Abstract
Background
We examined total opioid consumption during hospitalisation across manual (MA-THA), computer-assisted (CA-THA), and robotic-assisted (RA-THA) total hip arthroplasty.
Methods
This retrospective cohort study (2016–2023, Premier Healthcare database) of patients undergoing unilateral elective THA assessed opioid consumption during hospitalisation and by postoperative day (POD). Two-part mixed-effects models were conducted: first of a logistic regression for opioid use (yes/no) and second a linear regression model of log(opioid consumption) among opioid consumers.
Results
During hospitalisation of 584 578 patients, CA-THA was associated with lower odds of any opioid consumption than MA-THA (odds ratio [OR], 0.87; 95% confidence interval [95% CI], 0.80–0.95). RA-THA was associated with higher odds of any opioid consumption than CA-THA (OR, 1.22; 95% CI, 1.08–1.39). Among opioid consumers, CA-THA and RA-THA were associated with a 2% lower total opioid consumption than MA-THA (both multiplicative effects [ME], 0.98; 95% CI, 0.97–0.99). The strongest effect was observed on POD 1: lower odds of opioid consumption for both CA-THA (OR, 0.78; 95% CI, 0.74–0.83) and RA-THA (OR, 0.84; 95% CI, 0.80–0.88) than for MA-THA, and among those who consumed opioids, there was 9% lower (ME, 0.91; 95% CI, 0.90–0.93) and 6% lower (ME, 0.94; 95% CI, 0.93–0.96) opioid consumption for CA-THA and RA-THA, respectively, than for MA-THA.
Conclusions
Among those who consumed opioids, technology-assisted THA was associated with lower opioid consumption than MA-THA, primarily due to differences observed on POD 1. Although these differences are not clinically significant enough to affect decision-making, technology-assisted THA might be associated with anaesthetic techniques that confer improved pain relief in the immediate postoperative period.
Keywords: computer assisted, opioids, postsurgical pain, robotic assisted, total hip replacement
Pain management is a key component of perioperative care as acute postoperative pain is directly associated with acute postoperative opioid consumption, which in turn is one of the most important independent predictors of developing chronic pain and chronic opioid use disorder.1 In the setting of total hip arthroplasty (THA), acute pain management might also have a more functional dimension, as it has been described to trigger a ‘chain reaction’ of improved short-term, and therefore long-term, functionality, patient satisfaction, and the overall patient experience post-THA.2, 3, 4
The volume of robotic-assisted (RA-THA) and computer-assisted (CA-THA) THA has increased relative to manual THA (MA-THA) over the past several years.5 The former two have been described to improve several radiological parameters measuring accuracy of component placement.6 Given this shift in practice, a relatively unexplored factor that may potentially impact perioperative pain and opioid use among THA recipients is the use of technology.
Existing studies support the idea that RA-THA leads to less opioid consumption during hospitalisation than MA-THA, whereas one study supports the same finding for CA-THA compared with MA-THA for postoperative day 1 (POD 1).5,7,8 To the best of our knowledge, no direct comparison has been made between CA-THA and RA-THA. In the face of increased use and constant evolution of technology-assisted THA, using a multi-institutional, multi-year database with recent data could provide a more accurate understanding of how surgical modality might affect opioid requirements.
Therefore, using a national US database, we aimed to examine opioid consumption during hospitalisation across MA-THA, CA-THA, and RA-THA. We hypothesised that RA-THA and CA-THA would be associated with less opioid consumption during hospitalisation compared with MA-THA.
Methods
Study design and setting
This retrospective cohort study was approved by the Institutional Review Board (IRB #2012-050) at the senior author’s institution and followed the STROBE guidelines.9 The Premier Healthcare claims dataset (Premier Healthcare Solutions, Inc., Charlotte, NC, USA) was queried. It includes International Classification of Diseases, 10th revision, Clinical Modification (ICD-10 CM) and Procedure Codes (ICD-10 PCS), Current Procedural Terminology (CPT) codes, and standardised billing information for >25% of US healthcare.10
Participants
All adults (18 yr or older) undergoing primary elective unilateral THA during 2016–2023 were assessed for eligibility. We excluded patients with missing demographic information or undergoing simultaneous bilateral THA or THA for neoplasm, fracture, or trauma. Cases with concomitant removal of hardware or conversion arthroplasties were also excluded. To ensure normality of outcome data distribution, we excluded patients with opioid consumption above the cohort’s 95th percentile11 and those treated at hospitals performing fewer than 50 THA cases per year.12
Variables
The exposure of interest was surgical modality (MA-THA, CA-THA, and RA-THA) and three comparison groups were formed. Baseline patient characteristics collected were sex, age, Elixhauser Comorbidity Index Group (ECIG), race, ethnicity, insurance type, discharge status and year. We also documented history of neuropathic pain syndrome, complex regional pain syndrome (CRPS), chronic pain (syndrome or other), fibromyalgia, or psychiatric conditions (depression, anxiety, posttraumatic stress disorder, bipolar disorder, personality disorder). Hospital characteristics collected were location (urban, rural), teaching status, size (large=500+ beds, medium=300–499 beds, small=0–299 beds), and region. Perioperative/postoperative variables retrieved were surgical indication, procedural setting (inpatient/outpatient), anaesthesia type, surgical duration, and length of stay (LOS). Because orthopaedic complications could affect opioid requirements,13 we collected periprosthetic fracture, dislocation, or other. In similar fashion, non-orthopaedic perioperative complications collected were myocardial infarction (MI), pulmonary embolism (PE), acute kidney injury (AKI), delirium, stroke, and mortality. Multimodal analgesia use, hospital, and surgeon were retrieved and used as adjustments in regression models.
The primary outcome was total opioid consumption during hospitalisation. Secondary outcomes were total opioid consumption during the day of surgery (DOS), POD 1, and on and after postoperative day 2 until discharge (POD ≥2). We opted to cluster POD ≥2 as previous analyses have shown that 2 days are the 75th percentile of LOS for THA patients.14
Data sources/measurements
CA-THA and RA-THA were defined by extracting patients with their respective ICD-10 PCS and CPT codes from the whole THA cohort. If a surgical event included both RA-THA and CA-THA codes, it was classified as RA-THA.15 As there is no specific code for MA-THA, the procedure was considered MA-THA if it was not associated with a RA-THA or CA-THA code. ICD-10 CM codes were also used to retrieve perioperative orthopaedic and non-orthopaedic complications. A full list of the ICD-10 and CPT codes used is available in Supplementary Table 1. Anaesthesia type was defined by billing information. Similarly, multimodal analgesia use was extracted using billing information for non-opioid agents we have previously defined.16 As an adjustment in models, multimodal analgesia was used as a categorical variable: 0–1, 2, or 3+ components.17 Duration of surgery was calculated using standard procedure duration billings adjusted by a multiplier to account for deviations from billed time. Opioid consumption was extracted using billing information for opioid medication used. It was then converted and measured in morphine milligram equivalents (MME) using conversion factors.
