Clinical Orthopaedics and Related Research®

, Volume 468, Issue 9, pp 2363–2371

Factors That Predict Short-term Complication Rates After Total Hip Arthroplasty

Authors

    • Department of Orthopaedic SurgeryUCLA School of Medicine
  • Eugene Farng
    • Department of Orthopaedic SurgeryUCLA School of Medicine
  • Jay R. Lieberman
    • Department of Orthopaedic SurgeryUniversity of Connecticut
  • Lauchlan Chambers
    • Department of Orthopaedic SurgeryUCLA School of Medicine
  • David S. Zingmond
    • Department of General Internal Medicine and Health Services ResearchUCLA School of Medicine
Open AccessSymposium: Complications of Hip Arthroplasty

DOI: 10.1007/s11999-010-1354-0

Cite this article as:
SooHoo, N.F., Farng, E., Lieberman, J.R. et al. Clin Orthop Relat Res (2010) 468: 2363. doi:10.1007/s11999-010-1354-0

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 [46]. 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.

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