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Length of stay, pain and opioid consumption following manual versus robotic-assisted total hip arthroplasty
, Aaron I. Weinblatta, Stephen Lymana, Shu-Han Wanga, Michael Paridesa, Francesca Coxea, Gwo-Chin Leea, Amar S. Ranawata, Alejandro Gonzalez Della Vallea
⁎Corresponding author: Felix C. Oettl. felix.oettl@balgrist.ch
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Received: ,
Accepted: ,
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.
Abstract
Abstract
Robotic assistance in total hip arthroplasty (THA) has increased, but the influence on outcomes compared to manual THA remains uncertain. With the growing emphasis on reducing opioid consumption after arthroplasty, we studied whether robotic assistance was associated with length of stay (LOS), pain, and opioid use after THA.
We included 14,501 opioid-naïve patients who underwent THA at a single institution between 2019 and 2023 (8900 manual and 5601 robotic). In-hospital pain scores (NRS), LOS, and opioid consumption patterns were collected. Opioid dosages were converted to morphine milligram equivalents (MMEs). After preliminary bivariate analysis, multivariable linear regression analyses were performed adjusting for age, sex, race, BMI, ASA-class, smoking status, cement use, marital status, year of surgery, surgeon experience, approach and periarticular injection.
Robotic THA was associated with significantly shorter LOS (Estimate: 6.8 h, 95 %CI: 8.0, −5.6, p < 0.0001). Robotic THA patients had higher minimal and mean pain scores (Estimate: 0.03, 95 %CI: 0.02–0.05, p < 0.001; Estimate: 0.08, 95 %CI: 0.03, 0.14, p = 0.0042). Robotic THA patients used less MMEs per hour of hospitalization (Estimate −0.11 MMEs, 95 %CI -0.174, −0.039, p = 0.0021), but were prescribed more MMEs at discharge (Estimate: 3.59 MMEs, 95 %CI: 0.323, 6.856, p = 0.0312). The differences in MMEs refilled after discharge and total 90-day opioid prescription patterns were not significant.
Robotic assistance in THA was independently associated with a slightly shorter LOS. The significantly higher pain scores (0.08 points of NRS) and lower in-hospital opioid consumption (0.11 MMEs/hour) suggest that while some statistically significant differences exist between robotic-assisted and manual THA, these differences may not be clinically meaningful.
Keywords
Robotic-assisted total hip arthroplasty
Total hip arthroplasty
Postoperative pain
Opioid utilization
Length of hospital stay
1 Introduction
The past decade has witnessed an intensified focus on mitigating the adverse effects linked to prescription opioids, particularly opioid use disorder and opioid-related fatalities. Orthopedic surgical procedures have been identified as a substantial contributor to the opioid prescription burden, constituting approximately 10 % of all opioid prescriptions dispensed in the United States.1–4
Inadequate postoperative pain management is linked to prolonged hospital stays, increased complications, and diminished patient satisfaction.5 In the context of total hip arthroplasty (THA), effective pain control is a multifaceted challenge, and achieving optimal pain management remains a key objective for both orthopedic surgeons and their patients.
Robotic arm-assisted THA (raTHA) is a technological advancement over manual THA (mTHA). While raTHA is reported to improve implant positioning and theoretically reduce pain, opioid consumption, and accelerate rehabilitation through smaller incisions and less soft tissue trauma, its purported postoperative benefits are debated due to limitations in existing studies (e.g., small sample sizes, insufficient risk adjustment).6–13 While the potential benefits of raTHA have been extensively discussed, there is a lack of substantial evidence specifically examining the impact of raTHA on postoperative pain management and opioid consumption.14
The primary objectives of this investigation are to conduct a comparative analysis between raTHA and mTHA across key postoperative variables. Specifically, we aim to evaluate hospital length of stay (LOS), postoperative pain, and opioid consumption patterns both during hospitalization and extending to 90 days postoperatively.
2 Methods
Following institutional review board approval, a retrospective cohort study was conducted in patients undergoing primary, elective THA at a single academic institution between January 2019 and January 2024. Patient data was retrieved from the electronic medical record system.
The outcomes assessed were: 1) time in postoperative acute care unit (PACU) in minutes, 2) Length of hospitalization (LOS) in hours, 3) postoperative pain levels (numeric pain rating scale [NRS]) and 4) opioid consumption converted to morphine milligram equivalents (MME) and related metrics.
