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ImportanceCutaneous squamous cell carcinoma (CSCC) risk stratification is central to management, and physicians rely on tumor staging systems to estimate risk. The Brigham and Women’s Hospital (BWH) T staging system predicts risk based on 4 tumor risk factors (RFs). However, stage is not precisely associated with the number of RFs, as BWH stage T2b includes CSCCs with 2 and 3 RFs.ObjectiveTo determine how RF number is associated with the risk of recurrence, metastasis, and disease-related death.Design, Setting, and ParticipantsThis retrospective multination cohort study of CSCCs diagnosed between October 1, 1991, and July 19, 2023, was conducted at 12 centers in the US (10), Spain (1), and Brazil (1). Invasive CSCCs with confirmed negative margins longer than 14 days were included. Tumors were excluded if they were metastatic at presentation or received adjuvant therapy. Data were analyzed from October 2023 to August 2024.Interventions or ExposuresCSCCs were stratified by the number of the following RFs (0, 1, 2, 3, or 4): a diameter of 2 cm or larger, poorly differentiated histology, tumor extension beyond subcutaneous fat, and large caliber nerve invasion.Main Outcomes and MeasuresFive-year cumulative incidences of local recurrence, nodal metastasis, distant metastasis, and disease-specific death.ResultsA total of 16 844 CSCCs were included (5978 female individuals [35.5%]; median [IQR] age, 73.9 [65.7-81.8] years), with 0 (12 657 [75.1%]), 1 (2892 [17.2%]), 2 (1015 [6.0%]), 3 ( 225 [1.3%]) or 4 (55 [0.3%]) RFs. Median (IQ) follow up time was 33.6 (14.5-60.3) months. For local recurrence, the risk increased as the number of RF increased from 0 (1.7%; 95% CI, 1.5%-2.0%) to 1 (5.0%; 95% CI, 4.1%-5.9%) to 2 (8.8%; 95% CI, 7.0%-11.0%) to 3 (16.0%; 95% CI, 11.0%-22.0%) to 4 (33.0%; 95% CI, 19.0%-47.0%; P < .001 for between-group differences). This increase was also observed for nodal metastasis (0.6% [95% CI, 0.4%-0.7%] vs 3.6% [95% CI, 2.9%-4.4%] vs 11.0% [95% CI, 9.2%-13.0%] vs 20.0% [95% CI, 15.0%-26.0%] vs 28.0% [95% CI, 15.0%-42.0%], respectively; P < .001), distant metastasis (0.2% [95% CI, 0.1%-0.3%] vs 1.1% [95% CI, 0.7%-1.6%] vs 2.3% [95% CI, 1.4%-3.4%] vs 7.9% [95% CI, 4.6%-12.0%] vs 8.4% [95% CI, 2.6%-19.0%], respectively; P < .001), and disease-specific death (0.3% [95% CI, 0.2%-0.4%] vs 1.9% [95% CI, 1.4%-2.7%] vs 5.4% [95% CI, 4.0%-7.0%] vs 11.0% [95% CI, 6.7%-16.0%] vs 25% [95% CI, 12%-39%], respectively; P < .001). CSCCs with 3 RFs had higher cumulative incidences of local recurrence (1.6-fold), nodal metastasis (1.9-fold), distant metastasis (4.3-fold), and disease-specific death (1.9-fold) compared with those with 2 RFs.Conclusions and RelevanceThe results of this cohort study suggest that the number of RFs is an indicator of risk, and among BWH T2b tumors, those with 3 RFs represent a higher risk subset.

Migraine with aura(MwA) is associated with an increased risk of stroke and adverse vascular outcomes compared to those with migraine without aura (MwoA). AI-ECG prediction models developed at our institution can evaluate the probability of atrial fibrillation (AF) and estimate a patient’s age based on a normal sinus rhythm (NSR) ECG. Delta age, AI-ECG estimated age minus chronological age, indicates endothelial dysfunction and is associated with cardiovascular mortality. We aim to assess how AI-ECG prediction model outputs, specifically the AF probability and delta age, are associated with adverse vascular outcomes in patients with migraine. Adult patients diagnosed with MwA and MwoA from 2000-2020 with at least one digital, standard 12-lead ECG were identified. The first ECG that showed NSR is used as the index ECG. Patients with any adverse vascular outcomes before the index ECG were excluded. Adverse vascular outcomes used as endpoints include acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism, AF, and carotid artery dissection. A total of 31301 patients (13783 MwA, 17518 MwoA) were included in our analysis. The mean age at the time of the index ECG was 44.3 (14.5), and the average follow-up time was 71.6 (69.8) months. Based on the index ECG, the mean AF probability was significantly higher in MwA compared to MwoA (2.39 vs 2.06, p< 0.001), while the delta age was lower in MwA compared to MwoA (3.7 vs 4.2, p <.001). The prevalences of all vascular outcomes are summarized in Table 1. Multivariate Cox regression models showed that a higher AF prediction model output (every 10% increase), as well as a higher delta age (every 10-year increase), are associated with an increased risk of composite vascular outcomes (HR1.20, 95%CI 1.17-1.23, p<.001, and HR1.19, 95%CI 1.14-1.23, p < .001) [Table 2]. The Kaplan-Meier curve of adverse vascular outcome-free survival by AF prediction model output, using 1% as the cutoff (HR 1.49, 95% CI 1.41, 1.57, p<.001), and by delta age, using 2-year as the cutoff (HR 1.14, 95% CI 1.08, 1.21, p <. 001) are presented in Figure 1. Having aura, older age, male, coronary artery disease, congestive heart failure, diabetes, hypertension, and tobacco use were also associated with a higher risk of adverse vascular outcomes. Our results demonstrated the potential of using AI-ECG AF prediction model output and delta age to identify migraine patients at risk for adverse vascular outcomes.

