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Introduction: Studies suggest that stroke and hypoperfusion from heart failure (HF) may contribute to cognitive decline. However, the relationship between the HF subtypes and cognition remains poorly described in real-world populations. Hypothesis: We hypothesized that HF subtypes are differentially associated with cognitive impairment and that lower left ventricular ejection fraction (LVEF) correlates with poorer cognitive performance. Methods: We conducted a cross-sectional study of patients aged ≥18 years seen at a Heath System in 2022 who underwent both echocardiogram and cognitive assessment within one year. HF was classified into four groups based on 2022 American Heart Association HF guideline: No heart failure, HFpEF (HF with preserved LVEF), HFmrEF (HF with mid-range LVEF), and HFrEF (HF with reduced LVEF). Cognitive impairment was categorized using the Montreal Cognitive Assessment (MOCA) as normal (≥26), mild (18–25), moderate (10–17), and severe (<10). Multivariable linear regression assessed the correlation between EF and MoCA, and logistic regression evaluated the association between HF subtypes and severity of cognitive loss, adjusting for age, sex, race, hypertension, diabetes, dyslipidemia, chronic kidney disease, stroke. Results: A total of 1,695 patients were included in the study (mean age, 75.3±14 years; 48.3% female). 82.0% had HFpEF; 7.1%, HFmrEF; 7.9%, HFrEF, and 3.0% no HF. LVEF was positively associated with MoCA score (β=0.028, p=0.032). Patients with LVEF ≤40% had lower MoCA scores (17.7 ± 6.2 vs. 19.1 ± 5.9, p=0.008) and higher adjusted odds of moderate/severe cognitive impairment (aOR 2.49, 95%CI 1.20-5.15, p=0.014), compared to those with LVEF >40%. Adjusted odds of moderate to severe cognitive impairment were 1.14 for HFpEF, 0.99 for HFmrEF, and 2.81 for HFrEF compared to patients with no heart failure (trend-P=0.031, Table). Conclusions: Lower LVEF was independently associated with worse cognitive performance. Patients with HFrEF had a 2.5-fold higher risk of moderate to severe cognitive impairment. This association was not observed in patients with HFpEF or HFmrEF, suggesting differential cognitive effects across HF subtypes. Further studies are warranted to confirm these findings and evaluate the impact of interventions targeting EF and HF subtypes on cognitive outcomes.

Introduction: Cerebral amyloid angiopathy (CAA) is a progressive cerebrovascular disease caused by amyloid-beta (Aβ) deposition in cerebral vasculature. While CAA is a recognized cause of intracerebral hemorrhage (ICH) and cognitive decline, emerging evidence suggests that seizures are among a broader array of CAA clinical manifestations. However, the risk of seizures and status epilepticus in CAA remains poorly characterized. This study aimed to estimate incidence of seizures and status epilepticus in CAA using real-world data. Methods: A retrospective, longitudinal study was conducted using a nationally representative sample of administrative claims from US Medicare and commercial insurance beneficiaries (2017–2024). Patients with CAA (aged > 50 years with > 1 physician encounter with a validated ICD-10 code (I68.0)) were matched 1:4 to age- and sex-matched controls. The primary outcome was occurrence of seizure, ascertained using validated ICD-10 codes during acute care; the secondary outcome was hospitalization for status epilepticus. Follow-up continued until the end of enrollment or death. Descriptive statistics summarized patient characteristics and Poisson regression estimated incidence rates with 95% CIs. Cox proportional hazards regression assessed associations between CAA and outcomes of interest vs controls, adjusted for chronic kidney disease, liver disease, prior ICH, and seizure history. Results: We included 7294 patients with CAA and 29176 controls (median age: 77 years; 53.7% women; median follow-up: 29.4 months). Patients with CAA had higher rates of prior ICH (32.4% vs 0.5%) and prior seizure (18.7% vs 1.8%) than controls. The incidence rate of seizures was higher in patients with CAA than controls: 8.1 per 100 person-years (PY; 95% CI: 7.6–8.6) vs 0.4 per 100 PY (95% CI: 0.4–0.5). In the CAA cohort, seizure incidence rate was higher for those with prior ICH than those with no ICH history: 13.6 per 100 PY (95% CI: 12.5–14.8) vs 5.7 per 100 PY (95% CI: 5.2–6.3). Status epilepticus rates were higher in patients with CAA than controls: 0.7 per 100 PY (95% CI: 0.6–0.9) vs 0.03 per 100 PY (95% CI: 0.02–0.04). Adjusted models showed that CAA was associated with higher risk of seizures (HR: 6.7, 95% CI: 5.8–7.7) and status epilepticus (HR: 8.1, 95% CI: 4.8–13.7) than controls (Table 1). Conclusions: Patients with CAA have an increased risk of seizures and status epilepticus. These findings broaden the possible clinical spectrum of CAA.

