Minimal Important Clinical Difference Values Are Not Uniformly Valid in the Active Duty Military Population Recovering from Shoulder Surgery.

BACKGROUND: There are multiple methods for calculating the minimal clinically important difference (MCID) threshold, and previous reports highlight heterogeneity and limitations of anchor-based and distribution-based analyses. The Warfighter Readiness Survey assesses the perception of a military population's fitness to deploy and may be used as a functional index in anchor-based MCID calculations. The purpose of the current study in a physically demanding population undergoing shoulder surgery was to compare the yields of two different anchor-based methods of calculating MCID for a battery of PROMs, a standard receiver operator curve (ROC) -based MCIDs and baseline-adjusted ROC MCIDs. METHODS: All service members enrolled prospectively in a multicenter database with prior shoulder surgery that completed pre- and postoperative PROMs at a minimum of 12 months were included. The PROMs battery included Single Assessment Numeric Evaluation (SANE), American Shoulder and Elbow Surgeons score (ASES), Patient Reported Outcome Management Information System (PROMIS) Physical Function (PF), PROMIS Pain Interference (PI), and the Warfighter Readiness survey. Standard anchor-based and baseline-adjusted ROC MCIDs were employed to determine if the calculated MCIDs were both statistically and theoretically valid (95% confidence interval either completely negative or positive). RESULTS: There were 117 patients (136 operations) identified, comprised of 83% males with a mean age of 35.7 ± 10.4 years and 47% arthroscopic labral repair/capsulorrhaphy. Using the standard, anchor-based ROC MCID calculation, the area under the curve (AUC) for SANE, ASES, PROMIS PF, and PROMIS PI were greater than 0.5 (statistically valid). For ASES, PROMIS PF, and PROMIS PI, the calculated MCID 95% CI all crossed 0 (theoretically invalid). Using the baseline-adjusted ROC MCID calculation, the MCID estimates for SANE, ASES, and PROMIS PI were both statistically and theoretically valid if the baseline score was less than 70.5, 69, and 65.7. CONCLUSION: When MCIDs were calculated and anchored to the results of standard, anchor-based MCID, a standard ROC analysis did not yield statistically or theoretically valid results across a battery of PROMs commonly used to assess outcomes after shoulder surgery in the active duty military population. Conversely, a baseline-adjusted ROC method was more effective at discerning changes across a battery of PROMs among the same cohort.

Athlete Health and Human Performance Will Not Improve Without Transdisciplinary Collaboration and Data Sharing in Elite Sport.

There are two largely competing models for an athletics organization at both the collegiate and professional levels: the High Performance Model and the Medical Model. The High Performance Model arises from international Football perspective that places a "Performance Director" at the center of teams supporting the athletes. The Medical Model, supported by both the National Athletic Trainers Association and the NCAA, separates off medical staff (athletic trainers, physical therapists, and physicians, predominantly) and emphasizes the autonomy of medical decisions. The Medical Model has left clinicians in a "medical silo", limiting our ability to care for the individual athletes as holistic people and limits our wider impact in the field of athlete health and injury mitigation. We argue that Medical. Model is consistent with the High Performance Model only if we reject the notion that the "Performance Director" is an administrative person and instead conceptualize this as a "Health and Performance Information Hub" which facilitates transdisciplinary collaboration. This Commentary details how a data broker system can be used to accelerate transdisciplinary collaboration within an athletic organization, leading to better healthcare for athletes and improved team and individual performance. Furthermore, a transdisciplinary organization with data sharing is able to turn real-world data into real-world evidence, enhancing the care and performance of athletes locally as well as facilitating the creation of generalizable knowledge in the area of sports medicine and human performance.

Asking the Right Question Is Key to Getting a Valuable Answer.

Letter to the Editor-in-Chief in response to JOSPT article "The Relationship Between Neck Strength and Sports-Related Concussion in Team Sports: A Systematic Review With Meta-analysis" by Garrett JM et al. J Orthop Sports Phys Ther 2023;53(11):726. doi:10.2519/jospt.2023.0204.

