Abstract
Background
There is variability in the trajectories of pain intensity and magnitude of incapability after shoulder arthroplasty. A better understanding of the degree to which variation in recovery trajectories relates to aspects of mental health can inform the development of comprehensive biopsychosocial care strategies.
Questions/purposes
(1) Do pain intensities at baseline and the trajectories during recovery differ between groups when stratified by mental health composite summary score, arthroplasty type, and revision surgery? (2) Do magnitudes of capability at baseline and the trajectories during recovery differ between these groups?
Methods
We used a registry of 755 patients who underwent shoulder arthroplasty by a single surgeon at a specialized urban orthopaedic hospital that recorded the mental component summary (MCS) score of the Veterans RAND 12, a measure of shoulder-specific comfort and capability (American Shoulder and Elbow Surgeons [ASES] score, which ranges from 0 to 100 points, with a score of 0 indicating worse capability and pain and 100 indicating better capability and pain and a minimum clinically important difference of 6.4), and the VAS for pain intensity (range 0 [representing no pain] to 10 [representing the worst pain possible], with a minimum clinically important difference of 1.4) preoperatively, 2 weeks postoperatively, and 6 weeks, 3 months, 6 months, and 1 year after surgery. Forty-nine percent (368 of 755) of the patients were men, with a mean age of 68 ± 8 years, and 77% (585) were treated with reverse total shoulder arthroplasty (rTSA). Unconditional linear and quadratic growth models were generated to identify the general shape of recovery for both outcomes (linear versus quadratic). We then constructed conditional growth models and curves for pain intensity and the magnitude of capability showing mean baseline scores and the rates of recovery that determine the trajectory, accounting for mental health (MCS) quartiles, primary or revision arthroplasty, and TSA or reverse TSA in separate models. Because pain intensity and capability showed quadratic trends, we created trajectories using the square of time.
Results
Patients in the worst two MCS quartiles had greater pain intensity at baseline than patients in the best quartile (difference in baseline for bottom quartile: 0.93 [95% CI 0.72 to 1.1]; p < 0.01; difference in baseline for next-worst quartile: 0.36 [95% CI 0.16 to 0.57]; p < 0.01). The rates of change in recovery from pain intensity were not different among groups (p > 0.10). Patients with revision surgery had greater baseline pain (difference: 1.1 [95% CI 0.7 to 1.5]; p < 0.01) but no difference in rates of recovery (difference: 0.031 [95% CI 0.035 to 0.097]; p = 0.36). There were no differences in baseline pain intensity and rates of recovery between patients with reverse TSA and those with TSA (baseline pain difference: -0.20 [95% CI -0.38 to -0.03]; p = 0.18; difference in rate of recovery: -0.005 [95% CI -0.035 to 0.025]; p = 0.74). Patients in the worst two MCS quartiles had worse baseline capability than patients in the best quartile (difference in baseline for bottom quartile: -8.9 [95% CI -10 to -7.4]; p < 0.001; difference in baseline for the next-worst quartile: -4.9 [95% CI -6.4 to -3.4]; p < 0.01), with no differences in rates of recovery (p > 0.10). Patients with revision surgery had lower baseline capability (difference in baseline: -13 [95% CI -15 to -9.7]; p < 0.01), with a slower rate of recovery (difference in rate of recovery: -0.56 [95% CI -1.0 to -0.079]; p = 0.021). There were no differences in baseline capability or rates of recovery between TSA and reverse TSA.
Conclusion
The observation that preoperative and 1-year comfort and capability are associated with mental health factors and with similar recovery trajectories reminds us that assessment and treatment of mental health is best considered an integral aspect of musculoskeletal care. Future studies can address how prioritization of mental health in musculoskeletal care strategies might reduce variation in the 1-year outcomes of discretionary surgeries such as shoulder arthroplasty.
Level of Evidence
Level III, therapeutic study.