INTRODUCTION: Reliable biomarkers for the diagnosis of chronic pancreatitis (CP) and pancreatic fibrosis severity are lacking, hindering effective treatment and management. Histologic fibrosis is a hallmark of late-stage CP, but noninvasive methods to evaluate fibrosis progression are limited. We used urine proteomics to discover biomarkers that identify patients with CP and predict fibrosis severity. METHODS: We performed a cross-sectional study of 130 total subjects (CP n = 50) selected based on clinical criteria in a tertiary care setting. Urine proteomics samples were quantified using data-independent acquisition mass spectrometry. Differential biomarker candidates were identified with false discovery rate-corrected pairwise comparisons. These proteins were validated with an independent paired urine and plasma sample cohort (n = 36). Machine learning was used to develop a protein panel that predicted Ammann scores for patients with histologic fibrosis. RESULTS: We found 34 proteins consistently differentially expressed between CP and controls in pairwise false discovery rate-controlled tests. Of these, 25 urine proteins outperformed 19 previously suggested CP blood-based biomarkers in an independent validation cohort. Isocitrate dehydrogenase (IDH1), calcyphosin (CAPS), synuclein gamma (SNCG), and protein S100-P (S100P) all produced receiver operator curve area under the curve values >0.95, while the best plasma marker was interleukin 2 receptor subunit alpha (receiver operator curve area under the curve = 0.80). A 12-protein panel of identified markers predicted fibrosis severity with a linear correlation R 2 value of 0.61. DISCUSSION: We identified a panel of proteins that may diagnose CP in children and developed a model to predict pancreatic fibrosis severity, offering promising tools for improving diagnostics and patient care.

Abstract Background We aimed to describe the real-world durability of clinical and endoscopic outcomes of upadacitinib (UPA) in ulcerative colitis (UC). Methods We retrospectively analyzed an ongoing multi-center cohort of UC patients(pts) in the United States who were started on UPA for active disease. Primary outcome was clinical remission at 1yr assessed by partial Mayo score(pMS)(resolution of all UC related symptoms and physician global assessment). Secondary outcomes assessed at various timepoints, including after re-escalation, included clinical response(&gt;50%reduction in symptoms utilizing the pMS), endoscopic response (Mayo endoscopic score≤1or absence of erosions/ulcerations) and histological remission(defined as normal or chronic inactive). Durable secondary outcomes achieved at both 6months and at 1yr and beyond were also assessed. Descriptive statistics were performed. Adverse events(AEs) investigated included cardiovascular events and herpes zoster (HZ). Results 416pts were included(Table 1) with median values for age at initiation of36.00yrs(range27-48), disease duration7.00yrs(range3-14)and follow-up 993days(IQR,672-1103), with206(49.52%)with follow-up at 1yr or beyond. Of these, 66.1%had extensive colitis. Induction duration was8wks in 86.5%and16wks in 4.8 %(20/416). Most received an induction dose of45mg(97.1%)and maintenance dose of30mg(85.8%). Among the evaluable population at1yr and beyond, 62.5%(45/80)were in clinical remission, 75.9%(22/29)in endoscopic response with58.6%(17/29) in remission and66.6%(20/30)in histological remission. At 6months, clinical remission was51.6%(42/93), endoscopic response was 75%(24/32) of which43.75%(14/32)were in remission and histologic remission was seen in 40.8%(20/49).Durable secondary outcomes of clinical