The association between obesity and risk of complications after elective total joint arthroplasty (TJA) is clear. Numerous studies confirm a greater risk of medical, surgical, and wound complications after TJA in individuals with obesity or severe obesity than for those whose body mass index (BMI) is within the normal range [25-27, 42, 44]. Less is known about the risks of surgery for the minority of underweight patients undergoing TJA, although increased risk of complications has been documented in this group for other major surgical procedures . Although the financial  and functional burden of a high BMI in TJA has been demonstrated [2, 13], little is known about the association between BMI and mortality after surgery.
Studies using registry data sets have reported the paradox of a lower risk of all-cause mortality between 30 days and 1 year after TJA in overweight versus normal-weight patients as well as a higher risk for underweight patients [18, 40]. The results for obese groups, however, are more variable with some reporting an association between severe obesity and higher risk of all-cause mortality 1 year after TJA  and others showing no association between obesity and mortality within the first 90 days . Population-level data also suggest that being overweight confers a lower risk of all-cause mortality relative to those who are normal weight, whereas being underweight or severely obese, a higher risk of mortality is demonstrated . The inherent limitations in population-level studies and registry reports are notable and include a large number of missing values  and underrepresentation  or misrepresentation of subgroups . Furthermore, we could find no evidence of an association between BMI and all-cause mortality after TJA beyond 1 year. Understanding BMI-mortality associations after TJA allows providers to evaluate current practices and to identify patient groups who might benefit from nutritional intervention before surgery.
The purpose of this study, therefore, was to examine, in the context of two large, independent arthroplasty registries on different continents, the association between longer term all-cause mortality and underweight, overweight, and obese patient groups undergoing TJA.
Materials and Methods
A retrospective study using data from the Kaiser Permanente Total Joint Replacement Registry (KPTJRR) and the St Vincent’s Melbourne Arthroplasty Outcomes (SMART) Registry was conducted. The KPTJRR covers the largest integrated healthcare system in the United States . Data collection procedures, coverage, participation rate, and data and tools available from the KPTJRR have already been published [31-33]. Briefly, the KPTJRR was implemented in 2001 and covers all joint arthroplasties performed in the Kaiser Permanente membership population of > 10.6 million patients  in seven geographic US regions (ie, Southern California, Northern California, Northwest, Hawaii, Colorado, Georgia, and Mid-Atlantic). The Kaiser Permanente membership population has been shown to be mostly sociodemographically representative of the larger geographic areas it covers [20, 21]. The registry reported a > 95% voluntary participation rate in 2011 .
The SMART Registry is a clinical registry in Australia that captures information from surgeons performing joint arthroplasty at one major metropolitan hospital. Data collection procedures, outcome measures, and participation rates have been previously described  and participants are demographically representative of the Australian patient population . Registry data collection started in 1998 and > 11,000 procedures are now registered with 800 new yearly registrations. The Registry has complete capture of all pre- and postoperative encounters and achieves 98% followup of patient-reported outcome measures at 1 year [10, 11].
Adult (aged ≥ 18 years old) patients undergoing primary TKA and THA for any elective indication for surgery registered between January 1, 2002, and December 31, 2013, were included in the sample. Patients who underwent same-day bilateral THA or patients who underwent hemiarthroplasty were not included.
The final KPTJRR TKA cohort (n = 109,333) included arthroplasty procedures from 51 hospitals and 450 surgeons and the final SMART TKA cohort (n = 3,453) included arthroplasty procedures from one hospital and 26 surgeons. The median age of the KPTJRR cohort was 67 years (interquartile range [IQR], 61-74) and 62% were women, whereas the median age of the SMART cohort was 71 years (IQR, 64-77) and 66% were women (Table 1). There was a higher proportion of obese (class I-III) patients in the SMART Registry cohort (65%) than in the KPTJRR (55%). The KPTJRR cohort had a median followup of 4 years (IQR, 1.8-6.6; range, 0-12 years). The SMART cohort had a median followup of 5 years (IQR, 2.5-7.6; range, 0-12 years). No patients were lost to followup in either cohort.
The final KPTJRR THA cohort (n = 57,049) included arthroplasty procedures from 51 hospitals and 379 surgeons and the final SMART THA cohort (n = 2950) included arthroplasty procedures from one hospital and 26 surgeons. In the KPTJRR cohort, the median age was 66 years (IQR, 58-74) and 58% were women (Table 1). In the SMART cohort, the median age was 69 years (IQR, 61-75) and 59% were women. There was a higher proportion of obese patients (class I-III) in the SMART cohort (45%) than in the KPTJRR cohort (40%) (Table 1). The median followup in the KPTJRR cohort was 4 years (IQR, 1.9-6.9; range, 0-12 years). The median followup in the SMART cohort was 5 years (IQR, 2.4-7.6; range, 0-12 years). No cases were lost to followup in either cohort.