Introduction
Background
Many people experience notable improvement in pain intensity and magnitude of incapability after total shoulder arthroplasty (TSA) and reverse shoulder arthroplasty (rTSA) [4, 23, 30]. However, there is notable variation in recovery trajectories and final comfort, capability, and satisfaction [2, 26]. Technical factors and pathophysiologic variations alone cannot account for all variation in recovery trajectories. Prior studies of people with osteoarthritis (OA) of the knee found that mindset factors might have a greater association with levels of capability than the radiographic grade of OA [8, 9, 18, 33], and among patients undergoing shoulder arthroplasty, worse baseline mental health is associated with greater incapability and pain intensity after surgery [5, 36].
Based on this line of evidence, it is increasingly recognized that opportunities for better mental and social health are important aspects of musculoskeletal health [7, 8, 10, 14, 18, 29, 33, 34], and musculoskeletal care strategies are evolving to match this evidence [13, 21].
Rationale
Studies using cluster analysis and latent class analysis have helped further the evidence that unhealthy mindsets might account for more variation in comfort and capability than pathophysiologic severity [8, 19, 21]. Statistical techniques that can measure differences in recovery trajectories can further solidify the finding that technical and pathophysiologic factors such as arthroplasty type or having revision surgery have lesser associations with pain intensity and capability than mindset factors. A better understanding of the personal factors associated with recovery courses after shoulder arthroplasty could aid the design of comprehensive biopsychosocial approaches to care in order to improve ultimate comfort and capability.
Questions
Using a registry of patients recovering from shoulder arthroplasty, we asked: (1) Do pain intensities at baseline and the trajectories during recovery differ between groups when stratified by mental health composite summary score, arthroplasty type, and revision surgery? (2) Do magnitudes of capability at baseline and the trajectories during recovery differ between these groups?
Patients and Methods
Study Design and Setting
In this registry database study, all English-speaking new and return-visit adult patients who underwent shoulder arthroplasty at a specialized orthopaedic urban hospital from a single-surgeon arthroplasty registry were included if they underwent primary or revision TSA between 2015 and 2021. Of those, we considered patients if the mental component summary score (MCS) of the Veterans RAND 12 and the American Shoulder and Elbow Surgeons (ASES) score and pain intensity at baseline and 1 year after surgery were available. Of 1261 patients who have been entered in the registry since 2015, 39% (489 of 1261) of patients were excluded from the analysis because of missing data. A further 1.3% (17 of 1261) were excluded owing to infections during recovery, leaving 60% (755 of 1261) for analysis.
Descriptive Data
Forty-nine percent (368) were men, and the mean age was 68 ± 8 years (Table 1). Twenty-three percent (170) were treated with TSA and 77% (585) were treated with rTSA. Twenty-eight patients (3.7%) underwent revision arthroplasty.
Table 1.
Demographics of cohort (n = 755)
| Variable | Value |
| Age in years | 68 ± 8 |
| Men | 49% (368) |
| Surgery type | |
| rTSA | 77% (585) |
| Revision | |
| No | 96% (727) |
| MCS (preoperative) | 58 (49 to 64) |
| Pain intensity | |
| Preoperative | 60 (40 to 76) |
| 2 weeks postoperative | 20 (10 to 41) |
| 6 weeks postoperative | 11 (0 to 30) |
| 3 months postoperative | 10 (0 to 22) |
| 1 year postoperative | 0 (0 to 10) |
| ASES score | |
| Preoperative | 36 (25 to 49) |
| 2 weeks postoperative | 45 (33 to 53) |
| 6 weeks postoperative | 57 (47 to 68) |
| 3 months postoperative | 77 (61 to 87) |
| 1 year postoperative | 90 (77 to 95) |
Data presented as mean ± SD, % (n), or median (IQR).
Variables
The ASES score is a 100-point scale that combines pain intensity and magnitude of capability [22]. Scores range from 0 to 100, with a score of 0 indicating worse capability and pain and 100 indicating better capability and pain, with a minimum clinically important difference (MCID) of 6.4 [25]. Pain intensity was measured separately using a VAS ranging from 0 (representing no pain) to 10 (representing the worst pain possible), with an MCID of 1.4 [31]. To account for missing data, we carried forward the value of the previous timepoint for each timepoint that was missing. For pain intensity, this was done for 19 observations at T1, 21 observations at T2, 77 observations at T3, and 486 observations at T4. For ASES score, this was done for 22 observations at T1, 28 observations at T2, 81 observations at T3, and 494 observations at T4.
