Evaluating factors associated with telehealth appropriateness in outpatient rheumatoid arthritis encounters using the Encounter Appropriateness Score for You (EASY).

OBJECTIVE: Telehealth has been proposed as a safe and effective alternative to in-person care for rheumatoid arthritis (RA). The purpose of this study was to evaluate factors associated with telehealth appropriateness in outpatient RA encounters. METHODS: A prospective cohort study (1/1/21-8/31/21) was conducted using electronic health record data from outpatient RA encounters in a single academic rheumatology practice. Rheumatology providers rated the telehealth appropriateness of their own encounters using the Encounter Appropriateness Score for You (EASY) immediately following each encounter. Robust Poisson regression with Generalized Estimating Equations (GEE) modeling was used to evaluate the association of telehealth appropriateness with patient demographics, RA clinical characteristics, comorbid non-inflammatory causes of joint pain, previous and current encounter characteristics, and provider characteristics. RESULTS: During the study period, 1,823 outpatient encounters with 1,177 unique RA patients received an EASY score from 25 rheumatology providers. In the final multivariate model [Relative Risk (95% Confidence Interval)], factors associated with increased telehealth appropriateness included higher average provider preference for telehealth in prior encounters [1.26 (1.21-1.31)], telehealth as the current encounter modality [2.27 (1.95-2.64)], and increased patient age [1.05 (1.01-1.09)]. Factors associated with decreased telehealth appropriateness included moderate [0.81 (0.68-0.96)] and high [0.57 (0.46-0.70)] RA disease activity and if the previous encounter were conducted via telehealth [0.83 (0.73-0.95)]. CONCLUSION: In this study, telehealth appropriateness was most associated with provider preference, the current and previous encounter modality, and RA disease activity. Other factors like patient demographics, RA medications, and comorbid non-inflammatory causes of joint pain were not associated with telehealth appropriateness.

Evaluating Provider Perceptions of Telehealth Utility in Outpatient Rheumatology Telehealth Encounters.

OBJECTIVE: This study aims to explore the factors associated with rheumatology providers' perceptions of telehealth utility in real-world telehealth encounters. METHODS: From September 14, 2020 to January 31, 2021, 6 providers at an academic medical center rated their telehealth visits according to perceived utility in making treatment decisions using the following Telehealth Utility Score (TUS) (1 = very low utility to 5 = very high utility). Modified Poisson regression models were used to assess the association between TUS scores and encounter diagnoses, disease activity measures, and immunomodulatory therapy changes during the encounter. RESULTS: A total of 481 telehealth encounters were examined, of which 191 (39.7%) were rated as "low telehealth utility" (TUS 1-3) and 290 (60.3%) were rated as "high telehealth utility" (TUS 4-5). Encounters with a diagnosis of inflammatory arthritis were significantly less likely to be rated as high telehealth utility (adjusted relative risk [aRR], 0.8061; p = 0.004), especially in those with a concurrent noninflammatory musculoskeletal diagnosis (aRR, 0.54; p = 0.006). Other factors significantly associated with low telehealth utility included higher disease activity according to current and prior RAPID3 scores (aRR, 0.87 and aRR, 0.89, respectively; p < 0.001) and provider global scores (aRR, 0.83; p < 0.001), as well as an increase in immunomodulatory therapy (aRR, 0.70; p = 0.015). CONCLUSIONS: Provider perceptions of telehealth utility in real-world encounters are significantly associated with patient diagnoses, current and prior disease activity, and the need for changes in immunomodulatory therapy. These findings inform efforts to optimize the appropriate utilization of telehealth in rheumatology.

Encounter Appropriateness Score for You Model: Development and Pilot Implementation of a Predictive Model to Identify Visits Appropriate for Telehealth in Rheumatology.

OBJECTIVE: We aimed to develop a decision-making tool to predict telehealth appropriateness for future rheumatology visits and expand telehealth care access. METHODS: The model was developed using the Encounter Appropriateness Score for You (EASY) and electronic health record data at a single academic rheumatology practice from January 1, 2021, to December 31, 2021. The EASY model is a logistic regression model that includes encounter characteristics, patient sociodemographic and clinical characteristics, and provider characteristics. The goal of pilot implementation was to determine if model recommendations align with provider preferences and influence telehealth scheduling. Four providers were presented with future encounters that the model identified as candidates for a change in encounter modality (true changes), along with an equal number of artificial (false) recommendations. Providers and patients could accept or reject proposed changes. RESULTS: The model performs well, with an area under the curve from 0.831 to 0.855 in 21,679 encounters across multiple validation sets. Covariates that contributed most to model performance were provider preference for and frequency of telehealth encounters. Other significant contributors included encounter characteristics (current scheduled encounter modality) and patient factors (age, Routine Assessment of Patient Index Data 3 scores, diagnoses, and medications). The pilot included 201 encounters. Providers were more likely to agree with true versus artificial recommendations (Cohen's κ = 0.45, P < 0.001), and the model increased the number of appropriate telehealth visits. CONCLUSION: The EASY model accurately identifies future visits that are appropriate for telehealth. This tool can support shared decision-making between patients and providers in deciding the most appropriate follow-up encounter modality.

