Wu KA, Mai E, Warburton CS, Dhanjani S, Choudhury A, Docters F, Wong L, Prabhat A, Raza M, Maayan O, Song J, Shatkin M, Moucha CS, Hayden BL. Obesity and postoperative opioid utilization after total knee arthroplasty: A propensity-matched analysis. World J Clin Cases 2026; 14(25): 125054 [DOI: 10.12998/wjcc.125054]
Corresponding Author of This Article
Kevin A Wu, MD, Researcher, Department of Orthopedics, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, United States. kevin.wu2@mountsinai.org
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Orthopedics
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Wu KA, Mai E, Warburton CS, Dhanjani S, Choudhury A, Docters F, Wong L, Prabhat A, Raza M, Maayan O, Song J, Shatkin M, Moucha CS, Hayden BL. Obesity and postoperative opioid utilization after total knee arthroplasty: A propensity-matched analysis. World J Clin Cases 2026; 14(25): 125054 [DOI: 10.12998/wjcc.125054]
Kevin A Wu, Eric Mai, Christopher S Warburton, Suraj Dhanjani, Anjali Prabhat, Marium Raza, Omri Maayan, Junho Song, Michael Shatkin, Brett L Hayden, Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
Ankit Choudhury, Department of Orthopaedic Surgery, Medical College of Wisconsin, Milwaukee, WI 53226, United States
Francesca Docters, Laurel Wong, Icahn School of Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
Calin S Moucha, Leni and Peter W. May Department of Orthopaedic Surgery, Mount Sinai Medical Center, New York, NY 10029, United States
Author contributions: Wu KA conceived and designed the study and drafted the manuscript; Choudhury A performed the data acquisition, statistical analysis, and assisted with manuscript drafting and editing; Mai E, Warburton CS, Dhanjani S, Docters F, Wong L, Prabhat A, Raza M, Maayan O, Song J and Shatkin M contributed to data interpretation and manuscript revision; Moucha CS and Hayden BL supervised the study and provided critical revision of the manuscript; and all authors read and approved the final manuscript.
AI contribution statement: No artificial intelligence tools were used in the preparation of this manuscript.
Institutional review board statement: This retrospective cohort study used de-identified patient data from a national database and did not require Institutional Review Board approval.
Informed consent statement: This study used de-identified data, did not require IRB approval, and informed consent was not required.
Conflict-of-interest statement: The authors declare no conflict of interest.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement- checklist of items.
Data sharing statement: The data that support the findings of this study are derived from a third-party de-identified electronic health record database and are not publicly available due to data-use restrictions. De-identified data may be available from the corresponding author upon request and subject to database permissions.
Corresponding author: Kevin A Wu, MD, Researcher, Department of Orthopedics, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, United States. kevin.wu2@mountsinai.org
Received: July 7, 2026 Revised: July 31, 2026 Accepted: August 20, 2026 Published online: September 6, 2026 Processing time: 66 Days and 11.1 Hours
Abstract
BACKGROUND
Obesity is associated with increased complications following total knee arthroplasty (TKA), but its relationship with postoperative opioid prescribing remains unclear. Understanding whether body mass index (BMI) independently influences opioid prescribing patterns may help guide postoperative pain management and opioid stewardship strategies.
AIM
To evaluate the association between BMI and postoperative opioid prescribing following primary TKA.
METHODS
A retrospective cohort study was performed from March 2005 to March 2024 including adult patients undergoing primary TKA without prior opioid prescriptions. Patients were stratified into BMI cohorts: < 30 kg/m², 30-34.9 kg/m², 35-39.9 kg/m², and ≥ 40 kg/m², with additional grouped comparisons including BMI ≥ 30 kg/m² vs < 30 kg/m² and BMI ≥ 40 kg/m² vs < 40 kg/m². Six direct comparisons were performed between BMI groups. Propensity score matching (1:1) was conducted based on demographic factors and comorbidities including age, sex, race, mood disorders, anxiety disorders, chronic pain conditions, diabetes, hyperlipidemia, hypertension, chronic kidney disease, nicotine dependence, and coronary artery disease. Opioid prescriptions within 2 years following TKA were assessed using odds ratios (ORs) with 95%CI. The number of postoperative opioid prescriptions was also compared between groups.
