Citations

Abstract MGMT is primarily inactivated via promoter methylation, but MGMT homozygous deletion (hmzdel) has been rarely reported as an alternative inactivating mechanism. Although functionally similar to a methylated MGMT promoter, MGMT hmzdel is associated with an unmethylated MGMT promoter test result reflecting the MGMT promoter status of the non-neoplastic cells invariably present in any tumor specimen. Thus, patients whose tumors have MGMT hmzdel (and unmethylated MGMT promoter) would be inaccurately predicted to lack benefit from alkylating chemotherapy based on MGMT promoter methylation testing alone. To evaluate the impact of this clinical scenario, we estimated the frequency and distribution of MGMT hmzdel in routine clinical testing of CNS tumors. Among 12,424 CNS tumors (3,044 with provided diagnosis of glioblastoma) tested using Oncoscan chromosomal microarray (2015-2025), 10 tumors (0.08%) had MGMT hmzdel: 8 with entire gene hmzdel and 2 with partial gene hmzdel favored to be inactivating (RNA-sequencing underway to exclude possibility of activating rearrangements). All tumors were supratentorial and non-midline. Median age at diagnosis was 58.5 years (range, 34-81), with 3:2 male:female. Of the 10 cases, 9 (of 3,044; 0.3%) had integrated diagnosis of glioblastoma, IDH-wildtype; the remaining case was a recurrent astrocytoma, IDH-mutant, CNS WHO grade 4. All 10 cases had loss of chromosome 10q and a highly unmethylated MGMT promoter by droplet digital PCR testing of downstream CpG sites 75-80/84-87, with median fraction abundance of 0.19% (range, 0-0.85). In summary, MGMT hmzdel only occurred in high-grade adult-type diffuse astrocytic gliomas, and is a rare potential pitfall for MGMT promoter methylation testing leading to inaccurate patient stratification. It is critical that laboratories performing routine CNS tumor copy number testing include MGMT in the curated list of reportable genes, and that clinicians be aware of this rare clinical scenario to accurately stratify patients despite misleading MGMT promoter test results.

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .

Abstract Background Stress echocardiography (SE) was recently upgraded to the ABCDE protocol: step A, regional wall motion abnormalities; step B, B-lines; step C, left ventricular contractile reserve; step D, Doppler-based coronary flow velocity reserve in left anterior descending coronary artery; and step E, EKG-based heart rate reserve. Aim to assess the prognostic value of ABCDE-SE in a prospective, large-scale, multicenter, international, effectiveness study. Methods From July 2016 to November 2024, we enrolled 7896 all-comers (age 65±11 years, 4742 [60.0%] males, ejection fraction 59±10%) with known or suspected chronic coronary syndromes (CCS) referred from 27 certified laboratories. All patients underwent clinically indicated ABCDE-SE, with ACE in all patients, step D in 6286, step B in 5951 and all 5 steps in 5918 pts. The employed stress modality was exercise (n=2407) or pharmacological stress (n=5489; with vasodilator, n=4503; or dobutamine, n=986). Each step was scored from 0 (normal) to 3 (severely abnormal) following EACVI recommendations 2024. SE response ranged from a score of 0 (all steps normal) to a score of 15 (all steps severely abnormal). All-cause death was the only end-point. Results During a median follow-up of 685 days (interquartile range: 387-1227), 370 deaths occurred. Step A negativity was associated with a 1.5% annual mortality. Annual mortality rate ranged from 0.4% persons/year for score 0 (n=958, 12.1%) up to 6.19% person/year for score ≥10 (n=346, 4.3%). (Figure). At multivariate logistic regression analysis, age (hazard ratio [HR] 1.008, 95% interquartile range [IQ]: 1.006-1.010, p<0.001), diabetes (HR 1.564, 95% CI: 1.263-1.936, p<0.001), resting ejection fraction (HR 0.983, 95% CI: 0.974-0.993, p=0.001), and ABCDE score 3-5 (HR 3.111, 95% CI 1.597-6.060, p=0.001), score 6-9 (HR 5.442, 95% CI 2.761-10.729, p<0.001), and score ≥10 (HR 8.826, CI 4.197-18.654, p<0.001) were independent predictors of mortality. Conclusion In patients with known or suspected CCS, SE negativity by simple wall motion criterion is unable to identify a low-risk group. ABCDE-SE allows an effective prediction of survival.Figure

