Abstract
Background:
The objectives of this study were to determine the baseline patient characteristics associated with preoperative opioid use and to establish whether preoperative opioid use is associated with baseline patient-reported outcome measures in patients undergoing common hand surgeries.
Methods:
Patients undergoing common hand surgeries from 2015 to 2018 were retrospectively reviewed from a prospective orthopedic registry at a single academic institution. Medical records were reviewed to determine whether patients were opioid users versus nonusers. On enrollment in the registry, patients completed 6 Patient-Reported Outcomes Measurement Information System (PROMIS) domains (Physical Function, Pain Interference, Fatigue, Social Satisfaction, Anxiety, and Depression), the Brief Michigan Hand Questionnaire (BMHQ), a surgical expectations questionnaire, and Numeric Pain Scale (NPS). Statistical analysis included multivariable regression to determine whether preoperative opioid use was associated with patient characteristics and preoperative scores on patient-reported outcome measures.
Results:
After controlling for covariates, an analysis of 353 patients (opioid users, n = 122; nonusers, n = 231) showed that preoperative opioid use was associated with higher American Society of Anesthesiologists class (odds ratio [OR], 2.88), current smoking (OR, 1.91), and lower body mass index (OR, 0.95). Preoperative opioid use was also associated with significantly worse baseline PROMIS scores across 6 domains, lower BMHQ scores, and NPS hand scores.
Conclusions:
Preoperative opioid use is common in hand surgery patients with a rate of 35%. Preoperative opioid use is associated with multiple baseline patient characteristics and is predictive of worse baseline scores on patient-reported outcome measures. Future studies should determine whether such associations persist in the postoperative setting between opioid users and nonusers.
Keywords: opioids, pain, hand, baseline, preoperative, PROMIS
Introduction
The overprescription of opioids for pain management has become a growing concern in recent decades, and the field of orthopedic surgery has a critical role in the opioid crisis. Although orthopedic surgeons make up only 2.5% of physicians in the United States, they prescribe more opioids than any other surgical specialty. 1 Importantly, the vast majority of surveyed hand surgeons acknowledge opioid overuse in their field. 2 It has also become increasingly common for patients to use opioids chronically before undergoing orthopedic surgery, which is associated with increased morbidity, worse outcomes, and extended postoperative opioid use.3-7 Furthermore, recent studies have shown that preoperative opioid reduction can improve surgical outcomes in total joint arthroplasty patients and counseling can decrease short-term postoperative opioid use in both the hand and arthroplasty populations.6,8-13
While studies in the hand population have focused on postoperative pain management strategies and opioid use,7-11,14-20 there are little data regarding preoperative opioid use and its association with other clinical factors in patients undergoing elective hand surgery. Kazmers et al 7 recently reported the effect of baseline opioid use on new hand clinic patients, showing worse physical function, greater pain, and higher levels of depression and anxiety. Such baseline comparisons, however, are lacking in patients undergoing elective hand surgery in the preoperative setting.
In a cohort of patients undergoing common hand procedures, the objectives of this study were to determine the baseline patient characteristics associated with preoperative opioid use and to establish whether preoperative opioid use is associated with baseline patient-reported outcome measures. We hypothesized that various baseline patient characteristics would be associated with preoperative opioid use, and preoperative opioid use would be negatively associated with baseline patient-reported outcome measures related to pain, function, activity levels, and expectations for surgery.
Materials and Methods
An institutional review board–approved, prospective orthopedic registry, the Maryland Orthopaedic Registry, 21 was queried for patients undergoing elective hand surgeries at a single urban academic center between April 2015 and November 2018. Patients were included in the study if they underwent an elective hand surgery, were 17 years or older, were English speaking, were not incarcerated or a ward of the state, and were willing and able to participate in the study. Of the 495 patients enrolled in the registry, 137 patients were excluded because of incomplete baseline data. An additional 5 patients were excluded because of missing responses for the preoperative opioid use question, leaving 353 patients who underwent an elective hand surgery by 9 surgeons. Baseline patient characteristics obtained from the registry included age, sex, race, body mass index (BMI), comorbidities, American Society of Anesthesiologists (ASA) score, smoking status, insurance type, education, employment status, income, and procedure performed.