RESULTS
A total of 12830 opioid-naive patients undergoing primary TKA were identified, from which BMI-defined cohorts were derived. Patients with BMI ≥ 30 kg/m² were more likely to receive postoperative opioid prescriptions compared to those with BMI < 30 kg/m² (OR: 1.174; 95%CI: 1.056-1.305; P = 0.003). However, patients with BMI ≥ 40 kg/m² were not significantly more likely to receive opioid prescriptions compared to patients with BMI < 40 kg/m², BMI < 30 kg/m², BMI 30-34.9 kg/m², or BMI 35-39.9 kg/m². Additionally, no significant difference was observed between BMI 35-39.9 kg/m² and BMI 30-34.9 kg/m². There were no significant differences in the total number of postoperative opioid prescriptions across any BMI comparison.
CONCLUSION
While patients with BMI ≥ 30 kg/m² demonstrated a slightly increased likelihood of receiving postoperative opioid prescriptions following TKA, increasing levels of obesity were not associated with higher opioid prescribing rates or greater numbers of prescriptions. These findings suggest BMI alone may not be a major driver of postoperative opioid prescribing following TKA.
Core Tip: This large propensity-matched national cohort study examined how body mass index (BMI) relates to opioid prescribing after primary total knee arthroplasty (TKA) in opioid-naive patients. Patients with obesity (BMI 30 kg/m² or greater) had a modestly higher likelihood of receiving postoperative opioid prescriptions, but greater obesity severity, including morbid obesity (BMI 40 kg/m² or greater), was not associated with more prescriptions. BMI alone may therefore be a limited predictor of postoperative opioid prescribing after TKA, and individualized evaluation of psychosocial and pain-related risk factors may better guide opioid stewardship.
Citation: Wu KA, Mai E, Warburton CS, Dhanjani S, Choudhury A, Docters F, Wong L, Prabhat A, Raza M, Maayan O, Song J, Shatkin M, Moucha CS, Hayden BL. Obesity and postoperative opioid utilization after total knee arthroplasty: A propensity-matched analysis. World J Clin Cases 2026; 14(25): 125054
Total knee arthroplasty (TKA) is one of the most commonly performed orthopaedic procedures and remains an effective treatment for end-stage knee osteoarthritis, providing substantial improvements in pain, function, and quality of life[1,2]. As procedural volume continues to rise in the United States, increasing attention has been directed toward optimizing perioperative outcomes and minimizing complications following TKA[3-6]. Among these concerns, postoperative opioid utilization has become a major public health issue due to the ongoing opioid epidemic and the potential for prolonged opioid dependence following orthopaedic surgery[7-9].
Orthopaedic surgeons remain among the highest prescribers of postoperative opioids, with TKA frequently associated with substantial postoperative pain and opioid consumption[10-12]. Although multimodal pain management protocols and enhanced recovery pathways have reduced opioid requirements in many patients, persistent postoperative opioid use continues to be reported following TKA[13,14]. Prolonged opioid exposure has been associated with increased healthcare utilization, poorer functional outcomes, decreased patient satisfaction, and higher rates of chronic opioid dependence[15,16]. Consequently, identifying patient-specific risk factors associated with postoperative opioid utilization remains critical for improving perioperative pain management and guiding opioid stewardship initiatives[17].
Obesity is a well-established risk factor for adverse outcomes following TKA and has been associated with increased rates of infection, wound complications, readmissions, and revision surgery[18-20]. Additionally, obesity is highly prevalent among patients undergoing TKA, making it an important variable in perioperative risk stratification[21]. Prior studies have suggested that obesity may influence pain perception, functional recovery, and postoperative analgesic requirements[22]. Patients with elevated body mass index (BMI) often present with greater baseline pain, higher rates of medical comorbidities, and chronic inflammatory states, all of which may contribute to increased postoperative opioid utilization[23,24]. However, the relationship between obesity severity and postoperative opioid prescribing patterns after TKA remains poorly defined.
Existing literature evaluating BMI and opioid use following TKA has produced inconsistent findings. While some studies have demonstrated increased opioid consumption among obese patients, others have reported minimal or no clinically meaningful differences after controlling for medical and psychosocial comorbidities[25-27]. Furthermore, few studies have evaluated opioid prescribing patterns across varying obesity classes, particularly among patients with morbid obesity (BMI ≥ 40)[28]. As obesity rates continue to increase nationally, understanding whether higher BMI independently contributes to postoperative opioid utilization is increasingly important for both perioperative optimization and patient counseling[29]. A recent national database study reported that higher BMI was associated with greater opioid prescribing after lumbar spine surgery, with the highest risk among patients with class III obesity, but whether a comparable relationship exists after TKA has not been established[30].
