Abstract
Background
Periprosthetic infection has been linked to risk factors such as diabetes, obesity, and smoking amongst others. This study examined the relationship between common patient comorbidities and hip periprosthetic infection outcomes.
Methods
We retrospectively reviewed the records of 149 culture-positive periprosthetic hip infections at our tertiary care center that underwent treatment between 2005 and 2015. Baseline characteristics as well as common comorbidities were analyzed with relation to rates of successfully treated infection, total surgeries for infection, and cumulative length of hospitalization using multivariable analysis.
Results
Patients with coronary artery disease or anemia had significantly lower rate of successfully treated infection. Patients with anemia or chronic pulmonary disease underwent significantly more surgery, and patients with chronic pulmonary disease, psychiatric disease, anemia, or diabetes spent significantly longer time in hospital.
Conclusion
Potentially modifiable cardiovascular, respiratory, and psychiatric diseases were associated with decreased rate of successfully treated infection, more surgery, and longer hospitalization in treatment for hip peri-prosthetic infection in multivariable analysis.
Keywords: periprosthetic infection, hip arthroplasty, comorbidity
INTRODUCTION
Although hip arthroplasty has been a uniquely successful surgery [1], periprosthetic infection is a devastating complication of hip arthroplasty that accounts for 15% of all hip arthroplasty revision [2]. Treatment for infection is contentious [3] as well as costly, burdening patients with extra surgery and hospitalization [4]. Current projections suggest that there will be 500,000 primary total hip arthroplasties performed each year in 2030 [5]. With an infection rate of 1 – 2%, there may be 5,000 – 10,000 new periprosthetic hip infections to treat each year in the near future. Even as the demand for arthroplasty has been increasing, patients undergoing primary total hip arthroplasty are now suffering from greater comorbidity [6]. Modifiable risk factors [7] such as obesity, pre-operative anemia, malnutrition, alcohol abuse, diabetes, rheumatoid arthritis, and coagulopathy along with risk factors that are less modifiable such as cardiovascular disease, mood disorders, and renal failure have been associated with development of periprosthetic infection [8–10]. Further, some patients have modifiable surgical site infection risk factors at the time of their index operation [11].
While risk factors for development of periprosthetic infection have been established, there is relatively little known about how specific common comorbidities affect outcomes in treatment of hip periprosthetic infection. The staging system proposed by McPherson et. al. stratifies patients into stages based on the number and/or perceived severity of their comorbidities, but the individual contribution of comorbidities to outcomes has remained unclear [12]. Recently, Gomez and colleagues reported on the impact of the Charlson comorbidity score, rheumatoid arthritis, and diabetes on reimplantation rates in a series of hip and knee infections that had undergone antibiotic spacer placement and had at least 1 year of follow-up after their initial spacer placement. The authors noted that high Charlson comorbidity index and rheumatoid arthritis were both associated with lower reimplantation rates though the analysis was not stratified by which joint was infected [13]. A separate study identified morbid obesity as an additional risk factor for failure of two-stage exchange hip arthroplasty [14].
High cumulative comorbidity as measured by the Charlson comorbidity index, rheumatoid arthritis, and morbid obesity may impact success after 2-stage exchange in infected hip or knee arthroplasty. However, there has not been a study to the author’s knowledge that examines the relationship of the most prevalent chronic comorbidities with hip periprosthetic infection outcomes. As arthroplasty patients have increasing baseline comorbidity and as the demand for arthroplasty rises, it will be increasingly important to recognize the role of specific patient comorbidities in treatment of hip periprosthetic infection. The aim of this study was to examine the impact of common patient comorbidities on the outcomes of 1) rate of successfully treated infection, 2) surgeries for infection, and 3) length of hospitalization during treatment for hip periprosthetic infection.
