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Importance There is a lack of long-term efficacy and safety data on hereditary transthyretin amyloidosis with polyneuropathy (hATTR-PN) and on RNA interference (RNAi) therapeutics in general. This study presents the longest-term data to date on patisiran for hATTR-PN. Objective To present the long-term efficacy and safety of patisiran in adults with hATTR-PN. Design, Setting, and Participants This global open-label extension (OLE) of the APOLLO randomized clinical trial and phase 2 OLE study enrolled patients from 43 hospitals or clinical centers across 19 countries between July 2015 and August 2017, with follow-up until November 2022. Of 212 eligible patients with hATTR who completed the phase 3 APOLLO or phase 2 OLE parent studies, 211 enrolled in and 138 completed the global OLE. Intervention Patisiran, 0.3 mg/kg, intravenously once every 3 weeks for up to 5 years. Main Outcomes and Measures Outcomes evaluated at year 5 of the global OLE included disability (polyneuropathy disability [PND] score); polyneuropathy severity (Neuropathy Impairment Score [NIS]), nutritional status (modified body mass index [mBMI]), quality of life (Norfolk Quality of Life–Diabetic Neuropathy [Norfolk QOL-DN]), and Rasch-Built Overall Disability Scale (R-ODS), with no statistical hierarchy. Safety, survival probability, and mortality were also assessed. Results At the global OLE baseline, the mean (SD) age was 61.3 (12.3) years, and 156 patients (73.9%) were male. In 138 patients completing the study, PND scores remained stable or improved in 89 patients (65.0%), NISs showed a mean (SD) change of 10.9 (14.7), and mean (SD) mBMI (calculated as weight in kilograms divided by height in meters squared times serum albumin in grams per liter) increased by 46.4 (120.7) over 5 years from baseline. Norfolk QOL-DN and R-ODS scores showed mean (SD) changes of 4.1 (16.7) and –3.7 (6.2), respectively. Adverse events (AEs) leading to study withdrawal occurred in 47 patients (22.3%). Infusion-related reactions were the most common treatment-related AE (n = 34 [16.1%]). Overall, 41 patients (19.4%) died during the study. Patisiran treatment in the parent study and low familial amyloid polyneuropathy score at parent study baseline were associated with significantly improved survival. Conclusions and Relevance In the longest study of an RNAi therapeutic for any disease, patisiran treatment resulted in modest changes for patients with hATTR-PN with an acceptable safety profile. These results highlight the importance of initiating early treatment for hATTR and the potential of RNAi therapeutics in medicine. Trial Registration ClinicalTrials.gov Identifier: NCT02510261

Abstract BMT CTN (Blood and Marrow Transplant Clinical Trials Network) 1506 (“MORPHO”) was a randomized study of gilteritinib compared with placebo as maintenance therapy after hematopoietic cell transplantation (HCT) for patients with FLT3-ITD–mutated acute myeloid leukemia (AML). A key secondary end point was to determine the impact on survival of before and/or after HCT measurable residual disease (MRD), as determined using a highly sensitive assay for FLT3-ITD mutations. Generally, gilteritinib maintenance therapy was associated with improved relapse-free survival (RFS) for participants with detectable peri-HCT MRD, whereas no benefit was evident for those lacking detectable MRD. We conducted a post hoc analysis of the data and found that the level of MRD detected with this approach correlated remarkably with RFS and relapse risk, and that MRD detectable at any level negatively affected RFS. In the placebo arm, 42.2% of participants with detectable FLT3-ITD MRD relapsed compared with 13.4% of those without detectable MRD. We found that 14.8% of participants had multiple FLT3-ITD clones detected as MRD and had worse survival irrespective of treatment arm. Finally, we examined the kinetics of FLT3-ITD clonal relapse or eradication and found that participants on the placebo arm with detectable MRD relapsed rapidly after HCT, often within a few weeks. MRD-positive participants on the gilteritinib arm relapsed either with FLT3 wild-type clones (assessed by capillary electrophoresis), after cessation of gilteritinib with persistent MRD, or on progression of multiclonal disease. These data demonstrate the potential of FLT3-ITD MRD to guide therapy with gilteritinib for this subtype of AML. This trial was registered at www.clinicaltrials.gov as #NCT02997202.