Therefore, the purpose of this study was to evaluate the association between BMI and postoperative opioid prescribing following primary TKA using a large national database. We hypothesized that patients with elevated BMI would demonstrate increased postoperative opioid prescription rates compared to patients with lower BMI, with progressively greater prescribing observed among patients with higher obesity classes.
MATERIALS AND METHODS
Data source and study design
A retrospective cohort study was performed using a large national federated electronic health record database containing de-identified patient information from 110 healthcare organizations across the United States (TriNetX LLC, Cambridge, MA, United States). The database includes demographic information, diagnoses, procedures, medications, and laboratory data derived from inpatient and outpatient encounters. Data from March 2005 through March 2024 were queried for this study. Because all patient information within the database is de-identified in accordance with the Health Insurance Portability and Accountability Act, this study was exempt from institutional review board approval.
Patient selection
Adult patients aged 18 years or older who underwent primary TKA between March 1st, 2005 through March 1st, 2024 were identified using Current Procedural Terminology code 27447. Patients were required to have a recorded encounter at least 2 years after the index procedure to ensure adequate follow-up for outcome ascertainment. To minimize the influence of preexisting opioid dependence or chronic opioid use, patients with any opioid prescription at any time on or before the date of TKA were excluded, identified using Veterans Affairs National Drug File classification CN101 and Anatomical Therapeutic Chemical classification N02A. Prior exposure was assessed across the entire available patient record rather than a fixed preoperative lookback interval, applying a conservative definition of opioid-naive status.
Patients were stratified into BMI cohorts using the curated BMI variable available within the database (TNX: 9083). Cohorts included BMI < 30 kg/m², BMI 30-34.9 kg/m², BMI 35-39.9 kg/m², and BMI ≥ 40 kg/m². Additional grouped cohorts were created for BMI ≥ 30 kg/m² vs BMI < 30 kg/m² and BMI ≥ 40 kg/m² vs BMI < 40 kg/m². Six direct cohort comparisons were subsequently performed: BMI ≥ 30 kg/m² vs BMI < 30 kg/m², BMI ≥ 40 kg/m² vs BMI < 40 kg/m², BMI ≥ 40 kg/m² vs BMI < 30 kg/m², BMI ≥ 40 kg/m² vs BMI 30–34.9 kg/m², BMI ≥ 40 kg/m² vs BMI 35-39.9 kg/m², and BMI 35-39.9 kg/m² vs BMI 30-34.9 kg/m².
Propensity score matching
To reduce the influence of demographic and medical comorbidity differences between cohorts, propensity score matching was performed in a 1:1 fashion for each comparison. Matching variables included age, sex, race, mood disorders, anxiety disorders, chronic pain, chronic pain syndrome, diabetes mellitus, hyperlipidemia, hypercholesterolemia, hypertension, chronic kidney disease, nicotine dependence, and coronary artery disease. These variables were selected based on known associations with postoperative opioid utilization and TKA outcomes.
Following matching, cohort balance was assessed using standardized mean differences, with values less than 0.1 considered indicative of acceptable matching. For rare categorical variables with small cell counts, database small-cell suppression can inflate standardized mean differences despite negligible absolute differences. Standardized mean differences for all six comparisons are reported in Table 1 and Supplementary Tables 1-5, and residual imbalances exceeding 0.1 are addressed in the Discussion.
Table 1 Demographics and medical comorbidities for body mass index ≥ 40 vs body mass index < 30 cohorts before and after propensity score matching, n (%)/mean ± SD.
The primary outcome of interest was postoperative opioid prescription within 2 years following primary TKA. The 2-year window (opioid prescriptions occurring 1 days to 730 days after the index procedure) was selected a priori to capture prolonged and persistent postoperative opioid prescribing, which is the pattern most relevant to long-term opioid dependence and stewardship, and to maintain consistency with prior national database methodology evaluating BMI and opioid prescribing after spine surgery[30]. Opioid prescriptions were identified using medication classifications within the database. Rates of opioid prescriptions between matched cohorts were compared using odds ratios (ORs) with 95%CIs.