MATERIALS AND METHODS
After institutional review board approval, we reviewed records of all patients at our institution having undergone treatment for total or partial hip arthroplasty infection over the past 10 years. The analysis and reporting of this retrospective cohort study was conducted in accordance with the STROBE statement, a consensus statement intended to strengthen the reporting of observational studies [15]. Each patient’s clinical course was determined from the time of their first positive hip culture to their final hospitalization or surgery for infection and then through follow-up. All patients with positive hip culture and consideration for operative treatment of infection were considered for inclusion in the analysis [16]. Baseline information such as race, gender, age at first positive hip culture at our institution, and any record of previous treatment for hip infection at outside hospital (OSH) were recorded. Medical comorbidities associated with each patient before their final hospitalization or treatment for infection were extracted from patient records using International Classification of Diseases version 9 (ICD-9) codes developed by Quan et. al. based on comorbidities in the Charlson-deyo comorbidity index [17]. If a prevalent comorbidity did not appear in the Charlson-Deyo comorbidity index, ICD-9 codes corresponding to the appropriate comorbidity were used. Only comorbidities with greater than 10% prevalence in our study population were included in the analysis.
After determining patient characteristics, the following outcomes relevant to periprosthetic infection treatment were recorded: treatment success, number of surgeries for infection, and cumulative length of hospitalizations related to infection. Treatment success was defined as having appropriate arthroplasty components in place without need for further surgery or intravenous antibiotics. This definition was consistent with the Delphi-based international multidisciplinary consensus statement defining treatment success in periprosthetic infection [18]. Also, according to the consensus statement, patients were only included in the analysis if they had a minimum of 1 year of follow-up with orthopedic providers at our institution after their final surgery or hospitalization for infection. Out of 301 hip arthroplasty infections treated at our institution between September 2005 and September 2015, 149 separate hip arthroplasty infections met follow-up criteria for the study (1 year of follow-up after final surgery or hospitalization for infection) and were included in the analysis. No potentially-eligible hip infections were excluded for any other reason.
Infection type and host grade as described by McPherson et. al. were calculated [12] in order to improve understanding about the patient factors related to infection in this series. The authors were able to distinguish between acute post-operative (Stage I) and late post-operative (Stage II/III) infections. Additionally, systemic host grade could be categorized into “uncompromised,” “compromised,” and “significantly compromised” based on patient diagnoses. Because of the retrospective nature of the investigation and variance of data collected at the time of the infection, lab values and assessment of chronic active infection at another site were not included in the staging analysis. Lastly, local extremity grade could not be determined retrospectively.
JMP Pro version 12.0.1 from Statistical Analysis Software was used to conduct univariate analysis assessing the impact of each of our comorbidities and baseline characteristics on the outcomes of interest (treatment success, surgeries, length of hospitalization). Each covariate with univariate p-value less than 0.05 was included in multivariable models of each outcome, regardless of the direction of the association. Those covariates with p-value less than 0.05 in multivariable models are reported as significant.
RESULTS
Table 1 demonstrates that patients were about 61 years old, 69% were Caucasian, and 52% female. 24% had previous infection treatment at OSH. The majority of patients had a systemic host grade of “B” and had a late post-operative infection (stage II/III). Approximately 86% of patients had successful treatment of their infection after an average of 2.65 surgeries and 17.27 days in hospital. Appendix Table 1 shows a further breakdown of the surgical treatments and outcomes.
Table 1.
Baseline characteristics with averages and 95% confidence intervals (CI’s) are shown. The proportion of each McPherson stage is shown. Infection type is broken into acute postoperative (Stage I) and late post-operative (Stage II/III). Host grade is given by A - “uncompromised,” B – “compromised,” and C – “significantly compromised.” Outcomes of successful treatment, surgeries for infection, and days in hospital for infection are shown with proportions or averages and 95% CI’s.
