Comparing co-morbidities in total joint arthroplasty patients using the RxRisk-V, Elixhauser, and Charlson Measures: a cross-sectional evaluation

The prevalence of multi-morbidity is increasing [1, 2], thus the number of patients with multiple co-morbidities undergoing total joint arthroplasty has also increased in the last two decades [3, 4]. Studies in the United States (US) report multiple co-morbidities in 62 % of patients over 65 years old and this was associated with higher healthcare utilisation, cost, and overall impairment of patients [2]. In the US in the mid-2000s, patients older than 65 years undergoing total joint arthroplasty had an average of two co-morbidities when measured by the diagnosis based Elixhauser co-morbidity measure [3, 4]. The number of co-morbidities as well as specific conditions are related to patients’ surgical outcome during total joint arthroplasty [5‐12].

Appropriate measurement of patient co-morbidities in those undergoing joint arthroplasty is critical because of the high prevalence of co-morbidities, the higher service utilisation, and the poorer surgical outcomes in those patients. Several co-morbidity measures exist [13‐15]. The validated and most frequently used diagnostic based coding measure have been shown to be predictive of several healthcare outcomes in different study populations (including orthopaedics and other disciplines) and are used in case mix adjustment [15, 16]. Two of the most commonly used co-morbidity measures in health services and orthopaedics research are the Charlson [17] and Elixhauser [18] measures. Both measures have been used in studies evaluating total joint arthroplasty patients. Prescription medication based co-morbidity measures, such as the RxRisk-V [19] and Medication Based Disease Burden Index [20], were also developed to estimate the prevalence of co-morbidities in different populations and have been used in case mix adjustment and outcome prediction. However, to our knowledge, no previous study has evaluated the prevalence of co-morbidities in a total joint arthroplasty sample using a validated prescription-based coding measure.

Each method to ascertain co-morbidities has different strengths and limitations. For example, pharmacy based measures may provide more detailed information on active chronic conditions for which patients are actually receiving care and not only serious conditions that diagnostic based measures may identify. Further, difference in coding practices between countries may make one measure more relevant in a certain country. In Australia, for example, the use of the diagnoses based measures is limited to inpatient encounters as outpatient encounters are not recorded using the same coding system necessary for identification of the conditions. Additionally, in Australia and the US, hospital coding does not have an official mechanism to include diagnoses other than those relevant to the hospital stay [21]. This is probably avoided by countries such as the United Kingdom, which implemented in 2010 a list of required comorbidities that must be reported at the time of hospitalisation [15].

It is clinically and scientifically important to have an accurate, comprehensive, and standardized method to identify the presence of co-morbidities at the time of surgery for several reasons. One reason is to inform surgeons, healthcare workers, and patients on the proper course of treatment and risk of procedure, especially when multiple co-morbidities are present. Another is for standardizing research, specifically when evaluating outcomes in different cohorts of patients where proper case mix adjustment must be conducted to assure cohorts can be properly compared. Our study sought to (1) determine the prevalence of co-morbidities in an Australian cohort of total hip arthroplasty (THA) and total knee arthroplasty (TKA) patients using two diagnoses based co-morbidity measures (Charlson [17] and Elixhauser [18]) and one prescription based measure (RxRisk-V [19, 22]) and (2) compare the agreement of individual co-morbidities amongst the measures.

During the study period 11,848 patients underwent THA and 18,972 TKAs. Both cohorts had a slightly higher proportion of males (50.2 % THA and 52.3 % TKA) than females and procedures were mostly performed in private hospitals (95.6 % THA and 96.6 % TKA). The median age was 80.9 (IQR 76.4-84.4) years old for patients with THAs and 79.8 (IQR = 74.7-83.5) years old for patients with TKAs. See Table 1 for sample details.