Statistical analysis
Categorical variables were reported as N/n and percentage and continuous variables as medians and interquartile ranges. Given the large sample size, group differences were assessed by calculating absolute standardised mean differences (SMDs), and an absolute SMD >0.1 was considered notable. Three-group SMD values were calculated using the ‘tableone’ package in R version 4.4.3 (R Foundation for Statistical Computing, Vienna, Austria) which takes the average of all pairwise SMDs when more than two groups are being compared. For opioid consumption across timepoints, we reported means and standard deviations, and medians and interquartile ranges. To measure associations between the interventions and outcomes of interest, mixed-effects models were conducted in pairwise fashion. Normality of opioid consumption was seen by Q-Q plots, and the distribution was determined to be non-normal. We handled non-normality of the opioid consumption variables by (1) removing all patients with total opioid consumption greater than the 95th percentile (540 MME)18 and (2) conducting a two-part hurdle model to manage the substantial proportion of zero values at all timepoints.19,20 The two-part model consisted first of a logistic regression which modelled the binary outcome of any opioid consumption (yes/no). The second part was a linear regression model of log(opioid consumption) among those who consumed opioids. This model produces a multiplicative fixed-effects (ME) output, which represents the ratio in opioid consumption between the groups of interest and can be interpreted as the percent increase or decrease. Q-Q plots of log-transformed opioid consumption during hospitalisation showed adequate approximation of normality (Supplementary Fig. 1). Fixed effects included race, age, sex, insurance, ECIG, hospital characteristics, history of neuropathic pain syndrome, CRPS, chronic pain, fibromyalgia or psychiatric condition, surgical indication, anaesthesia type, multimodal analgesia, complications, LOS, and procedural setting. We included a random effect for hospital and surgeon, as different levels of experience with RA-THA have been reported to affect perioperative outcomes and efficiency.21,22 For our primary outcome, the full models were presented in detail. For the secondary outcomes, only patients who were in the hospital for the timepoint of interest were considered. We reported odds ratios (ORs), MEs, and 95% confidence intervals (CI). Statistical significance was defined as P-value <0.05. To reduce the possibility of false positive results in our primary outcome as a result of three pairwise comparisons, we implemented the Benjamini–Hochberg procedure. Both adjusted and unadjusted P-values were reported in the tables, but only adjusted P-values were considered. Secondary outcomes and other variables were exploratory and therefore unadjusted P-values were considered. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA).
Results
Participants
During 2016–2023, 646 506 adults undergoing primary elective THA were assessed for eligibility. After applying our exclusion criteria, 584 578 patients were included in the study (Fig. 1): 550 466 (94.2%) underwent MA-THA, 14 841 (2.5%) underwent CA-THA, and 19 271 (3.4%) underwent RA-THA. Baseline and perioperative characteristics are reported in Table 1. Differences between groups were notable for race, hospital teaching status, hospital size, hospital region, ECIG, discharge status, discharge year, proportion of cases with osteoarthritis, anaesthesia type, age, surgical duration, and LOS (Table 1).
Fig 1.
Study flowchart. THA, total hip arthroplasty.
Table 1.
Baseline and perioperative characteristics. ∗Mean and range. ∗∗Median and IQR. AKI, acute kidney injury; CA-THA, computer-assisted total hip arthroplasty; CRPS, complex regional pain syndrome; ECIG, Elixhauser Comorbidity Index Group; IQR, interquartile range; LOS, length of stay; MA-THA, manual total hip arthroplasty; MI, myocardial infarction; PE, pulmonary embolism; RA-THA, robotic-assisted arthroplasty; SMD, standardised mean difference.
| MA-THA |
CA-THA |
RA-THA |
SMD | ||||
|---|---|---|---|---|---|---|---|
| (n=550 466) |
(n=14 841) |
(n=19 271) |
|||||
| n | % | n | % | n | % | ||
| Gender | 0.021 | ||||||
| Female | 307 903 | 55.9 | 8419 | 56.7 | 10 630 | 55.2 | |
| Male | 242 563 | 44.1 | 6422 | 43.3 | 8641 | 44.8 | |
| Race | 0.11 | ||||||
| Asian | 4233 | 0.8 | 189 | 1.3 | 110 | 0.6 | |
| Black | 47 365 | 8.6 | 864 | 5.8 | 1685 | 8.7 | |
| Other | 21 208 | 3.9 | 397 | 2.7 | 538 | 2.8 | |
| Unknown | 8045 | 1.5 | 225 | 1.5 | 342 | 1.8 | |
| White | 469 615 | 85.3 | 13 166 | 88.7 | 16 596 | 86.1 | |
| Hispanic ethnicity | 0.091 | ||||||
| No | 452 546 | 82.2 | 12 241 | 82.5 | 15 256 | 79.2 | |
| Yes | 15 336 | 2.8 | 542 | 3.7 | 502 | 2.6 | |
