Physician Consensus Research Articles

Team Physician Consensus Statement: Return to Sport/Return to Play and the Team Physician: A Team Physician Consensus Statement-2023 Update.

Team Physician Consensus Statement: Return to Sport/Return to Play and the Team Physician: A Team Physician Consensus Statement-2023 Update.

Initial Assessment and Management of Select Musculoskeletal Injuries: A Team Physician Consensus Statement.

Musculoskeletal injuries occur frequently in sport during practice, training, and competition. Injury assessment and management are common responsibilities for the team physician. Initial Assessment and Management of Musculoskeletal Injury-A Team Physician Consensus Statement is title 23 in a series of annual consensus documents written for the practicing team physician. This statement was developed by the Team Physician Consensus Conference, an annual project-based alliance of six major professional associations. The goal of this document is to help the team physician improve the care and treatment of the athlete by understanding the initial assessment and management of selected musculoskeletal injuries.

Team Physician Consensus Statement: Return to Sport/Return to Play and the Team Physician: A Team Physician Consensus Statement-2023 Update.

Team Physician Consensus Statement: Return to Sport/Return to Play and the Team Physician: A Team Physician Consensus Statement-2023 Update.

Non-Fusion Versus Fusion Surgery in Pediatric Idiopathic Scoliosis: What Trade-Offs in Outcomes Are Acceptable for the Patient and Family?

Vertebral body tethering and other non-fusion techniques for the treatment of pediatric idiopathic scoliosis are increasing in popularity. There is limited physician consensus on this topic as the result of a paucity of published data regarding which patients most benefit from non-fusion strategies. Thus, much of the decision-making is left to patients and parents, who must select a treatment based on their goals and values and the information available from health-care providers, the internet, and social media. We sought to understand patient and family preferences regarding the attributes of fusion versus non-fusion surgery that drive these choices. Patients and families were recruited from 7 pediatric spine centers and were asked to complete a survey-based choice experiment that had been jointly developed with the U.S. Food and Drug Administration (FDA) to evaluate patient preferences. Choices between experimentally designed alternatives were analyzed to estimate the relative importance of outcomes and requirements associated with the choice options (attributes). The attributes included appearance, confidence in the planned correction, spinal motion, device failure, reoperation, and recovery period. The inclusion criteria were (1) an age of 10 to 21 years and (2) a diagnosis of adolescent idiopathic scoliosis in patients who were considering, or who had already undergone, treatment with fusion or non-fusion surgery. Preference weights were estimated from the expected changes in choice given changes in the attributes. A total of 344 respondents (124 patients, 92 parents, and 128 parent/patient dyads) completed the survey. One hundred and seventy-three patients were enrolled prior to surgery, and 171 were enrolled after surgery. Appearance and motion were found to be the most important drivers of choice. For the entire cohort, fusion was preferred over non-fusion. For patients who were considering surgery, the most important attributes were preservation of spinal motion and appearance. Patients and families seeking treatment for idiopathic scoliosis value appearance and preservation of spinal motion and, to a lesser extent, reoperation rates when considering fusion versus non-fusion surgery.

Initial Assessment and Management of Select Musculoskeletal Injuries: A Team Physician Consensus Statement.

Musculoskeletal injuries occur frequently in sport during practice, training, and competition. Injury assessment and management are common responsibilities for the team physician. Initial Assessment and Management of Musculoskeletal Injury-A Team Physician Consensus Statement is title 23 in a series of annual consensus documents written for the practicing team physician. This statement was developed by the Team Physician Consensus Conference, an annual project-based alliance of six major professional associations. The goal of this document is to help the team physician improve the care and treatment of the athlete by understanding the initial assessment and management of selected musculoskeletal injuries.

Prediction of 90 day home time among patients with low baseline ASPECTS undergoing endovascular thrombectomy: results from Alberta’s Provincial Stroke Registry (QuICR)

BackgroundThe benefit of endovascular thrombectomy (EVT) in stroke patients with a low baseline Alberta Stroke Program Early CT Score (ASPECTS, ≤5) is uncertain. We aim to use random forest regression...

Automated detection of causal relationships among diseases and imaging findings in textual radiology reports.

Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect causal associations among diseases and imaging findings from their co-occurrence in radiology reports. This IRB-approved and HIPAA-compliant study analyzed 1702462 consecutive reports of 1396293 patients; patient consent was waived. Reports were analyzed for positive mention of 16839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P < 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth. 2742 of 16839 RGO entities were included, 53849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold. Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports. This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients

To assess the clinical acceptability of a commercial deep-learning-based auto-segmentation (DLAS) prostate model that was retrained using institutional data for delineation of the clinical target volume (CTV) and organs-at-risk (OARs) for postprostatectomy patients, accounting for clinical and imaging protocol variations. CTV and OARs of 109 prostate-bed patients were used to evaluate the performance of the vendor-trained model and custom retrained DLAS models using different training quantities. Two new models for OAR structures were retrained (n=30, 60 data sets), while separate models were trained for a new CTV structure (n=30, 60, 90 data sets), with the remaining data sets used for testing (n=49, 19). The dice similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. Six radiation oncologists performed a qualitative evaluation scoring both preference and clinical utility for blinded structure sets. Physician consensus data sets identified from the qualitative evaluation were used toward a separate CTV model. Both the 30- and 60-case retrained OAR models had median DSC values between 0.91 to 0.97, improving significantly over the vendor-trained model for all OARs except the penile bulb. The brand new 60-case CTV model had a median DSC of 0.70 improving significantly over the 30-case model. DLAS (60-case model) and manual contours were blinded and evaluated by physicians with contours deemed acceptable or precise for 87% and 94% of cases for DLAS and manual delineations, respectively. DLAS-generated CTVs were scored precise or acceptable in 54% of cases, compared with the manual delineation value of 73%. The 30-case physician consensus CTV model did not show a significant difference compared with the randomly selected models. Custom retraining using institutional data leads to performance improvement in the clinical utility and accuracy of DLAS for postprostatectomy patients. A small number of data sets are sufficient for building an institutional site-specific DLAS OAR model, as well as for training new structures. Data indicates the workload for identifying training data sets could be shared among groups for the male pelvic region, making it accessible to clinics of all sizes.

An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI.

Protocolling, the process of determining the most appropriate acquisition parameters for an imaging study, is time-consuming and produces variable results depending on the performing physician. The purpose of this study was to assess the potential of an artificial intelligence-based semiautomated tool in reducing the workload and decreasing unwarranted variation in the protocolling process. We collected 19,721 MR imaging brain examinations at a large academic medical center. Criterion standard labels were created using physician consensus. A model based on the Long Short-Term Memory network was trained to predict the most appropriate protocol for any imaging request. The model was modified into a clinical decision support tool in which high-confidence predictions, determined by the values the model assigns to each possible choice, produced the best protocol automatically and low confidence predictions provided a shortened list of protocol choices for review. The model achieved 90.5% accuracy in predicting the criterion standard labels and demonstrated higher agreement than the original protocol assignments, which achieved 85.9% accuracy (κ = 0.84 versus 0.72, P value < .001). As a clinical decision support tool, the model automatically assigned 70% of protocols with 97.3% accuracy and, for the remaining 30% of examinations, achieved 94.7% accuracy when providing the top 2 protocols. Our model achieved high accuracy on a standard based on physician consensus. It showed promise as a clinical decision support tool to reduce the workload by automating the protocolling of a sizeable portion of examinations while maintaining high accuracy for the remaining examinations.

526. Antimicrobial Selection Based on Precision Metagenomics Compared with Standard Urine Culture/Susceptibility: A Reliability and Inter-rater Agreement Analysis for Application in Patients with Urinary Tract Infections

Abstract Background Management of urinary tract infections (UTIs) is highly variable and ∼50% of affected women may receive inappropriate antimicrobials. Precision Metagenomics-based detection of pathogens and antimicrobial resistance (AMR) markers can inform antimicrobial selection in support of stewardship practices. We investigated inter-physician agreement of culture and metagenomic results in a retrospective analysis. Methods We used de-identified urine samples with results of urine culture &amp; antimicrobial susceptibility testing (AST, n=25, 76% culture-positive) and the Explify® Urinary Pathogen ID/AMR Panel (UPIP, IDbyDNA Inc). UPIP is a Precision Metagenomics method to detect &amp;gt;170 uropathogens and &amp;gt;2,000 AMR markers. Four physicians reviewed culture/AST and UPIP results independently to assess if and how they would treat based on each set of results in a middle-aged, female patient with uncomplicated UTI (no comorbidities or medication allergies). Consensus was defined by simple majority; treatment decisions and antimicrobial choice were adjudicated by an infectious disease trained pharmacist. Inter-rater agreement (Fleiss’ kappa) and reliability for accurate case management (reference: adjudicator) were estimated for each method. Results Analytical agreement for uropathogen identification between the two methods was 72% with 95% positive agreement for common uropathogens. Consensus on whether or not to treat was reached for 96% (24/25) of cases for both culture/AST and UPIP; decisions were concordant between methods for 88% of cases (22/25). Inter-rater agreement was high (culture: k=0.73, UPIP: k=0.68; p&amp;lt; 0.05). Physicians were more likely to decide to treat based on culture results (17/25, 68%) than UPIP (15/25, 60%). UPIP detected additional organism(s) and/or AMR marker(s) compared with culture/AST alone that were informative for stewardship-guided treatment decisions in 28% (7/25) of samples. Figure 1: Physician Consensus Treatment Decisions Based on Results of Urine Sample Analysis by Precision Metagenomics or Urine Culture/AST Four infectious disease physicians independently reviewed paired results from analysis of a set of 25 clinical remnant urine samples by an investigational Precision Metagenomics method (UPIP) and by standard urine culture/AST and determined whether or not treatment would be warranted. Figure 2:Summary of Changes in Antimicrobial Selection based on Interpretation of Precision Metagenomics vs Culture/AST Results An infectious disease pharmacist reviewed and adjudicated hypothetical antimicrobial selections. All antimicrobial selections based on the results of the Precision Metagenomics method (UPIP) were found to be appropriate; in 7/15 cases, hypothetical treatment based on UPIP results would have resulted in a change in management that was evaluated to be consistent with stewardship practices. Conclusion Physician agreement on hypothetical treatment recommendations was comparable between the investigational method (UPIP) and the reference method (urine culture/AST). In no case was there inappropriate consensus to treat based on UPIP results alone. The findings from this pilot analysis support the feasibility of metagenomics-guided management of UTIs. Disclosures Benjamin Briggs, MD, PhD, CloudCath: Advisor/Consultant|CosmosID: Employee Benjamin Briggs, MD, PhD, CloudCath: Advisor/Consultant|CosmosID: Employee Robert Schlaberg, MD, MPH, IDBYDNA INC: Board Member|IDBYDNA INC: Explify-related IP|IDBYDNA INC: Employee|IDBYDNA INC: Stocks/Bonds.

Building an Institutional-Consensus Artificial Intelligence Model for Prostate Bed Auto-Segmentation through Incremental Learning

<h3>Purpose/Objective(s)</h3> Clinical target volume (CTV) definition for post-operative prostate bed radiotherapy is subject to large intra- and inter-observer variations for radiation oncologists, despite various publications of group consensus guidelines. The aim is to study the utility of artificial intelligence (AI) transferred incremental learning for building institutional group Intelligence-based models for post-operative prostate bed CTV and organs-at-risk (OARs) delineation. <h3>Materials/Methods</h3> One hundred and ten prostate bed patients were retrospectively collected and randomly divided into training (n=60) and testing (n=50) groups. Each dataset included physician-approved prostate bed CTV and five OARs (bladder, rectum, left/right femoral head, and penile bulb). The training group dataset was used to build two customized AI models (OAR and CTV) from the existing vendor-provided models. Quantitative and qualitative evaluations were performed for AI-generated contours on the 50 testing datasets in comparison with the corresponding manual ones. The qualitative evaluation was conducted amongst six expert radiation oncologists (RO) tasked to score at four levels (precision, acceptable, minor revision, and manual redraw) and indicated clinical preference given two sets of contours blindly (AI vs. manual). Scoring subjectivity was studied in cross-comparison amongst six ROs. Physician editing times was studied among all six ROs for five patients that had the CTV scored as <i>minor corrections</i> by physician consensus. <h3>Results</h3> Physicians chose the AI-generated OAR and CTV over the manual ones 34% and 32% of the time (range: 2%-54% and 12%-60%). The AI- generated and manual OARs were scored precise or acceptable 90% and 98% of cases (median values), respectively. The AI-generated CTVs were scored equal to or better than the corresponding manual delineation 53% of the time (ranges: 22% to 88% for the six experts). The average physician editing time from the AI-generated CTV was 3m 10s ± 36s. The time required to train the custom models were 27h 20m ± 8m. <h3>Conclusion</h3> Blinded scoring with benchmarking to the manual contours eliminates personal subjectivity. Contour comparison metrics and physician scoring demonstrated comparable results between AI-generated and manual OAR contours. Although almost half of the AI-generated CTVs required physician edit, the editing time is considered moderate. The custom-trained AI models using the transferred learning can facilitate OAR and target contouring efficiency and consistency for prostate bed patients.

Tumor necrosis factor (TNF) inhibitors for juvenile idiopathic arthritis-associated uveitis.

Uveitisis the most common extra-articular manifestation of juvenile idiopathic arthritis (JIA) and a potentially sight-threatening condition characterized by intraocular inflammation. Current treatment for JIA-associated uveitis (JIA-U) is largely based on physician experience, observational evidence and consensus guidelines, resulting in considerable variations in practice. OBJECTIVES: To evaluate the effectiveness and safety of tumor necrosis factor (TNF)inhibitors used for treatment of JIA-U. We searched the Cochrane Central Register of Controlled Trials (CENTRAL); Ovid MEDLINE; Embase.com; PubMed; Latin American and Caribbean Health Sciences Literature Database (LILACS); ClinicalTrials.gov, and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). We last searched the electronic databases on 3 February 2022. We included randomized controlled trials (RCTs) comparing TNF inhibitors with placebo in participantswith a diagnosis of JIA and uveitis who were aged 2 to 18 years old. We used standard Cochrane methodology and graded the certainty of the body of evidence for seven outcomes using the GRADE classification. We included three RCTs with 134 participants. One study conducted in the USA randomized participants to etanercept or placebo (N = 12). Two studies, one conducted in the UK (N = 90) and one in France (N = 32), randomized participants to adalimumab or placebo. All studies were at low risk of bias.Initial pooled estimates suggested that TNF-inhibitors may result in little to no difference on treatment success defined as 0 to trace cells on Standardization of Uveitis Nomenclature (SUN)-grading; or two-step decrease in activity based on SUN grading (estimated risk ratio (RR) 0.66; 95% confidence interval (CI) 0.21 to 2.10; 2 studies; 43 participants;low-certainty evidence) or treatment failure defined as a two-step increase in activity based on SUN grading (RR 0.31; 95% CI 0.01 to 7.15; 1 study; 31 participants;low-certainty evidence). Further analysis using the individual trial definitions of treatment response and failure suggested a positive treatment effect of TNF inhibitors; a RR of treatment success of 2.60 (95% CI 1.30 to 5.20; 3 studies; 124 participants; low-certainty evidence), and RR of treatment failure of 0.23 (95% CI 0.11 to 0.50; 3 studies; 133 participants). Almost all the evidence was on adalimumab and the evidence on etanercept was very limited. For secondary outcomes, one study suggests that adalimumab may have little to no effect on risk of recurrence after induction of remission at three months (RR 2.50, 95% CI 0.31 to 20.45; 90 participants; very low-certainty evidence) and visual acuity, but the evidence is very uncertain; mean difference in longitudinal logMAR score change over six months was -0.01 (95% CI -0.06 to 0.03) and -0.02 (95% CI -0.07 to 0.03) using the best and worst logMAR measurement, respectively (low-certainty evidence). Low-certainty evidence from one study suggested that adalimumab treatment results in reduction of topical steroid doses at six months (hazard ratio 3.58; 95% CI 1.24 to 10.32; 74 participants who took one or more topical steroid per day at baseline). Adverse events, including injection site reactions and infections, were more common in the TNF inhibitor group. Serious adverse events were uncommon. Adalimumab appears to increase the likelihood of treatment success and decrease the likelihood of treatment failure when compared with placebo. The evidence was less conclusive about a positive treatment effect with etanercept. Adverse events from JIA-U trials are in keeping with the known side effect profile of TNF inhibitors. Standard validated JIA-U outcome measures are required to homogenize assessment and to allow for comparison and analysis of multiple datasets.

Improving new cancer diagnosis education in a metropolitan cancer center.

251 Background: Oncologists are faced with sharing complex information to their patients when making new cancer diagnoses. Patients often find themselves overwhelmed with the amount of information given to them at this initial visit. This challenging and emotionally charged context often result in patient dissatisfaction. Currently, most patient education is done verbally despite studies showing that patients only retain 14% of verbal communication, compared to 85% when using visual aids (VA).. Gunn et al. showed that cancer patients may be vulnerable to poorer outcomes during treatment if they lack the necessary skills such as health literacy to meet the high informational demands and manage psychosocial stressors. Garcia-Retamero et al showed that the use of VA enables patients across all literacy levels to arrive at better diagnostic inferences; however, the quality and quantity of VA used is not uniform nationwide and little data exists on their use in cancer diagnosis education. Our initiative aims to improve the patient education process at our cancer center. Methods: We developed VA for breast, colon, gastric, and lung cancer clinics. New patients were divided into two, a control arm (CA) which received the current standard education and an intervention arm (IA), which also received VA. Patient surveys based on physician consensus were used to collect patient outcomes. The primary outcomes included patients’ understanding of their diagnosis, the stage of their cancer, and the goal of treatment (cure versus life prolongation). Secondary outcomes included duration of clinic visit and retention of information assessed in a follow up phone call. Results: Sixteen (16) patients participated in the study with 8 in the CA and 8 in the IA group, including 11 females and 5 males. There were 8 Caucasian, 4 African American, 2 Asian and 2 Hispanic patients. Five patients (63%) had breast cancer and 3 (38%) had lung cancer in each arm. CA had 5 patients (63%) with Stage IV disease and IA had 4 patients (50%) with Stage IV disease. IA had better understanding of the goal of treatment (100% IA vs 75% CA, p = 0.13); on follow up, IA were able to better recall the stage of their cancer (88% IA vs 63% CA, p = 0.17) and treatment goal (88% IA vs 50% CA, p = 0.08). IA arm expressed better ability to understand what was communicated to them during the visit (mean score: CA = 4.83, IA = 3.67, p = 0.01). All patients who received the pictorial educational material felt that it helped them better understand the medical information. The total visit time in minutes was 58.67 in in the CA and 44.50 in the IA (p = 0.16). Conclusions: Despite being a small study, we demonstrated a trend towards improved understanding of cancer stage and treatment goal in patients with the use of VA vs standard verbal education. In patients with newly diagnosed breast and lung cancer, the intervention also resulted in lower total visit time. This proof of concept should be reproduced in a larger study.

Persisting symptoms after concussion: Time for a paradigm shift.

Persisting symptoms after concussion: Time for a paradigm shift.

Preparing for the MR-Linac: Analyzing an Educational Curriculum

Background Image guided radiotherapy (IGRT) has made multiple advances that allow for more precise delivery of dose. Incorporation of magnetic resonance imaging (MRI) into workflows has provided the next incremental step. MRI capable linear accelerators (MR-Linac) provides the ability for treatment adaptation and replanning, which requires significant alteration of clinical workflows. Treatment on MR-Linacs has proven cumbersome for this reason, with multiple feasibility studies failing on time constraints. Minimal data is available regarding departmental preparation for the MR-Linac, despite expansion to multiple institutions. This trial set out to create and apply an educational curriculum regarding the MR-Linac, addressing this gap for future expansion. Methods In this IRB approved study (NU STU00216145), intradepartmental radiation therapists (RTs) and resident physicians (RPs) were recruited to participate in the educational curriculum and subsequent contouring assessment. The course consists of lectures and demonstrations tailored to each cohort. Proficiency of the course will be assessed by volumetric analysis of contours delineated on multiple MRI series both before and after the course, with requested organs specific to each cohort. Collected data will be anonymized. Each volume will be compared to a gold standard multiple physician consensus for Dice coefficient analysis. A post-education questionnaire will be distributed for analysis of the quality of the course and assessments. Results A total of 5 RTs and 6 RPs were recruited. The full course consisted of 4 hours for each cohort. RTs were each given 5 cases for both pre- and post education analysis, while RPs were given 3 cases. Currently, all RTs have completed their pre- and post-education cases with 100% retention. RP education is still underway. The null hypothesis predicts no increase in either individual Dice scores or total cohort Dice scores from pre- to post-education MRI volumes. Surveys will be distributed after completion of the trial. Discussion This is a novel educational study performed during the implementation of an MR-Linac in an academic hospital. The objective of this study is to prepare personnel for treatment adaptation while assessing feasibility of an educational series. Feasible implementation will be signaled by significant improvement from pre- to post-education contours on MRI series when compared to a gold standard. Results of this study will show the extent of education required to adequately prepare both RTs and RPs to perform MRI-based contouring during adaptive radiotherapy. If proven to be feasible and well received on surveys, this course could be formalized and exported. Image guided radiotherapy (IGRT) has made multiple advances that allow for more precise delivery of dose. Incorporation of magnetic resonance imaging (MRI) into workflows has provided the next incremental step. MRI capable linear accelerators (MR-Linac) provides the ability for treatment adaptation and replanning, which requires significant alteration of clinical workflows. Treatment on MR-Linacs has proven cumbersome for this reason, with multiple feasibility studies failing on time constraints. Minimal data is available regarding departmental preparation for the MR-Linac, despite expansion to multiple institutions. This trial set out to create and apply an educational curriculum regarding the MR-Linac, addressing this gap for future expansion. In this IRB approved study (NU STU00216145), intradepartmental radiation therapists (RTs) and resident physicians (RPs) were recruited to participate in the educational curriculum and subsequent contouring assessment. The course consists of lectures and demonstrations tailored to each cohort. Proficiency of the course will be assessed by volumetric analysis of contours delineated on multiple MRI series both before and after the course, with requested organs specific to each cohort. Collected data will be anonymized. Each volume will be compared to a gold standard multiple physician consensus for Dice coefficient analysis. A post-education questionnaire will be distributed for analysis of the quality of the course and assessments. A total of 5 RTs and 6 RPs were recruited. The full course consisted of 4 hours for each cohort. RTs were each given 5 cases for both pre- and post education analysis, while RPs were given 3 cases. Currently, all RTs have completed their pre- and post-education cases with 100% retention. RP education is still underway. The null hypothesis predicts no increase in either individual Dice scores or total cohort Dice scores from pre- to post-education MRI volumes. Surveys will be distributed after completion of the trial. This is a novel educational study performed during the implementation of an MR-Linac in an academic hospital. The objective of this study is to prepare personnel for treatment adaptation while assessing feasibility of an educational series. Feasible implementation will be signaled by significant improvement from pre- to post-education contours on MRI series when compared to a gold standard. Results of this study will show the extent of education required to adequately prepare both RTs and RPs to perform MRI-based contouring during adaptive radiotherapy. If proven to be feasible and well received on surveys, this course could be formalized and exported.

Prevalence, incidence and survival outcomes of cardiac sarcoidosis in the South Island, New Zealand

AimsCardiac sarcoidosis (CS) is an important cause of mortality in patients with sarcoidosis. The aim of this retrospective cohort study was to characterize the prevalence, incidence, clinical features and outcomes of CS in the southern region of New Zealand. Methods and results45 patients were identified: 23 fulfilling international classification criteria, 9 fulfilling physician consensus criteria, and 13 classified as possible CS. As of June 2021, 26 patients were living and domiciled in the Canterbury district; estimated point prevalence was 4.43 cases per 100,000 people. The average annual incidence was 0.24 cases per 100,000 people between 2016 and 2020. We estimated a 5.14% frequency of CS in patients with sarcoidosis. Median age at presentation was 56 years (range 31–72). Common presentations included heart failure, heart block and life threatening ventricular and supraventricular arrhythmias. Electrocardiogram abnormalities were found in 93.3% and cardiac MRI was often relied upon by physicians for diagnosis. The 10-year survival was 94% (95% CI 78–99%). ConclusionOur study provides further insight into the epidemiology of CS. In this retrospective cohort the frequency of CS amongst patients with sarcoidosis was estimated at 5%, whilst the estimated point prevalence of the disease was twice that of a contemporary report from the Northern hemisphere. The 10-year survival was similar to contemporaneous reports from other developed countries.

Anatomical Core Material of the Superior Extremity Musculosceletal System for Medical Student Learning

Background: Anatomy education is the essential basic science as a physician to run the clinical practice. The global development leads to the transformation of the medical curriculum which then results in the reduced time allotment for anatomic learning. In musculoskeletal system, superior extremity is one of the sections having the wide range of material coverage which cannot be learned in a short length of time. As a result, the students find it difficult to learn the material. A large number of anatomic material in superior extremity musculoskeletal system which need to be learnt in a limited time underlies the researcher to conduct this research.Methods: This research employed qualitative study using two-round Delphi method. The research subjects were 20 general physicians in Indonesia selected by purposive sampling. The initial questionnaire was arranged by the researcher based on 3 anatomic textbooks, 1 research on core anatomic material, 1 research on the anatomic component of clinical orthopedic, the substance recapitulation of national anatomy curriculum, and nomina anatomica. In the first Delphi round, the panel was asked to select the component which is important to be learned based on the clinical cases as well as to add whether the component has not been written yet by the researcher. In the second Delphi round, the panel ranked from 1-4. The consensus determined by the researcher is 35%.Results: In the first Delphi round, there is no topic added yet 53 components are removed. Thus, a number of component in the first round was decreased from 510 to 457 components. The second Delphi round obtains 26 core anatomic material out of 457 topics.Conclusion: General physician consensus is 26 from the core material out of the entire anatomic material in the superior extremity musculoskeletal system. There are also clinical reasons or correlation from the material of superior extremity muscukoskeletal system which are considered important by the panels. The results obtained from the panels who come from various regions to represent the general physician in Indonesia. Keywords: anatomy; core material; Delphi method; general physician consensus; musculoskeletal system,

Successful Development of an Outpatient Chimeric Antigen Receptor (CAR) T Cell Therapy Program

Chimeric antigen receptor (CAR) T-cell therapies have transformed the treatment landscape in relapsed or refractory B-cell malignancies and multiple myeloma. However, the risk of life-threatening complications such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) have led many centers to integrate CAR T-cell infusion as an inpatient procedure. Few reports document the implementation of an entirely outpatient CAR T-cell therapy program or the impact on patient outcomes. Here, we share our experience in the development and implementation of an outpatient model for CAR T-cell therapies. We describe a systematic approach for coordination of care, allocation of resources, education of staff, patients and caregivers, and toxicity management to make CAR T-cell therapies more accessible on an outpatient basis.Development of our outpatient CAR T-cell therapy program began in 2017 with the first outpatient CAR T-cell infusion in September 2019. The Table summarizes key aspects of our outpatient program with a focus on 6 important areas - creating a multidisciplinary team, training clinically competent nursing staff, alerting and educating providers in the community, augmenting patient knowledge and support, acquiring physical space, and continuously reviewing outcomes and procedures. Outpatients were monitored after cell infusion with daily provider visits on days 1-14 then three times per week on days 15-28. Response was assessed using the Lugano response criteria for all lymphoma patients by Positron emission tomography (PET) scan at 1 and 6 months, and by bone marrow biopsy and minimal residual disease (MRD) testing for acute lymphoblastic leukemia (ALL) patients.From September 2019 to May 2021, 20 patients (19 adults and 1 child) received commercial CAR T-cell products: 13 axicabtagene ciloleucel (axi-cel) for diffuse large B-cell lymphoma (DLBCL), 6 tisagenlecleucel (tisa-cel) (3 for ALL and 3 for DLBCL), 1 brexucabtagene autoleucel (brex-cel) for mantle cell lymphoma. Eighteen patients (90%) received outpatient infusions. Two patients receiving axi-cel were infused while inpatient based on physician consensus for high disease burden and co-morbidities. Of the 18 outpatients, 5 (28%) were admitted within 72 hours and 14 (78%) within 30 days after infusion. No patient required a visit to the emergency department. Median days to admission was 4 (range 1-28) and median days admitted was 9 (range 1-30). Indications for admission were fever in 12 cases (86%) and neurological symptoms in 2 cases (14%). Of 12 (67%) patients with CRS, 1 had grade 3. Of 6 (33%) patients with neurotoxicity, 1 had grade 3. Seven patients (39%) received an IL-6 inhibitor with a median of 1 dose (range 1-5) and 7 received dexamethasone for a median duration of 4 days (range 1-21). There were no treatment-related deaths among those infused as outpatients. At one month, the overall response rate (ORR) was 82% (9 of 11 patients) with axi-cel (6 complete responses (CR) and 3 partial responses (PR)), 100% with tisa-cel (5 CR and 1 PR; all 3 ALL patients were MRD negative) and a CR in 1 patient with brex-cel. At 6 months, 50% (4 of 8; 3 have not reached the 6-month time point yet), 75% (3 of 4) and 100% (1 of 1) of these patients continued to respond with axi-cel, tisa-cel, and brex-cel, respectively. Early outcomes are comparable to those seen in pivotal clinical trials (ZUMA-1/2, ELIANA, and JULIET). Outpatient administration and management of CAR T-cell therapy was safe and feasible with our approach. With the ongoing expansion of new indications for CAR T-cell products, strategies for safe outpatient administration are relevant to the practical clinical utility of these therapies. [Display omitted] DisclosuresIbrahimi: Karyopharm Theraputics: Divested equity in a private or publicly-traded company in the past 24 months. Wieduwilt: Gilead: Membership on an entity's Board of Directors or advisory committees; Reata: Current holder of stock options in a privately-held company.

A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care.Lay summaryHere we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.Graphical abstract

86. Investigation and validation of deep learning algorithms to identify cervical ossification of posterior longitudinal ligaments on radiography

<h3>BACKGROUND CONTEXT</h3> A deep learning algorithm capable of detecting cervical ossification of the posterior longitudinal ligament (OPLL) on radiography has the potential to decrease the incidence of misdiagnosis and OPLL overlook, and facilitate the provision of adequate and timely therapy. Further, it may improve patient safety and decrease radiation exposure by minimizing the need for computer tomography (CT). <h3>PURPOSE</h3> The purpose of present study was to validate the diagnostic yield of our deep learning algorithm created for detecting cervical OPLL on radiography, and to compare its diagnostic accuracy with that of experienced Japanese spine physicians. <h3>STUDY DESIGN/SETTING</h3> Cross-sectional study. <h3>PATIENT SAMPLE</h3> A total of 486 patients (243 patients with OPLL and 243 age and sex matched controls). <h3>OUTCOME MEASURES</h3> The accuracy and the area under curve (AUC) of the receiver operating characteristic curve were calculated. Subsequently, using 50 randomly selected samples, numbers of correct diagnoses were compared between the algorithm and the consensus of four spine physicians. <h3>METHODS</h3> The presence/absence of OPLL was confirmed via CT. The deep learning algorithm, which diagnosed the presence/absence of OPLL on cervical radiography and highlighted areas of ossification in positive cases, was constructed using a convolution neural network model called EfficientNetB2 with radiography and CT scan data. <h3>RESULTS</h3> The diagnostic accuracy of our deep learning algorithm was 0.88; and the AUC determined was 0.94. Results could have been affected by institutions where radiographic images were taken, and the type and level of the OPLL. Our deep learning algorithm had significantly more correct diagnoses than did the spine physician consensus (47/50 vs 39/50, respectively; p = 0.041, Fisher's exact test). <h3>CONCLUSIONS</h3> The accuracy of our deep learning algorithm in the diagnosis of cervical OPLL on radiography was significantly higher than that of the experienced spinal physician consensus. The algorithm may improve OPLL diagnostic accuracy by using different diagnostic criteria than physicians, and may help both physicians and patients by facilitating the provision of adequate therapy with minimal radiation exposure. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.