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Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P<.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P<.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P<.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P<.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Abstract Introduction The Geriatric Emergency Care Applied Research Network 2.0‐Advancing Dementia Care (GEAR 2.0‐ADC) aims to advance research efforts to improve the emergency care of persons living with dementia (PLWDs). Objective To support this objective, GEAR 2.0‐ADC convened a virtual consensus conference to prioritize emergency care research opportunities for PLWDs inclusive of perspectives of PLWDs to ensure identification of research gaps in response to their experiences and priorities. Inclusion of PLWDs as research partners is increasingly recognized as a best practice, however, approaches to facilitating consensus participation are lacking. Methods Best practices for supporting the engagement of PLWDs in a consensus conference, applied across its three phases (pre‐conference, during the conference, and post‐conference), include: establishing a learning environment focused on research priorities before the event, presenting information in ways that align with participants' learning preferences while accommodating cognitive impairments, and providing multiple opportunities and methods for gathering post‐conference feedback from PLWDs. Results These strategies were identified by PLWDs and care partners (CPs) through semi‐structured interviews, who were involved in the convening process, aimed at exploring ways to enhance facilitation techniques for participants. Conclusion Additionally, these summarized insights aim to encourage the use of community‐engaged approaches in discussions and consensus‐building around research priorities in emergency care, particularly for PLWDs and their CPs.

AbstractIntroductionThe International Hidradenitis Suppurativa Severity Score System (IHS4) is a validated tool that measures inflammatory lesions, including draining tunnels, in hidradenitis suppurativa (HS).ObjectiveTo evaluate secukinumab efficacy using IHS4 in patients with moderate to severe HS.MethodsData from the SUNSHINE and SUNRISE trials, which assessed subcutaneous secukinumab 300 mg every 2 (SECQ2W) and 4 (SECQ4W) weeks in adults with moderate to severe HS, were analyzed. Assessments included changes from baseline in IHS4 and severity classification up to Week 52; IHS4‐55, IHS4‐75, IHS4‐90 responses (55%, 75% and 90% reduction in IHS4) and concordance between IHS4‐55 and HS clinical response (HiSCR), at Weeks 16 and 52.ResultsIn total, 1084 patients (SECQ2W = 361; SECQ4W = 360; placebo = 363) were analyzed. At Week 16, SECQ2W and SECQ4W demonstrated a numerically higher reduction in IHS4 from baseline versus placebo (adjusted mean [95% CI]: −10.80 [−12.30 to −9.30] and −9.46 [−10.96 to −7.96] vs. −4.92 [−6.43 to −3.41]); the reduction was maintained until Week 52 in both dose regimens. A greater proportion of patients achieved IHS4‐55 with SECQ2W (43.4%) and SECQ4W (39.5%) versus placebo (31.5%) at Week 16, with further improvement at Week 52. Similar trends were observed for IHS4‐75 and IHS4‐90 responses. While no patients had mild disease based on IHS4 (80.7% had severe and 19.3% had moderate HS) at baseline, a greater proportion of patients were categorized as having mild disease at Week 16 in the SECQ2W (25.9%) and SECQ4W (24.0%) groups versus placebo (16.4%); this trend continued up to Week 52 in both dose regimens. Strong concordance (>85%) was observed between IHS4‐55 and HiSCR.ConclusionsBoth SECQ2W and SECQ4W demonstrated efficacy in improving treatment response as measured by IHS4 and reducing disease severity versus placebo at Week 16 and these improvements were sustained through Week 52. These findings support that the dynamic and dichotomous IHS4 can efficiently detect treatment response changes in clinical trial settings.Clinical Trials RegistrationSUNSHINE (NCT03713619); SUNRISE (NCT03713632).