| Unknown | 82 583 | 15.0 | 2058 | 13.9 | 3513 | 18.2 | |
| Insurance type | 0.09 | ||||||
| Commercial | 181 758 | 33.0 | 4664 | 31.4 | 6919 | 35.9 | |
| Medicaid | 28 599 | 5.2 | 570 | 3.8 | 982 | 5.1 | |
| Medicare | 319 748 | 58.1 | 9133 | 61.5 | 10 688 | 55.5 | |
| Uninsured | 2625 | 0.5 | 56 | 0.4 | 69 | 0.4 | |
| Unknown | 17 736 | 3.2 | 418 | 2.8 | 613 | 3.2 | |
| Procedural setting | 0.27 | ||||||
| Inpatient | 463 267 | 84.2 | 9925 | 66.9 | 15 700 | 81.5 | |
| Outpatient | 87 199 | 15.8 | 4916 | 33.1 | 3571 | 18.5 | |
| Urban/rural | 0.08 | ||||||
| Rural | 54 704 | 9.9 | 1096 | 7.4 | 2080 | 10.8 | |
| Urban | 495 762 | 90.1 | 13 745 | 92.6 | 17 191 | 89.2 | |
| Teaching hospital | 0.18 | ||||||
| No | 286 665 | 52.1 | 9647 | 65.0 | 11 459 | 59.5 | |
| Yes | 263 801 | 47.9 | 5194 | 35.0 | 7812 | 40.5 | |
| Hospital size | 0.18 | ||||||
| Large | 142 880 | 26.0 | 4267 | 28.8 | 4212 | 21.9 | |
| Medium | 156 555 | 28.4 | 3263 | 22.0 | 6397 | 33.2 | |
| Small | 251 031 | 45.6 | 7311 | 49.3 | 8662 | 45.0 | |
| Region | 0.45 | ||||||
| Midwest | 138 324 | 25.1 | 1665 | 11.2 | 3279 | 17.0 | |
| Northeast | 105 316 | 19.1 | 1924 | 13.0 | 5067 | 26.3 | |
| South | 225 766 | 41.0 | 6768 | 45.6 | 8848 | 45.9 | |
| West | 81 060 | 14.7 | 4484 | 30.2 | 2077 | 10.8 | |
| ECIG | 0.21 | ||||||
| 0 | 167 964 | 30.5 | 6682 | 45.0 | 6273 | 32.6 | |
| 1 | 133 673 | 24.3 | 3014 | 20.3 | 4572 | 23.7 | |
| 2 | 116 560 | 21.2 | 2533 | 17.1 | 3921 | 20.4 | |
| 3 | 72 021 | 13.1 | 1470 | 9.9 | 2431 | 12.6 | |
| 4 | 60 248 | 10.9 | 1142 | 7.7 | 2074 | 10.8 | |
| Discharge status | 0.12 | ||||||
| Dead | 164 | 0.0 | 0 | 0.0 | 10 | 0.1 | |
| Facility | 68 767 | 12.5 | 1128 | 7.6 | 1928 | 10.0 | |
| Home | 480 938 | 87.4 | 13 661 | 92.1 | 17 328 | 89.9 | |
| Other | 597 | 0.1 | 52 | 0.4 | 5 | 0.0 | |
| Discharge year | 0.13 | ||||||
| 2016 | 83 607 | 15.2 | 1567 | 10.6 | 1519 | 7.9 | |
| 2017 | 90 759 | 16.5 | 1642 | 11.1 | 2093 | 10.9 | |
| 2018 | 91 163 | 16.6 | 1931 | 13.0 | 2632 | 13.7 | |
| 2019 | 96 258 | 17.5 | 2645 | 17.8 | 3912 | 20.3 | |
| 2020 | 61 220 | 11.1 | 1435 | 9.7 | 2407 | 12.5 | |
| 2021 | 47 644 | 8.7 | 1421 | 9.6 | 2076 | 10.8 | |
| 2022 | 43 637 | 7.9 | 1932 | 13.0 | 2189 | 11.4 | |
| 2023 | 36 178 | 6.6 | 2268 | 15.3 | 2443 | 12.7 | |
| Initial diagnosis | |||||||
| Osteoarthritis | 442 656 | 80.4 | 12546 | 84.5 | 15 302 | 79.4 | 0.24 |
| Osteonecrosis | 34 572 | 6.3 | 550 | 3.7 | 1225 | 6.4 | 0.08 |
| Posttraumatic | 12 606 | 2.3 | 294 | 2.0 | 494 | 2.6 | 0.03 |
| Congenital | 3651 | 0.7 | 65 | 0.4 | 121 | 0.6 | 0.02 |
| Inflammatory | 22 835 | 4.2 | 496 | 3.3 | 788 | 4.1 | 0.03 |
| Other arthritis | 36 906 | 6.7 | 890 | 6.0 | 1257 | 6.5 | 0.02 |
| Anaesthesia type | 0.19 | ||||||
| General | 240 106 | 43.6 | 6795 | 46.0 | 7742 | 40.2 | |
| Neuraxial | 85 842 | 15.2 | 2046 | 13.8 | 4015 | 20.8 | |
| General+neuraxial | 31 715 | 5.8 | 1168 | 7.9 | 720 | 3.7 | |
| Other | 123 623 | 22.5 | 3072 | 20.7 | 4884 | 25.3 | |
| Unknown | 69 180 | 12.6 | 1760 | 11.9 | 1910 | 9.9 | |
| Orthopaedic complications | |||||||
| Periprosthetic fracture | 467 | 0.1 | 3 | 0.0 | 11 | 0.1 | 0.02 |
| Periprosthetic dislocation | 714 | 0.1 | 11 | 0.1 | 15 | 0.1 | 0.01 |
| Other | 319 | 0.1 | 5 | 0.0 | 6 | 0.0 | 0.01 |
| Non-orthopaedic complications | |||||||
| MI | 487 | 0.1 | 16 | 0.1 | 18 | 0.1 | 0.004 |
| PE | 321 | 0.1 | 9 | 0.1 | 13 | 0.1 | 0.002 |
| AKI | 7995 | 1.6 | 205 | 1.4 | 277 | 1.4 | 0.004 |
| Delirium | 1419 | 0.3 | 25 | 0.2 | 38 | 0.2 | 0.01 |
| Cerebrovascular | 762 | 0.2 | 7 | 0.1 | 12 | 0.1 | 0.02 |
| Neuropathic pain | 0.03 | ||||||
| Yes | 37 969 | 6.9 | 879 | 5.9 | 1311 | 6.8 | |
| No | 512 497 | 93.1 | 13 962 | 94.1 | 17 960 | 93.2 | |
| CRPS diagnosis | 0.01 | ||||||
| Yes | 189 | 0.0 | 3 | 0.0 | 8 | 0.0 | |
| No | 550 277 | 100.0 | 14 838 | 100.0 | 19 263 | 100.0 | |
| Chronic pain diagnosis | 0.03 | ||||||
| Yes | 32 877 | 6.0 | 828 | 5.6 | 1246 | 6.5 | |
| No | 517 589 | 94.0 | 14 013 | 94.4 | 18 025 | 93.5 | |
| Fibromyalgia diagnosis | 0.01 | ||||||
| Yes | 10 065 | 1.8 | 247 | 1.7 | 372 | 1.9 | |
| No | 540 401 | 98.2 | 14 594 | 98.3 | 18 899 | 98.1 | |
| Psychiatric diagnosis | 0.07 | ||||||
| Yes | 99 915 | 18.2 | 2247 | 15.1 | 3648 | 18.9 | |
| No | 450 551 | 81.9 | 12 594 | 84.9 | 15 623 | 81.1 | |
| Age∗ | 66.2 | 18.0, 89.0 | 67.1 | 18.0, 19.0 | 65.4 | 18.0, 89.0 | 0.1 |
| Adjusted cost∗∗ | 16 101.6 | 13 236.9–19 989.5 | 16 350.1 | 13 878.9–19 296.9 | 17 477.4 | 14 542.8–20 844.5 | 0.09 |
| Total minutes∗∗ | 135 | 118.0–177.84 | 135 | 120.0–10.0 | 150 | 120.0–210.0 | 0.13 |
| LOS (days)∗∗ | 1 | 1.0–2.0 | 1 | 0.0–2.0 | 1 | 1.0–2.0 | 0.26 |
Main results
Median total opioid consumption during hospitalisation was 135 (75–225) MMEs for MA-THA, 120 (60–210) MMEs for CA-THA, and 122 (65.5–202.5) MMEs for RA-THA (Table 2).
Table 2.