ABSTRACT Nowadays, consent to use donor bodies for medical education and research is obtained from the body donors and their families before the donation. Recently, the International Federation of Associations of Anatomists (IFAA) published guidelines that could restrict the appearance of cadaveric images in commercial anatomical resources such as textbooks and other educational products. These guidelines state that the donor must expressly consent to using such images for this purpose. Cadaveric photos and drawings made from dissections of cadavers have been used in anatomy textbooks and atlases for hundreds of years. They are invaluable for anatomy students and clinical/surgical practitioners. The IFAA guidelines should not restrict the use of those older books; to do so would infringe the rights of those seeking knowledge from these resources. As the images in such textbooks and atlases are anonymized and are used for teaching and research, and the donors and their families are informed about this before the donation, we believe no additional consent is needed. It is impossible to separate educational from “commercial” usage entirely in any situation, e.g., publications from publishers and the use of cadavers in medical schools. Therefore, our best efforts to avoid unethical use of cadaveric images by following traditional consent processes are still needed so that more people will reap the benefits from them. As senior textbook/atlas authors/editors from over 10 countries, we believe that using cadaveric images in anatomy textbooks is appropriate, and no additional consent should be necessary. Such usage falls within the good faith of professionals using these invaluable gifts.

Abstract Background and Aims Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk. Methods An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed. Results The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), −.1 (6.0), 4.7 (8.7), and −1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24–2.78), 1.89 (1.46–2.43), 1.90 (1.55–2.33), and 1.76 (1.67–1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47–3.37), 1.94 (1.39–2.70), 1.58 (1.06–2.35), and 1.79 (1.62–1.97) in these cohorts compared with the normal group. Conclusions The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.

AbstractPrior case reports have suggested that treating thoracic outlet syndrome (TOS) may relieve intractable migraine headaches, but there has been no case series large enough to show when underlying TOS should be suspected as a contributor to migraine burden. This observational followed by questionnaire study was performed in an outpatient neurology practice to identify clinical features of patients with migraine in which TOS contributed to migraine burden. We report the clinical features of 50 consecutive patients (48 women, 2 men, age = 43.9+/12.7 years) who were treated for chronic migraine and TOS (20 migraine with aura, 28 migraine without aura, two hemiplegic migraines). Headaches had become chronic within 1 year of onset in 21 patients (42%) with characteristics as follows (percentages are based on known data): greater severity ipsilateral to TOS limb pain (38/50=76%), presence of limb swelling (32/48=67%), and worsened by recumbency (32/38=84%). Thirty‐two patients eventually needed surgery, which included percutaneous transluminal venoplasty, 1st rib removal, scalenectomy, pectoralis minor tenotomy, and/or vein patching. The mean improvement of headaches on the treated side was 72 ± 26.7%; 12 patients experienced complete resolution of headaches after treatment of TOS (follow‐up 7.2 ± 5.2 months). Questionnaire responders reported significant reductions in headache days (18.3 ± 8.6 to 11.1 ± 10.8 days/month, p < 0.0016), severity (7.8+/2.5 to 5.4 ± 2.9, p < 0.00079), and need for emergency care (3.6 ± 4.0 to 0.71 ± 1.3 visits/year, p < 0.0029). Chronic migraines can be important manifestations of TOS. Early transition to a chronic state, lateralized limb pain, and headaches worsened by recumbency are clues to the contribution of TOS pathology. Addressing the TOS contribution to migraine can significantly reduce migraine headache burden.

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).

Abstract Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for LVDD detection has opened new avenues. Yet, whether AI-ECG-detected LVDD correlates with long-term AF risk remains unexplored. Methods We performed a retrospective study among all patients with a comprehensive LVDD assessment from the test population of the AI-ECG LVDD study between September 2001 and June 2022. Patients with prior documented AF according to health records or previous ECG were excluded. Patients were classified by AI-ECG as normal diastolic function (DF), grade 1 (G1), grade 2 (G2), or grade 3 (G3) LVDD. We assessed the risk of AF across AI-ECG LVDD grade, with normal DF as reference, with competing risk models. Results Of 97,023 patients free of AF at baseline, 63,533 (65.4%), 10,776 (11.1%), 18,940 (19.5%), and 3,774 (3.9%) were classified as normal DF, G1-LVDD, G2-LVDD, and G3-LVDD, respectively. Worse AI-ECG LVDD was associated with older age, lower left ventricular ejection fraction (LVEF), and higher rates of HF, diabetes, hypertension, chronic pulmonary disease, renal failure, and cardiovascular disease (p-of-trend&lt;0.001 for all). Over a median follow-up of 4.8 years (interquartile range 1.8-9.2 years), new-onset AF occurred in 6,795 (7.0%) patients. In multivariable survival analysis, adjusted to multiple risk factors including LVEF and clinical HF, G2 and G3 LVDD, representing patients with high filling pressures, were independently associated with a higher AF-risk [G2-LVDD: adjusted hazard ratio (aHR) 1.64 (95% confidence interval (95%CI) 1.52-1.73), G3-LVDD: aHR 2.24 (95%CI 2.02-2.48)]. Conclusion The AI-ECG LVDD algorithm independently predicts long-term AF risk, regardless of LVEF and clinical HF status, highlighting its potential for identifying high-risk populations and guiding proactive monitoring and management.