All enrolled patients were given the following questionnaires preoperatively: 6 Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive testing domains, including Physical Function (PF), Pain Interference (PI), Fatigue, Social Satisfaction (SS), Anxiety, and Depression, as well as the Brief Michigan Hand Questionnaire (BMHQ). Patients’ preoperative expectations were evaluated with the Musculoskeletal Outcomes Data Evaluation and Management System (MODEMS) Expectations questionnaire. Patients’ activity levels were assed with the Marx Upper Extremity Activity Rating Scale (Marx). Pain in the operative hand and the rest of the body was assessed with 2 Numeric Pain Scales (NPSs). Patients were excluded if they did not complete all PROMIS domain questionnaires and the BMHQ before surgery. Medical records were reviewed for comorbid conditions. Patients were stratified as “opioid users” or “opioid nonusers” by determining whether they had an opioid prescription marked as active during the preoperative visit or preoperative check-in on the day of surgery. The electronic medical record at our institution requires a full medication reconciliation before undergoing an elective procedure. Study data were collected and stored using the Research Electronic Data Capture electronic data capture tools.
Statistical Analyses
Continuous data were presented as mean and standard deviation, whereas categorical variables were presented as counts and percentages. Continuous variables were compared with independent t tests or the Wilcoxon rank sum test depending on normality, and categorical variables were compared with Fisher exact tests or Pearson χ2 tests. All variables that were deemed significant in bivariate analyses (a posteriori), as well as those deemed clinically relevant (a priori), were used to create multivariable logistic or linear regression models for predictors of baseline opioid use and baseline patient-reported outcome measures, respectively. The selection procedure was set to remove factors at a P ≤ .05 threshold. A priori variables included age, sex, race, BMI, ASA score, insurance status, and surgery for a fracture. 22 A posteriori variables included smoking and employment status. All statistical analyses were performed using SPSS version 25.0 (IBM Corp, Armonk, New York). Differences with P ≤ .05 were considered statistically significant.
Results
Table 1 shows the baseline patient and surgical characteristics. Based on preoperative opioid use, 122 patients (35%) were considered “opioid users,” and 231 patients (65%) were considered “opioid nonusers.” Preoperative opioid users were significantly older (P = .05), had a lower BMI (P < .01), had more ASA III and IV class patients (P = .04), had more current smokers (P < .01), had fewer college graduates (P = .03), and had lower employment (P = .03). No other baseline differences existed. In a multivariable logistic regression, only ASA III or IV (odds ratio [OR], 2.88; 95% confidence interval [CI], 1.37-6.05; P < .01), current smoking status (OR, 1.91; 95% CI, 1.10-3.35; P = .02), and BMI (OR, 0.95; 95% CI, 0.92-0.98; P < .01) were predictive of preoperative opioid use (Table 2).
Table 1.
Baseline Patient and Surgical Characteristics.