Background: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating “hallucinations”. Objective: To combine the deterministic framework of traditional NLP with the probabilistic capabilities of LLMs, we developed the AI Virtual Assistant (AIVA) Platform. This system utilizes a retrieval-augmented generation (RAG) architecture, integrating Gemini 2.0 Flash with a medically verified knowledge base via Google Vertex AI, to safely deliver dynamic, patient-facing postoperative guidance grounded in validated clinical content. Methods: The AIVA Platform was evaluated through 750 simulated patient interactions derived from 250 unique postoperative queries across 20 high-frequency recovery domains. Three blinded physician reviewers assessed formal system performance, evaluating classification metrics (accuracy, precision, recall, F1-score), relevance (SSI Index), completeness, and consistency (5-point Likert scale). Safety guardrails were tested with 120 out-of-scope queries and 30 emergency escalation scenarios. Additionally, groundedness, fluency, and readability were assessed using automated LLM metrics. Results: The system achieved 98.4% classification accuracy (precision 1.0, recall 0.98, F1-score 0.9899). Physician reviews showed high completeness (4.83/5), consistency (4.49/5), and relevance (SSI Index 2.68/3). Safety guardrails successfully identified 100% of out-of-scope and escalation scenarios. Groundedness evaluations demonstrated strong context precision (0.951), recall (0.910), and faithfulness (0.956), with 95.6% verification agreement. While fluency and semantic alignment were high (BERTScore F1 0.9013, ROUGE-1 0.8377), readability was 11th-grade level (Flesch–Kincaid 46.34). Conclusion: The simulated testing demonstrated strong technical accuracy, safety, and clinical relevance in simulated postoperative care. Its architecture effectively balances flexibility and safety, addressing key limitations of standalone NLP and LLMs. While readability remains a challenge, these findings establish a solid foundation, demonstrating readiness for clinical trials and real-world testing within surgical care pathways.

Objective To provide evidence‐based and expert guidance for the treatment and management of non‐renal systemic lupus erythematosus (SLE); treatment and management of lupus nephritis are addressed in a separate guideline. Methods Clinical questions for treatment and management of SLE were developed in the PICO format (population, intervention, comparator, and outcome). Systematic literature reviews were developed for each PICO question, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess evidence quality and formulate recommendations. The Voting Panel achieved a consensus of ≥70% agreement on the direction (for or against) and strength (strong or conditional) of each recommendation. Results We present recommendations and ungraded, consensus‐based good practice statements for the treatment and management of SLE that are applicable to pediatric and adult patients. Recommendations emphasize uniform treatment with hydroxychloroquine, limiting duration of glucocorticoid use, and early introduction of conventional and/or biologic immunosuppressive therapies to achieve and maintain control of SLE inflammation (remission or a low level of disease activity), reduce SLE‐related morbidity and mortality, and minimize medication‐related toxicities. Conclusion This guideline presents direction regarding treatment and management of SLE and provides a foundation for well‐informed, shared clinician–patient decision‐making. These recommendations should not be used to limit or deny access to therapies, as treatment decisions may vary due to the unique clinical situation and personal preferences of each person with SLE.