The main exposure of interest was BMI. The World Health Organization definitions were used to group patients according to their intraoperative BMI as follows: underweight (< 18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), obese class I (30.0-34.9 kg/m2), obese class II (35.0-39.9 kg/m2), and obese class III (≥ 40 kg/m2). BMI was captured by the KPTJRR using Kaiser Permanente integrated healthcare system electronic medical records. The body and height measures closest in date to the patient’s surgery were used for the BMI calculation. In the SMART Registry, BMI was obtained from the anesthetist’s chart on the day of surgery.
The outcome of interest was all-cause mortality from the day of surgery to the end of the study followup (December 31, 2013). Kaiser Permanente analytical data sets were made available postrelease of their 2014 report and the current study was subsequently started in late 2015. The KPTJRR captured mortality from the electronic medical records of people who are Kaiser Permanente members and the enrollment/membership roll of the institution yearly when analytical data sets for the KPTJRR are prepared. The Kaiser Permanente enrollment/membership files are validated against the US Social Security Administration periodically. The exact date of death is captured.
The SMART Registry captured mortality data from the hospital medical records on an ongoing basis. In addition, on an annual basis, death data are verified using data obtained from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) on all TJAs performed at the hospital. The AOANJRR link their data with the National Death Index (NDI), which is a Commonwealth database that contains records of deaths registered in Australia since 1980. The NDI draws this information from the Registrars of Births, Deaths and Marriages in each jurisdiction, the National Coronial Information System, and the Australian Bureau of Statistics. Exact date of death is captured.
Covariates of interest were baseline demographic and patient characteristics including age (per 10-year increment), sex, race (in the Kaiser Permanente registry specifically), Australian-born (in the SMART Registry specifically), American Society of Anesthesiologists (ASA) Physical Status Classification , year of operation (per 1-year increment), surgical indication (osteoarthritis, rheumatoid arthritis, osteonecrosis, posttraumatic arthritis, and other), and comorbidities. These data were obtained from the patient’s medical record for entry into the Registries. In addition, in the SMART Registry, smoking status, socioeconomic status (SES) using the fAustralian Bureau of Statistics SES index for area scores (1-10) , and rurality using the geographic accessibility index (ARIA+)  were covariates of interest. In both SMART and KPTJRR, diabetes was evaluated as a confounder.
All analyses were conducted independently for each registry and by THA and TKA cohorts and pooling of registry data was not performed. Our intent was to present the results of both cohorts separately so that local-level data (either United States or Australia) could be used by clinicians to inform their decision-making around TJA. Each study cohort was described using frequencies, proportions, means, SDs, medians, and IQRs. Followup was defined as the difference between the joint replacement surgery date and the date of death or the end date of the study period. Survival analyses were performed using Kaplan-Meier curves and Cox proportional hazard models to assess risk of death by each weight group with normal weight being the reference category. All variables associated with mortality (p < 0.2) were included in the final models as possible confounders. ASA Physical Status Classification scores were collapsed a priori (1 and 2 versus ≥3). Post hoc analysis using ASA levels 1 to 4 (ASA 5 was grouped with 4 because there were < 10 cases) revealed minimal differences (data not shown) and therefore the original analyses are presented. Effect modification by age group and gender was also investigated. Proportionality assumptions for survival by obesity level were verified testing the interaction of the obesity variable with time and visually assessed using log(-log[survival]) versus log of survival time graphs. To account for potential lack of independence among the observations, the model was adjusted for surgeon clustering . We used a fixed effect-only model and used robust standard errors (to adjust for the nesting of surgical cases within surgeons). Arthroplasty procedures with missing data (KPTJRR TKA cohort: 2459 [5%] and KPTJRR THA cohort: 1369 [5%]) were excluded from the final KPTJRR models. Sensitivity analyses that also handled missing data with multiple imputations were conducted (similar results found, data not shown). Hazard ratios (HRs), 95% confidence intervals (CIs), and Wald chi square p values are provided by registry and cohort. Data were analyzed using SAS Version 9.2 software (SAS Institute, Cary, NC, USA) with α = 0.05 as the statistical threshold for significance.
This study was approved by the Southern California Kaiser Permanente institutional review board (#5488) and the Human Research Ethics Committee of St Vincent’s Hospital Melbourne (HREC-A 100/14).