The secondary outcome was the total number of postoperative opioid prescriptions issued within 2 years following TKA. Mean prescription counts between matched cohorts were compared using independent t-tests.
Statistical analysis
Statistical analyses were performed within the database analytics platform. Continuous variables were reported as means with standard deviations, while categorical variables were reported as frequencies and percentages. ORs with corresponding 95%CI were calculated for categorical outcomes. Independent t-tests were used to compare continuous variables between matched cohorts. Because the number of opioid prescriptions represents right-skewed count data, medians are reported alongside means and standard deviations, and independent t-tests were interpreted in the context of large matched sample sizes, which support parametric comparison under the central limit theorem. Statistical significance was defined as P < 0.05 for all analyses.
RESULTS
A total of 276125 adult patients underwent primary TKA within the network during the study period, of whom 12830 had no opioid prescription on or before the index procedure and formed the opioid-naive study population. BMI-defined cohorts were derived from this population for each comparison: BMI < 30 kg/m² (n = 3835), BMI ≥ 30 kg/m² (n = 4210), BMI 30 kg/m² to 34.9 kg/m² (n = 2119), BMI 35 kg/m² to 39.9 kg/m² (n = 1190), BMI ≥ 40 kg/m² (n = 883), and BMI < 40 kg/m² (n = 6566). Patients without a recorded BMI were not eligible for BMI stratification. Because each cohort was generated by a separate query within the network, counts for the same BMI stratum could differ between comparisons; the BMI ≥ 40 kg/m² cohort comprised 883 patients in the comparisons against BMI < 30 kg/m², BMI 30-34.9 kg/m², and BMI 35-39.9 kg/m², and 799 patients in the comparison against BMI < 40 kg/m², and the sum of the individual obesity subclasses differs from the pooled BMI ≥ 30 kg/m² cohort by 18 patients. These differences reflect independent query execution rather than misclassification. Patient selection and cohort derivation are summarized in Figure 1.
Figure 1 Study cohort selection and propensity score matching workflow for patients undergoing total knee arthroplasty by body mass index category.
BMI: Body mass index.
After 1:1 propensity score matching, cohorts were well balanced across baseline demographic characteristics and medical comorbidities (Table 1; Supplementary Tables 1-5). All covariates achieved a standardized mean difference below 0.1 in the BMI ≥ 30 kg/m² vs BMI < 30 kg/m² comparison (maximum 0.085) and in the BMI 35 kg/m² to 39.9 kg/m² vs BMI 30 kg/m² to 34.9 kg/m² comparison (maximum 0.078). Residual imbalance above this threshold persisted for a small number of variables in comparisons involving the BMI ≥ 40 kg/m² cohort. The Asian race category remained imbalanced in three comparisons (standardized mean difference 0.158 vs BMI < 30 kg/m², 0.160 vs BMI 30-34.9 kg/m², and 0.166 vs BMI 35-39.9 kg/m²); in each instance this reflected a suppressed cell count of 10 or fewer patients in the BMI ≥ 40 kg/m² cohort matched against zero patients in the comparator rather than a meaningful absolute difference. Unspecified hyperlipidemia remained imbalanced in the BMI ≥ 40 kg/m² vs BMI < 30 kg/m² (0.112) and BMI ≥ 40 kg/m² vs BMI < 40 kg/m² (0.104) comparisons, and essential hypertension in the BMI ≥ 40 kg/m² vs BMI < 30 kg/m² comparison (0.161). In each case the covariate was more prevalent in the higher BMI cohort.
Patients with BMI ≥ 30 kg/m² demonstrated significantly greater odds of receiving postoperative opioid prescriptions within 2 years following TKA compared to patients with BMI < kg/m² 30 (OR: 1.174; 95%CI: 1.056-1.305; P = 0.003; Table 2). However, no significant differences in postoperative opioid prescription rates were identified among patients with more severe obesity classifications.
Table 2 Odds of opioid prescription between patients with different body mass index levels within 2 years of total knee arthroplasty after propensity score matching, n (%).
