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This Viewpoint discusses the use of aortic valve intervention vs watchful waiting for patients with asymptomatic aortic stenosis.

ImportanceUnderstanding the relationship between patients’ clinical characteristics and outcomes is fundamental to medicine. When critically ill patients die after withdrawal of life-sustaining therapy (WLST), the inability to observe the potential for recovery with continued aggressive care could bias future clinical decisions and research.ObjectiveTo quantify the frequency with which experts consider patients who died after WLST following resuscitated cardiac arrest to have had recovery potential if life-sustaining therapy had been continued.Design, Setting, and ParticipantsThis prospective cohort study included comatose adult patients (aged ≥18 years) treated following resuscitation from cardiac arrest at a single academic medical center between January 1, 2010, and July 31, 2022. Patients with advanced directives limiting critical care or who experienced cardiac arrest of traumatic or neurologic etiology were excluded. An international cohort of experts in post-arrest care based on clinical experience and academic productivity was identified. Experts reviewed the cases between August 24, 2022, and February 11, 2024.ExposurePatients who died after WLST.Main Outcome and MeasuresThree or more experts independently estimated recovery potential for each patient had life-sustaining treatment been continued, using a 7-point numerical ordinal scale. In the primary analysis, which involved the patient cases with death after WLST, a 1% or greater estimated recovery potential was considered to be clinically meaningful. In secondary analyses, thresholds of 5% and 10% estimated recovery probability were explored.ResultsA total of 2391 patients (median [IQR] age, 59 [48-69] years; 1455 men [60.9%]) were included, of whom 714 (29.9%) survived to discharge. Cases of uncertain outcome (1431 patients [59.8%]) in which WLST preceded death were reviewed by 38 experts who rendered 4381 estimates of recovery potential. In 518 cases (36.2%; 95% CI, 33.7%-38.7%), all experts believed that recovery potential was less than 1% if life-sustaining therapies had been continued. In the remaining 913 cases (63.8%; 95% CI, 61.3%-66.3%), at least 1 expert believed that recovery potential was at least 1%. In 227 cases (15.9%; 95% CI, 14.0%-17.9%), all experts agreed that recovery potential was at least 1%, and in 686 cases (47.9%; 95% CI, 45.3%-50.6%), expert estimates differed at this threshold.Conclusions and RelevanceIn this cohort study of comatose patients resuscitated from cardiac arrest, most who died after WLST were considered by experts to have had recovery potential. These findings suggest that novel solutions to avoiding deaths based on biased prognostication or incomplete information are needed.

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.

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.

Background and Aims: Clinical hepatology research often faces limited data availability, underrepresentation of minority groups, and complex data-sharing regulations. Synthetic data—artificially generated patient records designed to mirror real-world distributions—offers a potential solution. We hypothesized that diffusion models, a state-of-the-art generative technique, could produce synthetic liver transplant waitlist data from the United Network for Organ Sharing database that maintains statistical fidelity, replicates clinical correlations and survival patterns, and ensures robust privacy protection. Approach and Results: Diffusion models were used to generate synthetic patient cohorts mirroring the United Network for Organ Sharing liver transplant waitlist database between the years 2019 and 2023. Statistical fidelity was assessed using maximum mean discrepancy (MMD) and Wasserstein distance, correlation analysis, and variable-level metrics. Clinical utility was evaluated by comparing transplant-free survival via Kaplan–Meier curves and the MELD score performance. Privacy was quantified using the Distance to Closest Record (DCR) and attribute disclosure risk assessments. The synthetic dataset was nearly indistinguishable from the original dataset (MMD=0.002, standardized Wasserstein distance <1.0), preserving clinically relevant correlations and survival patterns as evidenced by similar median survival times (110 vs. 101 days) and 5-year survival rates (22.2% vs. 22.8%). MELD-based 90-day mortality prediction was maintained (original AUC=0.839 vs. synthetic AUC=0.844). Privacy metrics indicated no identifiable patient matches, and mean DCR values ensured that synthetic individuals were not direct replicas of real patients. Conclusion: Artificial intelligence–generated synthetic data derived from diffusion models can faithfully replicate complex hepatology datasets, maintain key clinical signals, and ensure strong privacy safeguards. This approach can help address data scarcity, enhance model generalizability, foster multi-institutional collaboration, and accelerate progress in hepatology research.