PACU time was recorded in hours (h) and defined as the time spent in the PACU immediately after surgery. LOS was recorded in hours and defined as time passed between admission of patient to the hospital and discharge from the hospital. Pain levels were collected by the nursing staff on an NRS of 0–10 several times after surgery. 3 outcomes are derived from that: minimal pain, which resembles the lowest pain score recorded, average pain, resembling the mean of all collected pain scores and highest pain, which represents the highest pain score measured during hospitalization. Opioid utilization was recorded at multiple intervals between admission and 90-days post-surgery. In-hospital MMEs were calculated by converting administered opioids to MMEs. Opioids utilized at and after discharge are calculated by converting prescription dosages to MMEs.
2.1 Patients
We identified 20,104 patients having undergone primary THA between February 1, 2019 and January 30, 2023. We excluded patients that underwent THA prior to February 2019 due to a pain management protocol change at our institution at this time coupled with increasing utilization of raTHA, which may have skewed our results.15 Patients that underwent computer assisted (e.g. navigation, but without robotic arm) THA, and patients that were not opioid naïve were excluded. Ultimately, we included 14,501 patients in the study. 8900 of those included were mTHA, the remaining 5601 were raTHA (Table 1).
| All | Manual THA | Robotic THA | p-value | Standardized mean difference | |
| N = 14,501 | N = 8900 | N = 5601 | |||
| Mean (SD) | Mean (SD) | Mean (SD) | |||
| Age | 64.9 (11.3) | 65.33 (11.12) | 64.21 (11.12) | <0.001 | 0.101 |
| BMI | 28.6 (5.9) | 28.5 (6) | 28.7 (5.7) | 0.003 | 0.035 |
| N (%) | N (%) | N (%) | p-value | ||
| Women | 8556 (59) | 5354 (60.16) | 3202 (57.17) | <0.001 | 0.061 |
| Race | 0.024 | 0.068 | |||
| White or Caucasian | 12,369 (85.3) | 7596 (85.35) | 4773 (85.22) | ||
| Black or African American | 947 (6.53) | 617 (6.93) | 330 (5.89) | ||
| American Indian or Alaska Native | 30 (0.21) | 21 (0.24) | 9 (0.16) | ||
| Native Hawaiian or other Pacific Islander | 15 (0.1) | 8 (0.09) | 7 (0.12) | ||
| Other | 516 (3.56) | 286 (3.21) | 230 (4.11) | ||
| Unavailable | 320 (2.2) | 191 (2.15) | 129 (2.31) | ||
| Smoking | 0.524 | 0.025 | |||
| Nonsmoker | 9108 (62.81) | 5600 (62.92) | 3508 (62.63) | ||
| Former Smoker | 5183 (35.74) | 3181 (35.74) | 2002 (35.74) | ||
| Smoker | 133 (0.92) | 77 (0.87) | 56 (1) | ||
| Unknown | 77 (0.53) | 42 (0.47) | 35 (0.62) | ||
| ASA Level | 0.01 | 0.058 | |||
| 1 | 595 (4.13) | 367 (4.15) | 228 (4.09) | ||
| 2 | 11,669 (80.9) | 7094 (80.15) | 4575 (82.09) | ||
| 3 | 2151 (14.91) | 1386 (15.66) | 765 (13.73) | ||
| 4 | 9 (0.06) | 4 (0.05) | 5 (0.09) | ||
| Charlson Comorbidity Index | 0.402 | 0.029 | |||
| 0 | 10,244 (70.65) | 6246 (70.19) | 3998 (71.39) | ||
| 1 | 2742 (18.91) | 1711 (19.23) | 1031 (18.41) | ||
| 2 | 963 (6.64) | 593 (6.66) | 370 (6.61) | ||
| 3+ | 550 (3.79) | 3493.92) | 201 (3.59) | ||
| Spinal anesthesia | 14,023 (96.7) | 8626 (96.92) | 5397 (96.36) | 0.226 | |
| Approach | <0.001 | 0.266 | |||
| Direct anterior approach | 4079 (28.13) | 2905 (32.64) | 1174 (20.96) | ||
| Posterior approach | 10,422 (71.87) | 5995 (67.36) | 4427 (79.04) | ||
| Cemented | 1453 (10.08) | 930 (10.52) | 523 (9.39) | 0.029 | 0.038 |
| Married | 9608 (66.26) | 5907 (66.37) | 3701 (66.08) | 0.008 | 0.075 |
| Surgery Year | <0.001 | 0.488 | |||
| 2019 | 2723 (18.78) | 2143 (24.08) | 580 (10.36) | ||
| 2020 | 2432 (16.77) | 1761 (19.79) | 671 (11.98) | ||
| 2021 | 2823 (19.47) | 1628 (18.29) | 1195 (21.34) | ||
| 2022 | 3222 (22.22) | 1660 (18.65) | 1562 (27.89) | ||
| 2023 | 3301 (22.76) | 1708 (19.19) | 1593 (28.44) | ||
| Experienced surgeon | 10,320 (71.17) | 8009 (89.99) | 2311 (41.26) | <0.001 | 1.195 |
| Periarticular Injection | 11,157 (79.94) | 5983 (67.22) | 5174 (92.4) | <0.001 | 0.66 |