| Factor | Total cohort (N = 353) | Opioid users (n = 122) | Nonusers (n = 231) | P value |
|---|---|---|---|---|
| Age, y | 44.9 ± 15.2 | 46.9 ± 14.9 | 43.8 ± 15.3 | .05 |
| Sex (male) | 165 (47) | 62 (51) | 103 (45) | .26 |
| Race | ||||
| Black | 133 (38) | 45 (37) | 88 (39) | .88 |
| White | 181 (52) | 63 (52) | 118 (51) | |
| Other | 33 (10) | 13 (11) | 23 (9) | |
| BMI, kg/m2 | 30.0 ± 7.8 | 28.7 ± 7.0 | 30.8 ± 8.1 | <.01 |
| Comorbidities | 1.2 ± 1.3 | 1.4 ± 1.3 | 1.2 ± 1.3 | .14 |
| Income (≥$70,000) | 131 (37) | 43 (35) | 88 (38) | .60 |
| ASA score | ||||
| I-II | 310 (88) | 101 (83) | 209 (91) | .04 |
| III-IV | 43 (12) | 21 (17) | 22 (9) | |
| Current smoker | 66 (19) | 33 (27) | 33 (14) | <.01 |
| Insurance | ||||
| Private/Employer | 251 (71) | 81 (67) | 170 (74) | .23 |
| Medicare/Medicaid | 95 (27) | 37 (30) | 58 (25) | |
| Other | 7 (2) | 4 (3) | 3 (1) | |
| Education (college degree) | ||||
| College degree | 131 (38) | 36 (30) | 95 (41) | .03 |
| High school degree only | 195 (56) | 75 (61) | 120 (53) | |
| No GED/High school degree | 22 (6) | 11 (9) | 11 (5) | |
| Employment | ||||
| Employed/Student | 237 (67) | 73 (60) | 164 (71) | .03 |
| Not currently employed | 111 (32) | 47 (39) | 64 (28) | |
| Procedure | ||||
| Fracture ORIF | 111 (31) | 41 (33) | 70 (30) | .53 |
| Carpal/Cubital tunnel release | 83 (24) | 26 (21) | 57 (25) | .48 |
| Trigger finger release | 36 (10) | 8 (7) | 28 (12) | .10 |
| Mass excision | 26 (7) | 5 (4) | 21 (9) | .09 |
| De Quervain release | 14 (4) | 7 (6) | 7 (3) | .22 |
| CMC arthroplasty | 13 (4) | 4 (3) | 9 (4) | .77 |
| Tendon repair/Transfer | 12 (4) | 7 (6) | 5 (2) | .08 |
| Other | 58 (16) | 24 (20) | 34 (15) | .23 |
Note. Values are given as mean ± standard deviation or as number with percentage in parentheses. Bold values signify significant P values. BMI = body mass index; ASA = American Society of Anesthesiologists; GED = General Educational Development; ORIF = open reduction internal fixation; CMC = carpometacarpal.
Table 2.
Multivariable Logistic Analysis for Predictors of Preoperative Opioid Use.
| Factors | OR and 95% CI | P value |
|---|---|---|
| ASA III-IV, ref. I-II | 2.88 (1.37-6.05) | <.01 |
| Current smoker, ref. no | 1.91 (1.10-3.35) | .02 |
| BMI, per point | 0.95 (0.92-0.98) | <.01 |
Note. Bold values signify significant P values. OR = odds ratio; CI = confidence interval; ASA = American Society of Anesthesiologists; BMI = body mass index.
Table 3 shows a comparison of baseline patient-reported outcome measures between opioid users and nonusers. All 6 baseline PROMIS domains were significantly worse in opioid users, including lower PF (−2.7 points; P = .01), higher PI (+2.2 points; P < .01), higher Fatigue (+4.7 points; P < .01), lower Social Satisfaction (−3.6 points; P < .01), more Anxiety (+4.0 points; P < .01), and higher Depression (+2.7 points; P = .02). A greater proportion of opioid users had an Anxiety score greater than or equal to 62 (28% vs 17%; P = .02), which is associated with moderate anxiety.23,24 There were no significant differences in the proportion of opioid users or nonusers with Depression scores greater than 60 (16% vs 14%, respectively; P = .52), which is associated with moderate depression. 25 Opioid users also had significantly lower baseline BMHQ scores (−9.3 points; P < .01), higher NPS hand scores (+0.9 points; P < .01), and lower expectations (−0.2 points; P = .04). Only the NPS for the rest of the body and Marx scores failed to reach statistical significance between groups.
Table 3.