The mean number of RxRisk-V, Elixhauser, and Charlson co-morbidities in the THA (5.5 (SD = 3.3), 0.9 (SD = 1.3), 0.4 (SD = 0.8), respectively) and TKA (5.4 (SD = 3.5), 0.9 (SD = 1.2), 0.4 (SD = 0.7), respectively) cohorts was similar. The three most common co-morbidities identified by the RxRisk-V were also similar between the THA and TKA cohort, and included: the musculoskeletal conditions of pain treated with anti-inflammatories (58.7 % THA, 55.9 % TKA) and pain treated with narcotics (55.0 % THA, 50.9 % TKA), and cardiovascular diseases that involved treatment with anticoagulation agents (52.6 % THA, 48.4 % TKA). Using the Elixhauser measure, the three most prevalent co-morbidities in patients having a THA were: hypertension (22.0 %), arrhythmias (14.7 %), and fluid and electrolyte disorders (7.5 %); in patients with TKAs the conditions were: hypertension (23.4 %), arrhythmias (13.5 %), and diabetes with chronic complications (7.7 %). Using the Charlson measure the three more prevalent co-morbidities of THA and TKA patients were: uncomplicated diabetes (7.1 % THA, 8.1 % TKA), diabetes with chronic complications (6.3 % THA, 7.3 % TKA), and chronic pulmonary disease (5.5 % THA, 4.5 % TKA). See Table 2 for co-morbidities by the three measures and overall prevalence of conditions.

Combining all measures, 64 unique co-morbidities were identified, of these 28 were only identified using the RxRisk-V, 11 using the Elixhauser, and 2 using the Charlson. The most prevalent co-morbidities only identified by the RxRisk-V included: pain treated with anti-inflammatories, pain treated with narcotics, gastric acid disorder, hyperlipidaemia, ischemic heart disease (both in combination with angina and hypertension), diseases treated with antiplatelets agents, and reactive airway disease. The most prevalent co-morbidities only identified by the Elixhauser included fluid and electrolyte disorders, obesity, valvular disease, history of solid tumour, deficiency anaemia, and obesity. The co-morbidities only identified by Charlson were cerebrovascular disease and myocardial infarction. See Table 2 other co-morbidities specific to each measure.

There was a relationship between co-morbidity measures for common indicators (Table 3). RxRisk-V and the Elixhauser, diabetes was the only co-morbidity with substantial agreement between the two measures (κ = 0.63, 95 % CI 0.60-0.65 for THA, κ = 0.61, 95 % CI 0.59-0.63 for TKA). Similarly, when comparing the RxRisk-V and Charlson measures, diabetes was also the only co-morbidity with substantial agreement ((κ = 0.63, 95 % CI 0.60-0.65 for THAs, κ = 0.61, 95 % CI 0.59-0.63 for TKAs). When comparing the two diagnostic based measures, the Charlson and Elixhauser, for conditions that did not have identical coding, the agreement was almost perfect for uncomplicated diabetes, diabetes with chronic complications, peptic ulcer disease, rheumatoid arthritis, and renal failure. Mild liver disease had substantial agreement between the Charlson and Elixhauser (κ = 0.71, 95 % CI 0.56-0.85 for THA and κ = 0.76, 95 % CI 0.67-0.86 for TKA).

The co-morbidity measures evaluated in this study yielded a different prevalence of co-morbidities and a wider variation of agreement between common conditions. In our sample of joint arthroplasty patients, the most common non-musculoskeletal related co-morbidities were hypertension, disorders that required anticoagulation agents, and gastric acid disorders. We also found that except for diabetes, which had good agreement between all three co-morbidity measures, there was low agreement between the prescription based RxRisk-V and the diagnoses based Elixhauser and Charlson co-morbidity measures for common conditions. As expected, between the diagnoses based measures there was substantial agreement between common co-morbidities.