Opioid consumption summary statistics by group and endpoint. CA-THA, computer-assisted total hip arthroplasty; DOS, day of surgery; IQR, interquartile range; LOS, length of stay; MA-THA, manual total hip arthroplasty; POD, postoperative day; RA-THA, robotic-assisted total hip arthroplasty.
| Opioid-free |
Opioid consumption among non-opioid free |
||||||
|---|---|---|---|---|---|---|---|
| MA-THA | CA-THA | RA-THA | MA-THA | CA-THA | RA-THA | ||
| DOS total | 550 466 | 14 841 | 19 271 | DOS total | 514 615 | 13 834 | 18 151 |
| Opioid-free | 35 835 (6.5) | 1009 (6.8) | 1118 (5.8) | Mean (sd) | 114.3 (86.8) | 118.1 (93.4) | 106.4 (81.5) |
| Median (IQR) | 90 (50–150) | 92.5 (45–165) | 88 (45–145) | ||||
| POD 1 total | 463 267 | 9925 | 15 700 | POD 1 total | 351 735 | 6990 | 11 770 |
| Opioid-free | 111 647 (24.1) | 2938 (29.6) | 3925 (25.0) | Mean (sd) | 44.7 (37.2) | 35.7 (31.8) | 41.5 (34.6) |
| Median (IQR) | 35 (20–60) | 25 (15–45) | 30 (15–56) | ||||
| POD ≥2 total | 244 565 | 3865 | 7368 | POD ≥2 total | 181 281 | 2756 | 5412 |
| Opioid-free | 63 342 (25.9) | 1109 (28.7) | 1960 (26.6) | Mean (sd) | 51.3 (50.1) | 44.2 (45.1) | 50.7 (49.7) |
| Median (IQR) | 35 (16–63) | 30 (15–55) | 33.9 (16.0–60.0) | ||||
| LOS total | 550 466 | 14 841 | 19 721 | LOS total | 525 184 | 14 051 | 18 604 |
| % 0 opioids | 25 321 (4.6) | 790 (5.3) | 682 (3.5) | Mean (sd) | 162.6 (115.9) | 146.8 (111.6) | 147.4 (107.6) |
| Median (IQR) | 135 (75–225) | 120 (60–210) | 122 (65.5–202.5) | ||||
Total opioid consumption during hospitalisation
CA-THA was associated with lower odds of any opioid consumption than MA-THA (OR, 0.87; 95% CI, 0.80–0.95; adjusted P=0.03). RA-THA was not associated with statistically significant different odds of any opioid consumption compared with MA-THA but was associated with higher odds of any opioid consumption than CA-THA (OR, 1.22; 95% CI, 1.08–1.39; adjusted P=0.002). The two most important predictors of any opioid consumption were number of multimodal analgesia components and anaesthesia type (Table 3).
Table 3.
Effect estimates for all predictors of yes vs no opioid consumption during the entire hospitalisation. Parentheses represent adjusted P-values after using the Benjamini–Hochberg procedure. CA-THA, computer-assisted total hip arthroplasty; CI, confidence interval; CRPS, complex regional pain syndrome; LOS, length of stay; MA-THA, manual total hip arthroplasty; OR, odds ratio; RA-THA, robotic-assisted total hip arthroplasty.
| Variable | OR | 95% CI | P-value |
|---|---|---|---|
| Surgical technique | |||
| CA-THA vs MA-THA | 0.87 | 0.80–0.95 | 0.002 (0.03) |
| RA-THA vs MA-THA | 1.07 | 0.97–1.17 | 0.18 (0.44) |
| RA-THA vs CA-THA | 1.22 | 1.08–1.39 | 0.001 (0.002) |
| Race | |||
| Asian vs White | 0.61 | 0.52–0.71 | <0.0001 |
| Black vs White | 1.35 | 1.26–1.44 | <0.0001 |
| Other vs White | 1.11 | 1.01–1.22 | 0.03 |
| Unknown vs White | 0.96 | 0.86–1.07 | 0.43 |
| Procedural setting | |||
| Inpatient vs outpatient | 0.71 | 0.66–0.76 | <0.0001 |
| Region | |||
| Midwest vs Northeast | 2.24 | 1.55–3.23 | <0.0001 |
| South vs Northeast | 2.35 | 1.66–3.34 | <0.0001 |
| West vs Northeast | 2.82 | 1.88–4.24 | <0.0001 |
| Gender | |||
| Female vs male | 1.07 | 1.03–1.09 | <0.0001 |
| Insurance | |||
| Medicaid vs commercial | 1.12 | 1.03–1.21 | 0.01 |
| Medicare vs commercial | 1.05 | 1.01–1.10 | 0.03 |
| Uninsured vs commercial | 0.88 | 0.70–1.11 | 0.27 |
| Unknown vs commercial | 1.03 | 0.94–1.14 | 0.49 |
| Elixhauser Comorbidity Index Group | |||
| 1 vs 0 | 1.15 | 1.10–1.21 | <0.0001 |
| 2 vs 0 | 1.29 | 1.23–1.36 | <0.0001 |
| 3 vs 0 | 1.47 | 1.39–1.57 | <0.0001 |
| 4 vs 0 | 1.4 | 1.31–1.50 | <0.0001 |
| Hospital size | |||
| Large vs small | 0.89 | 0.62–1.28 | 0.53 |
| Medium vs small | 1.19 | 0.89–1.59 | 0.25 |
| Teaching | |||
| No vs yes | 1.18 | 0.90–1.55 | 0.24 |
| Urban/rural | |||
| Rural vs urban | 1.3 | 0.95–1.78 | 0.1 |
| Discharge year | |||
| 2017 vs 2016 | 1.21 | 1.14–1.29 | <0.0001 |
| 2018 vs 2016 | 1.32 | 1.23–1.40 | <0.0001 |
| 2019 vs 2016 | 0.89 | 0.84–0.84 | <0.0001 |
| 2020 vs 2016 | 0.72 | 0.67–0.77 | <0.0001 |
| 2021 vs 2016 | 0.81 | 0.76–0.87 | <0.0001 |
| 2022 vs 2016 | 0.86 | 0.80–0.92 | <0.0001 |
| 2023 vs 2016 | 0.85 | 0.79–0.92 | <0.0001 |
| Non-orthopaedic complications | |||
| Yes vs no | 0.83 | 0.74–0.94 | 0.003 |
| Orthopaedic complications | |||
| Yes vs no | 1.36 | 0.94–1.98 | 0.1 |
| Multimodal analgesia components | |||
| 3+ vs 1 | 2.61 | 2.50–2.72 | <0.0001 |
| 2 vs 1 | 2.9 | 2.76–3.06 | <0.0001 |
| Anaesthesia type | |||
| Neuraxial vs general | 0.39 | 0.37–0.42 | <0.0001 |
| Neuraxial+general vs general | 0.69 | 0.63–0.76 | <0.0001 |
| Other vs general | 0.56 | 0.53–0.61 | <0.0001 |
| Unknown vs general | 0.34 | 0.31–0.37 | <0.0001 |
| Neuropathic pain | |||
| Yes vs no | 1.19 | 1.11–1.27 | <0.0001 |
| CRPS pain | |||
| Yes vs no | 2.39 | 0.74–1.35 | 0.15 |
| Chronic pain | |||
| Yes vs no | 1.24 | 1.14–1.35 | <0.0001 |
| Fibromyalgia | |||
| Yes vs no | 1.19 | 1.02–1.38 | 0.02 |
| Psychiatric diagnosis | |||
| Yes vs no | 1.24 | 1.14–1.35 | <0.0001 |
| Age | 0.97 | 0.96–0.97 | <0.0001 |
| LOS | 1.25 | 1.23–1.27 | <0.0001 |
Among opioid consumers, CA-THA was associated with a 2% lower total opioid consumption than MA-THA (ME, 0.98; 95% CI, 0.97–0.99; adjusted P=0.002). Similarly, RA-THA was associated with a 2% lower total opioid consumption than MA-THA (ME, 0.98; 95% CI, 0.97–0.99; adjusted P=0.002). RA-THA and CA-THA resulted in no statistically significantly different opioid consumption during hospitalisation (ME, 1.00; 95% CI, 0.99–1.02; adjusted P=0.77). The two most important predictors of total opioid consumption were discharge year and anaesthesia type (Table 4).