Background: We previously developed deep-learning algorithms that identify coronary artery disease (CAD) risk based on (i) coronary artery calcium (CAC), (ii) obstructive CAD by angiography, and (iii) left ventricular akinesis in ≥1 segment by echocardiogram, using a 12-lead electrocardiogram (CAD ECG-AI). We tested the hypothesis that those with increased probability of CAD by CAD ECG-AI algorithms will have an increased risk of atherosclerotic cardiovascular disease (ASCVD) events and would refine the AHA Predicting Risk of Cardiovascular Disease Events (PREVENT TM ) equations’ predictive capabilities. Methods: We assessed a group of consecutive patients who sought primary care in Olmsted County, MN, between 1997 and 2003. Passive follow-up was conducted using the Rochester Epidemiology Project's record linkage system. Patients included met the same criteria of the original PREVENT TM equations. The probability output of each CAD ECG-AI algorithm was used to predict ASCVD (fatal and non-fatal myocardial infarction and ischemic stroke) and ASCVD-Plus [further including percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and all-cause mortality]. Events were validated in duplicate. Cox proportional hazard models adjusted for age, sex, and risk factors while modeling each CAD ECG-AI algorithm as an individual and additive predictor (see footnote). PREVENT TM risk categories stratified analysis to evaluate the effect of the CAD ECG-AI models on predicted ASCVD risk. Results: We included 21,193 patients, with mean ± SD age 51.6 ± 12.0, 54% women and 95% white. After 14.2±12.4 years follow-up, 1,351 (6.4%) developed ASCVD, and 2,808 (13.2%) had ASCVD-Plus. The risk of ASCVD and ASCVD-Plus increased with positivity for the CAD ECG-AI algorithms after adjustments for age, sex, and PREVENT TM equations factors, all p for trend <0.001 (data not shown). In additive analyses, the risk of ASCVD and ASCVD-Plus increased with increased CAD ECG-AI factors, all p for trend <0.001 ( Fig. A-B ). Furthermore, the CAD ECG-AI algorithm enhanced the predictive capabilities of PREVENT TM equations across most risk subgroups ( Fig. C-E ). Conclusions: The deep-learning CAD ECG-AI algorithms displayed an independent and additive association with long-term ASCVD and ASCVD-Plus events in the community and improved the PREVENT TM predicted ASCVD risk. The CAD ECG-AI algorithms could help identify individuals at risk in primary prevention of cardiovascular events.

Background: Our team recently developed an Artificial Intelligence model that enables the identification of increased left ventricular filling pressures using a single-lead electrocardiogram (AI-ECG) with excellent performance (AUC 0.91). However, its ability to predict incident heart failure (HF) in the community is yet to be evaluated. Hypothesis: We tested the hypothesis that the output from the AI-ECG will predict the long-term incidence of heart failure (HF) and HF with preserved ejection fraction (HFpEF), and would refine the HF predictive capabilities of the AHA Predicting Risk of Cardiovascular Disease EVENTs (PREVENT TM ) Equations. Methods: Leveraging the resources of the Rochester Epidemiology Project (REP) we analyzed data from patients who sought primary care in Olmsted County, MN, between 1997-2003 with passive follow-up until April 2024. We included those with a digital ECG at baseline and excluded those who were part of the original algorithm development or history of heart failure. The diagnosis of heart failure was ascertained by using ICD codes (ICD9-428,ICD10-I.50) and a word search for the terms related to heart failure. HFpEF was ascertained using the Ejection Fraction (EF) data closest to the heart failure diagnosis date, using EF more than 40% as the criterion. We tested the association between AI-ECG predicted probabilities and new-onset HF and HFpEF using Cox proportional hazard models. These models were also adjusted for cardiovascular risk factors and were stratified to evaluate the effect of the AI-ECG output on PREVENT TM -predicted HF at 10 years. Results: We included 20,276 subjects, mean age 55±14.03, 52% women, 92% non-Hispanic white. After 18.2±8.5 years of follow-up, 4,008 (19.76%) were diagnosed with HF and 3,812 (18.8%) HFpEF. Risks of HF and HFpEF significantly increased in patients with increased left ventricular filling pressures by AI-ECG, independent of risk factors, all p for trend <0.001, see Fig A-B , respectively. AI-ECG enhanced the prediction capabilities of the PREVENT TM score across all risk groups, see Fig-C. Conclusions: AI-ECG-Derived increased left ventricular filling pressure is associated with new-onset HF and HFpEF in the community and was associated with incidental heart failure at any PREVENT TM risk category. Those results suggest a potential role of AI ECG in predicting heart failure.