| Baseline characteristics | Average (lower 95% CI, upper 95% CI) or proportion | |
|---|---|---|
| Age at first infection | 60.6 (58.3, 62.9) | |
| Caucasian | 102 / 149 (68.5%) | |
| Female gender | 77 / 149 (51.7%) | |
| Previous infection treatment at outside hospital | 36 / 149 (24.2%) | |
| McPherson Stage | ||
| Infection type | Host grade | Proportion |
| Stage I (acute post-op, n=66) | A | 19 / 149 (12.8%) |
| B | 35 / 149 (23.5%) | |
| C | 12 / 149 (8.1%) | |
| Stage II/III (late post-op, n=83) | A | 22 / 149 (14.8%) |
| B | 36 / 149 (24.2%) | |
| C | 25 / 149 (16.8%) | |
| Outcomes | Average (lower 95% CI, upper 95% CI) or proportion | |
| Successful treatment rate | 128 / 149 (85.9%) | |
| Surgeries | 2.65 (2.26, 3.04) | |
| Days in hospital | 17.27 (14.09, 20.45) | |
| Follow-up (months) | 37.90 (33.93, 41.88) | |
Appendix Table 1.
Surgical treatments and outcomes broken down by irrigation and debridement(s) (I+D) alone, 1-stage or 2-stage resection(s) alone, or a combination of the 2 procedures.
| Operative treatment | Infection-free rate | Surgeries | Hospitalization days |
|---|---|---|---|
| I+D(s) alone | 65 / 68 (95.6%) | 1.47 (1.27, 1.67) | 9.72 (7.96, 11.49) |
| Resection(s) alone | 38 / 42 (90.5%) | 2.09 (1.86, 2.33) | 12.48 (10.34, 14.61) |
| Combination I+D and resection | 25 / 38 (65.8%) | 5.45 (4.44, 6.45) | 36.54 (27.03, 46.04) |
As shown in Table 2, the following diseases had prevalence greater than 10% in our study population: hypertension, anemia, obesity, hyperlipidemia, diabetes mellitus without complication (DM), chronic pulmonary disease (CPD), tobacco abuse, psychiatric disease including depression and psychosis, coronary artery disease (CAD), renal disease, avascular necrosis, sleep apnea, rheumatic disease, malignancy, congestive heart failure, and cerebrovascular disease.
Table 2.
Baseline patient characteristics and comorbidities. Percentages or proportions are shown along with the ICD-9 codes corresponding to the comorbidity of interest. “CD” indicates that the codes were derived from the Charlson-Deyo comorbidity index.
| Comorbidity | Proportion | ICD-9 codes |
|---|---|---|
| Hypertension | 105 / 149 (70.5%) | 401–406 |
| Anemia | 99 / 149 (66.4%) | 280–285 |
| Obesity | 68 / 149 (45.6%) | 278.00–278.03 |
| Hyperlipidemia | 63 / 149 (42.3%) | 272.0–272.5 |
| Diabetes mellitus without complicationCD | 39 / 149 (26.2%) | 250.4–250.6 |
| Chronic pulmonary diseaseCD | 37 / 149 (24.8%) | 490–506.0, 506.4 |
| Tobacco abuse | 33 / 149 (22.1%) | 305.1 |
| Psychiatric disease | 31 / 149 (20.8%) | 290.0–299.91 |
| Coronary artery disease | 24 / 149 (16.1%) | 410.00–412,414.0–414.9 |
| Renal diseaseCD | 23 / 149 (15.4%) | 582.0–583.7, 585.1–586, 588.0–588.9 |
| Avascular necrosis | 22 / 149 (14.8%) | 733.4–733.49 |
| Sleep apnea | 20 / 149 (13.4%) | 327.20–327.29 |
| Rheumatic diseaseCD | 19 / 149 (12.8%) | 710.0, 710.1, 710.4, 714.0–714.2, 714.81, 725 |
| MalignancyCD | 18 / 149 (12.1%) | 140.0–172.9, 174.0.–195.8, 200.0–208.92 |
| Congestive heart failureCD | 17 / 149 (11.4%) | 428.0–428.9 |
| Cerebrovascular diseaseCD | 17 / 149 (11.4%) | 430–438.9 |
As shown in Table 3, patients with coronary artery disease (66.7% vs 89.6%, p=0.0407) or anemia (81.2% vs 94.0%, p=0.0295) had lower rate of successfully treated infection in multivariable analysis. Odds ratios for anemia and CAD were 3.73 (1.13, 17.21) and 3.36 (1.05, 10.29), respectively. Though sleep apnea was associated with successful treatment of infection in univariate analysis, the association did not persist in multivariable analysis.