Table 4.
Effect estimates for all predictors for total opioid consumption during hospitalisation among those who consumed opioids. Parentheses represent adjusted P-values after using the Benjamini–Hochberg procedure. CA-THA, computer-assisted total hip arthroplasty; CI, confidence interval; CRPS, complex regional pain syndrome; LOS, length of stay; MA-THA, manual total hip arthroplasty; RA-THA, robotic-assisted total hip arthroplasty.
| Variable | Multiplicative effect | 95% CI | P-value |
|---|---|---|---|
| Surgical technique | |||
| CA-THA vs MA-THA | 0.98 | 0.97–0.99 | 0.001 (0.002) |
| RA-THA vs MA-THA | 0.98 | 0.97–0.99 | 0.001 (0.002) |
| RA-THA vs CA-THA | 1.00 | 0.99–1.02 | 0.77 (0.77) |
| Race | |||
| Asian vs White | 0.79 | 0.77–0.81 | <0.0001 |
| Black vs White | 1.05 | 1.05–1.06 | <0.0001 |
| Other vs White | 0.99 | 0.98–1.01 | 0.34 |
| Unknown vs White | 0.97 | 0.95–0.98 | <0.0001 |
| Procedural setting | |||
| Inpatient vs outpatient | 0.82 | 0.81–0.82 | <0.0001 |
| Region | |||
| Midwest vs Northeast | 1.26 | 1.16–1.36 | <0.0001 |
| South vs Northeast | 1.14 | 1.06–1.23 | 0.001 |
| West vs Northeast | 1.22 | 1.12–1.33 | <0.0001 |
| Gender | |||
| Female vs male | 0.995 | 0.992–0.999 | 0.02 |
| Insurance | |||
| Medicaid vs commercial | 1.1 | 1.09–1.11 | <0.0001 |
| Medicare vs commercial | 1.05 | 1.045–1.056 | <0.0001 |
| Uninsured vs commercial | 1.02 | 0.99–1.05 | 0.22 |
| Unknown vs commercial | 1.04 | 1.03–1.05 | <0.0001 |
| Elixhauser Comorbidity Index Group | |||
| 1 vs 0 | 1.05 | 1.049–1.061 | <0.0001 |
| 2 vs 0 | 1.1 | 1.097–1.111 | <0.0001 |
| 3 vs 0 | 1.14 | 1.136–1.153 | <0.0001 |
| 4 vs 0 | 1.17 | 1.165–1.184 | <0.0001 |
| Hospital size | |||
| Large vs small | 1.02 | 0.94–1.10 | 0.69 |
| Medium vs small | 1.03 | 0.97–1.10 | 0.4 |
| Teaching | |||
| No vs yes | 1 | 0.95–1.06 | 0.88 |
| Urban/rural | |||
| Rural vs urban | 0.96 | 0.90–1.03 | 0.25 |
| Discharge year | |||
| 2017 vs 2016 | 0.91 | 0.90–0.92 | <0.0001 |
| 2018 vs 2016 | 0.82 | 0.81–0.82 | <0.0001 |
| 2019 vs 2016 | 0.74 | 0.73–0.74 | <0.0001 |
| 2020 vs 2016 | 0.68 | 0.67–0.69 | <0.0001 |
| 2021 vs 2016 | 0.67 | 0.66–0.68 | <0.0001 |
| 2022 vs 2016 | 0.67 | 0.66–0.69 | <0.0001 |
| 2023 vs 2016 | 0.65 | 0.64–0.66 | <0.0001 |
| Non-orthopaedic complications | |||
| Yes vs no | 0.89 | 0.88–0.91 | <0.0001 |
| Orthopaedic complications | |||
| Yes vs no | 1.21 | 1.17–1.26 | <0.0001 |
| Multimodal | |||
| 3 vs 1 | 0.93 | 0.92–0.94 | <0.0001 |
| 2 vs 1 | 0.9 | 0.89–0.90 | <0.0001 |
| Anaesthesia type | |||
| Neuraxial vs general | 0.62 | 0.61–0.63 | <0.0001 |
| Neuraxial+general vs general | 0.73 | 0.72–0.74 | <0.0001 |
| Other vs general | 0.82 | 0.81–0.83 | <0.0001 |
| Unknown vs general | 0.7 | 0.69–0.70 | <0.0001 |
| Neuropathic pain | |||
| Yes vs no | 1.07 | 1.06–1.07 | <0.0001 |
| CRPS pain | |||
| Yes vs no | 1.28 | 1.16–1.41 | <0.0001 |
| Chronic pain | |||
| Yes vs no | 1.15 | 1.14–1.16 | <0.0001 |
| Fibromyalgia | |||
| Yes vs no | 1.15 | 1.13–1.16 | <0.0001 |
| Psychiatric diagnosis | |||
| Yes vs no | 1.08 | 1.08–1.09 | <0.0001 |
| Age | 0.98 | 0.9831–0.9836 | <0.0001 |
| LOS | 1.13 | 1.12–1.13 | <0.0001 |
Total opioid consumption on day of surgery, postoperative day 1, and postoperative day ≥2
A forest plot for the odds of receiving opioids by timepoint of interest can be found in Figure 2a and a forest plot for the relative difference in opioid consumption among those who consumed opioids by timepoint of interest can be found in Figure 2b.
Fig 2.
Forest plots. (a) Odds ratios of opioid consumption by group and (b) multiplicative effects by group across timepoints of interest. CA-THA, computer-assisted total hip arthroplasty; CI, confidence interval; LOS, length of stay; MA-THA, manual total hip arthroplasty; RA-THA, robotic-assisted total hip arthroplasty.
The entire cohort was assessed for DOS. CA-THA was associated with lower odds of any opioid consumption than MA-THA (OR, 0.90; 95% CI, 0.83–0.98; P=0.03). Conversely, RA-THA was associated with higher odds of any opioid consumption than both MA-THA (OR, 1.18; 95% CI, 1.10–1.27; P<0.0001) and CA-THA (OR, 1.38; 95% CI, 1.15–1.42; P<0.0001). Among those who consumed opioids, a 3% higher opioid consumption than MA-THA was noted for both CA-THA (ME, 1.03; 95% CI, 1.01–1.04; P<0.0001) and RA-THA (ME, 1.03; 95% CI, 1.02–1.04; P<0.0001). No statistically significant differences were observed between RA-THA and CA-THA (Supplementary Table 2).