Patients with BMI ≥ 40 kg/m² were not significantly more likely to receive postoperative opioid prescriptions compared to patients with BMI < 40 kg/m² (OR: 1.085; 95%CI: 0.884-1.332; P = 0.433), BMI < 30 kg/m² (OR: 1.146; 95%CI: 0.941-1.395; P = 0.175), BMI 30-34.9 kg/m² (OR: 1.042; 95%CI: 0.854-1.270; P = 0.686), or BMI 35–39.9 kg/m² (OR: 1.011; 95%CI: 0.823-1.241; P = 0.917). Additionally, no significant difference in opioid prescription rates was observed between patients with BMI 35-39.9 and those with BMI 30–34.9 kg/m² (OR: 1.070; 95%CI: 0.907-1.263; P = 0.422; Table 2).
Analysis of postoperative opioid prescription burden demonstrated no significant differences in the mean number of opioid prescriptions issued within 2 years following TKA across any BMI comparison groups (Table 3). Reported means reflect the number of opioid prescriptions among patients who received at least one postoperative opioid prescription; the number of patients with a prescription in each cohort is shown in Table 2. Median prescription counts were 3 in all cohorts across every comparison (Table 3). Patients with BMI ≥ 30 kg/m² received a mean of 4.18 ± 4.5 opioid prescriptions compared to 3.96 ± 4.1 prescriptions among patients with BMI < 30 kg/m² (P = 0.207). Similarly, patients with BMI ≥ 40 kg/m² demonstrated no significant differences in the number of opioid prescriptions compared to patients with BMI 35–39.9 kg/m² (4.76 ± 7.0 vs 4.52 ± 5.1; P = 0.614), BMI 30–34.9 kg/m² (4.75 ± 6.9 vs 4.35 ± 4.4; P = 0.366), or BMI < 30 kg/m² (4.74 ± 6.8 vs 4.50 ± 5.1; P = 0.596). Additionally, no significant difference in opioid prescription burden was observed between patients with BMI 35-39.9 kg/m² and BMI 30-34.9 kg/m² (4.62 ± 5.0 vs 4.39 ± 4.7; P = 0.436).
Table 3 Number of opioid prescriptions between patients of different body mass index levels within 2 years of total knee arthroplasty after propensity score matching, mean ± SD.
Overall, while obesity defined as BMI ≥ 30 kg/m² was associated with a modest increase in the likelihood of postoperative opioid prescription following TKA, increasing obesity severity was not associated with progressively greater numbers of opioid prescriptions.
DISCUSSION
The principal finding of this study was that patients with obesity (BMI ≥ 30 kg/m²) demonstrated a modestly increased likelihood of receiving postoperative opioid prescriptions following primary TKA compared to patients with BMI < 30 kg/m². However, increasing obesity severity was not associated with progressively greater postoperative opioid prescribing. Specifically, patients with morbid obesity (BMI ≥ 40 kg/m²) did not demonstrate significantly increased opioid prescription rates or greater numbers of opioid prescriptions compared to patients in lower BMI categories. Furthermore, no significant differences were observed in the total number of postoperative opioid prescriptions across any BMI comparison groups. These findings suggest that while obesity may have a limited association with postoperative opioid exposure following TKA, BMI alone may not be a major independent driver of prolonged postoperative opioid prescribing.
The relationship between obesity and postoperative pain management following TKA remains complex. Obesity has historically been associated with worse baseline pain, decreased physical function, chronic inflammation, and increased medical comorbidity burden, all of which may theoretically contribute to increased postoperative analgesic requirements[22,24,31]. Additionally, obesity has been associated with poorer patient-reported outcomes and increased perioperative complications after TKA, leading some authors to hypothesize that patients with higher BMI may require greater postoperative opioid utilization[29,32]. Despite these concerns, our findings suggest that the influence of BMI on postoperative opioid prescribing may be less substantial than previously assumed, particularly after controlling for relevant demographic and medical comorbidities.