remission were seen in 71.4%(30/42), endoscopic improvement in 45.8%(11/24), and histologic remission in35%(7/20). Re-escalation to 45mg occurred in 35pts, of which48.6%(17/35)were re-escalated due to incomplete induction clinical response with all achieving response(11 in remission). The rest(18/35)were re-escalated for clinical relapse after initial remission where72.2%(13/18)were able to recapture remission. After re-escalation,54.3%(19/35)continued on 45mg daily. AEs were reported in12.7%with HZ in 7pts(2.1%). One pt aged53yrs had an episode of TIA after 1yr on UPA at 15mg, without pre-existing cardiovascular risk factors and UPA use is ongoing at the same dose. There were no reported episodes of worsening of pre-existing cardiovascular disease or DVT/PE. Conclusion UPA is durably effective and safe in a real-world clinical setting with re-escalation to 45mg capturing response in a majority of the pts. Ongoing recruitment will provide further evidence on long term effectiveness and safety. Conflict of interest: Ms. Huang, Katherine: No conflict of interest Chandnani, Arun: No conflict of interest Samaan, Sami: None Tan, Tingyi: I have no conflict of interests to declare. Ramesh, Prajith Raj: None Conlon, Caroline: No conflict of interest Klein, Jeremy: None Reddy, Nikhil: No conflict of interest Ayoub, Malek: None Cervantes Limon, Gabriela: none Myers, Melissa: None Bucaram, Martha: No conflict of interest Lieto, Stephen: No conflict of interest Scalzo, Nicholas: No conflict of interest Cohen, Benjamin: Abbvie - consulting and speaking ALPCO - consulting J & J Innovative Medicine - consulting Takeda - consulting and speaking Emmes Biopharma Services LLC - DSMB Pfizer - consulting Mohamed, Islam: nothing to declare Ciorba, Matthew: Advisory Board: Janssen, AbbVie, Pfizer, Geneoscopy Consulting: Janssen, AbbVie, Pfizer, Geneoscopy Grant contracts: Janssen, Pfizer, Incyte Shukla, Richa: Speakers bureau - Abbvie and Lilly Wright, Michael: None Xu, An Tao: No conflict of interest Chowla, Navreet: No conflict of interest Ogholikhan, Sina: Abbvie Medical Director Kujawski, Michelle: Abbvie Scientific Director Dulaney, David: Speaker bureau - Regeneron Speaker bureau - Sanofi Speaker bureau - Takeda Speaker bureau - Eli Lilly Fenster, Marc: none Benson, Caroline: No conflict of interest Alhobayb, Tamara: No conflict of interest Berinstein, Jeffrey: No conflict of interest Shivashankar, Raina: Abbvie and BMS - speaker bureau Janssen and Celltrion - grant for IBD fellowship funding Pfizer - consultant Bishu, Shrinivas: None Patel, Anish: speaker: abbvie, J & J, Lilly, BMS, Pfizer, Phathom, Takeda consultant: abbvie Ungaro, Ryan: Personal Fees: AbbVie, Bristol Myers Squibb, Genentech, Lilly, Pfizer, Janssen, Takeda Johnson, Amanda: Research grant: Abbvie, Spyre Therapeutics Pekow, Joel R.: CVS Health - Consulting Abbvie, Eli Lilly, Pfizer, Johnson and Johnson - Stocks Yarur, Andres: Personal Fees: Consultant for Takeda, Pfizer, Roche, Merck, Abbvie, Eli Lilly. Bristol Myers Squibb, Celltrion, Johnson and Johnson. Deepak, Parakkal: Parakkal Deepak has received research support under a sponsored research agreement unrelated to the data in the abstract from AbbVie, Johnson and Johnson, Sanofi, Merck, Teva, Direct Biologics, Tr1x, Boehringer Ingelheim, Bristol Myers Squibb, Pfizer, Prometheus Biosciences, Takeda Pharmaceuticals, Roche Genentech, Eli Lilly, AstraZeneca, Spyre and Agomab, has received consulting fees from Johnson and Johnson, Abbvie, Merck, Sobi, Celltrion, Fresenius Kabi, Asahi Kasei Pharma, Sandoz and CorEvitas, LLC and has served on the board of the Srategic Alliance for Intercultural Advocacy in GI.

