Citations

Abstract Objectives To determine the efficacy, safety and pharmacodynamics of belumosudil in patients with diffuse cutaneous systemic sclerosis (dcSSc) treated with background immunosuppressive therapies. Methods Eligible patients were randomised 1:1:1 to receive belumosudil 200 mg once daily (QD) or twice daily (BID), or placebo for 28 weeks (double-blind period). After unblinding, the patients who received belumosudil continued the same dose, whereas the patients who received placebo were re-randomised for one of the belumosudil doses for 24 weeks (open-label extension). Results Thirty-five and 31 patients were treated in the double-blind and open-label periods, respectively. The study was terminated prematurely, and target enrolment was not met. The primary end point, of CRISS score ≥0.60 at week 24, did not exhibit an efficacy signal in the belumosudil vs placebo groups [odds ratio: 1.06 (0.19–5.82; P = 0.9472) for the QD, and 0.39 (0.07–2.35; P = 0.3078) for the BID group]. Belumosudil was well tolerated and exhibited similar safety profiles in both double-blind and open-label periods. Tissue-based RNA sequencing analysis revealed FOXP3 upregulation and STAT3, IL23A and TGF-β downregulation in patients with CRISS score ≥0.60, which supported the mechanism of action of belumosudil. In blood and tissue samples, trends of decreased fibrosis biomarker levels were seen in the belumosudil-treated group vs placebo. Conclusion Efficacy signal for belumosudil could not be detected. Signalling pathway modulation analysis supported the mechanism of action of belumosudil. A trend for decreased fibrosis-related biomarkers was observed in the belumosudil-treated group. Trial registration ClinicalTrials.gov, https://clinicaltrials.gov, NCT03919799.

AbstractMetabolic syndrome (MetS) is a prevalent comorbidity in patients with chronic pain conditions. Emerging evidence suggests that the relationship between MetS and chronic pain is bidirectional. This interplay involves complex mechanisms, including proinflammatory mediators in nociceptive modulation and pain‐induced lifestyle changes affecting metabolic function. This scoping review examines the association between MetS and chronic pain. We conducted a scoping review, including 28 studies. This review followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Extension for Scoping Reviews guidelines. We included observational studies (cross‐sectional, case–control, and cohort studies) from the following databases: Ovid MEDLINE, EMBASE, Cochrane Central, Cochrane Database of Systematic Reviews, and Scopus. We identified studies on migraine (n = 9), spinal pain (n = 8), general chronic pain (n = 4), fibromyalgia syndrome (n = 3), carpal tunnel syndrome (n = 1), osteoarthritis (n = 1), shoulder pain (n = 1), and neuropathic pain (n = 1). Most studies used a cross‐sectional design and standard criteria to define MetS, with some exceptions. Individuals with MetS and spinal pain, fibromyalgia, or general chronic pain showed higher pain scores and increased functional limitations. MetS was common in patients with migraine, but its effect on pain severity was inconclusive. The relationship between MetS and other pain conditions requires further investigation to clarify the association and potential mechanisms. MetS is prevalent in patients with chronic pain and is associated with worse pain and functional outcomes. Health care providers should acknowledge the wide spectrum of MetS beyond obesity. This understanding may help identify those at risk for more severe chronic pain.

ABSTRACTObjectiveTo examine the outcomes of a clinical sample of young patients with restrictive eating disorders who received Family‐Based Treatment for Primary Care (FBT‐PC).MethodsParticipants were 134 youth (mean age = 15.7 years) and their caregiver(s). Participants and caregivers completed measures of eating disorder symptomatology, quality of life, and caregiver self‐efficacy.ResultsOf the 134 patients who received at least one session of FBT‐PC, 55.9% completed treatment, 20.1% were followed until they began a higher level of care, and 23.8% were non‐completers. Weekly measures for the full sample were evaluated using intention‐to‐treat analyses. Patient‐reported scores on the ED‐15 improved by −0.1 (SE = 0.02, F (1,133)= 26.4, p < 0.0001) per week, and caregivers' ratings of patient symptoms also improved by 0.85 (SE = 0.2, F (1,133)=13.1, p = 0.0003) per week. Patient's BMI percentile increased by 1.25 points per week (SE = 0.14, F (1,133) = 83.9, p < 0.0001). For patients who completed FBT‐PC, eating disorder symptoms, per patient‐ (M = −1.43, p < 0.0001) and caregiver‐report (M = −1.33 p < 0.0001) decreased significantly from baseline to end of treatment. Patient's quality of life increased significantly from baseline to end of treatment (M = 21.6, p < 0.0001) and caregivers showed significant increases in self‐efficacy (M = 3.41, p < 0.0001, d = 0.856). At the end of treatment, 62.5% of patients with complete data met criteria for full research remission, and 44.6% met criteria for full clinical remission.DiscussionFindings provide preliminary support for FBT‐PC as an effective treatment for youth with restrictive eating disorders. Additional research is needed to replicate these findings in other primary care settings and to understand the durability of treatment effects.