Interestingly, while patients with BMI ≥ 30 kg/m² demonstrated slightly greater odds of receiving postoperative opioid prescriptions, this association did not persist across progressively higher obesity classes. Patients with BMI ≥ 40 kg/m² were not significantly different from patients with BMI < 40 kg/m², BMI < 30 kg/m², BMI 30-34.9 kg/m², or BMI 35-39.9 kg/m² with respect to postoperative opioid prescriptions. Similarly, no differences were identified in the total number of opioid prescriptions issued postoperatively. These findings may reflect a threshold association rather than a dose-dependent relationship between obesity and opioid prescribing following TKA; however, this interpretation should be made with caution. The absence of a clear dose-response gradient may alternatively reflect reduced statistical power within the smaller higher BMI strata, residual confounding despite propensity score matching, or limitations of a prescription-based outcome assessed over a 2-year window, rather than a true biological threshold[33,34]. Alternatively, the observed increase in opioid prescribing among patients with BMI ≥ 30 kg/m² may reflect confounding factors related to perioperative pain perception, provider prescribing practices, or associated comorbidities rather than obesity severity itself.
Our findings are consistent with prior literature demonstrating that factors such as preoperative opioid exposure, chronic pain syndromes, depression, anxiety, and psychosocial variables may play a larger role in postoperative opioid utilization than BMI alone[35]. In a retrospective study of 1063 primary THA and TKA patients, Lendrum et al[36] reported that higher BMI was associated with increased inpatient opioid consumption, opioid refill rates, length of stay, and discharge to rehabilitation facilities. Notably, they also found that preoperative opioid use increased substantially across BMI categories, rising from 24% among patients with normal BMI to 40% among those with morbid obesity. These findings suggest that obesity frequently coexists with other factors known to influence postoperative opioid utilization, making it difficult to isolate the independent effect of BMI. Similarly, van Brug et al[37] demonstrated in a large Dutch arthroplasty registry that chronic preoperative opioid use was associated with approximately twofold higher risks of 1-year revision and mortality following THA and TKA. While BMI modified revision risk among TKA patients, it had minimal impact on postoperative pain, physical function, and quality-of-life outcomes, suggesting that increasing BMI may not independently drive postoperative outcomes to the same extent as opioid-related and other patient-specific risk factors. Notably, using an identical national database and analytic approach, a prior study reported a dose-dependent association between higher BMI and opioid prescribing after lumbar spine surgery, with class III obesity conferring the highest risk. The attenuation of this gradient in our TKA cohort suggests that the relationship between obesity severity and postoperative opioid prescribing may be procedure-specific and should not be assumed to generalize across surgical populations[30]. Furthermore, Takenoshita et al[29] identified higher BMI as a predictor of increased inpatient opioid consumption following TKA; however, opioid utilization was also strongly influenced by other patient- and perioperative-level characteristics, highlighting the multifactorial nature of postoperative opioid use. To better isolate the independent effect of obesity in our study, propensity score matching was performed controlling for several known confounding variables, including chronic pain, mood disorders, anxiety disorders, diabetes, nicotine dependence, and cardiovascular comorbidities. After matching, the absence of meaningful differences between obesity subclasses further supports the concept that postoperative opioid utilization is multifactorial and may not be driven predominantly by BMI severity.
From a clinical perspective, these findings have important implications for perioperative counseling and opioid stewardship efforts. Patients with elevated BMI are frequently considered higher risk surgical candidates and may be presumed to require greater postoperative analgesic support[38,39]. However, the present study suggests that severe obesity alone should not necessarily be viewed as a predictor of increased postoperative opioid utilization following TKA. Instead, emphasis may be better placed on identifying modifiable psychosocial and pain-related risk factors that more directly contribute to prolonged opioid exposure. As institutions continue implementing multimodal pain protocols and opioid reduction initiatives, individualized patient assessment may be more valuable than BMI-based assumptions alone[15,40-42].
This study has several limitations. First, the retrospective nature of the study introduces the possibility of selection bias and miscoding inherent to administrative database research. Second, opioid utilization was evaluated based on prescription data rather than actual medication consumption, which may not fully reflect patient opioid use patterns. Third, prescription dosage, morphine milligram equivalents, duration of therapy, and refill timing could not be comprehensively assessed. Fourth, despite propensity score matching, unmeasured confounding variables such as socioeconomic status, pain tolerance, surgical technique, institutional prescribing patterns, and postoperative rehabilitation protocols may have influenced outcomes. Furthermore, the database does not allow assessment of patient-reported pain scores or functional recovery, which may further affect opioid utilization patterns. Additionally, the 2-year outcome window, although selected to capture prolonged prescribing and to align with prior database methodology, may include opioid prescriptions related to conditions other than the index procedure, such as unrelated painful conditions, trauma, or subsequent operations; shorter clinically defined windows such as 90 days or 1 year were not separately analyzed and represent a direction for future work. Residual imbalance above a standardized mean difference of 0.1 persisted for a small number of variables in certain comparisons, driven primarily by rare categories subject to small-cell suppression, and unmeasured confounding cannot be excluded. Finally, prescription counts represent skewed count data summarized here with means and medians, and interquartile ranges and individual-level distributional testing were not available within the aggregate database output.