Table 3.
Multivariable logistic regression of successful treatment rate for all baseline characteristics in Table 1 and all comorbidities in Table 2 that achieved p-value less than 0.05 in univariate analysis. Adjusted p-values are shown that compare successful treatment rates of patients with exposure to the comorbidity against patients without exposure to the comorbidity. Raw successful treatment percentages as well as adjusted odds ratios with confidence intervals (CI) for successful treatment related to the comorbidity are shown.
| Comorbidity or characteristic | Successfully treated with exposure | Successfully treated without exposure | Adjusted odds ratio for successful treatment without comorbidity (lower 95% CI, upper 95% CI) | Adjusted p-value |
|---|---|---|---|---|
| Anemia | 81 / 99 (81.2%) | 47 / 50 (94.0%) | 3.73 (1.13, 17.21) | 0.0295 |
| CAD | 16 / 24 (66.7%) | 112 / 125 (89.6%) | 3.36 (1.05, 10.29) | 0.0407 |
| Sleep apnea | 14 / 20 (70.0%) | 114 / 129 (88.4%) | 2.23 (0.61, 7.49) | 0.2156 |
To provide more detail on the types of CAD in the study population, Appendix Table 2 shows ICD-9 codes separated into relevant categories and grouped based on when the diagnosis had been entered: 1) prior to first treatment, 2) during treatment, and 3) at any time prior to end of treatment. Among the 24 patients coded with CAD, some were labeled with several separate CAD codes prior to or during their treatment for infection. Two groups of codes were commonly entered at these time points: 412 (old myocardial infarction) and 414 (other forms of chronic ischemic heart disease). Prior to patients’ first treatment, 414 affected 18 / 24 patients (75%) and 412 affected 7 / 24 patients (29.2%); during patient’s treatment for infection, 414 affected 13 / 24 patients (54.2%) and 412 affected 8 / 24 patients (33.3%); and any time prior to final infection treatment, 414 affected 21 / 24 patients (87.5%) and 412 affected 12 / 24 patients (50%). Among patients with 414, the most common sub-classifications were 414.0 and 414.8: coronary atherosclerosis and other specified forms of chronic ischemic heart disease, respectively. Anemia codes were also further broken down into relevant subgroups and listed according to when the diagnosis had been entered. This information is provided in Appendix Table 3. Patients also often had several anemia codes listed. The two most common codes entered before patients’ first treatment date were 285.9 (unspecified anemia) affecting 50 / 99 (50.5%) and 285.1 (acute post-hemorrhagic anemia) affecting 27 / 99 (27.3%). The three most common anemia codes entered during patient’s treatment for infection included 285.9 (unspecified anemia) affecting 40 / 99 (40.4%), 285.1 (acute post-hemorrhagic anemia) affecting 32 / 99 (32.3%), 285.2 (anemia of chronic illness) affecting 15 / 99 (15.2%). The three most common codes among the 99 patients coded with anemia any time prior to final infection treatment were 285.9 (unspecified anemia) affecting 73 / 99 (73.7%), 285.1 (acute post-hemorrhagic anemia) affecting 51 / 99 (51.5%), and 285.2 (iron deficiency anemia) affecting 18 / 99 (18.2%). In order to further characterize the impact of anemia on treatment outcomes, hemoglobin levels were more closely analyzed. Hemoglobin values less than 8 or 11 mg/dL were separated according whether they occurred within 90 days prior to the start of infection treatment or during treatment for infection. These results are listed in Appendix Table 4. Successful treatment was related to pre-operative hemoglobin <11 and treatment hemoglobin <8. Total surgeries and length of hospitalization were both related to treatment hemoglobin <11 and <8.
Appendix Table 2.