Background: Prostate cancer is the second most common new cancer diagnosis in the United States. It is usually slow-growing, and when it is low-grade and confined to the prostate gland, it can be treated either conservatively (through active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that indicates a more aggressive cancer, and surgery may not be adequate. Methods: The challenge is that it is often difficult for radiologists reading prostate-specific imaging such as magnetic resonance images (MRIs) to differentiate malignant lymph nodes from non-malignant ones. An emerging field is the development of artificial intelligence (AI) models, including machine learning and deep learning, for medical imaging to assist in diagnostic tasks. Earlier research focused on implementing texture algorithms to extract imaging features used in classification models. More recently, researchers began studying the use of deep learning for both stand-alone feature extraction and end-to-end classification tasks. In order to tackle the challenges inherent in small datasets, this study was designed as a scalable hybrid framework utilizing pre-trained ResNet-18, a deep learning model, to extract features that were subsequently fed into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer. For comparison, two texture algorithms were implemented, namely the gray-level co-occurrence matrix (GLCM) and Gabor. Results: Using an institutional prostate lymph node dataset (42 positives, 84 negatives), the proposed framework achieved an accuracy of 76.19%, a sensitivity of 79.76%, and a specificity of 69.05%. Using GLCM features, the classification achieved an accuracy of 61.90%, a sensitivity of 74.07%, and a specificity of 42.86%. Using Gabor features, the classification achieved an accuracy of 65.08%, a sensitivity of 73.47%, and a specificity of 52.50%. Conclusions: Our results demonstrate that a hybrid approach, i.e., using a pre-trainined deep learning model for feature extraction, followed by a machine learning classifier, is a viable solution. This hybrid approach is especially useful in medical-imaging-based applications with small datasets.

OBJECTIVE The Woven EndoBridge (WEB) device was approved to treat wide-necked bifurcation aneurysms. The device is designed as an intrasaccular flow disruptor covering aneurysm widths up to 10 mm. Although prior studies combined all aneurysm sizes, it is known that aneurysms behave differently in response to endovascular treatment based on their size. Therefore, the authors’ objective was to identify ideal middle cerebral artery (MCA) aneurysm width and neck sizes most suitable for WEB treatment. METHODS The WorldWideWEB consortium is a large multicenter retrospective database that analyzes intracranial aneurysms treated with the WEB device. In this study, all unruptured MCA bifurcation aneurysms with available measurements were included. Cutoff values based on aneurysm width and neck in relation to aneurysm occlusion status were measured using the receiver operating characteristic (ROC) curve. Propensity score matching (PSM) was then used to compare treatment outcomes between aneurysms smaller and larger than the cutoff value for both width and neck size. RESULTS The ideal cutoff values for MCA bifurcation aneurysm width and neck were 6.1 mm and 4.6 mm, respectively. On PSM, 87 matched pairs were compared based on width size (≤ 6.1 mm and > 6.1 mm), and 77 matched pairs were compared based on neck size (≤ 4.6 mm and > 4.6 mm). There was a significant difference in adequate aneurysm occlusion between aneurysms smaller and larger than those cutoff values for both widths (93% vs 76%, p = 0.0017) and neck sizes (90% vs 70%, p = 0.0026). The retreatment rate was also significantly higher for larger aneurysms in both parameters. CONCLUSIONS This study shows that MCA bifurcation aneurysms ≤ 6.1 mm in width and ≤ 4.6 mm in neck size are significantly better candidates for WEB treatment, leading to improved occlusion status and reduced retreatment rate, which are important considerations when using WEB devices.

AbstractPatients in delirium require trustful communication and re‐orientation. We developed a flyer with positive, re‐orientating suggestions for families of delirious patients in intensive care units. Suggestions include creating a safe environment, interpreting unusual behaviours positively and fostering mental resilience. Additionally, families are encouraged to prioritize their own well‐being, recognizing their crucial role in supporting their loved ones. This flyer offers practical strategies across four key areas: ensuring security and orientation, reframing noises and body experiences, managing agitation and reshaping perceptions. By equipping families with knowledge and tools, this resource aims to promote understanding, resilience and strength to humanize delirium care.