Machine learning functional impairment classification with electronic health record data.

BACKGROUND: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment. METHODS: We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome. RESULTS: Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states. CONCLUSION: A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.

B-cell lymphoma 6 expression significantly differs by the uterine preparation method used for frozen embryo transfer.

OBJECTIVE: To determine whether B-cell lymphoma 6 (BCL6), an endometriosis-associated marker postulated to predict poor pregnancy outcomes, is differentially expressed in the window of implantation with various uterine preparation regimens commonly used for frozen embryo transfers. DESIGN: A retrospective cohort study. SETTING: An academic center. PATIENT(S): Patients with infertility who underwent endometrial biopsy for BCL6 evaluation INTERVENTION(S): Exogenous estradiol and/or progesterone. MAIN OUTCOME MEASURE(S): Endometrial BCL6 histological score (HSCORE) and overexpression (HSCORE >1.4) RESULT(S): Two hundred and forty-four patients were included in the analysis: 76 patients were sampled in a natural menstrual cycle without exogenous hormone exposure (NC), 25 under a modified natural cycle embryo transfer protocol with choriogonadotropin alfa injection followed by luteal phase vaginal progesterone supplementation (mNC), and 143 under a programmed cycle embryo transfer protocol, with estradiol administration followed by addition of intramuscular progesterone-in-oil injections (PC). Median HSCORE (interquartile range) was the highest in NC (3.0 [1.8-3.6]). BCL6 expression was significantly lower in mNC (1.1 [0.4-2.1]) and PC groups (0.8 [0.3-1.3]) compared with NC. In addition, BCL6 overexpression (HSCORE >1.4) was observed in 80.3% of NC, 40.0 % of mNC, and 23.1 % of PC. After adjusting for covariates, the endometrium exposed to exogenous progesterone had significantly lower odds of BCL6 overexpression than that of a natural menstrual cycle (adjusted odds ratio, 0.12 [95% CI 0.04-0.35] for mNC; and odds ratio, 0.08 [95% CI 0.04-0.17] for PC). CONCLUSION(S): BCL6 expression differs by the type of uterine preparation method, with lower levels observed with exogenous progesterone exposure. The validity and utility of BCL6 testing under medicated endometrial state warrants further investigation.

Telehealth Made EASY: Understanding Provider Perceptions of Telehealth Appropriateness in Outpatient Rheumatology Encounters.

OBJECTIVE: The purpose of this study was to evaluate a novel scoring system, the Encounter Appropriateness Score for You (EASY), to assess provider perceptions of telehealth appropriateness in rheumatology encounters. METHODS: The EASY scoring system prompts providers to rate their own encounters as follows: in-person or telehealth acceptable, EASY = 1; in-person preferred, EASY = 2; or telehealth preferred, EASY = 3. Assessment of the EASY scoring system occurred at a single academic institution from January 1, 2021, to August 31, 2021. Data were collected in three rounds: 1) initial survey (31 providers) assessing EASY responsiveness to five hypothetical scenarios, 2) follow-up survey (34 providers) exploring EASY responsiveness to 11 scenario modifications, and 3) assessment of EASYs documented in clinic care. RESULTS: The initial and follow-up surveys demonstrated responsiveness of EASYs to different clinical and nonclinical factors. For instance, less than 20% of providers accepted telehealth when starting a biologic for active rheumatoid arthritis, although more than 35% accepted telehealth in the same scenario if the patient lived far away or was well known to the provider. Regarding EASY documentation, 27 providers provided EASYs for 12,381 encounters. According to these scores, telehealth was acceptable or preferred for 29.7% of all encounters, including 21.4% of in-person encounters. Conversely, 24.4% of telehealth encounters were scored as in-person preferred. CONCLUSION: EASY is simple, understandable, and responsive to changes in the clinical scenario. We have successfully accumulated 12,381 EASYs that can be studied in future work to better understand telehealth utility and optimize telehealth triage.

AOASM Position Statement on Esports, Active Video Gaming, and the Role of the Sports Medicine Physician.

ABSTRACT: Electronic sports, or esports, has a global audience of over 300 million fans and is increasing in popularity, resulting in projected revenue of over $1 billion by the end of this past year. The global pandemic of 2020 had little to no effect on these increasing numbers because athletes have been able to continue to engage in sports because of its electronic nature and fans have been able to follow them virtually. Esports has been recognized as an organized sport by the International Olympic Committee, the US National Collegiate Athletic Association, and several secondary school athletic associations within the United States. In addition, professional teams have been established in several major cities within the United States, Canada, Europe, and Australia. With the growth of esports, the necessity of incorporating esports medicine into the practice of sports medicine physicians has become paramount. Esports can be played on a monitor or screen and played using physical activity in what has become known as active video gaming. Within both of these platforms, there have emerged certain conditions unique to esports. There are also certain conditions seen in other sports applicable to esports athletes. This document will review the evaluation of the esports athlete, introduce conditions unique to these athletes and review common conditions seen in esports, discuss diagnostics used in the evaluation of esports athletes, introduce treatment options for conditions unique to esports and review those for commonly seen injuries in esports, discuss prevention of injuries in esports, and introduce a framework for the future development of esports medicine that can be introduced into the daily practice of the sports medicine physician.