Low-load blood flow restriction reduces time-to-minimum single motor unit discharge rate.

Resistance training with low loads in combination with blood flow restriction (BFR) facilitates increases in muscle size and strength comparable with high-intensity exercise. We investigated the effects of BFR on single motor unit discharge behavior throughout a sustained low-intensity isometric contraction. Ten healthy individuals attended two experimental sessions: one with, the other without, BFR. Motor unit discharge rates from the tibialis anterior (TA) were recorded with intramuscular fine-wire electrodes throughout the duration of a sustained fatigue task. Three 5-s dorsiflexion maximal voluntary contractions (MVC) were performed before and after the fatigue task. Each participant held a target force of 20% MVC until endurance limit. A significant decrease in motor unit discharge rate was observed in both the non-BFR condition (from 13.13 ± 0.87 Hz to 11.95 ± 0.43 Hz, P = 0.03) and the BFR condition (from 12.95 ± 0.71 Hz to 10.9 ± 0.75 Hz, P = 0.03). BFR resulted in significantly shorter endurance time and time-to-minimum discharge rates and greater end-stage motor unit variability. Thus, low-load BFR causes an immediate steep decline in motor unit discharge rate that is greater than during contractions performed without BFR. This shortened neuromuscular response of time-to-minimum discharge rate likely contributes to the rapid rate of neuromuscular fatigue observed during BFR.

The Glenoid Track Paradigm Does Not Reliably Affect Military Surgeons' Approach to Managing Shoulder Instability.

Purpose: To report the frequencies of surgical stabilization procedures performed by military shoulder surgeons and to use decision tree analysis to describe how bipolar bone loss affects surgeons' decision to perform arthroscopic versus open stabilization procedures. Methods: The Military Orthopaedics Tracking Injuries and Outcomes Network (MOTION) database was queried for anterior shoulder stabilization procedures from 2016 to 2021. A nonparametric decision tree analysis was used to generate a framework for classifying surgeon decision making based on specified injury characteristics (labral tear location, glenoid bone loss [GBL], Hill-Sachs lesion [HSL] size, and on-track vs off-track HSL). Results: A total of 525 procedures were included in the final analysis, with a mean patient age of 25.9 ± 7.2 years and a mean GBL percentage of 3.6% ± 6.8%. HSLs were described based on size as absent (n = 354), mild (n = 129), moderate (n = 40), and severe (n = 2) and as on-track versus off-track in 223 cases, with 17% (n = 38) characterized as off-track. Arthroscopic labral repair (n = 428, 82%) was the most common procedure, whereas open repair (n = 10, 1.9%) and glenoid augmentation (n = 44, 8.4%) were performed infrequently. Decision tree analysis identified a GBL threshold of 17% or greater that resulted in an 89% probability of glenoid augmentation. Shoulders with GBL less than 17% combined with a mild or absent HSL had a 95% probability of an isolated arthroscopic labral repair, whereas a moderate or severe HSL resulted in a 79% probability of arthroscopic repair with remplissage. The presence of an off-track HSL did not contribute to the decision-making process as defined by the algorithm and data available. Conclusions: Among military shoulder surgeons, GBL of 17% or greater is predictive of a glenoid augmentation procedure whereas HSL size is predictive of remplissage for GBL less than 17%. However, the on-track/off-track paradigm does not appear to affect military surgeons' decision making. Level of Evidence: Level III, retrospective cohort study.

Missing Data in Sport Science: A Didactic Example Using Wearables in American Football.

