Citations

Abstract Aims We aimed to clarify the extent to which cardiac and peripheral impairments to oxygen delivery and utilization contribute to exercise intolerance and risk for adverse events, and how this relates to diversity and multiplicity in pathophysiologic traits. Methods and results Individuals with heart failure with preserved ejection fraction (HFpEF) and non-cardiac dyspnoea (controls) underwent invasive cardiopulmonary exercise testing and clinical follow-up. Haemodynamics and oxygen transport responses were compared. HFpEF patients were then categorized a priori into previously-proposed, non-exclusive descriptive clinical trait phenogroups, including cardiometabolic, pulmonary vascular disease, left atrial myopathy, and vascular stiffening phenogroups based on clinical and haemodynamic profiles to contrast pathophysiology and clinical risk. Overall, patients with HFpEF (n = 643) had impaired cardiac output reserve with exercise (2.3 vs. 2.8 L/min, p = 0.025) and greater reliance on peripheral oxygen extraction augmentation (4.5 vs. 3.8 ml/dl, p < 0.001) compared to dyspnoeic controls (n = 219). Most (94%) patients with HFpEF met criteria for at least one clinical phenogroup, and 67% fulfilled criteria for multiple overlapping phenogroups. There was greater impairment in peripheral limitations in the cardiometabolic group and greater cardiac output limitations and higher pulmonary vascular resistance during exertion in the other phenogroups. Increasing trait multiplicity within a given patient was associated with worse exercise haemodynamics, poorer exercise capacity, lower cardiac output reserve, and greater risk for heart failure hospitalization or death (hazard ratio 1.74, 95% confidence interval 1.08–2.79 for 0–1 vs. ≥2 phenogroup traits present). Conclusions Though cardiac output response to exercise is limited in patients with HFpEF compared to those with non-cardiac dyspnoea, the relative contributions of cardiac and peripheral limitations vary with differing numbers and types of clinical phenotypic traits present. Patients fulfilling criteria for greater multiplicity and diversity of HFpEF phenogroup traits have poorer exercise capacity, worsening haemodynamic perturbations, and greater risk for adverse outcome.

AbstractPurposeTo assess the accuracy of intraocular lens (IOL) power calculation in different age groups using various IOL calculation formulas.MethodsData from 421 eyes of 421 patients ≥60 years old (ages: 60–69, n = 131; 70–74, n = 105; 75–84, n = 158 and ≥85, n = 27), who underwent uneventful cataract surgery with SN60WF IOL implantation at John A. Moran Eye Center, Salt Lake City, USA, were retrospectively obtained. The SD of the prediction error (PE), median and mean absolute PEs and the percentage of eyes within ±0.25, ±0.50, ±0.75 and ±1.00 D were calculated after constant optimizations for the following formulas: Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Haigis, Hoffer Q, Hoffer QST, Holladay 1, Kane, Radial Basis Function (RBF) 3.0 and SRK/T. Results were compared between the different age groups.ResultsPredictability rates within 0.25D were lower for the eldest age group compared with the other groups using the EVO 2.0 (33% vs. 37%–53%, p = 0.045), Kane (26% vs. 35%–50%, p = 0.034) and SRK/T (22% vs. 31%–49%, p = 0.002). Higher median absolute refractive errors for all formulas were observed in the oldest group [range: 0.39 D (Haigis, Hoffer QSR)–0.48 D (Kane)], followed by the youngest group [range: 0.30 D (RBF 3.0)–0.39 D (Holladay 1, SRK/T)] but did not reach statistical significance. No significant differences between the groups in the distribution parameter were seen.ConclusionCurrent IOL power calculation formulas may have variable accuracy for different age groups. This should be taken into account when planning cataract surgery to improve refractive outcomes.