Importance Individuals with atopic dermatitis are frequently colonized and infected with Staphylococcus aureus . Empirical antibiotic therapy for individuals with atopic dermatitis is common, but data about the antimicrobial susceptibility profiles of S aureus strains isolated from these individuals are scarce for those living in particular geographic areas. Objective To determine the antimicrobial susceptibility of S aureus from individuals with atopic dermatitis and analyze differences according to the income level of the country of origin and the data collection period. Data Sources A meta-analysis of the literature was performed from the inception of the included databases (MEDLINE, Embase, Web of Science, Scopus, and Cochrane) to June 20, 2023, using predetermined Medical Subject Headings. Study Selection Studies were included if they reported antibiotic susceptibility profiles of 1 or more S aureus cutaneous isolates from individuals with atopic dermatitis. Articles written in English, Spanish, French, or German were included. Data Extraction and Synthesis Working in pairs, 6 of the authors conducted the data extraction. The guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) were followed. Main Outcomes and Measures The outcome of interest was antimicrobial susceptibility. Results A total of 61 studies reported 4091 S aureus isolates from individuals with atopic dermatitis. For 4 of the 11 commonly used antibiotics (36.4%), antimicrobial susceptibility was 85% or less, including for methicillin (binomial proportion, 0.85 [95% CI, 0.76-0.91]), erythromycin (binomial proportion, 0.73 [95% CI, 0.61-0.83]), fusidic acid (binomial proportion, 0.80 [95% CI, 0.62-0.91]), and clindamycin (binomial proportion, 0.79 [95% CI, 0.65-0.89]). Most studies (46; 75.4%) were conducted in high-income countries. Antimicrobial susceptibility to erythromycin, methicillin, and trimethoprim and sulfamethoxazole was significantly lower in lower middle–income countries and upper middle–income countries. Regarding the temporal trends, 33 studies (54.1%) reported data collected from 1998 to 2010. Antimicrobial susceptibility patterns have not changed over time. Conclusions and Relevance In this systematic review and meta-analysis, antimicrobial susceptibility of S aureus to β-lactams, erythromycin, clindamycin, and fusidic acid may be suboptimal for empirical use in individuals with atopic dermatitis. Significant differences in antimicrobial susceptibility patterns were found in high-income countries and in lower middle–income countries and upper middle–income countries for some antibiotics.

OBJECTIVES: Telecritical care (TCC) refers to the delivery of critical care using telehealth technologies. Despite increasing utilization, significant practice variation exists and literature regarding efficacy remains sparse. The Telecritical Care Collaborative Network sought to provide expert, consensus-based best practice recommendations for the design and delivery of TCC. DESIGN: We used a modified Delphi methodology. Following literature review, an oversight panel identified core domains and developed declarative statements for review by an expert voting panel. During three voting rounds, voters agreed or disagreed with statements and provided open-ended feedback, which the oversight panel used to revise statements. Statements met criteria for consensus when accepted by greater than or equal to 85% of voters. SETTING/SUBJECTS: The oversight panel included 18 multidisciplinary members of the TCC Collaborative Network, and the voting panel included 32 invited experts in TCC, emphasizing diversity of discipline, care delivery models, and geography. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified ten core domains: definitions/terminology; care delivery models; staffing and coverage models; technological considerations; ergonomics and workplace safety; licensing, credentialing, and certification; trust and relationship building; quality, safety, and efficiency, research agenda; and advocacy, leading to 79 practice statements. Of 79 original statements, 67 were accepted in round 1. After revision, nine were accepted in round 2 and two in round 3 (two statements were merged). In total, 78 practice statements achieved expert consensus. CONCLUSIONS: These expert consensus recommendations cover a broad range of topics relevant to delivery of TCC. Experts agreed that TCC is most effective when delivered by care teams with specific expertise and by programs with explicit protocols focusing on effective communication, technical reliability, and real-time availability. Interventions should be tailored to local conditions. Although further research is needed to guide future best practice statements, these results provide valuable and actionable recommendations for the delivery of high-quality TCC.