Introduction

Total hip arthroplasty (THA) is performed in 200 patients per 100,000 population in Organisation for Economic Cooperation and Development (OECD) countries yearly, which makes it one of the most common orthopedic surgeries [1]. The number of patients undergoing THA is continually increasing, and THA's efficiency is on the rise [2]. One of the causes of increasing effectiveness is a better assessment of a patient's health status to provide more personalized treatment based on their risk factors. According to research, 83.7% of patients undergoing hip surgery suffer from comorbidities [3]. Researchers indicate that concomitant diseases can affect the outcome of THA, including postoperative complications, risk of reoperation, cost of patient's treatment, future mobility of the patient, and outcomes represented by joint-specific measures including: Western and McMaster Universities Osteoarthritis Index (WOMAC), the Hip Disability and Osteoarthritis Outcome Score (HOOS), the Harris Hip Score (HHP), the Oxford Hip Score (OHS) and the Mayo Hip Score (MHS) [4]. Hence, the in-depth evaluation of comorbidities is vital for predicting THA outcomes [5]. The comorbidity index used for clinical practice should have simple computation, and data used for estimating should be easy to obtain. Most comorbidity indices are based on the International Statistical Classification of Diseases and Related Health Problems (ICD-10) coding, which provides better data assembling. ICD-10 codes are also collected in medical databases, which could be helpful for population-based or retrospective studies. Data for creating comorbidity indices could be obtained from a patient's exam, medical history, or prescription data, and the diseases used for estimating comorbidity indices should have a high prevalence and proven impact on THA outcome. There are also attempts to quantify comorbidities' influence by using weights assigned to each comorbidity to provide better risk assessment. Demographic factors such as age, body mass index (BMI) are often included in comorbidity indices [6].

Methods

The systemic search of medical databases Embase, PubMed, and Web of Science was conducted by two independent researcher’s MP and WK. To find the most valuable and recent data, we estimated the following search criteria: articles must be written in English, published between 2016 and 2020, and contain the following keywords: "HIP," "ARTHROPLASTY", "REPLACEMENT" linked with the keyword "COMORBIDITY INDEX" using the operator "AND". Articles in which THA was performed for femoral neck or acetabular fracture were excluded from research using the operator "NOT" and phrase "FRACTURE" in search criteria. Animal studies were also excluded using the operator "NOT" and the phrase "ANIMAL" and "ANIMALS". From the obtained literature collection, initial titles and abstracts selection were performed. The second step was to screen full texts and exclude publications that do not measure comorbidities' impact on THA outcomes and review articles. The last step was to choose publications that discuss the impact of comorbidity in clinical practice, including predicting postoperative complications, adverse events, physical status, quality of life revision rate, length of hospitalization, risk of readmission, and mortality in different periods. Data from the last collection was extracted into Table 1 to present a comprehensive overview of the most recent assessment tools. A search of reference lists of identified articles was performed to identify other relevant studies. This additional search aimed to find other, less often used indices, which could be a valuable tool for patient's health assessment.

Table 1 Comorbidity measurement tools used in THA studies
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Results

The search resulted in the identification of 564 publications suitable for initial criteria. A further selection of the final 26 publications is presented in Fig. 1. In this review, the majority of publications (23/26) were retrospective studies. This systematic review's primary purpose was to find recently used tools for assessing a patient's comorbidity. The investigation revealed the following indices, presented with the frequency of their appearance: The Charlson Comorbidity Index (18/26), Society of Anesthesiology classification (10/26), Elixhauser Comorbidity Method (6/26), and modified Frailty Index (5/26). The following outcomes were measured: quality of life and physical function (8/26), complications (10/26), mortality (8/26), length of stay (6/26), readmission (5/26), reoperation (2/26), satisfaction (2/26), blood transfusion (2/26), surgery delay or cancelation (1/26), cost of care (1/26), risk of falls (1/26), and use of painkillers (1/26). The selected articles are listed in Table 1.