Mental health was represented using the MCS, derived from the Veterans RAND 12 [27]. MCS scores are calculated using an algorithm that is scaled so that 50 represents the United States population mean according to the 2000 to 2002 United States Medical Expenditure Panel Survey Invitation [28]. Each 10 points above or below 50 represents one SD. Higher MCS scores represent better mental health.
Primary Study Outcomes
Our primary study goal was to identify whether pain intensity and capability at baseline and during recovery differed between groups stratified by MCS, arthroplasty type, and revision surgery. To achieve this, we created recovery trajectories including the mean baseline scores based on quartiles of the MCS, arthroplasty type (TSA versus rTSA), and revision (yes/no). Hemiarthroplasties were not available from the registry.
Ethical Approval
Ethical approval for this study was waived by the New England Baptist Hospital Institutional Review Board.
Statistical Analysis
Descriptive statistics were performed for all participants. Continuous variables are reported as the mean ± SD for normally distributed data and as median with IQR for nonparametric data. Categorical variables are reported as frequencies with percentages.
The shape of the general recovery trajectory of the full cohort was identified by fitting unconditional linear and quadratic growth models for pain intensity and ASES, using the Bayesian information criteria to assess the best-fitting shape (linear or quadratic). We found that the recovery trajectories of levels of pain and capability followed a quadratic trend, meaning that on average, the rate of improvement in pain and capability was exponential. To account for the quadratic trend in our recovery trajectories, we accounted for quadratic time (time2). We then constructed separate conditional growth models for pain intensity by MCS quartile (Fig. 1), arthroplasty type (Fig. 2), and revision (yes/no) (Fig. 3). For ASES score, we also constructed separate conditional growth models by MCS (Fig. 4), arthroplasty type (Fig. 5), and revision (yes/no) (Fig. 6). This was done in a stepwise approach to generate the most accurate estimates possible by examining the significance values of each variable as we added them to a multivariable model. This means also adding insignificant variables in the model to control for any variation attributed to that variable. We first accounted for the mean baseline score of the given explanatory variable in Step 1, after which the average rate of recovery was added to the model in Step 2. Only factors with a p value less than 0.05 in Steps 1 and 2 were considered statistically significant. Conditional growth curves (figures of trajectories) were generated to visualize differences in recovery among different subgroups of each explanatory variable.
Fig. 1.
This graph depicts the trajectories of pain intensity during recovery from shoulder arthroplasty by MCS quartiles. T1 = preoperative; T2 = 2 weeks; T3 = 3 months; T4 = 6 months; T5 = 1 year. Pain intensity was measured on a scale from 0 to 10, with higher scores indicating greater pain. A color image accompanies the online version of this article.
Fig. 2.
This graph depicts the trajectories of pain intensity during recovery from shoulder arthroplasty by arthroplasty type. T1 = preoperative; T2 = 2 weeks; T3 = 3 months; T4 = 6 months; T5 = 1 year. Pain intensity was measured on a scale from 0 to 10, with higher scores indicating greater pain. A color image accompanies the online version of this article.
Fig. 3.
This graph depicts the trajectories of pain intensity during recovery from shoulder arthroplasty by revision surgery. T1 = preoperative; T2 = 2 weeks; T3 = 3 months; T4 = 6 months; T5 = 1 year. Pain intensity was measured on a scale from 0 to 10, with higher scores indicating greater pain. A color image accompanies the online version of this article.
Fig. 4.
This graph depicts the trajectories of capability during recovery from shoulder arthroplasty by MCS quartiles. T1 = preoperative; T2 = 2 weeks; T3 = 3 months; T4 = 6 months; T5 = 1 year. ASES score was measured on a scale from 0 to 100, with higher scores indicating better capability. A color image accompanies the online version of this article.
Fig. 5.
This graph depicts the trajectories of capability during recovery from shoulder arthroplasty by arthroplasty type. T1 = preoperative; T2 = 2 weeks; T3 = 3 months; T4 = 6 months; T5 = 1 year. ASES score was measured on a scale from 0 to 100, with higher scores indicating better capability. A color image accompanies the online version of this article.
Fig. 6.