Table 1 reveals several significant differences between the patient population undergoing manual and robotic THA. Patients undergoing raTHA were slightly younger, had a higher BMI, and were less likely to be women. Racial distribution showed minor variations, with less of the Black and African American population undergoing raTHA. The robotic group had a higher proportion of ASA level 2 patients and fewer ASA level 3 patients. Robotic THA procedures more frequently used the posterior approach and periarticular injections. The use of robotic THA increased over time, with higher percentages in recent years. Manual THA was more often performed by experienced surgeons (defined as those with ≥15 years in practice). These findings highlight substantial differences in patient demographics, surgical techniques, and surgeon experience between manual and robotic THA procedures.
2.2 Pain management protocol
Patients underwent standardized anesthetic management, including spinal anesthesia and periarticular infiltration of local anesthetic agents (Table 1). The postoperative analgesic regimen comprised scheduled acetaminophen (650-1000 mg every 6h), meloxicam (7.5–15 mg daily), and as-needed oxycodone (5–10 mg every 4–6h). Intravenous hydromorphone was administered for breakthrough pain. Rehabilitation protocols-initiated weight-bearing as tolerated on either the day of surgery or the first postoperative day. Upon discharge, patients continued the established oral analgesic regimen and outpatient physical therapy. Discharge opioid prescriptions were individualized based on patient preference, tolerance, allergies, and prior opioid use. For additional opioid prescriptions, patients contacted the postoperative nurse practitioner or surgeon directly. In accordance with state regulations, prescribers were mandated to review the statewide prescription monitoring program to assess for potential substance abuse prior to dispensing controlled substances. In the absence of evidence suggestive of abuse, refills were provided, with quantities determined by the prescriber's clinical assessment of the patient's needs.
2.3 Data analyses
Continuous variables were reported as both mean (standard deviation [SD]) median, quartile 1 and quartile 3. An initial univariate analysis was conducted on all collected variables. The Mann-Whitney U test was used for continuous variables, and the Chi-squared test was used for categorical variables. Univariable linear regression has been utilized for comparison of continuous outcome variables. Due to the retrospective nature of the study missing datapoints were omitted from the analysis. Subsequently, multivariable regression analysis was performed to compare the respective groups, controlling for potential confounders identified through the univariate analysis. The confounding variables incorporated into the multivariable model included age, BMI, sex, ASA classification, race, smoking status, implant fixation method (cemented vs. uncemented), marital status, year of surgery, Charlson comorbidity index, surgeon experience (dichotomized as more or less than 15 years in practice), surgical approach, and administration of periarticular injection. We decided to control for periarticular injections, due to its suggested reduction of postoperative pain16,17. Statistical significance was established at a p-value <0.05. All statistical analyses were conducted using R (Version 4.3.2, R Foundation for Statistical Computing, Vienna, Austria) and RStudio (Version February 1, 5042, RStudio, Inc., Boston, MA).
3 Results
In univariate analysis the mean PACU time in raTHA (5.08h ± 2.92) was significantly shorter then in mTHA (5.21 ± 3.16; p = 0.013). Multivariable regression did not find a statistically significant difference in PACU time (Estimate 0.039; 95 % CI -0.09 - 0.17, p = 0.56).
In univariate analysis the mean LOS in raTHA (38.3h ± 29.19) was significantly shorter then in mTHA (45.03 ± 32.34; p < 0.0001). Multivariable regression similarly showed a statistically significant shorter LOS in raTHA (Estimate −6.815; 95 % CI -7.99 to −5.65, p < 0.0001).