Despite these limitations, this study utilized a large national cohort with propensity-matched analyses across multiple obesity classifications, allowing for a comprehensive evaluation of the relationship between BMI and postoperative opioid prescribing following TKA. The findings demonstrate that although obesity defined as BMI ≥ 30 may be associated with a small increase in postoperative opioid prescriptions, increasing obesity severity does not appear to independently increase postoperative opioid prescription burden following TKA.
CONCLUSION
In this propensity-matched analysis of a large national cohort of opioid-naive patients undergoing primary TKA, obesity defined as a BMI of 30 kg/m² or greater was associated with a modest increase in the likelihood of postoperative opioid prescribing, whereas higher obesity classes were not associated with progressively greater prescribing or a greater number of prescriptions. These findings indicate that BMI alone is unlikely to be a primary driver of postoperative opioid prescribing after TKA, and that individualized assessment of modifiable psychosocial and pain-related risk factors may be more informative than BMI based assumptions for opioid stewardship. Prospective studies incorporating filled prescriptions, morphine milligram equivalents, and shorter clinically defined postoperative windows are warranted to confirm these observations.
Chaudhry H, Ponnusamy K, Somerville L, McCalden RW, Marsh J, Vasarhelyi EM. Revision Rates and Functional Outcomes Among Severely, Morbidly, and Super-Obese Patients Following Primary Total Knee Arthroplasty: A Systematic Review and Meta-Analysis.JBJS Rev. 2019;7:e9.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 25][Cited by in RCA: 59][Article Influence: 9.8][Reference Citation Analysis (0)]
Pasqualini I, Huffman N, Redfern RE; Digital Health in Arthroplasty Writing Committee, Piuzzi NS. Despite Greater Improvement in Pain and Function Among Obese Patients Shortly After Total Knee Arthroplasty, There is No Difference in Patient-Reported Outcomes and Satisfaction Between Body Mass Index Classes at 1 Year Postoperatively.J Arthroplasty. 2024;39:1719-1725.e1.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 11][Cited by in RCA: 9][Article Influence: 4.5][Reference Citation Analysis (0)]
Abrecht CR, Cornelius M, Wu A, Jamison RN, Janfaza D, Urman RD, Campbell C, Smith M, Haythornthwaite J, Edwards RR, Schreiber KL. Prediction of Pain and Opioid Utilization in the Perioperative Period in Patients Undergoing Primary Knee Arthroplasty: Psychophysical and Psychosocial Factors.Pain Med. 2019;20:161-171.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 64][Cited by in RCA: 65][Article Influence: 9.3][Reference Citation Analysis (0)]
Takenoshita M, Fontalis A, Lisacek-Kiosoglous AB, Oliver T, Robinson A, Wignadasan W, Haddad FS. Factors associated with higher inpatient opioid consumption after primary total knee arthroplasty: a retrospective tertiary centre analysis.Knee. 2026;58:104279.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 2][Reference Citation Analysis (0)]
Van Brug HE, Nelissen RGHH, Rosendaal FR, Van Steenbergen LN, Bouvy ML, Dahan A, Gademan M. Association of preoperative chronic opioid use with 1-year revision rate, mortality, and patient-reported outcomes after primary hip and knee arthroplasty: age, sex and BMI matter - a Dutch register-based study.Acta Orthop. 2025;96:857-866.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 1][Cited by in RCA: 2][Article Influence: 2.0][Reference Citation Analysis (0)]
Madsen HJ, Gillette RA, Colborn KL, Henderson WG, Dyas AR, Bronsert MR, Lambert-Kerzner A, Meguid RA. The association between obesity and postoperative outcomes in a broad surgical population: A 7-year American College of Surgeons National Surgical Quality Improvement analysis.Surgery. 2023;173:1213-1219.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 79][Reference Citation Analysis (0)]
