The rates of THA are projected to increase between 43% and 70% from 2014 to 2030, with a corresponding proportional increase in rTHA [4]. Our analysis of 2006 to 2014 data demonstrated a 69.50% increase in patients receiving THA and a 28.50% increase in rTHA. 2015 data were not included in these calculations as they do not contain data from the full year. The increase in patients receiving these procedures may be indicative of expanding indications and increasing demand for THA, potentially driven by an aging population, higher rates of diagnosis and treatment of osteoarthritis, increased demand for improved quality of life [2], and generational improvements in implant design and longevity. As the volume of both index and revision procedures increases yearly, and as reimbursement systems shift towards value-based models, a heightened focus has been placed on resource utilization and quality of care delivery. In the setting of THA, this transition translates into a focus on limiting wasteful resource consumption while simultaneously improving perioperative outcomes. A failed THA results in a rTHA performed in an inpatient setting, with a reported average cost of $75,037 per procedure, totaling over $30,000,000,000 from 2006–2015. In order to decrease this revision burden, there is a need for continuously improving the technical aspects associated with the procedure in addition to perioperative medical and social optimization of THA recipients. Such optimization efforts would be substantially challenging without an in-depth understanding of the major demographic and medical comorbidities of this patient population.
While primary THA is considered one of the most successful procedures in medicine, its failure and subsequent revisions pose a significant negative impact on patient quality of life and the healthcare system. In this study, an increase in the rate of rTHA was noted between 2006 and 2015, with dislocation constituting the most common reason for revision during that time period. Previously reported literature also reported similar findings for reasons for rTHA [7, 8]. Ulrich et al. reported that in a cohort of 237 rTHA, 50% of patients had the revision surgery within five years of the index procedure. Furthermore, most revisions were performed for instability (33%) and infection (24%) [7]. The most crucial independent variable which may predispose patients to dislocation is implant position, specifically positioning of the acetabular shell [9, 10]. Robotic assisted THA, which helps with implant positioning, has been shown to help reduce dislocation rates compared to conventional THA. Shaw et al. found, in their cohort of 2247 patients, that the dislocation rate 0.60% with robotic assisted THA vs. 2.50% in the conventional THA cohort [11]. In addition to technical factors, patient characteristics and comorbidities also play a role in dislocation rates. Obesity, dementia, depression, Parkinson’s disease, chronic lung disease and inflammatory arthritis, amongst other factors, serve as independent risk factors to dislocation following THA [12, 13]. Preoperative optimization of these independent risk factors as well as taking care intraoperatively to ensure appropriate implant position may serve to decrease overall need for rTHA.
There are currently about 46 million geriatric adult patients living in the United States. This number is expected to grow to about 90 million geriatric adults based on a 2050 projection estimate [14]. In the years between 2020 and 2030 alone, the projection estimates another 18 million geriatrics adults [14]. Our study data revealed that the average age of patients undergoing pTHA was 65.98 and 68.54 for rTHA. As the population ages and “baby boomers” reach geriatric age, and the reported average age of THA is about 65 years, there is a projected significant increase in the amount of THA and corresponding increase in rTHA procedures. Increasing age would also account for an increased amount of comorbidities which would further impact rates of complications in hip arthroplasty patients.
With regards to other demographic variables, the findings of this study were in line with previously reported literature in terms of average age, gender distribution, and primary payor type, among other variables [15]. In terms of hospital location, we noted a higher rate of procedures performed in the urban setting, with a total of 89.90% for THA and 92.44% for rTHA. More interestingly, teaching institutions constituted a notably different rate between the procedures, with 56.72% for rTHA compared to a 49.27% for THA. While referral patterns of complex revisions for reasons such as lack of expertise or resources at smaller non-teaching centers may in part explain these differences, there are likely also financial incentives at play [16]. Previous literature has demonstrated that contemporary reimbursement models fail to adequately compensate for the additional resource utilization and costs that rTHA requires relative to THA [17, 18]. As such, non-teaching institutions may be incentivized to “cherry pick” primary procedures while “lemon dropping” the more complex and less financially rewarding revision procedures, consequently offloading the additional revision burden to teaching institutions [17, 19, 20]. Such financial disincentives have the potential to lead to access to care issues, and our findings support the idea that the Current Procedural Terminology (CPT) coding system and RVU allocation for revision procedures should be reexamined to better align incentives [21].
