Citations

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.

Abstract Studies assessing genetic associations with neuropathological features in Lewy body disease (LBD) have been limited to candidate gene investigations, and therefore, information is lacking regarding the genetic architecture of the neuropathology of LBD. In the current study, we examined a large series of neuropathologically confirmed LBD cases (n = 980 in the discovery series, n = 503 in the replication series) and performed genome-wide association studies of 11 different neuropathological outcome measures. The 11 neuropathological outcomes included Braak neurofibrillary tangle (NFT) stage, Thal amyloid phase, LBD subtype, Lewy body (LB) counts in five different brain regions, dorsolateral and ventromedial putaminal tyrosine hydroxylase immunoreactivity and neuronal loss in the ventrolateral part of the substantia nigra. Associations between variants and outcomes were assessed using regression models appropriate for the nature of the given neuropathological outcome and that were adjusted for age at death, sex and top principal components of genetic data. In the discovery series, APOE rs429358 (i.e. APOE ε4) was associated with a greater severity of each of Braak NFT stage [odds ratio (OR) = 3.07, P = 2.34 × 10−32], Thal amyloid phase (OR = 3.57, P = 3.28 × 10−29) and LBD subtype (OR = 1.78, P = 9.85 × 10−9), with similar findings observed in the independent replication series (Braak NFT stage, OR = 2.30, P = 2.70 × 10−11; Thal amyloid phase, OR = 3.17, P = 6.39 × 10−18; LBD subtype, OR = 2.68, P = 3.85 × 10−10). In the subgroup of cases with lower levels of Alzheimer's disease pathology (Braak NFT stage ≤ III and Thal amyloid phase ≤2), there was a strong association between APOE rs429358 and LBD subtype even when adjusting for Braak stage and Thal phase in the discovery series (n = 218, OR = 2.47, P = 0.007) and the replication series (n = 141, OR = 3.60, P = 0.006). Although additional genome-wide significant associations were identified in the discovery series between LINC01581/MCTP2 rs547411734 and lower middle frontal LB counts, between TLE3 rs3743309 and lower cingulate LB counts, and between GRIN2A/ATF7IP2 rs1097915 and lower parahippocampal LB counts, these findings were not observed in the replication series. Our results indicate that the APOE ε4 allele is the most prominent genetic determinant of severity of neuropathology in LBD. These findings represent a key step forward in our understanding of genetic drivers of neuropathological features in LBD. Future studies utilizing meta-analytical approaches will be important to more precisely assess other associations that were not quite genome-wide significant in the discovery series.

Abstract Objective Dravet syndrome (DS) is a developmental and epileptic encephalopathy. Diagnosis is clinical, but ~90% of patients have pathogenic variants in SCN1A . ATP6V0C has recently been proposed as a novel candidate gene for epilepsy, with or without developmental delay. Here we describe two adult patients with a clinical diagnosis of DS associated with ATP6V0C variants. Methods Patients with developmental and epileptic encephalopathies were evaluated by physicians who are experts in DS, and their clinical diagnosis was correlated with genetic findings. A subgroup of those patients with DS but without known genetic causes were evaluated through gene panels, whole exome sequencing, and chromosome microarray. Phenotype was determined by pediatric and adult chart reviews, interviews, and physical examinations. Results Of 753 patients with DS, two unrelated individuals with classic features of DS during childhood and adulthood were identified with heterozygous de novo missense variants in ATP6V0C (c.319G > C, p.(Gly107Arg) and c.284C > T, p.(Ala95Val), respectively). Both variants were absent in normal populations and computational prediction algorithms suggested deleterious effects on protein structure and/or function. No disease‐causing variants in other genes previously associated with DS were found. Significance Here we describe two adult patients with Dravet‐like syndrome and pathogenic/likely pathogenic variants in ATP6V0C . We propose that abnormal ATP6V0C function can, at the severe end of the clinical spectrum, be associated with Dravet‐like phenotype. This is relevant, as these patients would not qualify for disease‐modifying antisense nucleotide or gene therapies targeting SCN1A .

ABSTRACTOf the many burdens cancer patients face, the impact on personal finances is often invisible to clinicians. Financial toxicity refers to the negative impact on patients’ and families’ quality of life due to a combination of high out‐of‐pocket costs of medical treatment, diminished savings, and psychological distress as a result of diminished finances. Financial toxicity in cancer care has been more closely examined in the standard‐of‐care setting. Financial toxicity in the early phase clinical trial setting, and the ethical implications of making patients pay out of pocket to access experimental interventions that may not have therapeutic benefit, have yet to be explored. This article seeks to highlight hidden costs of clinical trial participation in the U.S., and to illustrate how patients are susceptible to financial toxicity from nonmedical direct costs and indirect costs even though a trial intervention itself is not charged to the patient. We argue that not informing prospective participants of the potential costs of trial participation threatens their autonomy and interferes with researchers’ prima facie duties to beneficence and nonmaleficence.

Abstract Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.