This graph depicts the trajectories of capability during recovery from shoulder arthroplasty by revision surgery. T1 = preoperative; T2 = 2 weeks; T3 = 3 months; T4 = 6 months; T5 = 1 year. ASES score was measured on a scale from 0 to 100, with higher scores indicating better capability. A color image accompanies the online version of this article.
Results
Trajectories of Pain Intensity
Patients in the worst two MCS quartiles had greater pain intensity at baseline than patients in the best quartile (difference in mean baseline for bottom quartile [0% to 25%]: 0.93 [95% CI 0.72 to 1.1]; p < 0.01; difference in mean baseline for next-worst quartile [26% to 50%]: 0.36 [95% CI 0.16 to 0.57]; p < 0.01). The rates of recovery of pain intensity were not different among groups (p > 0.10) (Table 2). Compared with patients in the best mental health quartile, the rate of recovery in pain intensity of the third quartile (51% to 75%) was 0.15 [95% CI -0.019 to 0.049]; p = 0.37), the rate of recovery in pain of the second quartile (26% to 50%) was 0.00087 [95% CI -0.035 to 0.034]; p = 0.96), and for the bottom quartile (0% to 25%) it was -0.029 [95% CI -0.063 to 0.0057]; p = 0.10) (Table 2). There were no differences in the mean baseline pain intensity and trajectories between patients who underwent rTSA and those with TSA (mean baseline pain difference -0.20 [95% CI -0.38 to -0.03]; p = 0.18; difference in rate of recovery: -0.005 [95% CI -0.035 to 0.025]; p = 0.74) (Table 3). Patients who underwent revision surgery had greater mean baseline pain (mean baseline pain difference: 1.1 [95% CI 0.7 to 1.5]; p < 0.01) but no difference in rate of recovery (0.031 [95% CI -0.035 to 0.097]; p = 0.36) (Table 4).
Table 2.
Conditional growth model for pain intensity and ASES score with variations in mean baseline scores and rate of recovery for MCS quartiles
| Pain intensity | ASES score | |||||
| Estimate (95% CI) | Step 1 p value | Step 2 p value | Estimate (95% CI) | Step 1 p value | Step 2 p value | |
| Step 1: Variation in mean baseline scores (main effects model) | ||||||
| Time | -0.22 (-0.24 to -0.21)a | < 0.01 | < 0.01b | 2.9 (2.8 to 3.0)a | < 0.01 | < 0.01b |
| MCS quartile (mean baseline) | ||||||
| 76%-100% (best) | Reference value | Reference value | ||||
| 51%-75% | -0.032 (-0.23 to 0.17)a | 0.76 | 0.40b | -0.95 (-2.4 to -0.51)a | < 0.01 | 0.92b |
| 26%-50% | 0.36 (0.16 to 0.57)a | < 0.01 | 0.01b | -4.9 (-6.4 to -3.4)a | < 0.01 | < 0.01b |
| 0%-25% (worst) | 0.93 (0.72 to 1.1)a | < 0.01 | < 0.01b | -8.9 (-10 to -7.4)a | < 0.01 | < 0.01b |
| Step 2: Variations in rate of recovery (interaction effects model) | ||||||
| MCS quartile*time (rate of recovery) | ||||||
| 76%-100% (best) | Reference value | Reference value | ||||
| 51%-75% | 0.015 (-0.019 to 0.049) | 0.37 | -0.18 (-0.43 to 0.071) | 0.16 | ||
| 26%-50% | 0.00087 (-0.035 to 0.034) | 0.96 | 0.047 (-0.20 to 0.30) | 0.72 | ||
| 0%-25% (worst) | -0.029 (-0.063 to 0.0057) | 0.10 | -0.058 (-0.31 to 0.19) | 0.65 | ||
Step 1model summary: pain intensity: r2 = 0.26, AIC 17336, BIC 17367; ASES: r2 = 0.53, AIC 32659, BIC 32690. Step 2 model summary: pain intensity: r2 = 0.27, AIC 17336, BIC 17386; ASES: r2 = 0.54, AIC 22026, BIC 22073.
aThese estimates are derived from the model in Step 1, where we only accounted for the effect of MCS quartiles on the mean baseline score (Step 1).
bThese p values are based on the model in which we accounted for the effect of MCS quartiles on rate of recovery (Step 2), where estimates for the mean baseline score are less accurate than in Step 1 and are therefore not presented. Variables are deemed clinically relevant when p values in both steps are significant.