Coronary artery disease codes displayed by the time that each code was entered.
| Time frame | Acute myocardial infarction (410) | Other acute and subacute forms of ischemic heart disease (411) | Old myocardial infarction (412) | Other forms of chronic ischemic heart disease (414) |
|---|---|---|---|---|
| Prior to first infection treatment | 3 / 24 (12.5%) | 0 / 24 (0%) | 7 / 24 (29.2%) | 18 / 24 (75%) |
| During infection treatment | 0 / 24 (0%) | 0 / 24 (0%) | 8 / 24 (33.3%) | 13 / 24 (54.2%) |
| Any time prior to final infection treatment | 3 / 24 (12.5%) | 0 / 24 (0%) | 12 / 24 (50%) | 21 / 24 (87.5%) |
Appendix Table 3.
Anemia codes displayed by the time that each code was entered.
| Time frame | Iron deficiency anemia (280) | Other deficiency anemia (281) | Hereditary hemolytic anemia (282) | Acquired hemolytic anemia (283) | Aplastic and other bone marrow anemia (284) | Acute post-hemorrhagic anemia (285.1) | Anemia of chronic illness (285.2) | Anemia unspecified (285.9) |
|---|---|---|---|---|---|---|---|---|
| Prior to first infection treatment | 9 / 99 (9.1%) | 7 / 99 (7.1%) | 3 / 99 (3%) | 1 / 99 (1%) | 4 / 99 (4%) | 27 / 99 (27.3%) | 9 / 99 (9.1%) | 50 / 99 (50.5 %) |
| During infection treatment | 9 / 99 (9.1%) | 2 / 99 (2%) | 5 / 99 (5.1%) | 0 / 99 (0%) | 2 / 99 (2%) | 32 / 99 (32.3%) | 15 / 99 (15.2%) | 40 / 99 (40.4 %) |
| Any time prior to final infection treatment | 18 / 99 (18.2 %) | 9 / 99 (9.1%) | 5 / 99 (5.1%) | 1 / 99 (1%) | 5 / 99 (5.1%) | 51 / 99 (51.5%) | 16 / 99 (16.2%) | 73 / 99 (73.7 %) |
Appendix Table 4.
Univariate association of two levels of significantly low hemoglobin (Hgb) levels on outcomes according to time of collection.
| Hemoglobin level | Treatment success without factor | Additional surgeries with factor | Additional days in hospital with factor |
|---|---|---|---|
| Hgb <11 within 90d prior to treatment (n=96) | 6.3 (1.4, 28.2), p=0.003 | 0.1 (−0.71, 0.92), p=0.8 | 2.73 (−3.97, 9.44), p=0.42 |
| Hgb <11 during treatment (n=131) | 3.1 (0.4, 24.3), p=0.22 | 1.88 (0.72, 3.04), p=0.002 | 12.8 (3.15, 22.45), p=0.01 |
| Hgb <8 within 90d prior to treatment (n=34) | 1.4 (0.5, 4), p=0.51 | 0.41 (−0.51, 1.34), p=0.38 | 3.04 (−4.61, 10.69), p=0.43 |
| Hgb <8 during treatment (n=87) | 3.5 (1.1, 11), p=0.018 | 1.94 (1.22, 2.67), p=<0.001 | 14.44 (8.35, 20.53), p=<0.001 |
As shown in Table 4, anemia (1.17 additional surgeries, p=0.0024) and chronic pulmonary disease (1.36 additional surgeries, p=0.0020) were each associated with increased surgery for infection in multivariable analysis. Though age, tobacco abuse, sleep apnea, uncomplicated diabetes, and obesity were each associated with the number of surgeries in univariate analysis, this relationship did not persist in multivariable analysis. Also in Table 4, patients with chronic pulmonary disease (9.41 additional days, 0.0059), psychiatric disease (9.52 additional days, 0.0075), anemia (7.53 additional days, 0.0120), sleep apnea (10.40 additional days, 0.0195), and diabetes (7.94 additional days, 0.0197) stayed in hospital for treatment longer than those patients without those diseases. Though age, tobacco abuse, renal disease, and obesity were associated with increased length of hospitalization in univariate analysis, this association did not persist in multivariable analysis. Appendix Tables 5 and 6 provide details on the rates of post-treatment oral antibiotic usage in the study cohort and demonstrate chronic oral antibiotic suppression (>6 months) was considerably more common in patients with unsuccessful treatment (52%) compared to patients with successful treatment (21%).