ObjectiveJuvenile localized scleroderma (jLS) is a chronic autoimmune disease commonly associated with poor outcomes, including contractures, hemiatrophy, uveitis, and seizures. Despite improvements in treatment, >25% of patients with jLS have functional impairment. To improve patient evaluation, our workgroup developed the Localized scleroderma Total Severity Scale (LoTSS), an overall disease severity measure.MethodsLoTSS was developed as a weighted measure by a consensus process involving literature review, surveys, case vignettes, and multicriteria decision analysis. Feasibility was assessed in larger Childhood Arthritis and Rheumatology Research Alliance groups. Construct validity with physician assessment and inter‐rater reliability was assessed using case vignettes. Additional evaluation was performed in a prospective patient cohort initiating treatment.ResultsLoTSS severity items were organized into modules that reflect jLS disease patterns, with modules for skin, extracutaneous, and craniofacial manifestations. Construct validity of LoTSS was supported by a strong positive correlation with the Physician Global Assessment (PGA) of severity and damage and weak positive correlation with PGA‐Activity, as expected. LoTSS was responsive, with a small effect size identified. Moderate‐to‐excellent inter‐rater reliability was demonstrated. LoTSS was able to discriminate between patient subsets, with higher scores identified in those with greater disease burden and functional limitation.ConclusionWe developed a new LS measure for assessing cutaneous and extracutaneous severity and have shown it to be reliable, valid, and responsive. LoTSS is the first measure that assesses and scores all the major extracutaneous manifestations in LS. Our findings suggest LoTSS could aid assessment and management of patients and facilitate outcome evaluation in treatment studies.image

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aβ and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aβ‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aβ+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aß and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aß‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aß+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.

Abstract Background Diagnosis of breast cancer in young women has been shown to affect their decision‐making with regard to fertility and family planning. Limited data are available from populations across the U.S. regarding this issue; thus, we sought to describe fertility concerns and efforts to preserve fertility in a national clinical trial population of young breast cancer patients. Methods The young and strong study was a cluster‐randomized controlled trial testing an intervention program for young women with breast cancer. Patients were surveyed within 3 months after diagnosis and at 3, 6, and 12 months after. Surveys asked about sociodemographics, psychosocial domains, fertility concerns, and fertility preservation strategies. Univariable and multivariable models were used to investigate sociodemographic, clinical, and psychosocial predictors of fertility concerns. Results Of 467 women from 54 clinical sites across the U.S. (14 academic, 40 community), 419 were evaluable regarding fertility concerns. Median age was 40 years (range 22–45), 11% were Black, 6% Hispanic, and 75% had children. Tumor stage was I (35%), II (51%), or III (14%); 82% received chemotherapy. At time of the treatment decision, 133 (32%) participants had fertility concerns, among whom 47% indicated this affected their treatment decisions. Sixty percent of participants reported having discussed fertility with their physician. Twenty percent of those with fertility concerns used fertility preservation strategies. History of difficulty becoming pregnant and younger age were associated with higher odds of fertility concerns in multivariable modeling. Conclusion Many young women with newly diagnosed breast cancer are concerned about fertility in a way that impacts their treatment decisions. Concerns were discussed, but few used fertility preservation strategies. These findings have implications for counseling young patients.

Importance Transoral robot-assisted surgery (TORS) continues to have a major role in the treatment of oropharyngeal cancer. As new iterations of robotic technology are increasingly utilized, it is important to share learning experiences and clinical outcomes data, to optimize technical efficiency and clinical care. Observations This was a retrospective review of a large academic institution’s initial clinical use of the da Vinci Single Port (SP) compared with the da Vinci Si (Si) system. A total of 205 TORS cases were reviewed: 109 in the SP group (November 22, 2018, through September 30, 2020), and 96 in the Si group (January 1, 2016, through November 12, 2018). Both groups had comparable operative times, rates of postoperative pharyngeal hemorrhage, length of hospital stay, and duration of nasogastric feeding tube use. There was no difference in pathological characteristics, rates of positive margins, or indications for or time to initiation of adjuvant therapy between the groups. The collective experience of 6 faculty members—who have trained 139 TORS surgeons for the SP system rollout—was compiled to provide a summary of learning experiences and technical notes on safe and efficient operation of the SP system. Conclusions and Relevance This Review found that the functional and oncologic outcomes were comparable between TORS cases performed with the Si and SP systems, and they had similar complication rates. Recognized advantages of the SP over the Si system include the availability of bipolar-energized instruments, a usable third surgical arm, and improved camera image quality.