Table 3.
Conditional growth model for pain intensity and ASES score with effects for type of surgery (mean baseline) and interactions of type of surgery*time (rate of recovery)
| Pain intensity | ASES score | |||||
| Estimate (95% CI) | Step 1 p value | Step 2 p value | Estimate (95% CI) | Step 1 p value | Step 2 p value | |
| Step 1: Variation in mean baseline scores (main effects model) | ||||||
| Time | -0.22 (-0.24 to -0.21)a | < 0.01 | < 0.01b | 2.9 (2.8 to 3.0)a | < 0.01 | < 0.01b |
| Type of surgery (mean baseline) | ||||||
| TSA | Reference value | Reference value | ||||
| rTSA | -0.20 (-0.38 to -0.027)a | 0.02 | 0.18b | 0.62 (-0.66 to 1.9)a | 0.34 | 0.34b |
| Step 2: Variations in rate of recovery (interaction effects model) | ||||||
| Type of surgery*time (rate of recovery) | ||||||
| TSA | Reference value | Reference value | ||||
| rTSA | -0.0050 (-0.035 to 0.025) | 0.74 | -0.044 (-0.26 to 0.17) | 0.69 | ||
Step 1model summary: pain intensity: r2 = 0.25, AIC 16958, BIC 16977; ASES: r2 = 0.51, AIC 32003, BIC 32022. Step 2 model summary: pain intensity: r2 = 0.25, AIC 16960, BIC 16985; ASES: r2 = 0.51, AIC 32005, BIC 32030.
aThese estimates are derived from the model in Step 1, where we only accounted for the effect of type of surgery on the mean baseline score.
bThese p values are based on the model in which we accounted for the effect of type of surgery on rate of recovery (Step 2), where estimates for the mean baseline score are less accurate than in Step 1 and are therefore not presented. Variables are deemed clinically relevant when p values in both steps are significant.
Table 4.
Conditional growth model for pain intensity and ASES score with variations in mean baseline scores and rate of recovery for revision surgery
| Pain intensity | ASES score | |||||
| Estimate (95% CI) | Step 1 p value | Step 2 p value | Estimate (95% CI) | Step 1 p value | Step 2 p value | |
| Step 1: Variation in mean baseline score (main effects model) | ||||||
| Time2 | -0.22 (-0.24 to -0.21)a | < 0.01 | < 0.01b | 2.9 (2.8 to 3.0) | < 0.01 | < 0.01 |
| Revision (mean baseline) | ||||||
| No | Reference value | Reference value | ||||
| Yes | 1.1 (0.71 to 1.5)a | < 0.01 | < 0.01b | -13 (-15 to -9.7) | < 0.01 | < 0.01 |
| Step 2: Variations in rate of recovery (interaction effects model) | ||||||
| Revision*time2 (rate of recovery) | ||||||
| No | Reference value | Reference value | ||||
| Yes | 0.031 (-0.035 to 0.097) | 0.36 | -0.56 (-1.0 to -0.079) | 0.02 | ||
Step 1model summary: pain intensity: r2 = 0.25, AIC 17165, BIC 17184; ASES: r2 = 0.52, AIC 32313, BIC 32331. Step 2 model summary: pain intensity: r2 = 0.25, AIC 17166, BIC 17191; ASES: r2 = 0.52, AIC 32210, BIC 32335.
aThese estimates are derived from the model in Step 1, where we only accounted for the effect of revision surgery on the mean baseline score.
bThese p values are based on the model in which we accounted for the effect of revision surgery on rate of recovery (Step 2), where estimates for the mean baseline score are less accurate than in Step 1 and are therefore not presented. Variables are deemed clinically relevant when p values in both steps are significant.
