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.

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).

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.

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.