Background: Our team has previously developed convolutional neural networks (CNNs) to estimate age and sex from a 10-second, 12-lead ECG as indicators of patient wellness. Here, we develop an additional wellness network to estimate body mass index (BMI) from the ECG input signal. Aims: To evaluate the performance of neural networks trained to classify and directly estimate BMI. Methods: We identified all adult patients in the Mayo Clinic system who had a 10-second, 12-lead ECG within 15 days of a BMI measurement. Of the&nbsp;174,863 patients in the cohort, 2,895 were underweight (BMI < 18.5), 42,387 were of normal weight (BMI 18.5-24.9), and 57,939 were overweight only (BMI 25.0-29.9). Among patients categorized as obese, 39,788 had class I obesity (BMI 30.0-34.9), 18,978 had class II obesity (BMI 35.0-39.9), and 12,876 had class III obesity (BMI ≥ 40.0). The cohort was split into training, validation, and testing datasets in an 8:1:1 ratio to develop networks for BMI classification and a direct&nbsp;estimation of BMI. Results: In detecting overweight in the testing set, the model achieved an AUC of 0.86 (95% CI 0.85, 0.87). In detecting class I, class II, and class III obesity, the model yielded AUCs of 0.84 (95% CI 0.83, 0.85), 0.84 (95% CI 0.83, 0.85), and 0.85 (95% CI 0.85, 0.86), respectively. The model to estimate BMI achieved a mean absolute error of 3.70. Conclusions: AI-enabled ECGs demonstrate efficient detection of overweight patients and all classes of obesity. Further research is needed to determine if discrepancies between the traditionally measured BMI and the AI-ECG BMI reflect the presence or lack of metabolic abnormalities.

Introduction: Artificial Intelligence-enabled electrocardiogram (AI-ECG) can estimate the probability of having atrial fibrillation (AFib) while in sinus rhythm. Recurrent stroke in subjects without known AFib may be due to undiagnosed AFib. We evaluated the association between AI-ECG AFib and recurrent ischemic stroke and with incident AFib Methods: Consecutive adults admitted to a Mayo Clinic or Mayo Clinic Health System hospital or to Olmsted Community Hospital for ischemic stroke between January,1,2000 and June,30,2022 were identified using the Rochester Epidemiology Project. Recurrent ischemic strokes were identified after reviewing medical records and imaging studies. We excluded those with known AFib or without ECGs within 3 years prior to the first ischemic stroke. The AI-ECG network was previously developed using 649,931 ECGs from > 180,000 subjects. The primary endpoint was recurrent ischemic stroke; secondary endpoint was incident AFib. Cox model adjusted for age, sex, and CHA2DS2-VASc2 score variables. Results: We included 4,816 subjects, mean age 70±15 yr, 54% male, 92% white, median CHA2DS2-VASc2 score 4 [3-5], median follow-up 3 [1-7] yr. 280 subjects (6%) had a second stroke and 366 (8%) were diagnosed with incident AFib over follow-up. Those with a positive AI-ECG AFib had an increased risk for recurrent ischemic stroke(HR 1.54, 95% CI 1.21-1.96, P<0.001 Figure A) This association remained significant after adjustment for CHA2DS2-VASc2 score(HR 1.37 95% CI 1.07-1.76, P=0.01). These associations were not affected by anticoagulation use over follow-up. AI-ECG AFib was also associated with incident AFib(HR 2.42 95% CI 1.96-2.98, P<0.001 Figure B) Conclusions: AI-ECG AFib predicts recurrent ischemic stroke in the community, possibly reflecting undiagnosed AFib that can be detected by AI-ECG, as it also predicts incident AFib. These results underscore the potential utility of AI-ECG for risk stratification of patients with ischemic stroke.