Table 4.
Multivariable linear regression of total surgeries for infection and total time in hospital related to infection for all baseline characteristics in Table 1 and all comorbidities in Table 2 that first achieved p-value less than 0.05 in univariate analysis. Adjusted p-values compare total surgeries for infection treatment between groups with and without comorbidity. Raw proportions of patients with each comorbidity shown is shown on the left. Multivariable regression estimates with 95% CI’s demonstrate increased surgery for infection associated with each comorbidity or characteristic.
| Comorbidity or characteristic | Average (lower 95% CI, upper 95% CI) or proportion | Adjusted additional surgeries (lower 95% CI, upper 95% CI) | Adjuste d p-value |
|---|---|---|---|
| Chronic pulmonary disease | 37 / 149 (24.8%) | 1.36 (0.51, 2.22) | 0.0020 |
| Anemia | 99 / 149 (66.4%) | 1.17 (0.42, 1.92) | 0.0024 |
| Age | 60.6 (58.3, 62.9) | −0.02 / year (−0.05, 0.00) | 0.0801 |
| Tobacco abuse | 33 / 149 (22.1%) | 0.75 (−0.13, 1.64) | 0.0958 |
| Sleep apnea | 20 / 149 (13.4%) | 0.94 (−0.17, 2.05) | 0.0973 |
| Uncomplicated diabetes mellitus | 39 / 149 (26.2%) | 0.49 (−0.36, 1.34) | 0.2601 |
| Obesity | 68 / 149 (45.6%) | −0.01 (−0.77, 0.76) | 0.9814 |
| Comorbidity or characteristic | Patients with comorbidity or average | Adjusted additional days in hospital (lower 95% CI, upper 95 %CI) | Adjusted p-value |
| Chronic pulmonary disease | 37 / 149 (24.8%) | 9.41 (2.75, 16.07) | 0.0059 |
| Psychiatric disease | 31 / 149 (20.8%) | 9.52 (2.58, 16.46) | 0.0075 |
| Anemia | 99 / 149 (66.4%) | 7.53 (1.68, 13.39) | 0.0120 |
| Sleep apnea | 20 / 149 (13.4%) | 10.40 (1.70, 19.09) | 0.0195 |
| Uncomplicated diabetes mellitus | 39 / 149 (26.2%) | 7.94 (1.29, 14.60) | 0.0197 |
| Age | 60.6 (58.3, 62.9) | −0.17 / year (−0.37, 0.03) | 0.0999 |
| Tobacco abuse | 33 / 149 (22.1%) | 4.24 (−2.58, 11.07) | 0.2213 |
| Renal disease | 23 / 149 (15.4%) | 4.89 (−3.17, 12.96) | 0.2325 |
| Obesity | 68 / 149 (45.6%) | −0.16 (−6.02, 5.71) | 0.9583 |
Appendix Table 5.
Patients on antibiotic suppression >6 months after final surgery or hospitalization for infection grouped by outcome of whether or not infections were successfully treated.
| Group | Proportion using antibiotic suppression |
|---|---|
| Overall | 38 / 149 (25.5%) |
| Successful treatment | 27 / 128 (21.0%) |
| Unsuccessful treatment | 11 / 21 (52.3%) |
Appendix Table 6.