Trajectories of Magnitude of Capability
Patients in the worst two MCS quartiles had worse capability at baseline than patients in the best quartile (difference in mean baseline for the bottom quartile [0% to 25%]: -8.9 [95% CI -10 to -7.4]; p < 0.01; difference in mean baseline for next-worst quartile [26% to 50%]: -4.9 [95% CI -6.4 to -3.4]; p < 0.01). The rates of recovery of capability were not different among groups (p > 0.10) (Table 2). Using the best mental health quartile of patients, the rate of recovery in capability of the third quartile (51% to 75%) was -0.18 ([95% CI -0.43 to 0.071]; p = 0.16), the rate of recovery in capability of the second quartile (26% to 50%) was 0.047 ([95% CI -0.20 to 0.30]; p = 0.72), and that for the bottom quartile (0% to 25%) was -0.058 ([95% CI -0.31 to 0.19]; p = 0.65) (Table 2). There were no differences in the mean baseline capability and trajectories between patients who underwent rTSA and those who underwent TSA (mean baseline capability difference: 0.62 [95% CI -0.66 to 1.9]; p = 0.34; difference in rate of recovery: -0.044 [95% CI -0.26 to 0.17]; p = 0.69) (Table 3). Patients who underwent revision surgery had worse mean baseline capability (mean baseline capability difference: -13 [95% CI -15 to -9.7]; p < 0.01) with a slower rate of recovery (-0.56 [95% CI -1.0 to 0.079]; p = 0.02) (Table 4).
Discussion
Variations in pain intensity and magnitude of incapability have a limited association with pathophysiologic severity and are accounted for largely by variation in unhelpful thoughts and distress about symptoms [11, 13, 14, 24]. Recent evidence suggests that recovery of comfort and capability over the course of 2 years after injury and surgery is tied to baseline mental health status [11]. Our study of trajectories of pain intensity and magnitude of capability after shoulder arthroplasty based on quartiles of the MCS found that people with a less healthy mindset had worse baseline levels of pain and capability with no differences in rates of recovery, suggesting that outcomes are worse throughout the trajectory if mental health is not addressed.
Limitations
First, to account for missing data, we used the last carried forward method, which might underestimate the rate of recovery. However, because missing data were limited and evenly distributed, this should not affect the statistical associations of individual factors with recovery trajectories. Second, there are mental health measures that may be more relevant to musculoskeletal symptoms than the MCS of the Veterans RAND 12, although mental health measures tend to correlate with each other. We estimate that more specific mindset measures might have a stronger association with comfort and capability. Future studies might address trajectories relative to more specific measures of unhelpful thoughts and distress regarding symptoms, such as measures that identify specific targets for interventions based on cognitive behavioral therapy. Third, rather than track specific mental health diagnoses, we addressed current mindset on a continuum. Studying formal diagnoses is problematic because people with a diagnosis may have well-treated symptoms or they may have a misdiagnosis. Further, some people have notable symptoms and no formal diagnosis. Additionally, it is important to address mental health on its continuum to limit mental health stigma. Fourth, this study was performed with data from a single-surgeon, single-center arthroplasty registry. This limits variation in surgeon communication strategies and patients a surgeon chooses to offer surgery to. Although these factors limit the generalizability of aspects such as the number of patients in each recovery trajectory, the existence of varied recovery trajectories should be reproducible. Specific implants and techniques may be overrepresented and others underrepresented compared with a multisurgeon or multicenter study. On the other hand, the homogeneity of surgeon technical choices, communication strategies, and indications for surgery could be an advantage in isolating associations with mental health factors.
Fifth, the ASES score has several items rating pain intensity. It might be better to have a measure that isolates capability to allow a study of comfort and capability separately. In future studies, it might be helpful to measure pain intensity and the magnitude of incapability separately using the Patient-Reported Outcome Measurement Information System Physical Function Upper Extremity computerized adaptive test, for example. Sixth, the indications for revision were not specified in the registry. Although revision surgery can address a wide variety of pathophysiology, major issues such as dislocation or infection are uncommon compared with radiographic signs of osteolysis or dissatisfaction with pain alleviation. We anticipate that the revision surgeries are representative.