Abstract Background Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is characterized by immune dysregulation1,2. Blood-based biomarkers in diagnosing patients with IBD are lacking. We tested the hypothesis that circulating immune cell types and immune repertoire could distinguish patients with IBD from controls. Methods We performed deep immunophenotyping with scRNA-seq with T-cell receptor (TCR) and B-cell receptor (BCR) sequencing on peripheral blood mononuclear cells (PBMCs) from patients with IBD and healthy controls (87 UC, 127 CD, 35 controls). We applied machine learning to quantify cell abundance (miloR3), construct diagnostic classifiers (immuneML4), and infer cell-cell ligand-receptor interactions (MultiNicheNet5) between patients with IBD and controls. Results A scRNA-seq atlas of over 1.1 million PBMCs consisting of 53 distinct cell types was generated (Fig1A). In classifying UC vs controls, support vector machine model had a balanced accuracy of 0.90, consisting of increased IgA and IgG plasma B cells, switched memory B cells, TRegs, and decreased CD16 monocytes, cDC1, non-switched B cells, and MAIT (Fig.1B). In classifying CD vs controls, K-nearest neighbors model had a balanced accuracy of 0.87, consisting of increased IgA plasma B cells, non-switched B cells, CD4 Th22, TReg cytotoxic, CD4 Th1/Th17, and pDC and decreased CD16 monocytes, gdT, CD4 Th17, CD4 Th2, and MAIT cells.Fig.1C). Inflammation was associated with increased IgG plasma B in patients with both UC and CD. Using 4-mer amino acid motif of the BCR complementary-determining region 3 (CDR3) logistic regression model, immuneML had a balanced accuracy of 0.82 (0.75-0.92) in classifying CD vs controls (Fig.1D with models showing top 25 BCR predictive features). Using 4-mer amino acid motif of the BCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.90 (0.70-1.00) in classifying UC vs controls (Fig.1E with models showing top 25 BCR predictive features). Using 3-mer amino acid motif of the TCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.76 (0.72-081) in classifying CD vs UC. (Fig.1F with models showing top 25 TCR predictive features). In UC vs controls, MultiNicheNet demonstrated increased signaling in ADAM10-NOTCH2 between TRegs and naïve B cells and IL15-IL2RA between CD14 monocytes and CD4 Th17 cells. In CD vs controls, there was increased signaling in IL15-IL2RA between CD14 monocytes and CD4 Th17 cells and ADAM10-TSPAN5 between CD14 monocytes and TRegs (Fig.1G). Conclusion Applying machine learning models to scRNA-seq and immune repertoire datasets enable blood-based deep immunophenotyping with diagnostic potential and reveals novel insights into cell signaling in IBD. References: 1.Kong L, Pokatayev V, Lefkovith A, Carter GT, Creasey EA, Krishna C, Subramanian S, Kochar B, Ashenberg O, Lau H, Ananthakrishnan AN, Graham DB, Deguine J, Xavier RJ. The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity. 2023 Feb 14;56(2):444-458.e5. doi: 10.1016/j.immuni.2023.01.002. Epub 2023 Jan 30. Erratum in: Immunity. 2023 Dec 12;56(12):2855. doi: 10.1016/j.immuni.2023.10.017. PMID: 36720220; PMCID: PMC9957882. 2.Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ, Regev A. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell. 2019 Jul 25;178(3):714-730.e22. doi: 10.1016/j.cell.2019.06.029. PMID: 31348891; PMCID: PMC6662628. 3.Dann E, Henderson NC, Teichmann SA, Morgan MD, Marioni JC. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol. 2022 Feb;40(2):245-253. doi: 10.1038/s41587-021-01033-z. Epub 2021 Sep 30. PMID: 34594043; PMCID: PMC7617075. 4.Pavlović M, Scheffer L, Motwani K, Kanduri C, Kompova R, Vazov N, Waagan K, Bernal FLM, Costa AA, Corrie B, Akbar R, Al Hajj GS, Balaban G, Brusko TM, Chernigovskaya M, Christley S, Cowell LG, Frank R, Grytten I, Gundersen S, Haff IH, Hovig E, Hsieh PH, Klambauer G, Kuijjer ML, Lund-Andersen C, Martini A, Minotto T, Pensar J, Rand K, Riccardi E, Robert PA, Rocha A, Slabodkin A, Snapkov I, Sollid LM, Titov D, Weber CR, Widrich M, Yaari G, Greiff V, Sandve GK. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat Mach Intell. 2021 Nov;3(11):936-944. doi: 10.1038/s42256-021-00413-z. Epub 2021 Nov 16. PMID: 37396030; PMCID: PMC10312379. 5.Browaeys, R., Gilis, J., Sang-Aram, C., De Bleser, P., Hoste, L., Tavernier, S., Lambrechts, D., Seurinck, R. and Saeys, Y., 2023. MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. BioRxiv, pp.2023-06. 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OBJECTIVE: Volatile anesthetics (VAs) are gaining renewed interest as a sedation strategy in the intensive care, offering an alternative to traditional IV agents. VAs provide several pharmacologic advantages, including rapid onset and offset, minimal systemic metabolism, and favorable recovery profiles. Advances in delivery systems enabled the safe and practical administration of volatile agents in the ICU. Thus, we aimed to describe the pharmacology and safety aspects of inhaled agents as well as the systems designed to deliver VAs in the ICU. DATA SOURCES: Relevant literature was identified through PubMed and MEDLINE databases. STUDY SELECTION: Original research, review articles, commentaries, and guidelines addressing safety, efficacy, and use of VAs in adult ICU patients were included. DATA EXTRACTION: Studies were reviewed by the authors, with key findings summarized and organized by pharmacologic properties, delivery systems, and safety domains. DATA SYNTHESIS: VAs are halogenated hydrocarbons whose mechanism of action is not fully understood. Although the CNS is the primary site of action, the end-tidal concentration of exhaled anesthetic is used to monitor clinical effects such as immobility to a noxious stimulus. Inhaled agents have unique pharmacokinetics, minimal metabolisms, and distinct recovery. The side effect profile is also unique, with malignant hyperthermia being the most feared, yet rare complication. Two systems for inhalational sedation delivery are available internationally, with one currently under evaluation in the United States. The systems are composed of a miniature vaporizer, delivery controller, and a monitor. The systems have distinct safety considerations, such as tidal volume limits. CONCLUSIONS: VAs can be used as sedative agents in the ICU. This article comprehensively reviews the pharmacology of VAs along with their safety profile and describes the structure and function of miniature vaporizers currently available on the world market.

Abstract Objective To develop a comprehensive risk stratification model to identify patients at high risk of cesarean surgical site infection (cSSI) based on information available at the time of hospital discharge. Methods This is a retrospective cohort study based on datasets from two independent level IV academic medical centers to improve generalizability. The first study includes 2336 cesarean deliveries for analysis with 109 instances of cSSI (4.7%). The second study includes 2936 cesarean deliveries with 72 instances of cSSI (2.5%). The first study cohort was utilized for both model training and internal validation, using the 15 shared features present in both datasets. Random search with cross‐validation was used to choose the type of machine learning (ML) model and its hyperparameters, with area under the receiver operating characteristic curve (AUC) as the validation metric. The second study cohort was then used to evaluate testing accuracy (external validation) and generalization capability of this model. The predictive importance of each feature was evaluated using permutation importance applied to the second cohort. Risk stratification cutoff scores were defined based on centiles over the first patient cohort and evaluated on the second cohort. Results The ML model with the highest validation AUC was an XGBoost classifier. This trained model achieved an AUC of 0.78 on the training data and a 0.72 cross‐validated AUC. When tested on data from the second study cohort, it achieved an AUC of 0.69. The features showing the strongest predictive generalization, as measured by permutation importance, were length of hospital stay, primary indication for cesarean delivery, and estimated blood loss. Using the second cohort to test our model, the model shows a 22.7% positive predictive value (PPV) of cSSI among women in the highest risk group, and a 0.8% PPV among women in the bottom risk group, compared with 2.5% cSSI incidence for the study population at large. Conclusion We developed and validated a predictive ML model for cSSI with information available to healthcare professionals at the time of hospital discharge. This model suggests that the most predictive risk factors for cSSI include length of hospital stay, primary indication for cesarean delivery, and estimated blood loss. Additionally, the model provides reasonable risk stratifications for individual patients. This tool may be used for identifying subgroups at higher risk of cSSI and identifying those who may require closer surveillance or clinical intervention.

Adults with congenital heart disease (CHD) constitute a growing population with challenges related to cardiovascular and neurological health. Many individuals born with CHD experience neurodevelopmental delays in childhood. Ongoing neurocognitive and psychological concerns impact education, employment, and quality of life. Despite this, interventions to address these issues are largely unexplored. Collaborations among multidisciplinary experts and patient partners culminated at a 2‐day Adult Congenital Heart Disease (ACHD)/Neuro Conference (April 2024) to review the current state of knowledge and guide future directions for research and clinical care. Three working groups separately focused on genetics and brain health, characterizing outcomes, and interventions among patients with ACHD. This article focuses on the work of the interventions group. A consensus statement was created to synthesize current knowledge, identify key gaps, and recommend next steps around 5 critical questions designed to guide research and clinical care for interventions with ACHD: (1) What interventions are effective for improving neurocognitive functioning? (2) What interventions are effective for improving psychological wellbeing? (3) What evidence do we have about safety and efficacy of psychotropic medications? (4) What interventions can improve health literacy? (5) What are modifiable facilitators and barriers to accessing high‐quality neurocognitive and psychological interventions? Limited research and fragmented health systems hinder interventions in ACHD. Efforts from pediatric congenital heart disease could inform the ACHD community. Although interventions are costly and resource intensive, neglecting neurocognitive and psychological needs could lead to lower quality of life and higher health care costs. By ultimately shifting to prevention and intervention, the ACHD community could facilitate positive outcomes.

Individuals with congenital heart disease are living well into adulthood, resulting in a growing population at risk for neurocognitive and psychosocial challenges. Numerous studies have described these risks in childhood, but significant gaps remain in our understanding of how those challenges evolve across stages of adulthood and how to optimally integrate neurocognitive and psychosocial care for adults with congenital heart disease. A multidisciplinary group of experts and patient partners assembled in April 2024 in Atlanta, GA, forming 3 working groups: (1) Genetics and Brain Health, (2) Characterizing Neuropsychological and Psychological Outcomes, and (3) Neuropsychological and Psychosocial Interventions. Current knowledge was reviewed, knowledge gaps were identified, and recommendations to address these gaps were formulated. Five critical questions regarding the characterization of neuropsychological and psychological outcomes of adults with congenital heart disease were prioritized for discussion: which neurocognitive and psychosocial domains should be prioritized in research and clinical care, what are the trajectories of neurocognitive and psychosocial functioning over the lifespan, how should neurocognitive and psychosocial screening be deployed, what should neurocognitive and psychosocial assessments include, and how do we make comprehensive neurocognitive and psychosocial care a reality. Although many gaps in knowledge were identified, particularly around characterizing neurocognitive trajectories throughout adulthood, there is substantial evidence of increased risk for poor psychosocial outcomes among adults with with congenital heart disease. Clinical and research advances must progress in tandem to address these risks. Specific recommendations for advancing this field over the next decade are offered.

With improvement in medical and surgical care, the number of adults with congenital heart disease (CHD) is soaring. Adults with CHD commonly have impairments in brain health. However, significant gaps in knowledge remain regarding the relevant types and prevalence of neurologic and psychiatric risk and their associated risk factors. We sought to review current evidence, identify gaps in knowledge, and develop key next steps to improve scientific understanding and clinical care. Three working groups—Genetics and Brain Health, Characterizing Neuropsychological and Psychological Outcomes, and Neuropsychological and Psychosocial Interventions—were composed of multidisciplinary experts in relevant clinical and research domains, as well as adults with CHD. Each group identified 5 key knowledge gaps and associated next investigations needed to address those gaps. For Genetics and Brain Health, 5 key knowledge gaps were identified: lack of a standardized neuroimaging protocol for adults with CHD, need to understand neuroradiological–pathological–neuropsychological correlates, role of gene–environment interactions, what can be learned from brain health risk models from other groups, and how existing multimodal approaches influence risk and neuroresilience. Adults with CHD can benefit from routine assessment of brain health, as well as increased clinical and basic research into the underlying factors that contribute to risk and neuroresilience for neurologic and psychiatric sequalae. Multidisciplinary collaborative efforts that incorporate adults with CHD across the research cycle are essential for all key next steps.