Fig. 1
figure 1

Summary of search and review process

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A Further examination of reference lists of 26 identified articles and combining them with systemic research resulted in creating a list of 11 indices suitable for predicting THA's outcome. The background information on the creation of each clinical tool and its essential characteristics is summarized in Table 2. The indices are subdivided into four categories depending on the tool’s scope. The index can be based on diagnosis, medical and demographic factors, prescription data, or general health status. The scoring method can vary between authors for the same clinical tool; in Table 3, the recommended scoring methods are described. Table 4 shows a detailed description of each instrument assessed in this review in the aspect of THA. The clinical conditions rated in each of the comorbidity indices are listed in Table 5. This systematic review revealed high heterogeneity in the methods used to assess THA patients' comorbidity, resulting from a lack of clinical guidelines.

Table 2 Background information on comorbidity measurement tools
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Table 3 Scoring methods of comorbidity measurement tools
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Table 4 The use of comorbidity measurement tools in total hip arthroplasty studies
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Table 5 Clinical conditions rated in comorbidity indices
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Discussion

The THA is one of the most common surgeries worldwide that 1–3% of patients aged over 65 years will undergo in their lifetime [12]. Due to the high effectiveness in improving patients functioning and quality of life, the procedure was described in 2007 in "The Lancet" as "Operation of the Century" [80]. Currently, the age of patients undergoing THA increases, as is the comorbidity burden [81]. In a systematic review conducted by Buirs et al. [82], 11 out of 13 studies (84.62%) showed a significant negative relationship between comorbidities and postoperative hip function. In another review by Olthof et al. [83], multimorbidity predisposed to the longer hospital stay after THA, and in 8 out of 9 studies, the relationship was statistically significant. In all out of two eligible studies, comorbidities were associated with a higher cost of care. Also, cognitive status and mental health before surgery can affect the functioning after THA. Psychiatric disorders are associated with less satisfactory functional outcomes and less improvement in life quality, pain and satisfaction after surgery, prolonged hospitalization, complications, and increased mortality [84, 85]. Undeniably, the coexisting diseases can impact THA results, both traditional outcomes like mortality, risk of adverse events, or revision, and patient-oriented outcomes such as quality of life, physical function, and satisfaction [4]. Identifying patients at high risk of complications can lead to adequate qualification for the procedure and initiation of more rigorous prophylaxis. On the other hand, low-risk patients could be subjected to fast-track surgery, reducing the length of stay and care-related costs [58]. The current methods used to assess health status among patients qualified for THA are very diverse among the authors, making it difficult to compare individual results in a pooled analysis. This review is intended to facilitate the selection of the appropriate tool and its proper application. Table 6 represents the summary of the strengths and limitations of included comorbidity assessment methods.

Table 6 Strengths and weaknesses of comorbidity indices used in THA studies
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The most commonly used comorbidity measure in THA patients is the ASA classification, and the second one is the CCI. These clinical tools often serve as a reference point for measuring other indices' performance, including mFI and ECM. Both ASA and CCI can successfully predict the THA outcomes such as quality of life, physical function, complications, mortality, length of stay, and hospital readmission. Nevertheless, the ASA classification was more predictive than CCI when indices were compared in terms of adverse events (any, minor and serious), length of stay, and discharge to the higher level facility after THA. The ASA could better reflect patients' health status because of its dynamic assessment of comorbidities, while indices like CCI only note the presence of the disease. The CCI, an objective, diagnose-based measure, has less predictive power than a subjective tool like ASA. However, the ASA class had less discriminative ability than age in all the aforementioned outcomes. The available variants of CCI are presented in Table 7 [12, 57].

Table 7 Development and changes in CCI modifications
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The recent publications demonstrate that the ASA score has a good predictive value, but it could present significant discrepancies over time because of its dynamic and subjective nature [12]. That is why other indices like ECM are still under investigation. The ECM is based on ICD codes, which can be acquired from administrative data, unlike the ASA score, collected and assessed prospectively. The ECM is the third most commonly used comorbidity index in THA studies. It outperformed CCI and mFI to predict serious complications, e.g., sepsis, myocardial infarction, bleeding, mortality, mechanical complications, infection, extended length of stay, and discharge to the facility [28]. Also, comparing to ASA, it can be a better predictor of outcome after orthopedic surgery [86]. However, the complexity of 30 variables that could provide a broad perspective of the patient’s preoperative health status could lead to data collection difficulties. Using scores consisting of many variables could provide a situation when comorbidities with different impacts on THA are put on equal. That is why creating appropriate weights was made, but studies do not prove the additional utility of weighted scores [28].