A total of 463 267 MA-THAs, 9925 CA-THAs, and 15 700 RA-THAs were assessed for POD 1. Both CA-THA (OR, 0.78; 95% CI, 0.74–0.83; P<0.0001) and RA-THA (OR, 0.84; 95% CI, 0.80–0.88; P<0.0001) were associated with lower odds of any opioid consumption than MA-THA. No statistically significant difference was observed between RA-THA and CA-THA (Supplementary Table 2). Among those who consumed opioids, CA-THA was associated with a 9% lower opioid consumption than MA-THA (ME, 0.91; 95% CI, 0.89–0.93; P<0.0001). RA-THA was associated with a 6% lower opioid consumption than MA-THA (ME, 0.94; 95% CI, 0.93–0.96; P<0.0001) and a 3% higher opioid consumption than CA-THA (ME, 1.03; 95% CI, 1.01–1.06; P=0.004).
A total of 244 565 MA-THAs, 3865 CA-THAs and 7368 RA-THAs were assessed for POD ≥2. Both CA-THA (OR, 0.86; 95% CI, 0.79–0.94; P=0.001) and RA-THA (OR, 0.87; 95% CI, 0.81–0.93; P<0.0001) were associated with lower odds of any opioid consumption than MA-THA. No statistically significant difference was observed between RA-THA and CA-THA (Supplementary Table 2). Among those who consumed opioids, only RA-THA was associated with a 4% lower opioid consumption than MA-THA (ME, 0.96; 95% CI, 0.94–0.99; P=0.003).
Discussion
Key results
In our analysis of 584 578 patients, CA-THA was associated with lower odds of opioid consumption than MA-THA, and RA-THA was associated with higher odds of opioid consumption than CA-THA. The two most important predictors of opioid consumption were number of multimodal analgesia components and anaesthesia type. Among patients who received opioids, CA-THA and RA-THA were associated with a 2% lower total opioid consumption than MA-THA, with discharge year and anaesthesia type as key predictors of total opioid use. The greatest differences were observed on POD 1: CA-THA had significantly lower odds of opioid consumption than MA-THA, and among opioid users, CA-THA and RA-THA were associated with 9% and 6% lower consumption, respectively, compared with MA-THA.
Interpretation
Whereas other studies have examined the association between technology-assisted THA and opioid use, ours adopted a different analytic approach.5,7,8,23 The first part of our two-step regression analysis examined the odds of consuming opioids in association with surgical modality. We found that CA-THA was associated with lower odds of opioid consumption than MA-THA and RA-THA. The proposed mechanism through which technology-assisted THA might affect opioid requirements is the need for less soft tissue manipulation and the more accurate and efficient acetabular cup placement, which could lead to decreased acute inflammatory response and lower pain.24, 25, 26, 27 Nevertheless, several studies have reported that an entirely opioid-free postoperative course is more related to the structure and implementation of analgesic protocols rather than pain severity alone.28, 29, 30 Thus, it is unlikely that surgical technique alone meaningfully altered the likelihood of remaining opioid-free—particularly because total LOS calculations include day-of-surgery opioid administration, which is often pre-emptive. The differences observed between techniques might reflect residual variation in analgesic pathways that could not be fully captured by the proxy variables available. Considering that CA-THA had almost double the proportion of cases in the outpatient setting compared with RA-THA and MA-THA, it could be that the surgical team and institutional protocols associated with CA-THA are more likely to have implemented an opioid-free enhanced recovery after surgery pathway.
Indeed, the most influential variables affecting opioid-free THA were anaesthesia technique, where neuraxial anaesthesia had the strongest opioid-free effect, and number of multimodal analgesia components, where an increasing number was associated with higher likelihood of receiving any opioids. In contrast, increasing incorporation of multimodal analgesia components was associated with a lower opioid dose among those who consumed opioids. Our lack of knowledge on whether an increasing amount of multimodal analgesia components represents a more comprehensive protocol or a need-based escalation limits the interpretation of this finding. The data pattern perhaps suggests the former, as a more comprehensive analgesic protocol would be more likely to include opioids, but at a lower dose owing to less frequent breakthrough pain and need for rescue medication.31, 32, 33
In contrast, opioid dosage can be influenced significantly more by pain severity.28, 29, 30 Among opioid consumers, we found that technology-assisted THA was associated with lower total opioid consumption. This association confirms the existing but limited data suggesting that RA-THA and CA-THA lead to less opioid consumption than MA-THA in the acute postoperative setting.5,7,8,23 As mentioned above, less soft tissue manipulation and increased accuracy and efficiency in component placement, specifically of the cup, could be the primary drivers behind this association.7,24,27 In theory and based on total knee arthroplasty reports,27 RA-THA and CA-THA might lead to less soft tissue manipulation, as the stereotactic help would allow cup placement to happen with less, but sufficient, visibility. This in turn could lead to a decreased acute inflammatory response that peaks on POD 1 according to a quantitative analysis of tissue injury for minimally invasive THA.34 This could very well explain why the strongest associations between technology use and opioid consumption were observed on POD 1. Unlike the total LOS results, the reduced likelihood of opioid-free status on POD 1 together with lower opioid consumption in technology-assisted THA likely reflects a reduction in early postoperative inflammation and pain. This finding is supported by Piple and colleagues,5 who also described reduction in POD 1 opioid use with technology-assisted THA. Similarly, Buchan and colleagues7 observed no difference in PACU opioid use but observed less opioid consumption with RA-THA on the hospital floor.
Even though results among studies are consistent, the clinical significance of this association is yet to be determined. We observed a 2% reduction in total opioid use and a 6–9% reduction on POD 1 with technology-assisted THA. These findings are similar to Piple and colleagues,5 who reported a 10% reduction on POD 1, and to Oettl and colleagues,23 who found a 0.11 hourly MME reduction with RA-THA. Buchan and colleagues7 reported a 19% reduction with RA-THA relative to MA-THA, but their result was not adjusted for confounding other than opioid tolerance. Based on the available data, a difference likely exists, but it is not clinically meaningful to the extent that it should affect surgical and perioperative decision-making. In fact, our models highlight the significance of opting for neuraxial anaesthesia to completely avoid or reduce opioid use, a fact well established in the perioperative literature.35 Furthermore, the strong association between lower opioid dose and recency of discharge year and the increasing amount of multimodal analgesia components highlight the evolving importance and incorporation of multimodal pain management strategies.16 Taken together, our results indicate that technology-assisted THA may provide small, complementary advantages to current multimodal pain management strategies, which can be thoughtfully incorporated into preoperative discussions with patients.
Limitations
This study is subject to limitations inherent in its retrospective and observational design, namely causal inference. Although Premier allows for unique population-level conclusions for >25% of US healthcare, selection bias of hospitals opting to be in the database is always a possibility and may be a limiting factor to the generalisability of our results. Moreover, although regular data entry checks are performed,10 entry errors are always possible. Lack of data on history of preoperative opioid use, case complexity, BMI, peripheral nerve block type, and surgical approach (anterior vs posterior) could have affected our results and could pose limitations on our interpretation.36,37 Nonetheless, there are no data that would suggest the distribution of these patients would be different among comparison groups.7 Selection of surgical modality was not randomised, allowing for unknown residual confounding. Nevertheless, the intraoperative complication rate was similar among groups, and surgical time differences were predictable.5 The lack of decision-making information extends to two important variables that affect opioid requirements: anaesthesia choice and multimodal analgesia. To address this, we adjusted for hospital and surgeon, which are the best available proxies for the multidisciplinary team of care as information on the anaesthetist is not available. Still, these two variables represent proxies and not perfect equivalents of institutional and care-team decision-making. Even though our models were adjusted for the number of multimodal analgesia components used, this is only a crude approximation of multimodal use and does not reflect dosing, timing, and adherence to protocols. Finally, patient-reported outcome measures of pain were not available in the dataset, which does not allow for a multidimensional assessment of pain management.