Data are often recorded from athletes to make decisions regarding the mitigation of injuries or the enhancement of performance. However, data collection in the real-world is difficult, and it is common for data to be missing from a particular training session due to equipment malfunction, athlete non-compliance, etc. The statistical community has long recognized that proper handling of missing data is vital to unbiased analyses and decision making, yet most dashboards in sport science and medicine do not recognize the issues introduced by missing data and practitioners are largely unaware that their displays are conveying biased information. The goal of this leading article is to show how real-world data can violate the 'missing completely at random' assumption in an American Football example and then demonstrate some potential imputation solutions which appear to maintain the underlying properties of the data in the presence of missingness. Whether data are aggregated on a dashboard as simple histograms and averages or with higher-level analytics, violating the 'missing completely at random' assumption results in a biased dashboard. Practitioners need to insist that dashboard developers perform missing data analyses and impute data as needed so valid data-driven decisions can be made.

An Exploratory Factor Analysis of the National Institutes of Health Patient-Reported Outcomes Measurement Information System and the Single Assessment Numeric Evaluation in Knee Surgery Patients.

INTRODUCTION: Patient-Reported Outcomes (PROs) have been suggested for use in measuring treatment effectiveness. To minimize patient burden, two approaches have been proposed: An orthopedic-specific Single Assessment Numeric Evaluation (SANE) or computer adaptive testing methods such as the National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS). The goal of this work was to examine the constructs measured by the SANE and PROMIS system in a military orthopedic population undergoing knee surgery. MATERIALS AND METHODS: In 732 patients and 2,166 complete observations, the SANE-Knee PRO and PROMIS surveys for Depression, Anxiety, Pain Interference, Sleep Disturbance, and Physical Function were obtained. A correlation matrix between the PROs was calculated, the number of latent factors to extract was determined via parallel plot, and the final principal axis exploratory factor analysis was performed. RESULTS: The parallel plot analysis indicated that two latent factors existed. One latent factor corresponded to measures of psychological distress (PROMIS Sleep Disturbance, Depression, and Anxiety) and the second latent factor corresponded to physical capability (SANE, PROMIS Physical Function, Pain Interference, and Sleep Disturbance). Both PROMIS Physical Function (0.83) and Pain Interference (-0.80) more strongly weigh on the physical capability latent factor than SANE (0.69). CONCLUSIONS: In a knee surgical population, the SANE, PROMIS Physical Function, and PROMIS Pain Interference measure the same human dimension of physical capability; however, PROMIS Physical Function and Pain Interference may measure this construct more effectively in isolation. The SANE may be a more viable option to gauge physical capability when computer adaptive testing is not possible.

All MCIDs Are Wrong, But Some May be Useful.

OBJECTIVE: To demonstrate how to apply a baseline-adjusted receiver operator characteristic curve (AROC) analysis for minimum clinically important differences (MCIDs) in an empirical data set and discuss new insights relating to MCIDs. DESIGN: Retrospective study. METHODS: This study includes data from 999 active-duty military service patients enrolled in the United States Military Health System's Military Orthopedics Tracking Injuries and Outcomes Network. Anchored MCIDs were calculated using the standard receiver operator characteristic analysis and the AROC analysis for the Patient-Reported Outcome Measure Information System (PROMIS) Pain Interference and Defense and Veterans Pain Rating Scale (DVPRS). Point estimates where confidence intervals (CIs) crossed the 0.5 identity line on the area-under-the-curve (AUC) analysis were considered statistically invalid. MCID estimates where CIs crossed 0 were considered theoretically invalid. RESULTS: In applying an AROC analysis, the region of AUC and MCID validity for the PROMIS Pain Interference score exists when the baseline score is greater than 61.0 but less than 72.3. For DVPRS, the region of MCID validity is when the baseline score is greater than 5.9 but less than 7.9. CONCLUSION: Baseline values influence not only the MCID but also the accuracy of the MCID. MCIDs are statistically and theoretically valid for only a discrete range of baseline scores. Our findings suggest that the MCID may be too flawed a construct to accurately benchmark treatment outcomes. J Orthop Sports Phys Ther 2022;52(6):401-407. doi:10.2519/jospt.2022.11193.

The Military Orthopedics Tracking Injuries and Outcomes Network: A Solution for Improving Musculoskeletal Care in the Military Health System.