Importance Semaglutide, a glucagonlike peptide-1 receptor agonist (GLP-1RA), has recently been implicated in cases of nonarteritic anterior ischemic optic neuropathy (NAION), raising safety concerns in the treatment of type 2 diabetes (T2D). Objective To investigate the potential association between semaglutide and NAION in the Observational Health Data Sciences and Informatics (OHDSI) network. Design, Setting, and Participants This was a retrospective study across 14 databases (6 administrative claims and 8 electronic health records). Included were adults with T2D taking semaglutide, other GLP-1RA (dulaglutide, exenatide), or non–GLP-1RA medications (empagliflozin, sitagliptin, glipizide) from December 1, 2017, to December 31, 2023. The incidence proportion and rate of NAION were calculated. Association between semaglutide and NAION was assessed using 2 approaches: an active-comparator cohort design comparing new users of semaglutide with those taking other GLP-1RAs and non–GLP-1RA drugs, and a self-controlled case-series (SCCS) analysis to compare individuals’ risks during exposure and nonexposure periods for each drug. The cohort design used propensity score–adjusted Cox proportional hazards models to estimate hazard ratios (HRs). The SCCS used conditional Poisson regression models to estimate incidence rate ratios (IRRs). Network-wide HR and IRR estimates were generated using a random-effects meta-analysis model. Exposures GLP-1RA and non–GLP-1RAs. Main Outcomes and Measures NAION under 2 alternative definitions based on diagnosis codes: one more inclusive and sensitive, the other more restrictive and specific. Results The study included 37.1 million individuals with T2D, including 810 390 new semaglutide users. Of the 43 620 new users of semaglutide in the Optum’s deidentified Clinformatics Data Mart Database, 24 473 (56%) were aged 50 to 69 years, and 26 699 (61%) were female. The incidence rate of NAION was 14.5 per 100 000 person-years among semaglutide users. The HR for NAION among new users of semaglutide was not different compared with that of the non–GLP-1RAs using the sensitive NAION definition—empagliflozin (HR, 1.44; 95% CI, 0.78-2.68; P = .12), sitagliptin (HR, 1.30; 95% CI, 0.56-3.01; P = .27), and glipizide (HR, 1.23; 95% CI, 0.66-2.28; P = .25). The risk was higher only compared with patients taking empagliflozin (HR, 2.27; 95% CI, 1.16-4.46; P = .02) using the specific definition. SCCS analysis of semaglutide exposure showed an increased risk of NAION (meta-analysis IRR, 1.32; 95% CI, 1.14-1.54; P &amp;lt; .001). Conclusions and Relevance Results of this study suggest a modest increase in the risk of NAION among individuals with T2D associated with semaglutide use, smaller than that previously reported, and warranting further investigation into the clinical implications of this association.

Background: Recent biomechanical evidence for adjustable suture anchor (ASA)–based posterior medial meniscus root (PMMR) fixation has shown promising results compared with conventional transtibial pull-out repair (TPOR). However, ASA fixation has not been evaluated in human tissue to 100,000 cycles. Hypothesis: ASA repair would lead to increased primary fixation strength and less cyclic displacement than conventional TPORs. Study Design: Controlled laboratory study. Methods: A total of 32 human medial menisci were used, 8 of which were intact specimens and served as native controls. For the others, PMMR tears were created and repaired using 3 different techniques (n = 8 group). Two conventional PMMR repairs were prepared consisting of two No. 2 simple sutures (TSS) and two No. 2 sutures in a Mason-Allen (MA) configuration, all tied over a cortical button. The knotless ASA repair was fixed in MA with repair sutures tensioned at 120 N (MA-120). The repairs’ initial force, stiffness, and relief displacement from the tensioned state toward repair unloading (2 N) were measured after fixation. All repair constructs were loaded for 100,000 cycles, with displacement and stiffness measured, and finally were pulled to failure. Results: The TPORs demonstrated similar primary fixation and cyclic loading behavior except for initial cyclic displacement (cycle 10). The ASA repair provided a higher initial repair load ( P < .001) and stiffness ( P < .001) with relief displacement similar to conventional TPORs. Lower initial cyclic displacement ( P < .011; cycle 10) with overall higher repair stiffness ( P < .011) resulted in significantly lower displacement ( P < .001) throughout testing for ASA repair. Although both TPORs were completely loose after 100,000 cycles, the ASA repair achieved near-native dynamic meniscal stabilization. The TSS repair had lower overall ultimate load ( P < .001) and ultimate stiffness ( P < .023) compared with the ASA repair. All repairs had lower ultimate stiffness and loads than the native meniscus ( P < .001). Conclusion: The ASA repair resulted in improved primary PMMR fixation that was stiffer with less cyclic displacement than conventional TPORs and approached that of the human meniscal function after 100,000 load cycles in a cadaveric model. However, all repair techniques had lower ultimate strength than the native human PMMR. Clinical Relevance: Knotless ASA meniscus root fixation resulted in higher tissue compression and less displacement in a cadaveric model; however, future clinical series with surveillance imaging will define the overall significance of healing rates.