Association Between BMI and Death After TKA
After adjusting for age, gender, year of operation, indication for surgery, ASA category, race (KPTJRR only), Australian-born (SMART Registry only), bilateral surgery (KPTJRR only), and diabetes, the risk of mortality after TKA was higher in both the KPTJRR (HR, 1.88; 95% CI, 1.33-2.64; p < 0.001) and SMART cohorts (HR, 3.72; 95% CI, 1.94-7.08; p < 0.001) for patients who were underweight at the time of their surgery compared with normal-weight patients. Compared with normal-weight patients, those who were obese class I (KPTJRR: HR, 0.71; 95% CI, 0.66-0.78; p < 0.001; SMART: HR, 0.66; 95% CI, 0.47-0.92; p = 0.015) or obese class II (KPTJRR: HR, 0.73; 95% CI, 0.66-0.81; p < 0.001; SMART: HR, 0.54; 95% CI, 0.35-0.82; p = 0.004) had a lower risk of mortality. Furthermore, in the KPTJRR TKA cohort, being overweight was also associated with a lower risk of mortality (HR, 0.76; 95% CI, 0.71-0.82; p < 0.001), whereas in the SMART cohort, there was no difference in the risk of mortality for overweight patients (HR, 0.79; 95% CI, 0.57-1.09; p = 0.146). No differences in the risk of mortality between the most severe level of obesity identified in our study (class III) compared with normal-weight patients were observed in either the KPTJRR (HR, 0.91; 95% CI, 0.80-1.04; p = 0.181) or the SMART (HR, 0.73; 95% CI, 0.46-1.16; p = 0.186) cohort (Table 2). No interactions (effect modification) by age groups and gender were observed (data not shown). The crude cumulative mortality rate at 5 years (Appendix, Supplemental Digital Content 1) and overall survival analyses (Fig. 1A-B) demonstrated a linear pattern between BMI group and risk of death with mortality rates highest in underweight patients and lowest in obese patient groups.
Association Between BMI and Death After THA
After adjusting for age, gender, year of operation, indication for surgery, ASA category, race (KPTJRR only), Australian-born (SMART Registry only), and diabetes, in the KPTJRR cohort, patients who were underweight had a higher risk of mortality (HR, 2.09; 95% CI, 1.65-2.64; p < 0.001) than normal-weight patients and those who were overweight (HR, 0.73; 95% CI, 0.67-0.80; p < 0.001), obese class I (HR, 0.68; 95% CI, 0.62-0.75; p < 0.001), or obese class II (HR, 0.71; 95% CI, 0.62-0.81; p < 0.001) were at a lower risk of mortality. For the SMART cohort, there was no difference in the risk of mortality for underweight (HR, 1.89; 0.76-4.71; p = 0.170) overweight (HR, 0.84; 95% CI, 0.63-1.12; p = 0.226), obese class I (HR, 0.86; 95% CI, 0.63-1.17; p = 0.332), and obese class II (HR, 0.77; 95% CI, 0.49-1.22; p = 0.265) patients compared with normal-weight patients. No differences in the risk of mortality between the most severe levels of obesity identified in our study (class III) compared with normal-weight patients were observed in either the KPTJRR (HR, 0.91; 95% CI, 0.75-1.12; p = 0.379) or the SMART (HR, 0.68; 95% CI, 0.35-1.33; p = 0.260) cohort (Table 2). No interactions (such as effect modification) by age groups and gender were observed (data not shown). The crude cumulative mortality rate at 5 years (Appendix 1) and overall survival analyses (Fig. 2A-B) demonstrated a linear pattern between BMI group and risk of death with mortality rates highest in underweight patients and lowest in obese patient groups.
TJA is one of the most commonly performed surgical procedures in the developed world with an estimated 7 million Americans living with a hip or knee arthroplasty in 2010 . For the majority of Organization for Economic Cooperation and Development countries, there have been increases in both TKA and THA in the previous 10 years with the strongest growth in TJA occurring in the younger population and in countries with the highest prevalence of obesity [29, 30]. Surgeons may make decisions about whether to offer elective TJA to patients based on whether they are obese or not because there is a perception that they have an increased surgical risk [16, 25-27]. Conversely, little attention is given to underweight patients, despite the increased risk of morbidity after major surgical procedures documented in this group . Because both being underweight or obese are associated with increased risk of surgical morbidity, it would seem logical that BMI would also be associated with mortality risk after TJA. However, the evidence to date is variable with some suggesting a paradoxic association between BMI and mortality in the first 30 days to 1 year after TJA, whereas others have demonstrated an increased risk of death associated with severe obesity but not with other BMI categories within the first year [18, 40]. We could find no studies examining BMI and risk of mortality after TJA beyond this early time period and therefore sought to fill this gap by examining the association between BMI and all-cause mortality beyond 1 year in patients undergoing TJA. This study had two important findings; first, being underweight was associated with a higher risk of all-cause mortality for both hip and knee arthroplasty and, second, a reduced risk of mortality in patients who are overweight, obese, and severely obese, which calls into question withholding arthroplasty from patients based on obesity alone.