This study reported on all comorbidities that constitute the Elixhauser comorbidity index, which has been extensively utilized in epidemiological studies and proven to be a superior tool for outcome prediction at the population level [6, 22, 23]. Understanding the medical comorbidity profile of THA and rTHA recipients might allow for improved preoperative optimization protocols and for development of appropriate risk stratification models that can subsequently guide establishment of fairer reimbursement models. A study by Dlott et al. reported reduced LOS and less subsequent emergency department visits among patients who underwent preoperative optimization [24]. While arthroplasty literature assessing correlation of various medical comorbidities with postoperative outcomes is quite extensive, a complete delineation of the impact of major comorbidities and their interaction on postoperative outcomes remains lacking. The data noted the most common comorbidities in both THA and rTHA groups to be hypertension, obesity, chronic pulmonary disease, hypothyroidism, uncomplicated diabetes, deficiency anemia, fluid/electrolyte imbalance and depression.
Obesity, one of the most studied comorbidities in arthroplasty, was found to be the second highest comorbidity in the THA group and the sixth highest in the rTHA group. Prior studies have shown that increasing BMI is associated with increased LOS, costs, and intraoperative blood loss, which in turn can lead to a variety of postoperative complications [25].
Other comorbidities such as diabetes have been shown to affect postoperative outcomes. Lovecchio et al. elucidated the increased risk of medical complications in both insulin-dependent and non-insulin-dependent diabetics [26]. Insulin dependence was also described to be associated with higher readmission rates. Additionally, hypertension has a variable effect on cardiac complications. A systematic review by Elsiwy et al. discussed four independent articles evaluating the effect of hypertension on hip and knee joint arthroplasty outcomes, with two showing a positive correlation and two exhibiting no effect. The authors concluded that history of cardiac disease bore the strongest association with postoperative cardiac complications [27].
Optimization from these pre-existing comorbidities may lead to decreased length of stay duration, decreased post-discharge ED visits, and provide value by reducing cost burden to the health care system.
More recently, the implementation of advanced data analysis tools, such as machine learning algorithms, to better understand impact of a combination of various medical comorbidities on postoperative outcomes has been popularized [28,29,30]. Harris et al. demonstrated an accurate predictive model for mortality and complications following joint arthroplasty with patient-specific variables [31]. Such efforts would benefit from a better understanding of critical comorbidities, which could potentially be incorporated as predictive variables for these algorithms and would allow for the development of risk-stratification models with increasing accuracy. Once such models are available, and once postoperative outcomes can be predicted with relatively high accuracy based on comorbidity profiles, patient-specific payment models with a more distributed reimbursement system can be implemented. Ramkumar et al. utilized a preliminary Bayesian machine learning model trained with patient factors to forecast LOS and to quantify patient risk. The algorithm proposed a staggered payment model that reimburses based on patient risk level to reduce patient selection bias and promote access [32]. Such reimbursement models would diminish concerns for provider “cherry-picking” through fairly accounting for the heightened risk undertaken by surgeons performing THA and rTHA on a more complex population.
Similar to most large database cross-sectional observational studies, this study has several limitations. While the NIS supplies a large amount of healthcare and resource utilization data at the population level, it remains prone to frequent errors due to reliance on suboptimal coding systems and human manual entry of data [33]. Despite this inherent potential weakness, the database has been validated for complication and comorbidity data, and is considered an excellent tool to conduct population-based observational epidemiological studies. Additionally, the NIS is strictly limited to inpatient data, and hence NIS does not allow for a complete assessment of postoperative clinical and economic outcomes beyond the immediate in-hospital period. While this was not an initial aim for this study, a better understanding of the long-term postoperative course would further help in improving optimization efforts, and subsequent studies could focus on shedding further light onto that aspect of the episode of care. Furthermore, the NIS registry does not provide information such as operative technique, preoperative diagnostic information or intraoperative complications. This data would be helpful in further highlighting reasons behind complications following hip arthroplasty.
Despite its inherent limitations, the present study has considerable strengths in design and analysis. To the authors’ knowledge, this study constitutes the largest available comprehensive report at the population level delineating the notable medical comorbidities of THA and rTHA recipients. The substantially large volume of data and long duration of the study provide a generalizable cross-sectional understanding of the demographics, comorbidities, clinical and economic outcomes following THA and rTHA, in addition to the type and reason for rTHA. This knowledge can potentially empower clinicians with a deeper understanding of perioperative conditions that might impact postoperative outcomes and allow for development and implementation of perioperative optimization pathways geared to target these conditions.