Objective To provide evidence‐based and expert guidance for the treatment and management of non‐renal systemic lupus erythematosus (SLE); treatment and management of lupus nephritis are addressed in a separate guideline. Methods Clinical questions for treatment and management of SLE were developed in the PICO format (population, intervention, comparator, and outcome). Systematic literature reviews were developed for each PICO question, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess evidence quality and formulate recommendations. The Voting Panel achieved a consensus of ≥70% agreement on the direction (for or against) and strength (strong or conditional) of each recommendation. Results We present recommendations and ungraded, consensus‐based good practice statements for the treatment and management of SLE that are applicable to pediatric and adult patients. Recommendations emphasize uniform treatment with hydroxychloroquine, limiting duration of glucocorticoid use, and early introduction of conventional and/or biologic immunosuppressive therapies to achieve and maintain control of SLE inflammation (remission or a low level of disease activity), reduce SLE‐related morbidity and mortality, and minimize medication‐related toxicities. Conclusion This guideline presents direction regarding treatment and management of SLE and provides a foundation for well‐informed, shared clinician–patient decision‐making. These recommendations should not be used to limit or deny access to therapies, as treatment decisions may vary due to the unique clinical situation and personal preferences of each person with SLE.

Retrieval-augmented generation (RAG) quality depends on how source documents are segmented before indexing; fixed-length chunks can split concepts or add noise, reducing precision. We evaluated whether proposition, semantic, and adaptive chunking improve accuracy and relevance for safer clinical decision support. Using a curated domain knowledge base with Gemini 1.0 Pro, we built four otherwise identical RAG pipelines that differed only in the chunking strategy: adaptive length, proposition, semantic, and a fixed token-dependent baseline. Thirty common postoperative rhinoplasty questions were submitted to each pipeline. Outcomes included medical accuracy and clinical relevance (3-point Likert scale) and retrieval precision, recall, and F1; group differences were tested with ANOVA and Tukey post hoc analyses. Adaptive chunking achieved the highest accuracy—87% (Likert 2.37 ± 0.72) versus baseline 50% (1.63 ± 0.72; p = 0.001)—and the highest relevance (93%, 2.90 ± 0.40). Retrieval metrics were strongest with adaptive (precision 0.50, recall 0.88, F1 0.64) versus baseline (0.17, 0.40, 0.24). Proposition and semantic strategies improved all metrics relative to baseline, though less than adaptive. Aligning chunks to logical topic boundaries yielded more accurate, relevant answers without modifying the language model, offering a model-agnostic, data-source-neutral lever to enhance the safety and utility of LLM-based clinical decision support.

Background: Critical congenital heart disease (CCHD) screening algorithms based on pulse oximetry have up to 27% false positive rate at high altitudes (>2500 m) despite altitude-specific cutoff changes. We examined the added value of an adult-based AI model applied to phonocardiography (AI-PCG) in the digital stethoscope EKO Core 500 in these settings. Research Question: What is the diagnostic performance of AI-PCG model compared to pulse oximetry in detecting neonatal CCHD across different altitudes in Latin America? Methods: This observational, prospective, case-control study included newborns born at different altitudes from 0 to 4380 meters in Peru, Mexico, Colombia and Bolivia. All underwent preductal and postductal oximetry at least 18 hours after birth, electrocardiography and PCG with the digital stethoscope. All CCHD cases were confirmed by echocardiography. Non-CCHD cases were defined by echocardiography or clinically if they were alive for 1 month and had 1) negative pulse oximetry, 2) no hospitalization due to cardiac or pulmonary causes and 3) no cyanosis or pneumonia. Those with abnormal pulse oximetry, genetic syndromes or murmur detection underwent echocardiography. Diagnostic performance metrics [area under the receiving operating characteristic curve (AUROC), sensitivity, specificity and false positive rate] were evaluated per modality. Results: A total of 1152 newborns were enrolled, 725 at <2500 m [13 (1.8%) with CCHD] and 427 at >2500 m [6 (1.4%) with CCHD]. Out of 19 CCHD cases, 17 had positive pulse oximetry and 9 had a positive AI-PCG model. One newborn with severe coarctation of the aorta and atrial septal defect had negative pulse oximetry and AI-PCG model. Pulse oximetry showed superior AUROC (0.932 vs 0.664), sensitivity (89.5% vs 47.4%), specificity (97% vs 85.5%) and lower false positive rate (3% vs 14.5%) compared to the AI-PCG model across altitudes. Diagnostic accuracy varied with altitude (Figure): at <2500 m, pulse oximetry showed higher AUROC (0.999 vs 0.612) and lower false positive rate (0.1% vs 16%) than AI-PCG model, but at >2500 m, pulse oximetry had similar AUROC (0.794 vs 0.774) and lower false positive rate (7.8% vs 11.9%) than the AI-PCG model. Conclusion: Pulse oximetry has superior diagnostic performance than an adult-based AI-PCG model when screening for neonatal CCHD at <2500 m, but similar performance is found >2500 m.