This study has limitations, most notably the data represent a snapshot in time in terms of measuring BMI. We do not know the patterns of weight loss or gain in our cohort before nor subsequent to their TJA; therefore, it remains unclear what impact this might have had on mortality after surgery. Although current research indicates that for the majority, weight remains stable or increases after TJA [7, 8], there is evidence to suggest that the impact of obesity on mortality is dependent on the length of time an individual is obese . Obesity that occurs in early adulthood may confer a different mortality risk than if occurring later in life. Furthermore, we did not have data on cause-specific mortality and therefore were unable to investigate associations between obesity and reasons for death.
Followup for some patients was quite short with the end date of both surgery and mortality followup being December 31, 2013, and this may also be considered a limitation. However, because both of our cohorts were longitudinally followed with varying surveillance periods, we conducted survival analyses, which account for the varying followup of the cohort. Using survival analysis, we have provided descriptive information on the survival of our cohort by reporting time-specific survival estimates. This means that short-term events do not bias the estimates because they are for the specific periods presented. Furthermore, excluding people with the shorter followup in such a study design would bias the survival estimates (excluding those who died within the first couple of years).
Collider stratification bias, an unmeasured confounding induced by selection bias, may also explain our contrary-to-expected finding. Collider stratification bias has been described  as a noncausal association induced between an exposure (like obesity) and an outcome (such as mortality) that can arise when analyzing data from only one highly restricted subgroup (eg, TJA) of people. However, we believe this provides at best a partial explanation. Although collider bias has been put forth as a common explanation for the obesity paradox, a recent study testing this supposition using counterfactual causal analysis in a range of scenarios found that the effect of collider bias was small relative to the causal relationships between variables. The authors concluded that collider bias was unlikely to be the main explanation for a reverse direction of an association to a true causal relationship and that alternative explanations of the obesity paradox should be explored .
Assessment for potential confounding was limited to available covariates in the two registries and, in that regard, neither reliable smoking data nor SES were available in KPTJRR. However, both smoking and SES data were available in SMART with no association observed between either variable on unadjusted analyses. Surprisingly, a recent systematic review  cited only one study examining risk of mortality after TJA among smokers, demonstrating an increased risk in the first 60 days after THA . A greater mortality rate in smokers after TKA in the longer term has also been demonstrated . In contrast, despite one study demonstrating a higher risk of adverse events after TJA in the socioeconomically disadvantaged, no association with mortality was observed in the short term .
Another limitation in our analyses arises from the very small number of arthroplasty procedures in the SMART Registry underweight group and, as such, the results for this subgroup may be unreliable. Therefore, no inferences about mortality and underweight patients should be drawn from the results of the SMART Registry data alone. The Kaiser Permanente Registry, however, had adequate representation of underweight patients, from which we could make a more robust assessment.
Many studies have suggested patients who are obese have a higher risk of medical complications [16, 25-27]. One would imagine, therefore, that obesity may infer a higher risk of death, but we found otherwise. Patients who were overweight and obese (class I and II) had a lower risk of mortality compared with normal-weight patients in this study. This finding has been reported in several patient populations, including those with coronary heart disease and heart failure . Although an explanation for this relationship remains elusive, in chronic heart disease, it has been postulated that both central obesity and body fat percentage provide more accurate markers of mortality risk than BMI . Conversely, patient selection bias may exist whereby only the healthiest of the patients who are obese are referred for major surgical procedures .
Most notably, we also observed that being underweight was associated with higher mortality. A higher risk of mortality in underweight patients undergoing TJA has been reported in the first 30 days to 1 year post-TJA . Our findings extend beyond the first year and are consistent with the suggestion that higher long-term mortality among underweight patients may occur as a result of a condition known as sarcopenia . In patients with hip osteoarthritis, reduced mobility at baseline assessment has been shown to predict lower survival at 14 years followup . However, further studies will be required to determine whether these factors are associated with mortality risk in patients undergoing TJA.
Many studies have suggested patients who are obese undergoing TJA have a higher risk of medical complications, but despite those, we did not find a higher risk of death; in fact, we found the opposite. TJA results in substantial improvements in quality of life for many and despite a higher risk of complications, withholding surgery on the basis of obesity alone may not be justified in light of our findings. Conversely, clinicians should be cautious when considering TJA in underweight patients without first considering their nutritional status. Randomized controlled trials may be warranted to examine the role of nutritional interventions in reducing morbidity and mortality risk for underweight patients considering TJA.
We thank all Kaiser Permanente orthopaedic surgeons and the staff of the Department of Surgical Outcomes and Analysis who have contributed to the success of the Total Joint Replacement Registry and all SMART Registry investigators and orthopaedic surgeons at St Vincent’s Hospital (Melbourne). We also thank the AOANJRR for providing mortality data.
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