Background: Surgical registries have been widely adopted in high-income countries to improve patient outcomes. However, similar data-driven initiatives are still scarce in low- and middle-income countries (LMICs). Aims: This study aimed to address the challenges of implementing a perioperative registry for otolaryngology-head and neck surgery (OHNS) in Mekelle, Ethiopia, and to assess strategies for ensuring its long-term sustainability. Methods: The registry was developed using REDCap, through a collaborative effort between otolaryngologists in the United States and Ethiopia, ensuring its relevance to the local context. On-site training sessions were conducted for 13 OHNS residents and four senior surgeons to facilitate their use of the registry. A Wi-Fi router was installed in the operating room to enable real-time data entry. Continuous support was provided through remote communication between the local team and the U.S. research team. Sustainability strategies focused on fostering local ownership, integrating the registry into existing workflows, and maintaining continuous data monitoring. Results: Despite facing challenges like intermittent internet connectivity and issues with workflow integration, the local team successfully integrated the registry into routine clinical and surgical practices. Key strategies included providing dedicated Wi-Fi routers, modifying registry fields for improved efficiency, and emphasizing the registry’s value to the institution. Ongoing collaboration between the local team and the U.S. team enabled continuous optimization and data collection. Conclusion: The successful implementation of this perioperative registry underscores the importance of engaging local stakeholders and integrating sustainable workflows. This initiative serves as a model for other LMICs seeking to establish surgical registries that enhance data-driven decision-making at both the patient and institutional levels.

OBJECTIVE In this study, the authors aimed to establish a supervised machine learning (ML) model based on multiple tree-based algorithms to predict long-term biochemical outcomes and intervention-free survival (IFS) after endonasal transsphenoidal surgery (ETS) in patients with Cushing’s disease (CD). METHODS The medical records of patients who underwent ETS for CD between 2013 and 2023 were reviewed. Data were collected on the patient’s baseline characteristics, intervention details, histopathology, surgical outcomes, and postoperative endocrine functions. The study’s primary outcome was IFS, and the therapeutic outcomes were labeled as "under control" or "treatment failure," depending on whether additional therapeutic interventions after primary ETS were required. The decision tree and random forest classifiers were trained and tested to predict long-term IFS based on unseen data, using an 80/20 cohort split. RESULTS Data from 150 patients, with a median follow-up period of 56 months, were extracted. In the cohort, 42 (28%) patients required additional intervention for persistent or recurrent CD. Consequently, the IFS rates following ETS alone were 83% at 3 years and 78% at 5 years. Multivariable Cox proportional hazards analysis demonstrated that a smaller tumor diameter that could be detected by MRI (hazard ratio 0.95, 95% CI 0.90–0.99; p = 0.047) was significantly associated with greater IFS. However, the lack of tumor detection on MRI was a poor predictor. The ML-based model using a decision tree model displayed 91% accuracy (95% CI 0.70–0.94, sensitivity 87.0%, specificity 89.0%) in predicting IFS in the unseen test dataset. Random forest analysis revealed that tumor size (mean minimal depth 1.67), Knosp grade (1.75), patient age (1.80), and BMI (1.99) were the four most significant predictors of long-term IFS. CONCLUSIONS The ML algorithm could predict long-term postoperative endocrinological remission in CD with high accuracy, indicating that prognosis may vary not only with previously reported factors such as tumor size, Knosp grade, gross-total resection, and patient age but also with BMI. The decision tree flowchart could potentially stratify patients with CD before ETS, allowing for the selection of personalized treatment options and thereby assisting in determining treatment plans for these patients. This ML model may lead to a deeper understanding of the complex mechanisms of CD by uncovering patterns embedded within the data.