Conclusions
Among those who consumed opioids, technology-assisted THA was associated with lower opioid consumption than MA-THA, primarily owing to differences observed on POD 1. Although these differences are not clinically significant enough to affect decision-making, patients could be informed before surgery that technology-assisted THA might contribute to an optimal pain management strategy that is primarily driven by the choice of anaesthesia technique and the evolving multimodal analgesia protocols.
Authors’ contributions
Conceptualisation: PG, JL, LP, SGM, RGM, JP, AGDV
Methodology: PG, JER, RR, LR, AI, AS, CC, JW, STZ, SEC, DM
Formal analysis: PG, JER, AI, JW
Writing—original draft: PG, AS
Writing—review and editing: all authors
Investigation: JER, AI, JW
Supervision: JL, SGM, RGM, JP, AGDV
Funding acquisition: SGM
Project administration: AGDV
Funding
Anesthesiology Departmental Mission Fund, Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery.
Declarations of interest
SGM is the holder of a US patent for a Multicatheter Infusion System (US-2017-0361063) and is the owner of SGM Consulting, LLC. JP, SGM, and JL are partners in Parvizi Surgical Innovations, LLC. RGM discloses the following relationships: 1st Vice President of International Society of Arthroscopy, Knee Surgery and Orthopedic Sports Medicine (ISAKOS), royalties from books published with Springer and Demos Health Equity, compensation for seat on science advisory board of Mend Nutrition Inc. AGDV reports a relationship with Hip and Knee Societies that includes Committee membership. AGDV holds stock options with Naviswiss, WishBone. AGDV discloses the following relationship with Enovis and MicroPort: consulting agreement. AGDV receives royalty payments from Orthodevelopment and Link. None of the aforementioned relations influenced the conduct of the present study. The other authors declare that they have no conflicts of interest.
Handling Editor: Joana Berger
Footnotes
A preliminary analysis of this work was presented at the 50th Annual Regional Anesthesiology and Acute Pain Medicine Meeting, 1–3 May 2025, Orlando, FL, USA.
Supplementary data to this article can be found online at https://doi.org/10.1016/j.bjao.2026.100528.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Wu C.L., King A.B., Geiger T.M., et al. American Society for Enhanced Recovery and Perioperative Quality Initiative joint consensus statement on perioperative opioid minimization in opioid-naive patients. Anesth Analg. 2019;129:567–577. doi: 10.1213/ANE.0000000000004194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Halawi M.J., Grant S.A., Bolognesi M.P. Multimodal analgesia for total joint arthroplasty. Orthopedics. 2015;38:e616–e625. doi: 10.3928/01477447-20150701-61. [DOI] [PubMed] [Google Scholar]
- 3.Hojer Karlsen A.P., Geisler A., Petersen P.L., Mathiesen O., Dahl J.B. Postoperative pain treatment after total hip arthroplasty: a systematic review. Pain. 2015;156:8–30. doi: 10.1016/j.pain.0000000000000003. [DOI] [PubMed] [Google Scholar]
- 4.de Souza D.N., Lorentz N.A., Charalambous L., Galetta M., Petrilli C., Rozell J.C. Comprehensive pain management in total joint arthroplasty: a review of contemporary approaches. J Clin Med. 2024;13:6819. doi: 10.3390/jcm13226819. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Piple A.S., Wang J.C., Hill W., et al. Postoperative outcomes and trends in computer-navigated and robotic-assisted total hip arthroplasty. Hip Int. 2024;34:569–577. doi: 10.1177/11207000241264256. [DOI] [PubMed] [Google Scholar]
- 6.Kunze K.N., Bovonratwet P., Polce E.M., Paul K., Sculco P.K. Comparison of surgical time, short-term adverse events, and implant placement accuracy between manual, robotic-assisted, and computer-navigated total hip arthroplasty: a network meta-analysis of randomized controlled trials. J Am Acad Orthop Surg Glob Res Rev. 2022;6 doi: 10.5435/JAAOSGlobal-D-21-00200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Buchan G.B.J., Bernhard Z., Hecht C.J., 2nd, Davis G.A., Pickering T., Kamath A.F. Improved perioperative narcotic usage patterns in patients undergoing robotic-assisted compared to manual total hip arthroplasty. Arthroplasty. 2023;5:56. doi: 10.1186/s42836-023-00211-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Zhuang T., Young B., Hooper J., Amanatullah D.F., Shapiro L., Kamal R.N. Opioid use after robotic-assisted versus conventional total hip arthroplasty: a national database analysis. Orthopedics. 2024;47:289–294. doi: 10.3928/01477447-20240702-03. [DOI] [PubMed] [Google Scholar]
- 9.Vandenbroucke J.P., von Elm E., Altman D.G., et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4 doi: 10.1371/journal.pmed.0040297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Premier Applied Sciences® tRDoPI. Premier Healthcare Database white paper: data that informs and performs. Updated 7 September 2020. Available from: https://learn.premierinc.com/white-papers/premier-healthcare-database-whitepaper (accessed 28 October 2024).