Baseline Patient-Reported Outcome Measures in Opioid Users and Nonusers.
| Factors | Total cohort | Opioid users | Nonusers | P value |
|---|---|---|---|---|
| PROMIS | ||||
| Physical Function | 45.5 ± 9.7 | 43.7 ± 9.9 | 46.4 ± 9.5 | .01 |
| Pain Interference | 60.0 ± 7.4 | 61.4 ± 7.7 | 59.2 ± 7.2 | <.01 |
| Fatigue | 52.0 ± 10.2 | 55.1 ± 9.3 | 50.4 ± 10.3 | <.01 |
| Social Satisfaction | 43.0 ± 9.8 | 40.6 ± 8.5 | 44.2 ± 10.2 | <.01 |
| Anxiety | 54.4 ± 10.0 | 57.1 ± 9.1 | 53.1 ± 10.2 | <.01 |
| Depression | 48.8 ± 9.9 | 50.6 ± 9.7 | 47.9 ± 9.8 | .02 |
| BMHQ | 45.7 ± 21.0 | 39.6 ± 20.0 | 48.9 ± 20.8 | <.01 |
| NPS—Hand | 5.3 ± 2.9 | 5.9 ± 2.9 | 5.0 ± 2.9 | <.01 |
| NPS—Body | 1.8 ± 2.7 | 2.1 ± 2.9 | 1.6 ± 2.5 | .07 |
| Marx Upper Extremity ARS | 48.9 ± 31.5 | 44.3 ± 32.7 | 51.2 ± 30.7 | .06 |
| Expectations (MODEMS) | 4.3 ± 0.8 | 4.2 ± 0.8 | 4.4 ± 0.8 | .04 |
Note. Values are given as mean ± standard deviation. Bold values signify significant P values. PROMIS = Patient-Reported Outcomes Measure Information System; BMHQ = Brief Michigan Hand Questionnaire; NPS = Numeric Pain Scale; ARS = Activity Rating Scale; MODEMS = Musculoskeletal Outcomes Data Evaluation and Management System.
Table 4 shows the multivariable linear regression analyses for predictors of baseline patient-reported outcome measures. Preoperative opioid use was a significant predictor for all 6 baseline PROMIS domains (PF, −2.21 points; PI, +1.85 points, Fatigue, +4.36 points; Social Satisfaction, −3.24 points; Anxiety, +3.48 points; and Depression, +2.47 points), the BMHQ (−8.11 points), and NPS hand score (+0.69 points) after controlling for other factors. Preoperative opioid use was not a significant predictor of surgical expectations after controlling for other factors.
Table 4.
Multivariable Linear Regression Analyses for Factors Associated With Baseline Patient-Reported Outcome Measures.
| Factors a | β | 95% CI | P value |
|---|---|---|---|
| PROMIS—Physical Function | |||
| Preoperative opioid use, ref. no | −2.21 | −4.24 to −0.18 | .03 |
| BMI, per point | −0.13 | −0.26 to −0.01 | .04 |
| Employed, ref. no | 6.77 | 4.72 to 8.82 | <.01 |
| PROMIS—Pain Interference | |||
| Preoperative opioid use, ref. no | 1.85 | 0.23 to 3.47 | .03 |
| Current smoker, ref. no | 2.31 | 0.31 to 4.30 | .02 |
| BMI, per point | 0.12 | 0.02 to 0.22 | .02 |
| Employed, ref. no | −2.47 | −4.12 to −0.82 | <.01 |
| PROMIS—Fatigue | |||
| Preoperative opioid use, ref. no | 4.36 | 2.22 to 6.51 | <.01 |
| BMI, per point | 0.17 | 0.04 to 0.30 | .01 |
| Employed, ref. no | −5.00 | −7.15 to −2.81 | <.01 |
| PROMIS—Social Satisfaction | |||
| Preoperative opioid use, ref. no | −3.24 | −5.35 to −1.13 | <.01 |
| Employed, ref. no | 3.47 | 1.33 to 5.60 | .02 |
| PROMIS—Anxiety | |||
| Preoperative opioid use, ref. no | 3.48 | 1.34 to 5.62 | <.01 |
| Employed, ref. no | −4.23 | −6.40 to −2.06 | <.01 |
| PROMIS—Depression | |||
| Preoperative opioid use, ref. no | 2.47 | 0.34 to 4.59 | .023 |
| Employed, ref. no | −4.66 | −6.87 to −2.44 | <.01 |
| Age, per year | −0.09 | −0.16 to −0.02 | .01 |
| BMHQ | |||
| Preoperative opioid use, ref. no | −8.11 | −12.57 to −3.65 | <.01 |
| Employed, ref. no | 9.35 | 4.82 to 13.88 | <.01 |
| NPS—Hand | |||
| Preoperative opioid use, ref. no | 0.69 | 0.06 to 1.32 | .03 |
| Current smoker, ref. no | 0.87 | 0.09 to 1.64 | .03 |
| Employed, ref. no | −1.07 | −1.71 to −0.43 | <.01 |
| Expectations (MODEMS) | |||
| Preoperative opioid use, ref. no | −0.10 | −0.28 to 0.09 | .30 |
| Current smoker, ref. no | −0.24 | −0.47 to −0.02 | .04 |
| Employed, ref. no | 0.21 | 0.02 to 0.40 | .03 |
| Age, per year | 0.01 | 0.001 to 0.01 | .02 |