Abstract Allogeneic hematopoietic stem cell transplant (alloHCT) is considered for all patients with myeloid neoplasms (MNs) harboring TP53 mutations (TP53mut). The aim of this international study across 7 transplant centers in the United States and Australia was to identify factors associated with improved post-alloHCT survival. Of 134 TP53mut MN cases who underwent alloHCT, 80% harbored complex karyotype; 94% of TP53 variants were localized to the DNA-binding domain (DBD). Most common comutations were ASXL1 (7%), TET2 (7%), and DNMT3A (6%). Median post-HCT survival was 1.03 years, and overall survival (OS) at 1 year, 2 years, and 3 years was 51.4%, 35.1%, and 25.1%, respectively. A total of 103 cases (76.9%) met the International Consensus Classification (ICC) criteria for MN with mutated TP53 (referred to as ICC-defined TP53mut MN hereafter). The 3-year OS of ICC-defined TP53mut was significantly shorter compared with that of other TP53mut MNs (3-year OS, 16.9% vs 54.9%; P = .002). ICC-defined TP53mut MNs was independently associated with inferior OS (hazard ratio [HR], 2.62; P = .019). The presence of non-DBD TP53mut only (HR, 3.40; P = .005), DNMT3A comutation (HR, 2.64; P = .016), and pre-alloHCT bone marrow blasts ≥5% (HR, 2.76; P = .006) was independently associated with inferior relapse-free survival (RFS), whereas melphalan-based conditioning was associated with superior RFS (HR, 0.52; P = .005). Combining these variables, we constructed a hierarchical model that stratified patients into low-, intermediate-, and high-risk categories with 1-year RFS of 81.3%, 31.3%, and 6.7%, respectively (P &lt; .001). In conclusion, a subset of MN harboring TP53mut who have low blasts pre-alloHCT and received melphalan-based conditioning derived long-term benefit from alloHCT.

Abstract This retrospective analysis aimed to provide evidence-based risk stratification of TP53-mutated (TP53mut) myeloid neoplasms (MNs). Of 580 MNs harboring TP53mut with variant allele frequency (VAF) ≥2%, 219 (37.8%), 194 (33.4%), 92 (15.9%), and 75 (12.9%) were classified as acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) with low blasts (MDS-LB), MDS with excess blasts (MDS-EB)-2, and -EB1 according to the revised fourth edition of the World Health Organization (WHO) classification, respectively. Hierarchical analysis identified the following 4 risk groups with distinct survival: (1) MDS-LB, (2) MDS-EB1/EB2/AML VAF &lt;10%, (3) MDS-EB1/EB2 VAF ≥10%, and (4) AML VAF ≥10%. We next evaluated the impact of allelic status, VAF, and complex karyotype (CK). In our cohort, the significance of biallelic status was limited to MDS with &lt;5% blasts and not for blasts 5% to 9%, as proposed by the International Consensus Classification (ICC), or 5% to 19%, as proposed by the fifth edition of the WHO (WHO-5). MDS-EB1 and -EB2 with VAF ≥10% had comparable survival (9.6 vs 7.2 months; P = .12), regardless of allelic status. Contrary to the ICC proposal, MDS-EB1/EB2 with VAF &lt;10% and CK had poor survival compared with those without CK, comparable to MDS-EB1/EB2 with VAF ≥10% (5.6 vs 26.2 vs 6.3 months; P = .003). Survival of TP53mut AML was poor (median 3.9 months) regardless of allelic/CK status. Thus, using ICC or WHO-5 may underestimate prognosis of MDS with blasts 5% to 19% and 5% to 9%, respectively. Collectively, the hierarchical model acknowledges poor survival of 91.9% TP53mut MDS and AML compared with 36.5% and 80.7% by WHO-5 and ICC, respectively.

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care.