Univariate analysis showed significantly higher minimal pain scores in raTHA (0.09 ± 0.55) compared to mTHA (0.06 ± 0.49, p < 0.0001). This finding remained significant in multivariable analysis (Estimate: 0.031, 95 %CI: 0.01–0.05, p < 0.001). Similarly, univariate analysis showed mean pain levels were higher in raTHA (3.04 ± 1.42) compared to mTHA (2.85 ± 1.4, p < 0.0001), which was preserved in multivariable analysis (Estimate: 0.08, 95 %CI: 0.03–0.14, p = 0.005). Peak pain levels did not show a statistical significance in univariate (p = 0.11) or multivariable (p = 0.988) analysis.
Univariate analysis showed significantly less total inpatient MME consumption in raTHA (74.1 ± 72.46) compared to mTHA (79.83 ± 80.39, p < 0.001), which was preserved after correcting for potential confounders (Estimate: 11.82, 95 %CI: 15.01 to −8.63, p < 0.001). Total inpatient MME/h was significantly higher in raTHA (2.2 ± 1.61) compared to mTHA (2.07 ± 1.71, p < 0.001), this relationship reversed in multivariable analysis, with raTHA showing less total inpatient MME/h consumption (Estimate: 0.107, 95 %CI: 0.17 to −0.04, p = 0.002). Investigating postoperative inpatient MME consumption, univariate analysis did not show a statistically significant difference between the groups (p = 0.1779), multivariable analysis showed significantly less postoperative inpatient MME in raTHA (Estimate: 3.065, 95 %CI -6.02 to −0.11, p = 0.042). Adding back that temporal relationship, postoperative inpatient MME/h shows greater opioid utilization in raTHA (2.12 ± 1.91) compared to mTHA (1.81 ± 1.73, p < 0.0001), this difference remained highly significant in multivariable regression (Estimate: 0.31, 95 %CI 0.24–0.38, p < 0.001).
MMEs prescribed at discharge were significantly less in raTHA (176.59 ± 77.83) than in mTHA (191.62 ± 94.35, p < 0.0001), however after correcting for potential confounders we found higher MMEs prescribed at discharge in raTHA (Estimate: 3.638, 95 %CI 0.37–6.9, p = 0.029).
Total MMEs utilized within 90-days (recorded in-hospital opioids + prescriptions after discharge) were significantly lower in raTHA (321.64 ± 256.62) than in mTHA (347.69 ± 446.98, p < 0.0001). Multivariable regression did not find a statistically significant difference between groups (Estimate: 12.472, 95 %CI -28.82 – 3.87, p = 0.135).
4 Discussion
To our knowledge, this study presents the largest analysis comparing opioid consumption, postoperative pain levels and resource utilization in raTHA versus mTHA performed at a single institution. The main finding of our study is that while robotic-assisted THA was associated with a statistically significant shorter length of stay, this difference is unlikely to be clinically meaningful, and neither are differences in postoperative pain and opioid consumption compared to manual THA.
This single-center retrospective study has several limitations. Its validity relies on adequately captured metrics by healthcare personnel. Intraoperative and postoperative protocols were not standardized, left to the surgeon's discretion, and while efforts were made to control for documented protocol differences (e.g., periarticular injection), uncaptured variations likely exist. Furthermore, estimated opioid utilization based on MMEs prescribed at discharge and further prescriptions may overestimate actual consumption. We also could not control for variations in attending surgeons' standard intraoperative and postoperative protocols, including weight-bearing restrictions and analgesic prescriptions, which were at their discretion. Multivariable regression controlled for administration of local anesthetic infiltration at the conclusion of the procedure. Our study is further limited by the non-randomized allocation of patients to robotic versus conventional THA, as the choice of surgical approach was left to individual surgeon preference and practice patterns. In our center, surgeons typically demonstrate consistent preferences, with some performing predominantly robotic procedures, others performing predominantly manual procedures, and some utilizing both approaches. While this reflects real-world clinical practice, it introduces potential selection bias that may confound the comparison between surgical approaches. Although we controlled for available surgeon factors in our multivariable analysis, residual confounding related to surgeon-specific techniques and patient selection criteria cannot be entirely excluded. Additionally, while surgeons in our center book operating rooms for full days and schedule cases at their discretion, which should minimize systematic bias related to time of surgery and its potential impact on length of stay, we were unable to control for variations in the number of procedures scheduled per day between robotic and conventional THA cases. This represents another potential source of bias in our length of stay outcomes that should be considered when interpreting our results. Furthermore, it is important to highlight the significant baseline differences between the two groups, as shown in Table 1. There were significant differences in the use of periarticular injections (92.4 % in robotic vs. 67.2 % in manual) and the experience of the operating surgeon (90 % of manual THA surgeons had >15 years of experience vs. 41 % for robotic THA). While we adjusted for these factors in our multivariable regression, residual confounding is possible, and these differences may have influenced our results.