Note. Bold values signify significant P values. CI = confidence interval; PROMIS = Patient-Reported Outcomes Measure Information System; BMI = body mass index; BMHQ = Brief Michigan Hand Questionnaire; NPS = Numeric Pain Scale; MODEMS = Musculoskeletal Outcomes Data Evaluation and Management System.
Additional factors included in the models but not shown.
Discussion
With increasing emphasis on reducing opioid use in the perioperative setting for hand surgery patients, understanding the factors associated with preoperative opioid use and its effect on baseline patient-reported outcome measures is essential. While prior studies have focused on ways to reduce postoperative opioid use through pain protocols and preoperative education,8-11,14-20 few have described the influence of preoperative opioid use on hand surgery patients.6,7 The results of this study demonstrate that preoperative opioid users have older age, lower BMI, higher ASA class, higher current smoking status, lower education, and lower employment status. After controlling for covariates, only ASA class, smoking, and BMI were significantly associated with preoperative opioid use. Preoperative opioid users also had significantly worse baseline PROMIS scores across 6 domains, lower BMHQ scores, lower NPS hand scores, and worse expectations for surgery. After controlling for covariates, preoperative opioid users had significantly worse scores for all baseline patient-reported outcome measures, except expectations for surgery.
To our knowledge, this is the first study to establish the baseline patient characteristics and patient-reported outcome measures for preoperative opioid users undergoing common hand surgeries. In new hand clinic patients, Kazmers et al 7 determined the association between baseline opioid use and PROMIS scores, but did not distinguish between patients who were scheduled to undergo surgery. This study closes this knowledge gap. We showed preoperative opioid use is significantly associated with worse baseline PROMIS PF (−2.7 points), PI (+2.2 points), Depression (+2.7 points), and Anxiety (+4.0 points) scores, as shown in the study by Kazmers et al 7 , but the magnitude of association was not as strong as the former study (−9.7, +7.1, +5.4, and +6.9 points, respectively). This could be explained by the more homogeneous disease burden for the patients included in our study as they all required surgery. Alternatively, the clinic patients’ disease severity may have ranged from mild to severe, and baseline opioid use may have selected the patients with worse pathology and worse baseline outcome scores. The opioid nonusers in the former study may have been mixed with a greater proportion of patients with more mild symptoms that could have skewed the baseline outcome scores to make differences between opioid users and nonusers greater than this study. This distinction is important, as the minimal clinically important differences (MCIDs) were exceeded in the former study for PROMIS PF (MCID, 8.6 points), 26 PI (MCID, 7.1 points), 26 and Depression (MCID, 4.5 points),27,28 whereas they did not reach MCIDs in this study. Like the former study, the MCID was achieved for Anxiety (MCID, 3.0-4.5 points), 27 which is supported by the higher proportion of opioid users (28% vs 17%) with moderate anxiety based on Anxiety scores.23,24 Although we did not assess the Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) like Kazmers et al, 7 we did show significantly worse BMHQ scores (−9.3 points) among opioid users, which achieved the MCID of 7.0 points and is highly correlated with QuickDASH. 29 Finally, we showed opioid users have significantly worse NPS hand scores (+0.9 points), but this failed to achieve the MCID of 1.0 point.
