracture of neck, shaft, or unspecified - femur
- 82010
fracture of neck, shaft, or unspecified - femur
- 82011
fracture of neck, shaft, or unspecified - femur
- 82012
fracture of neck, shaft, or unspecified - femur
- 82013
fracture of neck, shaft, or unspecified - femur
- 82019
fracture of neck, shaft, or unspecified - femur
- 8202
fracture of neck, shaft, or unspecified - femur
- 82020
fracture of neck, shaft, or unspecified - femur
- 82021
fracture of neck, shaft, or unspecified - femur
- 82022
fracture of neck, shaft, or unspecified - femur
- 8203
fracture of neck, shaft, or unspecified - femur
- 82030
fracture of neck, shaft, or unspecified - femur
- 82031
fracture of neck, shaft, or unspecified - femur
- 82032
fracture of neck, shaft, or unspecified - femur
- 8208
fracture of neck, shaft, or unspecified - femur
- 8209
fracture of neck, shaft, or unspecified - femur
- 821
fracture of neck, shaft, or unspecified - femur
- 8210
fracture of neck, shaft, or unspecified - femur
- 82100
fracture of neck, shaft, or unspecified - femur
- 82101
fracture of neck, shaft, or unspecified - femur
- 8211
fracture of neck, shaft, or unspecified - femur
- 82110
fracture of neck, shaft, or unspecified - femur
- 82111
fracture of neck, shaft, or unspecified - femur
- 8080
acetabulum, closed
- 8081
acetabulum, open
- 8082
pubis, closed
- 8083
pubis, open
- 80841
ilium, closed
- 80842
ischium, closed
- 80843
multiple pelvic, closed
- 80849
pelvic, other
- 80851
ilium, open
- 80852
ischium, open
- 80853
multiple pelvic, open
- 80850
other pelvic, open
- 8088
unspecified, pelvic, closed
- 71105
infection - hip
- 71165
infection - hip
- 71195
infection - hip
- 7300
infection - hip
- 73000
infection - hip
- 73005
infection - hip
- 7301
infection - hip
- 73010
infection - hip
- 73015
infection - hip
- 7302
infection - hip
- 73020
infection - hip
- 73025
infection - hip
- 7309
infection - hip
- 73090
infection - hip
- 73095
infection - hip
- 170
malignancy or pathoalogic fracture
- 1706
malignancy or pathoalogic fracture
- 1707
malignancy or pathoalogic fracture
- 1709
malignancy or pathoalogic fracture
- 1953
malignancy or pathoalogic fracture
- 1955
malignancy or pathoalogic fracture
- 198
malignancy or pathoalogic fracture
- 1985
malignancy or pathoalogic fracture
- 1990
malignancy or pathoalogic fracture
- 7331
malignancy or pathoalogic fracture
- 73314
malignancy or pathoalogic fracture
- V540
aftercare for removal of fracture plate or other fixation device
- 9964
complications of implant
- 9966
complications of implant
- 99660
complications of implant
- 99666
complications of implant
- 99667
complications of implant
- 9967
complications of implant
- 99670
complications of implant
- 99677
complications of implant
- 99678
complications of implant
Outcome diagnosis of Interest
* Code descriptions ending in an * also require a V-code (to specify the joint)
- 41511
DVT/PE - iatrogenic pulmonary embolism and infarction
- 41519
DVT/PE - pulmonary embolism and infarction, other
- 45340
DVT/PE - deep venous thrombosis of lower extremity
- 45341
DVT/PE - DVT of proximal lower extremity
- 45342
DVT/PE - DVT of distal lower extremity
- 711
infection - arthropathy associated with infections
- 7110
infection - pyogenic arthritis
- 71100
infection - pyogenic arthritis, site unspecified
- 71105
infection - pyogenic arthritis, pelvic region and thigh
- 7116
infection - mycotic arthropathy
- 71160
infection - mycotic arthropathy, site unspecified
- 71165
infection - mycotic arthropathy, pelvic region and thigh
- 7119
infection - unspecified infective arthritis
- 71190
infection - unspecified infective arthritis, site unspecified
- 71195
infection - unspecified infective arthritis, pelvic region and thigh
- 7300
infection - acute osteomyelitis
- 73000
infection - acute osteomyelitis, site unspecified
- 73005
infection - acute osteomyelitis, pelvic region and thigh
- 7301
infection - chronic osteomyelitis
- 73010
infection - chronic osteomyelitis, site unspecified
- 73015
infection - chronic osteomyelitis, pelvic region and thigh
- 7302
infection - unspecified osteomyelitis
- 73020
infection - unspecified osteomyelitis, site unspecified
- 73025
infection - unspecified osteomyelitis, pelvic region and thigh
- 7309
infection - unspecified
- 73090
infection - unspecified unspecified site
- 73095
infection - unspecified infection of bone, pelvic region and thigh
- 99640
mechanical complication - unspecified mechanical complication of internal orthopedic device, implant, graft *
- 99641
mechanical complication - mechanical loosening of prosthetic joint *
- 99642
mechanical complication - dislocation of prosthetic joint *
- 99643
mechanical complication - prosthetic implant joint failure *
- 99644
mechanical complication - peri prosthetic fracture around prosthetic joint*
- 99645
mechanical complication - peri-prosthetic osteolysis *
- 99646
mechanical complication - articular bearing surface wear of prosthetic joint *
- 99647
mechanical complication - other mechanical complication of prosthetic joint implant *
- 99649
mechanical complication - other mechanical complication of other internal orthopedic device, implant, and graft *
- 99811
hemorrahge, hematoma, or seroma complicating a procedure
- 99812
hemorrahge, hematoma, or seroma complicating a procedure
- 99813
hemorrahge, hematoma, or seroma complicating a procedure
- 9982
neurovascular - accidental puncture or laceration during procedure on vessel, nerve, organ
- 9966
infection and inflammatory reaction due to joint prosthesis *
- 786
removal of implanted device from bone
- 7860
removal of implanted device from bone, site unspecified
- 7865
removal of implant device from bone, femur
- 800
arthrotomy for removal of prosthesis
- 8000
arthrotomy for removal of prosthesis, site unspecified
- 8005
arthrotomy for removal of prosthesis, hip
- 801
arthrotomy, other
- 8010
arthrotomy, other, site unspecified
- 8015
arthrotomy, other, hip
- 7975
closed reduction, hip
- 7985
open reduction, hip
- 8153
revision arthroplasty - Revision of hip replacement
- 8622
I and D - excisional debridement of wound, infection, burn
- 8628
I and D - nonexcisional debridement of wound, infection, burn
- 7765
I and D - local excision of lesion or tissue of bone, femur
Valid V codes -- only used for outcomes with a *
- V4364
v - hip
Footnotes
One or more of the authors (NFS) received funding from the Orthopaedic Research and Education Foundation.