These factors highlight the need for cautious interpretation of the results and potential prospective randomized control trials to validate the findings. Finally, the outcome measures employed may lack the sensitivity to differentiate nuanced degrees of excellence in clinical outcomes.
The univariate decrease in PACU time for raTHA (Table 2) is likely due to the lower ASA level in raTHA patients (Table 1), as no significant difference was found after confounder adjustment (Table 3), furthermore the findings of Fontalis et al. neither show significant differences between the groups.18
| mTHA | raTHA | Mean Difference | 95 % CI | P-value | |||
| N = | 8900 | N = | 5601 | ||||
| Mean (SD) | Median (IQR) | Mean (SD) | Median (IQR) | ||||
| PACU time (h) | 5.21 (3.16) | 4 (4, 6) | 5.08 (2.92) | 4.00 (4, 6) | −0.14 | (-0.24, −0.04) | 0.0080 |
| LOS (h) | 45.03 (32.34) | 33 (28, 55) | 38.30 (29.19) | 30.00 (26, 49) | −6.73 | (-7.77, −5.69) | <0.0001 |
| NRS Lowest | 0.06 (0.49) | 0 (0, 0) | 0.09 (0.55) | 0.00 (0, 0) | 0.03 | (0.01, 0.05) | 0.0011 |
| NRS Average | 2.85 (1.4) | 2.81 (1.81, 3.85) | 3.04 (1.42) | 3.00 (2, 4) | 0.19 | (0.15, 0.24) | <0.0001 |
| NRS Highest | 7.24 (1.93) | 7.00 (7, 8) | 7.31 (1.87) | 7.00 (7, 8) | 0.07 | (0.00, 0.13) | 0.0386 |
| MME Total Inpatient | 79.83 (80.39) | 60.00 (32.5, 100) | 74.10 (74.26) | 57.50 (30, 95) | −5.74 | (-8.35, −3.13) | <0.0001 |
| MME/h Total Inpatient | 2.07 (1.71) | 1.69 (0.9, 2.8) | 2.20 (1.61) | 1.88 (1.01, 3.04) | 0.13 | (0.08, 0.19) | <0.0001 |
| MME Inpatient Postop | 60.84 (74.67) | 40.00 (17.5, 75) | 56.52 (68.48) | 40.00 (17.5, 70 | −4.32 | (-6.74, −1.90) | 0.0005 |
| MME/h Inpatient Postop | 1.81 (1.73) | 1.36 (0.63, 2.5) | 2.12 (1.91) | 1.63 (0.79, 2.92) | 0.31 | (0.25, 0.37) | <0.0001 |
| MME - Discharge | 191.62 (94.35) | 180.00 (150, 210) | 176.59 (77.83) | 180.00 (150, 180) | −15.03 | (-17.98, −12.07) | <0.0001 |
| MME - Total 90 Day | 347.69 (446.98) | 264.00 (207.5, 392.5) | 321.64 (256.62) | 249.00 (202.5, 361.5) | −26.05 | (-38.91, −13.19) | <0.0001 |
| Mean mTHA | Mean: raTHA | Estimate | 95 % low | 95 % high | P-value | |
| PACU time (h) | 6.4092 | 6.4479 | 0.039 | −0.09 | 0.17 | 0.556 |
| LOS (h) | 63.1572 | 56.3418 | −6.815 | −7.99 | −5.65 | <0.001 |
| NRS Lowest | 0.0308 | 0.0622 | 0.031 | 0.01 | 0.05 | <0.001 |
| NRS Average | 2.6619 | 2.7452 | 0.083 | 0.03 | 0.14 | 0.005 |
| NRS Highest | 7.6593 | 7.6599 | 0.001 | −0.08 | 0.08 | 0.988 |
| MME Total Inpatient | 84.4408 | 72.6213 | −11.82 | −15.01 | −8.63 | <0.001 |
| MME/h Total Inpatient | 1.7787 | 1.6717 | −0.107 | −0.17 | −0.04 | 0.002 |
| MME Inpatient Postop | 57.9233 | 54.8588 | −3.065 | −6.02 | −0.11 | 0.042 |
| MME/h Inpatient Postop | 1.2951 | 1.6052 | 0.31 | 0.24 | 0.38 | <0.001 |
| MME - Discharge | 169.4610 | 173.0985 | 3.638 | 0.37 | 6.9 | 0.029 |
| MME - Total 90 Day | 318.8622 | 306.3903 | −12.472 | −28.82 | 3.87 | 0.135 |
The shorter length of stay after raTHA found in both univariate and multivariable regression is in line with previously reported results.18–20 While a 6.8-h reduction in hospitalization time may seem modest, it could have meaningful effects on hospital efficiency.