Introduction: Corticosteroid therapy has been demonstrated to improve prognosis and reduce mortality in patients with severe Coronavirus Disease 2019 (COVID-19) infection by attenuating the exaggerated inflammatory response that emerges in the late phase of infection. However, its impact on patients with pre-existing cardiovascular disease, who are at higher risk of complications, has not been specifically studied. The aim of this study is to evaluate the effect of corticosteroid therapy on mortality and long-term COVID-19 symptoms in this high-risk population. Methods: We analyzed the prospective registry HOPE-2. Patients with previous cardiovascular disease were selected, and 18-month all-cause death was defined as the primary endpoint. Long-term COVID-19 symptoms were considered as secondary endpoints. A total of 1188 patients with previous heart disease were included, of which 453 received corticosteroid treatment. Propensity score matching analysis in a 1:1 fashion was performed based on baseline variables that exhibited a p-value < 0.05 in the univariant analysis and outcome variables that defined corticosteroid use, with a final matched population of 796 patients. Results: In patients with pre-existing heart disease, corticosteroid treatment was not associated with differences in 18-month all-cause mortality (p = 0.52). However, a shorter duration of hospitalization (median: 8 days [IQR: 4–14] and 11 days [IQR: 7–18]; p < 0.001) was observed in patients who received corticosteroids. No significant differences in long-term COVID-19 symptoms were observed between the two groups. Conclusions: In patients with pre-existing heart disease, the absence of a clear harmful effect suggests that the positive effects of corticosteroids may be offset by their potential adverse effects which could contribute to the persistence of long COVID symptoms. This finding may reflect a differential response to corticosteroids in this high-risk subgroup, highlighting the need for further studies to clarify the role of this therapy in such patients.

Abstract INTRODUCTION Preclinical Alzheimer's disease (AD) can be described relative to biomarker positivity onset time. METHODS We estimated time from amyloid positivity (A+) using sampled iterative local approximation (SILA) in a longitudinal autosomal dominant AD (ADAD) sample ( N = 379) with amyloid positron emission tomography. We compared (1) predicted age at A+ to imputed age, (2) estimated age at A+ to estimated age at symptom onset, and (3) variance in cognitive performance explained. RESULTS Mean error between imputed and SILA‐estimated age at A+ ( N = 26) was 1.15 years. Age at A+ explained 39% of estimated years to symptom onset (EYO) variance. Time from A+ explained 19% of cognitive composite variance and 14% of Clinical Dementia Rating Sum of Boxes CDR‐SB variance; EYO explained 43% and 57%, respectively. DISCUSSION SILA estimates A+ age in ADAD with reasonably good accuracy. SILA‐estimated time from A+ describes the start of pathology, but the time from A+ onset to symptoms is variable in ADAD and better described by EYO. Highlights Amyloid chronicity predicts a 14‐year preclinical AD phase in ADAD. SILA accurately estimates age at A+ (MAE < 2 years). EYO outperforms chronicity in predicting symptom onset. APP mutation carriers show atypical amyloid accumulation. Chronicity models help reveal AD heterogeneity in preclinical stages.