The prevalence of co-morbidities identified using all three measurements had some commonalties and differences with previous estimates in large cohorts of joint arthroplasty patients. Some of the differences are attributed to the method of co-morbidity ascertainment in different studies. The Elixhauser was the more commonly used measure in the published literature, such as Cram et al.’s US Medicare studies [3, 4], Kapoor et al.’s US DVA study [31], the Kaiser Permanente Total Joint Replacement registry studies [32, 33], and the US California and New York states cohort studies by Dy et al. [34, 35]. But other studies have used different measures, such as an US Medicare TKA study that used a combination of the Charlson and Elixhauser [5], and a Finish arthroplasty registry study that used a non validated measure inclusive of diagnostic codes, medication prescriptions, and drug reimbursement for certain conditions [36]. Most studies reported similar high prevalence of hypertension (range 43–70 %) [5, 31‐35], but the Finish study reported a lower prevalence (17.7 % in THA and 20.8 % in TKA) than these cohorts and ours [36]. The prevalence of diabetes was also similar between our cohort (approximately 10–13 %) and those of the US California and New York states studies (9 % in the THAs and 14 % in TKAs), US Medicare THA cohort (10-15 % recent estimates) and the Finish arthroplasty registry cohort (5.5–7.5 %) [3, 35, 36]. This prevalence was lower, however, than those reported by other US cohorts (Kaiser Permanente registry 18–26 %, Medicare TKA patients 22 %, and DVA patients 26.5 %) [4, 5, 31‐33]. The Kaiser Permanente registry ascertained diabetes using the institution’s diabetes registry, instead of the Elixhauser, which could contribute to the higher prevalence reported [37]. The prevalence of congestive heart failure (approximately 13 %) was also similar to that reported by one study reporting on the US TKA Medicare cohort (10 %) [6], but higher than all other studies reviewed (range 2.6–5.2 %) [3, 4, 31‐36]. Conversely, the prevalence of chronic obstructive pulmonary disease in our sample (5–6 %) was lower than that reported by almost all cohorts (range 11–18 %) [6, 31, 34, 35] but the Finish arthroplasty register cohort (6–8 %) [36]. While there are obvious differences in the cohorts of patients in these studies, disease prevalence differences are also due to how they were identified- highlighting the need for consideration in how co-morbidities are determined.

There was only a strong agreement between the co-morbidities identified by the Risk-V and the Elixhauser (10 common conditions) and Charlson (6 common conditions) for the condition of diabetes. For all other conditions, the agreement was weak. This agrees with previous comparison of the Charlson and RxRisk-V in a cohort of older non-joint arthroplasty Australian veterans [38]. Because of this lack of agreement amongst measurements, it is necessary to understand what measurements are used by different studies. A study using only diagnoses based measures to identify congestive heart failure, hypertension, or depression will underestimate these diagnoses, while a study using only RxRisk-V will underestimate renal disease/failure, dementia, and alcohol abuse. Underestimating these co-morbidities can impact study estimates. Utilizing validated measures is also recommended to insure that comparisons with other study results are possible.

In addition to the differences in prevalence estimates of co-morbidities, there are further considerations for choosing the appropriate measure for co-morbidity ascertainment. Specifically, within the 64 co-morbidities identified by all measures used in this study, 28 were only identified by the RxRisk-V, 11 only by the Elixhauser and two only by the Charlson. Studies requiring the nutritional/obesity related co-morbidities such as obesity, blood loss anaemia, deficiency anaemia, fluid and electrolyte disorders, and weight loss should use the Elixhauser measure. Studies that require detail on the specific cardiovascular disease a patient is actually being treated for (e.g. anticoagulation disorder, hyperlipidaemia, ischemic heart disease/angina, and ischemic heart disease/hypertension) should use the RxRisk-V for obtaining this information. The Charlson measure should be used if history of myocardial infarction or cerebrovascular disease are needed. If a more comprehensive understanding of a patients’ co-morbidity profile is necessary, we suggest using all the co-morbidity indices for both the inclusion of a greater number of conditions and a likely greater sensitivity in identifying certain conditions and provide greater amount of information to conduct case mix adjustment.

This study has several limitations. Information bias due to our use of administrative data, which can suffer from coding errors, missing data, linkage problems, and lack of detailed clinical information, was possible. Additionally, because of our sampling frame the prevalence of co-morbidities in our study may not be representative of the greater population undergoing joint arthroplasty in Australia or in other countries, where the median age of TKA and THA cohorts is between 65 and 70 years old. Ours is a sample of patients who are members of the Australian DVA system, an older patient population due to how their benefits are granted. Due to their older age we would expect them to have more co-morbidities than younger arthroplasty cohorts. However, they are representative of an increasingly greater number of patients undergoing joint arthroplasty later in life and therefore offer valuable information in regards to this specific demographic.

Our study strengths included the utilisation of a captive membership population with a comprehensive database of prescription medications dispensed to its members. Due to the nature of DVA services payments, all the hospitalisation and prescriptions our cohort of patients obtains within Australia is captured. A further strength of our study is that a previous validation study has shown acceptable results in using the RxRisk-V in the Australian population in identifying co-morbidities as compared to self-reported conditions [25]. Finally, all patients in our study cohort have unique identifiers, minimizing the likelihood of data handling bias when linking their hospitalisation, demographic, and hospitalisation information.