- 11.Cozowicz C., Olson A., Poeran J., et al. Opioid prescription levels and postoperative outcomes in orthopedic surgery. Pain. 2017;158:2422–2430. doi: 10.1097/j.pain.0000000000001047. [DOI] [PubMed] [Google Scholar]
- 12.Ekhtiari S., Pincus D., Ruangsomboon P., Paterson J.M., Ravi B. Inter-hospital variation in early major complication rates following total hip arthroplasty: a population-based study. Int Orthop. 2025;49:589–594. doi: 10.1007/s00264-025-06423-5. [DOI] [PubMed] [Google Scholar]
- 13.Chung B.C., Bouz G.J., Mayfield C.K., et al. Dose-dependent early postoperative opioid use is associated with periprosthetic joint infection and other complications in primary TJA. J Bone Joint Surg Am. 2021;103:1531–1542. doi: 10.2106/JBJS.21.00045. [DOI] [PubMed] [Google Scholar]
- 14.Reisinger L, Cozowicz C, Zhong H, et al. Trends in comorbidities and complications among octogenarians and nonagenarians undergoing primary total joint arthroplasty in the United States. Anesth Analg Advance Access published on July 23, 2025, doi:10.1213/ANE.0000000000007631. [DOI] [PubMed]
- 15.Chen E.A., Roof M.A., Lygrisse K.A., Kurapatti M., Hepinstall M.S., Schwarzkopf R. ICD-10 coding mismatch in computer and robotic assisted primary total hip arthroplasty. J Arthroplasty. 2021;36:3934–3937. doi: 10.1016/j.arth.2021.08.002. [DOI] [PubMed] [Google Scholar]
- 16.Giannakis P., Saleeb J., Illescas A., et al. Trends in use of nonopioid analgesic modes over time in inpatient and outpatient total knee arthroplasty. Anesthesiology. 2025;143:1123–1126. doi: 10.1097/ALN.0000000000005638. [DOI] [PubMed] [Google Scholar]
- 17.Memtsoudis S.G., Poeran J., Zubizarreta N., et al. Association of multimodal pain management strategies with perioperative outcomes and resource utilization: a population-based study. Anesthesiology. 2018;128:891–902. doi: 10.1097/ALN.0000000000002132. [DOI] [PubMed] [Google Scholar]
- 18.Cozowicz C., Bekeris J., Poeran J., et al. Multimodal pain management and postoperative outcomes in lumbar spine fusion surgery: a population-based cohort study. Spine (Phila Pa 1976) 2020;45:580–589. doi: 10.1097/BRS.0000000000003320. [DOI] [PubMed] [Google Scholar]
- 19.Chung A.R., Mather R.V., Gutierrez R., et al. Association of peripheral nerve blocks with increased postoperative pain and opioid use in orthopaedic surgery: a single-centre retrospective cohort study. Br J Anaesth. 2025;135:1286–1296. doi: 10.1016/j.bja.2025.05.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Santa Cruz Mercado L.A., Liu R., Bharadwaj K.M., et al. Association of intraoperative opioid administration with postoperative pain and opioid use. JAMA Surg. 2023;158:854–864. doi: 10.1001/jamasurg.2023.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Hecht C.J., Porto J.R., Sanghvi P.A., Homma Y., Sculco P.K., Kamath A.F. Contemporary analysis of the learning curve for robotic-assisted total hip arthroplasty emerging technologies. J Robot Surg. 2024;18:160. doi: 10.1007/s11701-024-01928-4. [DOI] [PubMed] [Google Scholar]
- 22.Hecht Ii C.J., Porto J.R., Sanghvi P.A., Homma Y., Sculco P.K., Kamath A.F. Navigating the learning curve: assessing caseload and comparing outcomes before and after the learning curve of computer-navigated total hip arthroplasty. J Robot Surg. 2024;18:104. doi: 10.1007/s11701-024-01855-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Oettl F.C., Weinblatt A.I., Lyman S., et al. Length of stay, pain and opioid consumption following manual versus robotic-assisted total hip arthroplasty. J Orthop. 2025;70:202–207. doi: 10.1016/j.jor.2025.08.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Goyal P., Lau A., Naudie D.D., Teeter M.G., Lanting B.A., Howard J.L. Effect of acetabular component positioning on functional outcomes in primary total hip arthroplasty. J Arthroplasty. 2017;32:843–848. doi: 10.1016/j.arth.2016.08.024. [DOI] [PubMed] [Google Scholar]
- 25.Guo D.H., Li X.M., Ma S.Q., Zhao Y.C., Qi C., Xue Y. Total hip arthroplasty with robotic arm assistance for precise cup positioning: a case-control study. Orthop Surg. 2022;14:1498–1505. doi: 10.1111/os.13334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Najarian B.C., Kilgore J.E., Markel D.C. Evaluation of component positioning in primary total hip arthroplasty using an imageless navigation device compared with traditional methods. J Arthroplasty. 2009;24:15–21. doi: 10.1016/j.arth.2008.01.004. [DOI] [PubMed] [Google Scholar]
- 27.American Academy of Orthopaedic Surgeons. Surgical Management of Osteoarthritis of the Knee. Rosemont, IL, USA: American Academy of Orthopaedic Surgeons, 2022. Available from: https://www.aaos.org/globalassets/quality-and-practice-resources/surgical-management-knee/smoak2cpg.pdf (accessed 1 August 2025).
- 28.Chassery C., Atthar V., Marty P., et al. Opioid-free versus opioid-sparing anaesthesia in ambulatory total hip arthroplasty: a randomised controlled trial. Br J Anaesth. 2024;132:352–358. doi: 10.1016/j.bja.2023.10.031. [DOI] [PubMed] [Google Scholar]
- 29.Turk R., Averkamp B., Hietpas K., et al. An opioid-free perioperative pain protocol is noninferior to opioid-containing management: a randomized controlled trial. J Bone Joint Surg Am. 2025;107:665–677. doi: 10.2106/JBJS.24.00460. [DOI] [PubMed] [Google Scholar]
- 30.Padilla J.A., Gabor J.A., Schwarzkopf R., Davidovitch R.I. A novel opioid-sparing pain management protocol following total hip arthroplasty: effects on opioid consumption, pain severity, and patient-reported outcomes. J Arthroplasty. 2019;34:2669–2675. doi: 10.1016/j.arth.2019.06.038. [DOI] [PubMed] [Google Scholar]
- 31.Mousad A.D., Nithagon P., Grant A.R., Yu H., Niu R., Smith E.L. Non-opioid analgesia protocols after total hip arthroplasty and total knee arthroplasty: an updated scoping review and meta-analysis. J Arthroplasty. 2025;40:1643. doi: 10.1016/j.arth.2024.11.013. 52.e6. [DOI] [PubMed] [Google Scholar]
- 32.Feng J.E., Mahure S.A., Waren D.P., et al. Utilization of a novel opioid-sparing protocol in primary total hip arthroplasty results in reduced opiate consumption and improved functional status. J Arthroplasty. 2020;35:S231–S236. doi: 10.1016/j.arth.2020.02.009. [DOI] [PubMed] [Google Scholar]
- 33.Fleischman A.N., Tarabichi M., Foltz C., et al. Cluster-randomized trial of opiate-sparing analgesia after discharge from elective hip surgery. J Am Coll Surg. 2019;229:335. doi: 10.1016/j.jamcollsurg.2019.05.026. 45.e5. [DOI] [PubMed] [Google Scholar]
- 34.Kwak S., Chun Y., Rhyu K., Cha J., Cho Y. Quantitative analysis of tissue injury after minimally invasive total hip arthroplasty. Clin Orthop Surg. 2014;6:279–284. doi: 10.4055/cios.2014.6.3.279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lee K.H., Wang J.C., Chang C.Y., et al. Regional anaesthesia modalities for primary total hip arthroplasty: a systematic review and component network meta-analysis. Br J Anaesth. 2025;135:469–482. doi: 10.1016/j.bja.2025.04.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Bovonratwet P., Chen A.Z., Shen T.S., Ondeck N.T., Kunze K.N., Su E.P. Postoperative patient-reported pain and opioid consumption after total hip arthroplasty: a comparison of the direct anterior and posterior approaches. J Am Acad Orthop Surg. 2022;30:e108–e117. doi: 10.5435/JAAOS-D-21-00082. [DOI] [PubMed] [Google Scholar]
- 37.Martinez M., Yep T., Chung S., Raouf M., Lojeski E. Opioid consumption patterns before and after elective joint surgery. J Arthroplasty. 2025;41:370. doi: 10.1016/j.arth.2025.06.053. 4.e2. [DOI] [PubMed] [Google Scholar]
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