Another example of an index that should also be considered in THA patients is the modified Frailty Index (mFI). With aging, the comorbidities burden increases, catabolic processes exacerbate, and the physiological reserve and resistance to stressors such as surgery declines. This state of organism exhaustion is referred to as frailty. The mFI is used to assess multimorbidity and frailty, and it is available in a version containing eleven components (mFI-11) and in a shortened version consisting of five items ("mFI-5"). Both versions effectively predict increased risk of prolonged hospitalization, complications, and reoperation after THA [61]. Due to its easy estimation, objectivity, and good predictive value of surgery outcomes, mFI is a promising clinical practice tool. It can be obtained retrospectively from medical records ICD coding. Previous studies have shown that mFI is a stronger predictor than age or ASA in predicting the length of hospitalization, complications, reoperation, and mortality after THA [17]. The mFI was recently proven to predict long-term functional outcomes (WOMAC) and length of hospital stay in patients after THA [18].

Other, less frequently used indices deliver a more diverse image of a patient's health status and provide additional predictive value than the beforementioned clinical tools. For example, the Functional Comorbidity Index (FCI) can predict postoperative patients' physical function and quality of life after THA. It includes aspects like obesity or mental status and focuses on physical function limitation. However, its predictive ability does not find reflection in recent studies, and it is not widely used in clinical practice. Moreover, The FCI, compared to CCI, has a worse predicting ability of mortality after THA [27]. Another less-commonly used index is RxRisk-V, a proven predictor of THA outcome with a unique calculation method based on a patient’s prescription data. The RxRisk-V provides good predictive value, as well as easy data collection. However, a medication-based index can lead to misclassifications when one medication is given to cure two comorbid diseases or medicament is given “off label” [42]. The Index of Coexistent Disease (ICED) is an example of an index considering both physical and functional status, but it is rarely used in orthopedic literature [32]. The Cumulative Illness Rating Scale (CIRS) differs from other indices because it rates each separate human body system. It could be a reliable and valid instrument for assessing comorbidity in THA patients. As a fast, objective, and easily quantified index, it is well suited to various research uses. [25]. As well as some lesser-known indices we presented in this review, demographic factors have a significant ability to predict the outcome of THA. Measurement tools like RRATHR and CMS-HCC combine demographic factors like age with comorbidities to create a more comprehensive reflection of a patient's health status. However, RRATHR was found to have no proven predictive value in THA, according to recent literature. Furthermore, their overwhelming complexity excludes them from everyday clinical practice instruments and adjusting care for patients' needs [54].

Studies discussing comorbidity indices' effectiveness highlighted that indices used in everyday practice should remain as easy as possible. Too many factors included in the index could lead to errors and hinder data assembling. Additionally, the index should be legible and straightforward for clinicians to provide a convenient and fast evaluation. That is why ASA and CCI are still widely used even though they do not precisely reflect a patient's health status. In opposition to more specific ones, general indices help assess which patient should receive more intensive peri/postoperative care. Using general indices also avoids the risk of equalizing different conditions in patients with the same comorbid disease [32]. Despite the variety of comorbidity assessment methods and measured outcomes, the majority of recent studies presented in this systemic review confirm the predicting ability of different comorbidity indices and convince that assessing patients' comorbid diseases is vital in clinical practice. This study does not contain all available comorbidity indices like Chronic disease score (CDS), Kaplan Feinstein Classification (KFC), Health-related Quality of Life Comorbidity Index (HRQL-CI) due to their absence in the orthopedic literature [87, 88].

Conclusions

  1. 1.

    The most commonly used comorbidity indices in THA studies are CCI and ASA.

  2. 2.

    Currently, researchers focus not only on mortality and complications but also on the quality of life, function, and patient satisfaction after THA.

  3. 3.

    There is high heterogeneity in the methods used to assess the health status of THA patients.

  4. 4.

    Comorbidity indices should be an integral part of clinical practice because it allows predicting the risk of complications and the THA's functional outcome.

  5. 5.

    Less common comorbidity indices may also prove useful for researchers in THA studies.