BACKGROUND Cutaneous lichen planus (LP) is a recalcitrant, difficult-to-treat, inflammatory skin disease characterized by pruritic, flat-topped, violaceous papules on the skin. Baricitinib is an oral Janus kinase (JAK) 1/2 inhibitor that interrupts the signaling pathway of IFN-γ, a cytokine implicated in the pathogenesis of LP. METHODS In this phase II trial, 12 patients with cutaneous LP received 2 mg daily baricitinib for 16 weeks, accompanied by in-depth spatial, single-cell, and bulk transcriptomic profiling of pre- and posttreatment samples. RESULTS An early and sustained clinical response was seen, with 83.3% of patients responsive at week 16. Our molecular data identified a unique, oligoclonal IFN-γ, CD8 + , and CXCL13 + cytotoxic T cell population in LP skin and demonstrated a rapid decrease in IFN signature within 2 weeks of treatment, most prominently in the basal layer of the epidermis. CONCLUSION This study demonstrates the efficacy and molecular mechanisms of JAK inhibition in LP. TRIAL REGISTRATION NCT05188521 FUNDING Eli Lilly, Appignani Benefactor Funds, 5P30AR075043, Mayo Clinic Clinical Trials Stimulus Funds.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89–0.98) and 0.88 (95% CI, 0.83–0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data‐derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84–0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87–0.94). Conclusions An artificial intelligence–enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult‐trained models offer good performance, improvements are seen when training pediatric‐specific models.

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P &lt; 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P &lt; 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.

Importance Office-based phototherapy is cost-effective for psoriasis but difficult to access. Home-based phototherapy is patient preferred but has limited clinical data, particularly in patients with darker skin. Objective To compare the effectiveness of home- vs office-based narrowband UV-B phototherapy for psoriasis. Design, Setting, and Participants The Light Treatment Effectiveness study was an investigator-initiated, pragmatic, open-label, parallel-group, multicenter, noninferiority randomized clinical trial embedded in routine care at 42 academic and private clinical dermatology practices in the US. Enrollment occurred from March 1, 2019, to December 4, 2023, with follow-up through June 2024. Participants were 12 years and older with plaque or guttate psoriasis who were candidates for home- and office-based phototherapy. Interventions Participants were randomized to receive a home narrowband UV-B machine with guided mode dosimetry or routine care with office-based narrowband UV-B for 12 weeks, followed by an additional 12-week observation period. Main Outcomes and Measures The coprimary effectiveness outcomes were Physician Global Assessment (PGA) dichotomized as clear/almost clear skin (score of ≤1) at the end of the intervention period and Dermatology Life Quality Index (DLQI) score of 5 or lower (no to small effect on quality of life) at week 12. Results Of 783 patients enrolled (mean [SD] age, 48.0 [15.5] years; 376 [48.0%] male), 393 received home-based phototherapy and 390 received office-based phototherapy, with 350 (44.7%) having skin phototype (SPT) I/II, 350 (44.7%) having SPT III/IV, and 83 (10.6%) having SPT V/VI. A total of 93 patients (11.9%) were receiving systemic treatment. At baseline, mean (SD) PGA was 2.7 (0.8) and DLQI was 12.2 (7.2). At week 12, 129 patients (32.8%) receiving home-based phototherapy and 100 patients (25.6%) receiving office-based phototherapy achieved clear/almost clear skin, and 206 (52.4%) and 131 (33.6%) achieved DLQI of 5 or lower, respectively. Home-based phototherapy was noninferior to office-based phototherapy for PGA and DLQI in the overall population and across all SPTs. Home-based phototherapy, compared to office-based phototherapy, was associated with better treatment adherence (202 patients [51.4%] vs 62 patients [15.9%]; P &amp;lt; .001), lower burden of indirect costs to patients, and more episodes of persistent erythema (466 of 7957 treatments [5.9%] vs 46 of 3934 treatments [1.2%]; P &amp;lt; .001). Both treatments were well tolerated with no discontinuations due to adverse events. Conclusions and Relevance In this randomized clinical trial, home-based phototherapy was as effective as office-based phototherapy for plaque or guttate psoriasis in everyday clinical practice and had less burden to patients. Trial Registration ClinicalTrials.gov Identifier: NCT03726489

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P &lt; .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.