INTRODUCTION: Musculoskeletal injuries are an endemic amongst U.S. Military Service Members and significantly strain the Department of Defense's Military Health System. The Military Health System aims to provide Service Members, military retirees, and their families the right care at the right time. The Military Orthopedics Tracking Injuries and Outcomes Network (MOTION) captures the data that can optimize musculoskeletal care within the Military Health System. This report provides MOTION structural framework and highlights how it can be used to optimize musculoskeletal care. MATERIALS AND METHODS: MOTION established an internet-based data capture system, the MOTION Musculoskeletal Data Portal. All adult Military Health System patients who undergo orthopedic surgery are eligible for entry into the database. All data are collected as routine standard of care, with patients and orthopedic surgeons inputting validated global and condition-specific patient reported outcomes and operative case data, respectively. Patients have the option to consent to allow their standard of care data to be utilized within an institutional review board approved observational research study. MOTION data can be merged with other existing data systems (e.g., electronic medical record) to develop a comprehensive dataset of relevant information. In pursuit of enhancing musculoskeletal injury patient outcomes MOTION aims to: (1) identify factors which predict favorable outcomes; (2) develop models which inform the surgeon and military commanders if patients are behind, on, or ahead of schedule for their targeted return-to-duty/activity; and (3) develop predictive models to better inform patients and surgeons of the likelihood of a positive outcome for various treatment options to enhance patient counseling and expectation management. RESULTS: This is a protocol article describing the intent and methodology for MOTION; thus, to date, there are no results to report. CONCLUSIONS: MOTION was established to capture the data that are necessary to improve military medical readiness and optimize medical resource utilization through the systematic evaluation of short- and long-term musculoskeletal injury patient outcomes. The systematic enhancement of musculoskeletal injury care through data analyses aligns with the National Defense Authorization Act (2017) and Defense Health Agency's Quadruple Aim, which emphasizes optimizing healthcare delivery and Service Member medical readiness. This transformative approach to musculoskeletal care can be applied across disciplines within the Military Health System.

Latent Factor Analysis of the PROMIS and Single Assessment Numeric Evaluation in Patients Undergoing Shoulder Surgery.

INTRODUCTION: Patient-reported outcomes (PROs) are reporting tools that quantify patients' perceptions of their mental and physical health. Many PROs may inadvertently measure the same or overlapping theoretical constructs (e.g., pain, function, depression, etc.), which is both inefficient and a patient burden. The purpose of this study was to examine the functional relationship of the Single Assessment Numeric Evaluation (SANE) score and general constructs measured with the NIH Patient-Reported Outcomes Measurement Information System (PROMIS) in young patients undergoing shoulder surgery. MATERIAL AND METHODS: This study was an institutional review board approved retrospective case control of the Military Orthopaedics Tracking Injuries and Outcomes Network using 805 patients and 1,373 observations. All patients underwent shoulder surgery and had multiple observations ranging from 28 days pre-surgery to 428 days post-surgery. Correlation matrices and exploratory factor analysis were used to examine how each of the measured variables (PROMIS physical function, PROMIS pain interference, PROMIS sleep disturbance, PROMIS anxiety, PROMIS depression, and SANE surveys) contribute or "weigh" on latent factors, which are then mapped to a theoretical construct. This statistical method helps uncover structural relationships between measured variables. RESULTS: The PROMIS and SANE surveys collectively weigh on two latent factors: psychological health (measured variables: PROMIS anxiety [0.95] and PROMIS depression [0.86]) and physical capabilities (measured variables: PROMIS physical function [0.81], PROMIS pain interference [-0.82], PROMIS sleep disturbance [-0.51], and SANE [0.68]). Although the physical capability construct is functionally related to psychological health (-0.45), there is no direct relation between SANE and measures of depression or anxiety. CONCLUSIONS: This study supports the use of the SANE as a valid single question to assess physical function providing similar information to the PROMIS in regard to measuring physical capabilities. Its simplicity makes it easy to use and implement with minimal uplift or change in workflow.