During hospitalization, raTHA showed greater minimal and average pain levels, while peak pain levels were similar in both univariate and multivariable analyses. While literature on immediate postoperative pain is sparse, existing studies show no difference or slightly more pain in mTHA.14,21 It is crucial here to differentiate between statistical significance and clinical relevance. A 0.08-point difference on the NRS, while statistically significant in our large cohort, is unlikely to be perceived by a patient and falls well below the threshold for a minimal clinically important difference..22
Patients undergoing raTHA patients used nearly 3 MMEs less during the post-operative period and raTHA 11.7 MMEs fewer during full hospitalization, the latter of which has shown to be clinically significant.23 While an understudied topic, the sparse literature shows similar findings, with raTHA consuming fewer opioids during hospitalization, which has also been shown in the literature.19 Overall, the observed reduction of MMEs, may not represent a clinically meaningful opioid-sparing effect.
Investigating opioid prescriptions at discharge however, raTHA shows over 3.6 MMEs more than mTHA, which together with the shorter length of stay in raTHA probably shrinks the initially displayed difference. This can also be appreciated by evaluating the MME consumption per hours of hospitalization (Table 2, Table 3).
For overall 90-day opioid consumption, univariate analysis showed significantly less opioid use in raTHA, but this difference became non-significant after confounder adjustment, suggesting similar overall consumption. Buchan et al. similarly reported no difference in total postoperative MME at 6 weeks in their opioid-naïve cohort, which aligns with our findings despite their lack of confounder correction and potential power limitations.14
While our single-center study allowed for critical investigation and confounding variable correction, findings may be influenced by facility-specific factors (resources, staffing, protocols) not representative of other centers. Despite efforts and multivariable regression, potential unmeasured or unaccounted confounding factors remain, limiting generalizability. While the trends observed may align with some previous reports, the strength of our study lies in its large sample size of over 14,000 patients. This provides high statistical power and robust, real-world evidence, serving as an important confirmation or rejection of findings from smaller, less generalizable studies.
5 Conclusion
Our findings from this large, single-center retrospective study suggest that the adoption of robotic technology in THA may not lead to clinically significant improvements in LOS, postoperative pain and opioid consumption. The decrease in in-hospital opioid consumption while statistically and clinically significant is not sustained at the 90-day period, suggesting no significant difference in opioid consumption. Further exploration of potential short-term benefits to robotic THA could help identify clinical advantages.
Informed consent
A waiver of informed consent was granted in accordance with IRB guidelines, given that the study involves the use of de-identified data, ensuring patient confidentiality and privacy.
Guardian/patient's consent
A waiver of informed consent was granted in accordance with IRBguidelines, given that the study involves the use of de-identified data, ensuring patient confidentiality and privacy.
Ethical approval
This study has been reviewed and approved by the Institutional Review Board (IRB) under Expedited Review Category #5. The approval was granted on May 3, 2024, and will expire on May 2, 2027 (Study# 2024-0747). All necessary privacy and confidentiality measures, including the assignment of unique study numbers, will be strictly adhered to.
Credit author statement
All listed authors have contributed substantially to this work: FCO, AIW and AGDV developed the idea for the present study. SL, SW and MP were responsible for methodology and statistical analysis. FCO, AIW and AGDV performed primary manuscript preparation. Editing and final manuscript preparation was performed by FC, GL ASR, AGDV, AIW and FCO. All authors read and approved the final manuscript.
Funding
This study was partially funded by the generous donation of the Peterson Foundation.
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