SCAT5 Sex Differences: Normative Data, Clinical Thresholds, and Relevance for Identification of Concussion.

OBJECTIVE: This study evaluated sex differences in performance on the Sport Concussion Assessment Tool-5 (SCAT5) Standardized Assessment of Concussion (SAC) and in baseline SCAT5 symptom reporting. It established clinically relevant cut points for low performance on the SAC based on both reliable chance indices (RCIs) and normative performance. This study also evaluated the diagnostic utility of the sex-adjusted SCAT5 SAC for identification of suspected concussion in collegiate athletes. METHOD: In total, 671 uninjured collegiate athletes were administered the SCAT5 and 264 of these athletes also completed SCAT5 testing ~1 year later. Fifty-four athletes were administered the SCAT5 after being removed from play due to suspected concussion. Sex differences in cognitive performance and symptom reporting at baseline were evaluated and sex-specific clinically relevant cut points were provided. Chi square and logistic regression models were used to evaluate if SAC performance was a significant predictor of concussion status. RESULTS: Female athletes outperformed male athletes on the SCAT 5 SAC and showed minimally higher symptom endorsement. Use of sex-corrected normative data improved performance of the SAC in identification of suspected concussion when a low score cut point was used. Logistic regression models showed that sex-corrected SAC change from baseline (RCI) improved the predictive value of the model after first accounting for other elements of the SCAT5. CONCLUSIONS: Present results support the use of sex-specific normative data for the SCAT5 SAC, particularly if using low performance without comparison to a baseline; however, reliable change from a pre-injury baseline may have somewhat higher diagnostic utility.

Diagnostic Accuracy of Synovial Lactate, Polymerase Chain Reaction, or Clinical Examination for Suspected Adult Septic Arthritis.

BACKGROUND: Adult septic arthritis can be challenging to differentiate from other causes of acute joint pain. The diagnostic accuracy of synovial lactate and polymerase chain reaction (PCR) remains uncertain. OBJECTIVE: Our aim was to quantify the diagnostic accuracy of synovial lactate, PCR, and clinical evaluation for adults with possible septic arthritis in the emergency department (ED). METHODS: We report a prospective sampling of ED patients aged ≥ 18 years with knee symptoms concerning for septic arthritis. Clinicians and research assistants independently performed history and physical examination. Serum and synovial laboratory testing was ordered at the discretion of the clinician. We analyzed frozen synovial fluid specimens for l- and d-lactate and PCR. The criterion standard for septic arthritis was bacterial growth on synovial culture and treated by consultants with operative drainage, prolonged antibiotics, or both. Diagnostic accuracy measures included sensitivity, specificity, likelihood ratios, interval likelihood ratios, and receiver operating characteristic area under the curve. RESULTS: Seventy-one patients were included with septic arthritis prevalence of 7%. No finding on history or physical examination accurately ruled in or ruled out septic arthritis. Synovial l- and d-lactate and PCR were inaccurate for the diagnosis of septic arthritis. Synovial white blood cell count and synovial Gram stain most accurately rule in and rule out septic arthritis. CONCLUSIONS: Septic arthritis prevalence in ED adults is lower than reported previously. History and physical examination, synovial lactate, and PCR are inadequate for the diagnosis of septic arthritis. Synovial white blood cell count and Gram stain are the most accurate tests available for septic arthritis.

The Pediatric Endurance Athlete.

Youth sports participation numbers continue to grow in the United States. A shift toward sport specialization has caused an increase in sport training frequency and intensity that places the growing athlete at risk for overtraining, nutritional deficits, and injuries. Individuals who participate in endurance sports are at especially high risk. Youth runners and swimmers are high-risk populations that require special attention to their training schedules, nutritional intake, and injuries. Appropriate scheduling of training, dedicating time to rest, and nutrition education can help prevent problems in the endurance athlete.

Approach to the Underperforming Athlete.

Children and adolescents who participate in intense sports training may face physical and psychologic stresses. The pediatric health care provider can play an important role in monitoring an athlete's preparation by obtaining a proper sports history, assessing sleep hygiene, discussing nutrition and hydration guidelines, and evaluating physiologic causes of fatigue. Educating parents and athletes on the potential risks of high-intensity training, inadequate rest and sleep, and a poor diet may improve the athlete's performance and prevent symptoms of overtraining syndrome. Infectious mononucleosis must also be considered a cause of fatigue among adolescents. The signs and symptoms of overtraining and burnout are discussed in this article.