Each author certifies that his or her institution approved the human protocol for this investigation, that all investigations were conducted in conformity with ethical principles of research, and that informed consent for participation in the study was obtained.
This work was performed at the UCLA School of Medicine.
References
- 1.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–619. doi: 10.1016/0895-4356(92)90133-8. [DOI] [PubMed] [Google Scholar]
- 2.Hip and Knee Arthroplasty: Annual Report 2009. Available at: http://www.dmac.adelaide.edu.au/aoanjrr/documents/aoanjrrreport_2009.pdf. Accessed March 23, 2010.
- 3.Karrholm J, Garellick G, Rogmark C, Herberts P. Swedish Hip Arthroplasty Register: Annual Report 2007. Available at: http://www.jru.orthop.gu.se/. Accessed March 23, 2010.
- 4.Katz JN, Losina E, Barrett J, Phillips CB, Mahomed NN, Lew RA, Guadagnoli E, Harris WH, Poss R, Baron JA. Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States medicare population. J Bone Joint Surg Am. 2001;83:1622–1629. doi: 10.1302/0301-620X.83B3.10487. [DOI] [PubMed] [Google Scholar]
- 5.Katz JN, Phillips CB, Baron JA, Fossel AH, Mahomed NN, Barrett J, Lingard EA, Harris WH, Poss R, Lew RA, Guadagnoli E, Wright EA, Losina E. Association of hospital and surgeon volume of total hip replacement with functional status and satisfaction three years following surgery. Arthritis Rheum. 2003;48:560–568. doi: 10.1002/art.10754. [DOI] [PubMed] [Google Scholar]
- 6.Mahomed NN, Barrett JA, Katz JN, Phillips CB, Losina E, Lew RA, Guadagnoli E, Harris WH, Poss R, Baron JA. Rates and outcomes of primary and revision total hip replacement in the United States Medicare population. J Bone Joint Surg Am. 2003;85:27–32. doi: 10.2106/00004623-200301000-00005. [DOI] [PubMed] [Google Scholar]
- 7.National Center for Health Statistics. CDC Web site. National Hospital Discharge Survey: 2002 Public Use Data File Documentation.Available at: http://www.cdc.gov/nchs/injury/injury_hospital.htm. Accessed March 23, 2010.
- 8.Puolakka TJ, Pajamaki KJ, Halonen PJ, Pulkkinen PO, Paavolainen P, Nevalainen JK. The Finnish Arthroplasty Register: report of the hip register. Acta Orthop Scand. 2001;72:433–441. doi: 10.1080/000164701753606608. [DOI] [PubMed] [Google Scholar]
- 9.Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived rom ICD-9-CCM administrative data. Med Care. 2002;40:675–685. doi: 10.1097/00005650-200208000-00007. [DOI] [PubMed] [Google Scholar]
- 10.Shervin N, Rubash HE, Katz JN. Orthopaedic procedure volume and patient outcomes: a systematic literature review. Clin Orthop Relat Res. 2007;457:35–41. doi: 10.1097/BLO.0b013e3180375514. [DOI] [PubMed] [Google Scholar]
- 11.SooHoo NF, Lieberman JR, Ko CY, Zingmond DS. Factors predicting complication rates following total knee replacement. J Bone Joint Surg Am. 2006;88:480–485. doi: 10.2106/JBJS.E.00629. [DOI] [PubMed] [Google Scholar]
- 12.Soohoo NF, Zingmond DS, Lieberman JR, Ko CY. Primary total knee arthroplasty in California 1991 to 2001: does hospital volume affect outcomes? J Arthroplasty. 2006;21:199–205. doi: 10.1016/j.arth.2005.03.027. [DOI] [PubMed] [Google Scholar]
- 13.Zingmond DS, Ye Z, Ettner SL, Liu H. Linking hospital discharge and death records–accuracy and sources of bias. J Clin Epidemiol. 2004;57:21–29. doi: 10.1016/S0895-4356(03)00250-6. [DOI] [PubMed] [Google Scholar]