A High-Sensitivity International Knee Documentation Committee Survey Index From the PROMIS System: The Next-Generation Patient-Reported Outcome for a Knee Injury Population.

BACKGROUND: Patient-reported outcomes (PROs) measure progression and quality of care. While legacy PROs such as the International Knee Documentation Committee (IKDC) survey are well-validated, a lengthy PRO creates a time burden on patients, decreasing adherence. In recent years, PROs such as the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function and Pain Interference surveys were developed as computer adaptive tests, reducing time to completion. Previous studies have examined correlation between legacy PROs and PROMIS; however, no studies have developed effective prediction models utilizing PROMIS to create an IKDC index. While the IKDC is the standard knee PRO, computer adaptive PROs offer numerous practical advantages. PURPOSE: To develop a nonlinear predictive model utilizing PROMIS Physical Function and Pain Interference to estimate IKDC survey scores and examine algorithm sensitivity and validity. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: The MOTION (Military Orthopaedics Tracking Injuries and Outcomes Network) database is a prospectively collected repository of PROs and intraoperative variables. Patients undergoing knee surgery completed the IKDC and PROMIS surveys at varying time points. Nonlinear multivariable predictive models using Gaussian and beta distributions were created to establish an IKDC index score, which was then validated using leave-one-out techniques and minimal clinically important difference analysis. RESULTS: A total of 1011 patients completed the IKDC and PROMIS Physical Function and Pain Interference, providing 1618 complete observations. The algorithms for the Gaussian and beta distribution were validated to predict the IKDC (Pearson = 0.84-0.86; R2 = 0.71-0.74; root mean square error = 9.3-10.0). CONCLUSION: The publicly available predictive models can approximate the IKDC score. The results can be used to compare PROMIS Physical Function and Pain Interference against historical IKDC scores by creating an IKDC index score. Serial use of the IKDC index allows for a lower minimal clinically important difference than the conventional IKDC. PROMIS can be substituted to reduce patient burden, increase completion rates, and produce orthopaedic-specific survey analogs.

Generating the American Shoulder and Elbow Surgeons Score Using Multivariable Predictive Models and Computer Adaptive Testing to Reduce Survey Burden.

BACKGROUND: The preferred patient-reported outcome measure for the assessment of shoulder conditions continues to evolve. Previous studies correlating the Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CATs) to the American Shoulder and Elbow Surgeons (ASES) score have focused on a singular domain (pain or physical function) but have not evaluated the combined domains of pain and physical function that compose the ASES score. Additionally, previous studies have not provided a multivariable prediction tool to convert PROMIS scores to more familiar legacy scores. PURPOSE: To establish a valid predictive model of ASES scores using a nonlinear combination of PROMIS domains for physical function and pain. STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 3. METHODS: The Military Orthopaedics Tracking Injuries and Outcomes Network (MOTION) database is a prospectively collected repository of patient-reported outcomes and intraoperative variables. Patients in MOTION research who underwent shoulder surgery and completed the ASES, PROMIS Physical Function, and PROMIS Pain Interference at varying time points were included in the present analysis. Nonlinear multivariable predictive models were created to establish an ASES index score and then validated using "leave 1 out" techniques and minimal clinically important difference /substantial clinical benefit (MCID/SCB) analysis. RESULTS: A total of 909 patients completed the ASES, PROMIS Physical Function, and PROMIS Pain Interference at presurgery, 6 weeks, 6 months, and 1 year after surgery, providing 1502 complete observations. The PROMIS CAT predictive model was strongly validated to predict the ASES (Pearson coefficient = 0.76-0.78; R2 = 0.57-0.62; root mean square error = 13.3-14.1). The MCID/SCB for the ASES was 21.7, and the best ASES index MCID/SCB was 19.4, suggesting that the derived ASES index is effective and can reliably re-create ASES scores. CONCLUSION: The PROMIS CAT predictive models are able to approximate the ASES score within 13 to 14 points, which is 7 points more accurate than the ASES MCID/SCB derived from the sample. Our ASES index algorithm, which is freely available online (https://osf.io/ctmnd/), has a lower MCID/SCB than the ASES itself. This algorithm can be used to decrease patient survey burden by 11 questions and provide a reliable ASES analog to clinicians.