After controlling for covariates, this study shows preoperative opioid use is associated with the 6 baseline PROMIS scores, BMHQ score, and NPS hand scores. Additional predictors of baseline patient-reported outcome measures included BMI, employment status, smoking status, and age to varying degrees. Among other factors, Jamieson et al 6 similarly found preoperative opioid use, unemployment, and age to be predictive of postoperative opioid use after hand surgery. Sabesan et al 30 showed only preoperative opioid use and fracture complexity were predictive of chronic opioid use in patients with proximal humerus fractures. In a heterogeneous cohort, Bot et al 31 demonstrated that preoperative opioid use was predictive of lower pain satisfaction following operative fracture treatment. The authors also showed mental illness and smoking were additional predictors of satisfaction with pain control. Future studies should compare baseline and postoperative outcome measures among preoperative opioid users and nonusers, controlling for these potential confounders.
This study also describes the prevalence and baseline characteristics of opioid users undergoing common hand procedures. The rate of preoperative opioid use in this study (35%) was higher than that reported for hand clinic patients (17%), which is likely related to the greater severity of pain in patients requiring surgery compared with a cohort of clinic patients. 7 Given how common preoperative opioid use has become, surgeons should elicit such information as it can help them better estimate opioid needs in the postoperative setting. 6 We showed patients are 5% less likely to be opioid users for every point increase in BMI. Jiang et al 32 similarly showed baseline opioid use was associated with a lower BMI in surgical patients. This finding likely has a ceiling effect, however, as a higher BMI could lead to more musculoskeletal ailments and more chronic pain. We also showed higher ASA class and smoking were associated with 2.88 and 1.91 higher odds, respectively, of preoperative opioid use. Although there is no literature to support these baseline opioid predictors, Sabesan et al 30 showed ASA class and smoking are not predictive of postoperative opioid dependence in patients undergoing upper extremity fracture surgery. Understanding the factors related to preoperative opioid use in hand surgery is imperative in the current medical climate, as Menendez et al 33 reported that preoperative opioid abuse was associated with increased morbidity and mortality after major orthopedic procedures. Preoperative opioid use has also been shown to be associated with worse postoperative outcomes after orthopedic shoulder, hip, and knee surgery.33-36 These data can assist hand surgeons in properly identifying patients using preoperative opioids to better manage these patients in the postoperative setting.
These findings contribute to a growing body of literature demonstrating a relationship between pain, psychosocial factors, and opioid use in hand surgery patients.7,19,20,37 In patients undergoing suture removal for minor hand surgery, Vranceanu et al 19 determined that a diagnosis of depression was an independent predictor of disability and pain. Additional studies have shown pain catastrophizing, which has similar characteristics to anxiety and depression, is a risk factor for increased postoperative pain and opioid consumption.20,38 While most of these studies focus on postoperative opioid consumption, other studies have shown increased anxiety and pain are predictive of function in hand clinic patients. 37 Our results contribute to the existing literature by showing preoperative opioid use is associated with worse baseline pain, function, and mental illness in patients undergoing common hand surgeries. Future studies should determine whether these baseline differences persist in the postoperative setting.