Antibiotics used in chronic suppression lasting >6 months.
| Antibiotic | Patient usage |
|---|---|
| Trimethoprim/sulfamethoxazole | 10 |
| Cephalexin | 9 |
| Minocycline | 6 |
| Ciprofloxacin | 4 |
| Doxycycline | 4 |
| Amoxicillin | 1 |
| Amoxicillin/clavulanate | 1 |
| Cefoxitin | 1 |
| Clindamycin | 1 |
| Dicloxacillin | 1 |
| Levofloxacin | 1 |
| Moxifloxacin | 1 |
A secondary analysis was performed on all infections meeting at least 2 years of follow-up after final surgery of hospitalization for infection. In univariate analysis of the reduced cohort of patients that had at least 2 years of follow-up after their final surgery or hospitalization for infection (n=95), CAD was associated with lower treatment success; anemia, COPD, lower age, obesity, and malignancy were associated with more surgery; and COPD, anemia, female gender, tobacco abuse, diabetes, psychiatric disease, obesity, and renal disease were associated with longer hospitalization. In multivariable analysis, CAD was associated with reduced treatment success; anemia was associated with more surgery (and chronic pulmonary disease approached significance at p=0.051); and anemia, chronic pulmonary disease, tobacco abuse and female gender were associated with longer hospitalization. While many of the same factors achieved univariate significance in both the reduced cohort and full cohort, differences in disease prevalence, outcomes, and sample size between the full and reduced cohort likely explain much of the differences seen between multivariable results.
DISCUSSION
This study demonstrates that coronary artery disease, anemia, chronic pulmonary disease, diabetes mellitus, psychiatric disease and sleep apnea are associated with poor outcomes in treatment for hip periprosthetic infection. While diabetes had previously been associated with poor 2-stage exchange success, none of the other risk factors for poor treatment outcome had been specifically identified. Patients, providers, and healthcare systems have an interest in understanding factors associated with poor outcomes.
Patients and providers may be most concerned with whether or not the infection is ultimately successfully treated. We found lower rates of successful treatment with anemia and coronary artery disease in multivariable analysis. Closer evaluation of anemia further demonstrated that low hemoglobin in the pre-operative and treatment periods were each associated with decreased cure rates. Patients with coronary artery disease had 23% lower rate of successful treatment than patients without coronary artery disease. From a biologic standpoint, decreased cardiovascular fitness as evidenced by diagnosis of coronary artery disease could be related to poor tissue perfusion, which may lead to impaired wound healing through decreased oxygen and inflammatory cell delivery [19]. Additionally, decreased gut drug absorption and impaired tissue micro-circulation in patients with significant heart disease may reduce drug availability at the intended target site [20, 21]. Alternatively, from the standpoint of operative planning, surgeons may choose to avoid operating on patients with significant CAD due to concerns of perioperative mortality. In this way, providers may be balancing the risks of continued infection with the risks of potential curative treatment. Patients with anemia had a 12% lower rate of successful treatment than patients without anemia. Similar to patients with CAD, anemic patients may have greater difficulty delivering oxygen to tissues [22] which may allow for growth of anaerobic organisms, impaired oxygen free-radical formation, and delayed tissue healing [19].
While successful treatment is important, patients are also impacted by the potential morbidity and mortality of each surgery they must undergo for successful treatment. Anemia and chronic pulmonary disease were associated with 1.2 to 1.4 more surgeries for infection treatment. In a manner similar to anemia, chronic pulmonary disease may decrease tissue oxygenation and degrade the healing of infected surgical sites. Although a number of other risk factors such as younger age, diabetes mellitus, tobacco abuse, psychiatric disease, and obesity were associated with increased surgery in unadjusted analyses, these factors were no longer significant in the multivariable model. This finding may suggest that these diseases have no direct role in increasing the number of surgeries for infection. Alternatively, these diseases may have a significant role in impacting the number of surgeries for infection that is not adequately captured by our sample size.
Multiple patient factors were associated with additional time spent in hospital for infection treatment such as chronic pulmonary disease, psychiatric disease, anemia, sleep apnea, and diabetes. The effects of diabetes on the immune and cardiovascular systems along with the increased medical management associated with diabetes care may account for the increased duration of hospitalization for infection. Psychiatric disease including depression and psychosis had an unexpected impact on hospitalization time. This risk factor could be related to patients’ adherence to their infection treatment regimen or a decreased motivation to leave the hospital and continue treatment in the outpatient setting. Though possible, it seems perhaps less likely that there is a physiological connection between psychiatric disease and extended hospitalization. The association of sleep apnea and greater time in hospital for infection treatment may be related to persistent infection in light of poor tissue perfusion during sleep. Alternatively, sleep apnea could be related to obesity, which has previously been shown to be related to poor outcomes in periprosthetic infection [14].
Anemia and chronic pulmonary disease were associated with more surgery and time in hospital for infection; however, we did not find a corresponding statistically significant increase in number of operations for patients with diabetes, psychiatric disease, or sleep apnea in multivariable analysis. This could suggest that patients with diabetes, psychiatric disease, or sleep apnea spend longer in hospital without having more surgery. On the other hand, the impact of these comorbidities on increased surgery may not be captured due to the limitations of our sample size.
Lastly, although we determined a univariate effect of tobacco abuse, obesity, and age on both number of surgeries and time in hospital, we did not demonstrate reproducible effect of these covariates in multivariable analysis on any of the outcomes. It is possible that these risk factors may be related to outcomes in hip periprosthetic infection treatment and that the lack of correlation in this study may be related to sample size. Additionally, the only previous study that has evaluated the effect of obesity on periprosthetic hip infection outcomes analyzed morbid obesity (BMI>40) rather than any BMI greater than 30 as in this study [14].
As with many retrospective studies, the quality of the charted data impacts the quality of the research. The optimal way to evaluate the impact of patient comorbidity on periprosthetic treatment outcome would be through prospective data collection. However, with a relatively small population of patients that develop hip periprosthetic infection nationally each year, assembling an appropriately sized cohort may be difficult without multi-center studies. One of this study’s strengths include its reliance on published treatment success and follow-up criteria. Further, the sample size is relatively large and only includes hip arthroplasties instead of combining infections from multiple sites of arthroplasty. However, although we limited our focus to patients with hip periprosthetic infection, we did not limit our scope by the type of treatment for hip periprosthetic infection. This was largely due to the vast heterogeneity in treatment that patients experience during infection treatment. For example, while a patient may have originally been treated with an incision and drainage, they may have progressed to needing a 2-stage exchange and a later explantation. However, in addition to assessing whether or not patients achieve a successful treatment, the number of surgeries or time in hospital may provide a reproducible estimate of burden experienced by the patient, provider, and healthcare system. Another limitation of this study is that soft tissue and local extremity grade, a component of the McPherson stage, could not be reported. Not all patients in this retrospective study had sufficient information in the records by which to consistently judge the quality of the tissues. The authors chose to limit reporting on the McPherson stage to only those components that could be more universally assessed and acknowledge that the tissue quality could play a role in outcomes. A final limitation of this study is the length of follow-up. Ideally, all patients undergoing infection treatment would have at least 2 years of follow-up after their final surgery or hospitalization for infection to assess persistence of treatment success. However, some patients were lost to follow-up between years 1 and 2. To address this limitation, the reduced cohort of patients with at least 2-year follow-up was also analyzed. Univariate results were similar between cohorts for each outcome, given the reduced sample size of the extended follow-up cohort. Multivariable results were also similar for the outcomes of treatment success and surgeries for infection. However, several factors that had been associated with increased length of hospitalization in the full cohort, including sleep apnea, diabetes, and age, were replaced with female gender and tobacco abuse in the reduced cohort. Alterations in prevalence, outcomes, and sample size likely drove differences between the full study cohort and the reduced cohort.
CONCLUSIONS
This is one of the first studies to correlate common patient comorbidities to poor outcomes in hip periprosthetic infection. In our tertiary care center, common patient comorbidities such as anemia, coronary artery disease, chronic pulmonary disease, diabetes, and psychiatric disease were associated with poor outcomes in hip periprosthetic infection. As patient comorbidity and demand for patient-centered, individualized care increases, comorbidity-specific information may be increasingly valuable to providers in their discussions with patients and policy makers. Several factors identified in this study such as anemia, COPD, and diabetes may be modifiable, leaving room for investigation into whether or not medical optimization could improve outcomes.
Footnotes
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