Perceived exertion and performance modulation: effects of caffeine ingestion and subject expectation.

BACKGROUND: It is well established that caffeine has ergogenic effects on endurance performance. This evidence often comes from studies in which subjects receive either caffeine or placebo in double-blind, randomized and counterbalanced order. Here, we propose a new methodology which aims to estimate the effects of participant expectancy of ergogenic or anti-ergogenic effects on performance. METHODS: Sixteen physically active participants (non-athletes engaged in systematic physical training >3 months, at least three times a week) performed three 30-minute running tests after being told they would be provided with either a harmful treatment (lactic acid), a beneficial treatment (caffeine) or a placebo. In each blinded case, subjects were given caffeine. The velocity and Rating of Perceived Exertion (RPE) during the time trial were examined in light of the participant's expectancy before and after the endurance event using Bayesian multilevel models. RESULTS: For pre-exercise expectancy, there is a 92% probability that caffeine expectation decreases RPE (posterior median±SD -0.65±0.36) and a 79% probability that lactic acid expectation increases RPE (posterior median±SD 0.58±0.47) with expectations for placebo and 'not sure' falling in between (posterior median±SD: -0.37±0.32 and -0.22±0.37, respectively). In general, our interventions suggest an 81% probability that caffeine lowers RPE. However, there was no effect of caffeine supplementation on running velocity (median±SD 0.04±0.08 km.h-1). CONCLUSIONS: When a participant believed they are under a potentially positive treatment, their RPE decreased but if they believed themselves to be under a harmful treatment, their RPE increased, regardless of the actual positive intervention; neither caffeine nor the expectancy of a particular intervention improved actual performance as measured by running velocity in a 30-minute period.

Anchored Minimal Clinically Important Difference Metrics: Considerations for Bias and Regression to the Mean.

Minimal clinically important differences (MCIDs) are used to understand clinical relevance. However, repeated observations produce biased analyses unless one accounts for baseline observation, known as regression to the mean (RTM). Using an International Knee Documentation Committee (IKDC) survey dataset, we can demonstrate the effect of RTM on MCID values by (1) MCID-estimate dependence on baseline observation and (2) MCID-estimate bias being higher when the posttest-pretest data correlation is lower. We created 10 IKDC datasets with 5000 patients and a specific correlation under both equal and unequal variances. For each 10-point increase in baseline IKDC, MCID decreased by 3.5, 2.7, 1.9, 1.2, and 0.7 points when posttest-pretest correlations were 0.10, 0.25, 0.50, 0.75, and 0.90, respectively, under equal variances. Not accounting for RTM resulted in a static 20-point MCID. Minimal clinically important difference estimates may be unreliable. Minimal clinically important difference calculations should include the correlation and variances between posttest and pretest data, and researchers should consider using a baseline covariate-adjusted receiver operating characteristic curve analysis to calculate MCID.

Acute:Chronic Workload Ratio: Conceptual Issues and Fundamental Pitfalls.

The number of studies examining associations between training load and injury has increased exponentially. As a result, many new measures of exposure and training-load-based prognostic factors have been created. The acute:chronic workload ratio (ACWR) is the most popular. However, when recommending the manipulation of a prognostic factor in order to alter the likelihood of an event, one assumes a causal effect. This introduces a series of additional conceptual and methodological considerations that are problematic and should be considered. Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data. Furthermore, there are known issues with the use of ratio data and unrecognized assumptions that negatively affect the ACWR metric for use as a causal prognostic factor. ACWR use in practical settings can lead to inappropriate recommendations, because its causal relation to injury has not been established, it is an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled), it has a lack of background rationale to support its causal role, it is an ambiguous metric, and it is not consistently and unidirectionally related to injury risk. Conclusion: There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts.