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Background: Non-ischemic cardiomyopathies can be primary (p-NICM) which involves mainly the heart muscle or caused by systemic disease with heart muscle involvement as it is seen in systemic infiltrative diseases (i-NICM). There is limited data on the safety and in-hospital outcomes of ventricular tachycardia (VT) ablation in i-NICM. Methods: We identified patients who underwent VT ablation between 2018-2021 using the National Inpatient Sample Database. i-NICM include amyloidosis, hemochromatosis, or sarcoidosis. In-hospital complications include mortality, acute kidney injury (AKI), acute heart failure (AHF), bleeding, vasopressor use, blood transfusion and cardiac tamponade. Multivariate logistic regression analyses were performed. Results : There were 7,420 VT ablations, 7,235 had P-NICM and 185 had i-NICM. A multivariable analysis did not reveal any difference in mortality (adjusted odds ratio [aOR]: 0.81; 95% confidence interval 0.32-1.98; p=0.62), AHF aOR: 0.88 (0.41-1.81; p=0.69, AKI aOR:1.17 (0.83-1.65); p=0.36, blood transfusion aOR: 0.63 (0.25-1.59); p=0.33, vasopressor aOR: 0.86( 0.51-1.49); p=0.61 or pericardial effusion/ tamponade aOR: 1.16 (0.67-2.1); p=0.58 Figure Conclusion: Patients with i-NICM had similar in-hospital complications compared to P-NICM further supporting the safety of VT ablation in managing recurrent VT in patients with i-NICM.

Background A variety of clinically important benchmarks of success (CIBS) have been defined for total shoulder arthroplasty (TSA) to quantify success. However, it is unclear how the preoperative status of the patient influences their likelihood of achieving each CIBS. Questions/purposes (1) What proportion of patients achieve commonly used CIBS after TSA? (2) Is there a relationship between a patients’ preoperative function and their probability of achieving different CIBS? (3) Does there exist preoperative ranges for each outcome measure that are associated with greater achievement of CIBS? Methods We retrospectively queried a multicenter shoulder arthroplasty database for primary anatomic TSA (aTSA) and reverse TSA (rTSA). A total of 2041 aTSAs and 3205 rTSAs were included. The mean ± SD age at time of surgery was 66 ± 9 years for aTSAs and 72 ± 8 years for rTSAs. In all, 51% (1049 of 2041) of aTSAs and 61% (1955 of 3205) of rTSAs were in patients who were women. For aTSAs, osteoarthritis with an intact rotator cuff was the most common preoperative diagnosis (92% [1869 of 2041]). For rTSAs, the most common preoperative diagnoses included rotator cuff tear arthropathy (40% [1280 of 3205]), osteoarthritis with an intact rotator cuff (24% [780 of 3205]), osteoarthritis with a torn rotator cuff (20% [632 of 3205]), and massive rotator cuff tear (10% [309 of 3205]). Outcomes were evaluated at latest follow-up (aTSA 61 ± 36 months, rTSA 47 ± 26 months) and consisted of ROM (abduction, forward elevation, external and internal rotation) as well as the most commonly used outcome scores: the Simple Shoulder Test (SST), Constant score, American Shoulder and Elbow Surgeons (ASES) score, University of California, Los Angeles (UCLA) score, Shoulder Pain and Disability Index (SPADI), and the Shoulder Arthroplasty Smart (SAS) score. The CIBS that we evaluated included the minimum clinically important difference (MCID), substantial clinical benefit (SCB), patient acceptable symptom state (PASS), minimum clinically important percentage of maximal possible improvement (MCI-%MPI), and substantial clinically important percentage of maximal possible improvement (SCI-%MPI). Prosthesis-specific anchor-based CIBS were adopted from prior publications on patients from this database. Multivariable regression was performed to identify the relationship between preoperative outcome measures and achievement of CIBS. Additionally, receiver operating characteristic (ROC) curve analyses were performed to determine whether thresholds in preoperative outcome measures were associated with achieving CIBS. Results For all ROM measures and outcome scores, poorer preoperative ROM was associated with greater odds of achieving the MCID and SCB but lower odds of achieving the PASS. For the SST and the two scores without ceiling effects (the Constant and SAS scores), poorer preoperative outcome scores were associated with greater odds of achieving the MCI-%MPI and SCI-%MPI, but no association was demonstrated for the ASES, SPADI, and UCLA scores. Graphical analysis demonstrated that patients with greater preoperative ROM and outcome scores had a lower probability of achieving the MCID and SCB but a higher probability of achieving the PASS. For outcome scores with known ceiling effects, patients with more favorable preoperative outcome scores were more likely to achieve the MCI-%MPI and SCI-%MPI than the MCID and SCB, respectively. For outcome scores without ceiling effects, patients undergoing aTSA were more likely to achieve the MCID, MCI-%MPI, and the SCI-%MPI than the SCB, but no clear trend was identified for patients undergoing rTSA. On ROC curve analysis, identified thresholds were reasonably accurate (area under the curve > 0.7) for achievement of measures of absolute improvement (the MCID and SCB), but not for absolute postoperative status (the PASS) or relative improvement (the MCI-%MPI and SCI-%MPI). Conclusion While most patients reported being “much better” (aTSA 75%, rTSA 76%) or “better” (aTSA 15%, rTSA 18%) compared with before surgery, 10% of aTSAs and 6% of rTSAs were either “unchanged” or “worse.” Patients’ likelihood of achieving CIBS depends in part on their baseline function and whether success is defined as absolute improvement, absolute postoperative status, or relative improvement. Patients with more favorable preoperative status are more likely to achieve CIBS that evaluate relative improvement (the MCI-%MPI and SCI-%MPI) than absolute improvement (the MCID and SCB). Future studies are needed to determine which individual CIBS or combinations thereof most accurately represent clinically relevant benefit. Level of Evidence Level III, therapeutic study.

Abstract Pharmacogenomics (PGx) is focused on the relationship between an individual's genetic makeup and their response to medications, with the overarching aim of guiding prescribing decisions to improve drug efficacy and reduce adverse events. The PGx and genomic medicine communities have worked independently for over 2 decades, developing separate standards and terminology, making implementation of PGx across all areas of genomic medicine difficult. To address this issue, the Clinical Genome Resource (ClinGen) Pharmacogenomics Working Group (PGxWG) was established by the National Institutes of Health (NIH)-funded ClinGen to initially create frameworks for evaluating gene–drug response clinical validity and actionability aligned with the ClinGen frameworks for evaluating monogenic gene–disease relationships, and a framework for classifying germline PGx variants similar to the American College of Medical Genetics (ACMG) and Association of Molecular Pathology (AMP) system for interpretation of disease-causing variants. These frameworks will leverage decades of work from well-established PGx resources facilitating buy-in among PGx stakeholders. In this report, we describe the background and major activities of the ClinGen PGxWG, and how this initiative will facilitate the critical inclusion of PGx into the larger context of genomic medicine.

Background: Reverse shoulder arthroplasty (RSA) is increasingly used in the treatment of displaced proximal humerus fractures (PHFs) with reliable clinical improvement. However, the preferred techniques for humeral stem fixation are varied and may be influenced by patient and injury characteristics, including bone quality and fracture pattern. This systematic review and meta-analysis sought to determine the effect of humeral component cementing and bone grafting on tuberosity healing rates and functional outcomes after RSA for PHFs. Methods: A systematic review was performed per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed/MEDLINE, EMBASE, Web of Science, and Cochrane were queried for clinical studies on RSA performed for PHFs that reported on the use of cemented humeral stems and autograft bone. The primary outcome was the rate of greater tuberosity healing between the various techniques. Secondarily, the authors compared clinical outcomes including postoperative external rotation, forward elevation, abduction, Constant score, and the incidence of complications and revision surgery. Outcomes were compared based on the use of an uncemented press-fit stem, a fully cemented stem without bone graft, or a partially cemented stem with humeral head autograft (i.e., black and tan technique). Results: Forty-eight studies reporting on 1,797 RSAs were included (mean patient age, 75 years; follow-up, 34 months; 81% female). Tuberosity healing was highest in the uncemented cohort, then the black and tan cohort, and lowest in the cemented cohort (80% vs. 70% vs. 61%, p = 0.006). No significant differences in postoperative range of motion, Constant score, complication rates, or revision rates were found. Conclusion: Uncemented fixation with a press-fit stem was associated with superior greater tuberosity healing rates; however, functional outcomes and complications did not differ among techniques. Level of Evidence: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P<0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P<0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P<0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P<0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P<0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Introduction Prognostication in myelofibrosis (MF) currently relies on manual assessment of clinical, pathological, and laboratory variables, which is inefficient and error-prone. The potential of large language models (LLMs) and rule-based programming to automate data extraction and prognostication in these patients remains unexplored and merits investigation. Methods Patients with MF were identified from a retrospective chart review. Data regarding clinical (age, constitutional symptoms, transfusion requirements), laboratory (hemoglobin, circulating white blood cell, platelet, and blast counts), and pathological (grade of bone marrow fibrosis, chromosomal analyses, cytogenetics) features was extracted from the latest free-text clinical notes and pathology reports using the generative pretrained transformer-4 (GPT-4) model in a zero-shot setting with the prompting parameters set to generate deterministic responses. The patients were randomly sampled into development (15%) and test (85%) sets, with the documents of patients in the development set used for iterative prompt engineering. The finalized prompts were used for data extraction in the test set. Rule-based programming was used for post-processing of the extracted data and ascertaining prognosis via Dynamic International Prognostic Scoring System (DIPSS), DIPSS-plus (DIPSS+), and Mutation-enhanced International Prognostic Scoring System-70 (MIPSS-70). Automatically extracted data and prognostic categories were compared with the manually generated human annotations. Mean accuracy, precision, and recall with 95% confidence intervals (CI) were computed. Detailed error and sensitivity analyses were conducted to assess performance. Results The study included 100 patients (development: 15; test: 85) with 100 clinical notes (development: 15; test: 85) and 90 pathology reports (development: 13; test: 77). The mean age of these patients was 70 (SD: 10.6); 55 (55%) patients were men, and 93 (93%) patients were White. In the development set, the mean accuracy, precision, and recall for data extraction from clinical notes were 94% (95% CI: 90-98%), 92% (84-99%), and 99% (97-100%). For data extraction from pathology reports, the mean accuracy, precision, and recall were 96% (93-100%), 98% (92-100%), and 83% (63-100%). For prognostication, the mean accuracy, precision, and recall were 87% (79-94%), 80% (73-87%), and 100% using DIPSS (accuracy: 80%; precision: 73%; recall: 100%), DIPSS+ (accuracy: 93%; precision: 83%; recall: 100%) and MIPSS-70 (accuracy: 87%, precision: 83%; recall: 100%). In the test set, the mean accuracy, precision, and recall for data extraction from clinical notes were 96% (95% CI: 93-98%), 91% (87-96%), and 98% (97-100%). For data extraction from pathology reports, the mean accuracy, precision, and recall were 95% (92-98%), 93% (87-99%), and 90% (80-100%). For prognostication the mean accuracy, precision, and recall were 95% (94-96%), 89% (88-90%) and 99% (96-100%) using DIPSS (accuracy: 94%; precision: 89%; recall: 100%), DIPSS+ (accuracy: 95%; precision: 88%; recall: 95%) and MIPSS-70 (accuracy: 94%, precision: 90%; recall: 100%). Error analyses identified incorrect extractions by GPT-4 were related to the circulating blasts and constitutional symptoms variables from the clinical notes. Sensitivity analyses excluding the associated variables improved overall accuracy from 94% to 97% and 96% to 97% in the development and test sets, respectively. Similarly, incorrect extractions from the pathology reports were related to the CALR gene mutation variables. Sensitivity analyses excluding the associated variables improved the overall accuracy from 96% to 98% and 95% to 97% in the development and test sets, respectively. Conclusion LLMs and rule-based programming can accurately extract data from the electronic health records and prognosticate MF patients via DIPSS, DIPSS+, and MIPSS-70. The automated approach could significantly streamline clinical and research workflows, providing an efficient and less resource-intensive alternative to manual prognostication.

Lipoprotein a (Lp(a)) is a lipid biomarker that binds cholesterol and bears independent cardiovascular risk. Strategies to lower the level of Lp(a) and mitigate such risk are important both for primary and secondary prevention. Currently there are no approved therapies targeting Lp(a) directly. Lipid lowering therapies prescribed routinely may have no effect on Lp(a) levels. Some agents such as niacin and estrogens can significantly decrease Lp(a), but their use is not recommended due to their adverse safety profile. Statins increase Lp(a) levels by 10–20%, questioning the benefit of such therapy when this biomarker is elevated. The Food and Drug Administration (FDA) endorses new agents to address dyslipidemia such as proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9-i) and Inclisiran, a small interfering RNA. These approaches have been shown to also significantly reduce Lp(a), but more clinical data is needed before implementing their use in clinical practice. Clinical trials are currently ongoing to test the efficacy of newly developed antisense oligonucleotides and small interfering RNAs targeting the gene encoding for Lp(a) in hepatocytes, while other investigations assess small molecules that inhibit Lp(a) assembly. This review summarizes the pathophysiology and clinical implications of Lp(a) elevation, and focuses on proposed Lp(a) therapies and the current state of the clinical trials of such novel agents.

Background: Osteochondritis dissecans (OCD) of the knee is a focal idiopathic alteration of subchondral bone and/or its precursor with risk for instability and disruption of adjacent cartilage. Treatment options focused on preventing premature osteoarthritis vary depending on multiple patient and lesion characteristics, including lesion mobility. Purpose: To differentiate lesion mobility before arthroscopy using a multivariable model that includes patient demographic characteristics and physical examination findings. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Demographic, preoperative physical examination, and radiographic data were collected from a multicenter national prospective cohort of patients with OCD of the knee. Inclusion criteria included patients <19 years of age and patients with arthroscopically confirmed mobility status based on the Research on Osteochondritis Dissecans of the Knee arthroscopy classification. Multivariable logistic regression analysis using stepwise model selection was used to determine factors associated with the likelihood of a mobile versus an immobile lesion. A 75% partition of the data was used for model training, and 25% was used as a validation cohort. Quantitative model fit statistics were computed using the holdout data, including sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), along with the corresponding 95% CI. Results: A total of 407 patients in the prospective cohort met inclusion criteria, and 62% were male. The mean ± SD age was 13.7 ± 2.2 years, height 161.8 ± 5.3 cm, and weight 59.2 ± 42.2 kg. Arthroscopic evaluation yielded 235 immobile and 172 mobile lesions. Multivariable analysis determined that the best model to predict lesion mobility included chronologic age ≥14 years ( P < .001), effusion on physical examination ( P < .001), and any loss of range of motion on physical examination ( P = .07), while controlling for male sex ( P = .38) and weight >54.4 kg ( P = .12). In the 25% holdout validation sample (n = 102), a sensitivity of 83%, a specificity of 82%, and an AUC of 0.89 (95% CI, 0.82-0.95) were achieved with these predictive factors. Conclusion: Age, effusion, and loss of motion can predict knee OCD lesion mobility at the time of arthroscopy. Education about lesion mobility can help with surgical planning and patient and family counseling.

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).