Seventh, there was an imbalance in the numbers of TSA and rTSA procedures, where rTSA was more frequent. Given the finding of no difference between the types of arthroplasty, this is unlikely to change with different proportions. Eighth, more timepoints within the first 6 months would have been desirable considering that mindset, comfort, and capability may vary more in that part of the recovery course. Including more timepoints was not possible because this was a database study, but we believe five timepoints within 1 year is sufficient to create recovery trajectories by mindset groupings. Finally, this study was not designed to determine whether mindset interventions would affect comfort and capability, which future studies should aim to address.
Trajectories of Pain Intensity and Magnitude of Incapability
The observation that worse mental health is associated with greater pain intensity and magnitude of incapability throughout the recovery course, independent of arthroplasty type, confirms that comfort and capability achieved by shoulder arthroplasty can be limited by modifiable mindset factors and supports the comprehensive, biopsychosocial treatment of musculoskeletal illness. This aligns with other studies of recovery trajectories after musculoskeletal surgery finding that unhealthier mindsets are associated with greater pain [11] and capability [14, 15] at baseline and until 2 years after surgery, as well as less likelihood that a patient will gain substantial benefits from surgery [37]. Baseline pain intensity was worse on average for people requesting revision arthroplasty [12]. Among people with osteolysis or other problems with their prosthesis, there may be some who seek care and consider revision, and there may also be a notable number of people who are not seeking care and are accommodating technical deficiencies, perhaps largely because they interpret associated sensations in an adaptive manner. Given that pain alleviation is a key goal of shoulder arthroplasty, and given the evidence that pain alleviation and improved levels of capability are not as well achieved in people with less healthy mindsets, there may be important aspects of unhealthy thinking and feelings of distress that can be addressed and even prioritized when considering discretionary quality-of-life surgeries [5, 13, 32, 33]. Orthopaedic surgeons and their patients might often focus on treating pathophysiology in the hope that mindsets will improve, but there is evidence that this does not occur [38]. In our view, the evidence that common musculoskeletal pathophysiology is often well accommodated [17, 20], that accommodation is based largely on healthy thinking and limited distress [17, 24], that care-seeking behavior is often a signal of opportunities for improved mindset [1], and that recovery trajectories from reconstructive surgery are limited by unaddressed less health mindsets point firmly to comprehensive biopsychosocial models of care that emphasize and prioritize mindsets.
Conclusion
The observation that less healthy mindsets are associated with greater pain intensity and magnitude of capability during recovery from shoulder arthroplasty reminds us that assessment and treatment of mental health is best considered an integral aspect of musculoskeletal care. Patients and surgeons might hope that the alleviation of pain and restoration of motion achieved by discretionary surgeries such as shoulder arthroplasties might alleviate feelings of distress and unhelpful thinking, but these seem to continue after pathophysiology is treated. Orthopaedic surgeons can anticipate unhelpful thinking and feelings of distress. Such feelings are ubiquitous and not only experienced by people diagnosed with major depression or generalized anxiety disorder. Nonoperative treatment can be more than medications, injections, or exercises, and the evidence is strong that treatment should address less healthy mindsets. The next step is to study the implementation of this knowledge and determine the most effective methods of reorienting unhelpful thinking and alleviating feelings of distress among patients seeking orthopaedic specialty care. Approaches with some supportive evidence that merit additional evaluation include cognitive behavioral therapy toolkits [35] and psychologically informed physical therapies [6], including cognitive functional therapy [3, 16].
Footnotes
One of the authors (DR) certifies receipt of personal payments or benefits, during the study period, in an amount of less than USD 10,000 from Skeletal Dynamics.
All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request.
Ethical approval for this study was waived by the New England Baptist Hospital Institutional Review Board, Boston, MA, USA.
This work was performed at Dell Medical School at the University of Texas at Austin, Austin, TX, USA, and the New England England Baptist Hospital, Boston, MA, USA.
Contributor Information
Melle Martijn Broekman, Email: melle.broekman@austin.utexas.edu.
Niels Brinkman, Email: niels.brinkman@austin.utexas.edu.
Daniel Swanson, Email: dswanson@bostonssc.com.
David Ring, Email: david.ring@austin.utexas.edu.
Michel van den Bekerom, Email: bekerom@gmail.com.
Andrew Jawa, Email: andrewjawa@gmail.com.
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