While opioid users were shown to have significantly lower preoperative expectations on bivariate analysis in this study, multivariable analysis revealed preoperative opioid use was not predictive of patient expectations. Kadzielski et al 39 similarly reported preoperative expectations were not correlated with postoperative outcomes after carpal tunnel release. Conversely, other authors have shown lower preoperative patient expectations to be associated with worse postoperative outcomes in patients undergoing upper extremity surgery. 40 Our study used a validated measure of patient expectations (MODEMS), but the lack of validated measures of patient expectations in prior studies makes it difficult to draw conclusions between studies.
This study is not without limitations. First, the study is a retrospective review and is subject to the limitations of such study designs. Second, medical records were reviewed to determine whether patients were using preoperative opioids at the time of study enrollment. Patients may not have felt comfortable admitting to opioid use for numerous reasons, including fear of scrutiny. The 35% rate of preoperative opioid use in this study is reasonable given 17% of hand clinic patients use opioids, and one would expect surgical patients to have more pain than the general hand clinic population. 7 Third, we did not assess the quantity of opioids used by patients in the preoperative setting, which is a future direction for further studies. Fourth, the study only focuses on the effect of preoperative opioid use on baseline patient-reported outcome measures. Future studies should determine whether the differences between opioid users and nonusers persist in the postoperative setting for the outcomes assessed in this study. The chronicity of preoperative opioid use should be analyzed as well to help inform clinicians about the possible varying impact of short-term or long-term opioid use. Finally, this study was performed at a single, urban academic institution, and the results may not be generalizable to other study populations.
Preoperative opioid use was found to be common in patients undergoing common hand surgeries with a rate of 35%. Patients with higher ASA scores, current smoking status, and lower BMIs were more likely to use opioids preoperatively. Opioid users had significantly worse baseline patient-reported outcome scores related to pain, function, and mental health. This information can be used by surgeons to highlight the widespread prevalence of opioid use in patients undergoing hand surgery and provides insight into the detrimental effects of opioid use on baseline outcome measures. Future studies should determine whether such baseline differences persist in the postoperative setting and whether opioid cessation can positively influence patient outcomes.
Acknowledgments
The authors thank J. Kathleen Tracy, PhD; Andrew Dubina, MD; Julio Jauregui, MD; Farshad Adib, MD; Craig Bennett, MD; Andrew Eglseder, MD; Mohit Gilotra, MD; S. Ashfaq Hasan, MD; Jonathan Packer, MD; Ebrahim Paryavi, MD; Michael Smuda; and Shaun Medina for their assistance with data collection.
Footnotes
Ethical Approval: This study was approved by the Institutional Review Board of the University of Maryland, Baltimore (HP-00062261).
Statement of Human and Animal Rights: All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. This research protocol was approved by our institutional review board.
Statement of Informed Consent: Informed consent was obtained from all patients included in the study.
Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: NMA is a current board or committee member of the American Academy of Orthopaedic Surgeons and American Society for Surgery of the Hand. RAP receives intellectual property royalties and is a paid consultant, paid presenter or speaker, and has stock or stock options for Globus Medical. RFH has previously received research support from Arthrex, Inc. All other authors certify that he or she has no commercial associations (eg, consultancies, stock ownership, equity interest, patent/licensing arrangements) that might pose a conflict of interest in connection with the submitted article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a grant from James Lawrence Kernan Hospital Endowment Fund, Incorporated.
ORCID iDs: Ali Aneizi 
https://orcid.org/0000-0001-5064-6617
Patrick M. J. Sajak
https://orcid.org/0000-0002-1830-3793
Vidushan Nadarajah
https://orcid.org/0000-0002-0526-5417
Tristan B. Weir
https://orcid.org/0000-0001-9773-3013
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