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (&gt;moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with &gt;moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

SummaryBackgroundFilgotinib, an oral, once‐daily, Janus kinase 1 preferential inhibitor, is an approved treatment for moderately to severely active ulcerative colitis.AimsThe aim of this study is to assess the safety and efficacy of continued filgotinib therapy over ~4 years in the long‐term extension of the phase 2b/3 SELECTION trial (SELECTIONLTE; NCT02914535).MethodsIn this interim analysis of SELECTIONLTE, SELECTION completers (week 10 responders to filgotinib who completed the maintenance study) continued their assigned treatment (double‐blind filgotinib 200 mg [FIL200] or filgotinib 100 mg) and SELECTION week 10 non‐responders received open‐label FIL200. We assessed safety by adverse events (AEs), and efficacy by partial Mayo Clinic Score (pMCS), inflammatory biomarkers and health‐related quality of life (HRQoL). We compared safety and efficacy between achievers and non‐achievers of a multi‐component endpoint, comprehensive disease control (CDC), comprising symptomatic, endoscopic, inflammatory biomarker and HRQoL improvements.ResultsData for completers (n = 250) and non‐responders (n = 372) were reported for ≤202 weeks. AE occurrences were low and consistent with previous analyses. The as‐observed proportion of FIL200‐treated patients in pMCS, biomarker and HRQoL remission during SELECTIONLTE remained high among completers (week 144: 80.0%, 86.4% and 86.0%, respectively) and increased among non‐responders (week 192: 62.1%, 76.7% and 59.3%, respectively). Significantly higher proportions of CDC achievers at SELECTION week 58 achieved pMCS, IBDQ and corticosteroid‐free pMCS remission than non‐achievers, up to LTE week 96.ConclusionsFilgotinib induced and maintained symptomatic remission and improved HRQoL over 4 years. Safety results showed a proven long‐term benefit–risk profile. FIL200‐treated CDC achievers had better long‐term outcomes than non‐achievers.

Importance The clinical effects of risankizumab (a monoclonal antibody that selectively targets the p19 subunit of IL-23) for the treatment of ulcerative colitis are unknown. Objective To evaluate the efficacy and safety of risankizumab when administered as an induction and a maintenance therapy for patients with ulcerative colitis. Design, Setting, and Participants Two phase 3 randomized clinical trials were conducted. The induction trial was conducted at 261 clinical centers (in 41 countries) and enrolled 977 patients from November 5, 2020, to August 4, 2022 (final follow-up on May 16, 2023). The maintenance trial was conducted at 238 clinical centers (in 37 countries) and enrolled 754 patients from August 28, 2018, to March 30, 2022 (final follow-up on April 11, 2023). Eligible patients had moderately to severely active ulcerative colitis; a history of intolerance or inadequate response to 1 or more conventional therapies, advanced therapies, or both types of therapies; and no prior exposure to risankizumab. Interventions For the induction trial, patients were randomized 2:1 to receive 1200 mg of risankizumab or placebo administered intravenously at weeks 0, 4, and 8. For the maintenance trial, patients with a clinical response (determined using the adapted Mayo score) after intravenous treatment with risankizumab were randomized 1:1:1 to receive subcutaneous treatment with 180 mg or 360 mg of risankizumab or placebo (no longer receiving risankizumab) every 8 weeks for 52 weeks. Main Outcomes and Measures The primary outcome was clinical remission (stool frequency score ≤1 and not greater than baseline, rectal bleeding score of 0, and endoscopic subscore ≤1 without friability) at week 12 for the induction trial and at week 52 for the maintenance trial. Results Among the 975 patients analyzed in the induction trial (aged 42.1 [SD, 13.8] years; 586/973 [60.1%] were male; and 677 [69.6%] were White), the clinical remission rates at week 12 were 132/650 (20.3%) for 1200 mg of risankizumab and 20/325 (6.2%) for placebo (adjusted between-group difference, 14.0% [95% CI, 10.0%-18.0%], P &amp;lt; .001). Among the 548 patients analyzed in the maintenance trial (aged 40.9 [SD, 14.0] years; 313 [57.1%] were male; and 407 [74.3%] were White), the clinical remission rates at week 52 were 72/179 (40.2%) for 180 mg of risankizumab, 70/186 (37.6%) for 360 mg of risankizumab, and 46/183 (25.1%) for placebo (adjusted between-group difference for 180 mg of risankizumab vs placebo, 16.3% [97.5% CI, 6.1%-26.6%], P &amp;lt; .001; adjusted between-group difference for 360 mg of risankizumab vs placebo, 14.2% [97.5% CI, 4.0%-24.5%], P = .002). No new safety risks were detected in the treatment groups. Conclusion and Relevance Compared with placebo, risankizumab improved clinical remission rates in an induction trial and in a maintenance trial for patients with moderately to severely active ulcerative colitis. Further study is needed to identify benefits beyond the 52-week follow-up. Trial Registration ClinicalTrials.gov Identifiers: NCT03398148 and NCT03398135