Carter, Rickey E.

To assess the diurnal patterns of postprandial glucose tolerance and insulin sensitivity, 19 subjects with type 2 diabetes (8 women; 60 ± 11 years; BMI 32 ± 5 kg/m2) and 19 anthropometrically matched subjects with no diabetes (ND; 11 women; 53 ± 12 years; BMI 29 ± 5 kg/m2) were studied during breakfast (B), lunch (L), and dinner (D) with identical mixed meals (75 g carbohydrates) on 3 consecutive days in a randomized Latin square design. Three stable isotopes of glucose were ustilized to estimate meal fluxes, and mathematical models were used in estimating indices of insulin action and β-cell function. Postmeal glucose excursions were higher at D versus B and at D versus L in type 2 diabetes (P < 0.05), while in ND they were higher at D versus B (P = 0.025) and at L versus B (P = 0.04). The insulin area under the curve was highest at B compared with L and D in type 2 diabetes, while no differences were observed in ND. Disposition index (DI) was higher at B than at L (P < 0.01) and at D (P < 0.001) in ND subjects, whereas DI was low with unchanging pattern across B-L-D in individuals with type 2 diabetes. Furthermore, between-meal differences in β-cell responsivity to glucose (F) and insulin sensitivity (SI) were concurrent with changes in the DI within groups. Fasting and postmeal glucose, insulin, and C-peptide concentrations, along with estimates of endogenous glucose production (EGP), Rd, SI, F, hepatic extraction of insulin, insulin secretion rate, extracted insulin, and DI, were altered in type 2 diabetes compared with ND (P < 0.011 for all). The data show a diurnal pattern of postprandial glucose tolerance in overweight otherwise glucose-tolerant ND individuals that differs from overweight individuals with type 2 diabetes. The results not only provide valuable insight into management strategies for better glycemic control in people with type 2 diabetes, but also improved understanding of daytime glucose metabolism in overweight individuals without impaired glucose tolerance or overt diabetes.

AbstractIn this short narrative, we highlight some of our experiences leading the US Convalescent Plasma Program at the beginning of the pandemic in the spring and summer of 2020. This includes a brief summary of how the program emerged and high‐level lessons we learned. We also share our impressions about why convalescent plasma was used at scale in the United States, early in the pandemic and share ideas that might inform the use of convalescent plasma in future outbreaks of novel infectious diseases.

Background: Transcatheter aortic valve replacement (TAVR) is now an approved alternative to surgical aortic valve replacement for the treatment of severe aortic stenosis. As the clinical adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to evaluate the impact of sex differences in aortic valve calcium score (AVCS) on long-term mortality following TAVR in a large patient sample. Methods: We included consecutive patients who successfully underwent TAVR for treatment of severe native aortic valve stenosis from June 2010 to May 2021 across all US Mayo Clinic sites with follow-up through July 2021. AVCS values were obtained from preoperative computed tomography of the chest. Additional clinical data were abstracted from medical records. Kaplan-Meier curves and Cox-proportional hazard regression models were employed to evaluate the effect of AVCS on long-term mortality. Results: A total of 2543 patients were evaluated in the final analysis. Forty-one percent were women, median age was 82 years (Q1: 76, Q3: 86), 18.4% received a permanent pacemaker following TAVR, and 88.5% received a balloon expandable valve. We demonstrate an increase in mortality risk with higher AVCS after multivariable adjustment ( P <0.001). When stratified by sex, every 500-unit increase in AVCS was associated with a 7% increase in mortality risk among women (adjusted hazard ratio, 1.07 [95% CI, 1.02–1.12]) but not in men. Conclusions: We demonstrate a notable sex difference in the association between AVCS and long-term mortality in a large TAVR patient sample. This study highlights the potential value of AVCS in preprocedural risk stratification, specifically among women undergoing TAVR. Additional studies are needed to validate this finding.

IMPORTANCE: Animal models and limited human studies have suggested a plausible role for platelets in the pathogenesis and resolution of acute respiratory distress syndrome (ARDS). However, there are little data regarding the role of platelets in ARDS development. OBJECTIVES: The objective of this study was to characterize the role of platelets in a postoperative ARDS model through an analysis of two platelet-specific biologic markers: thromboxane A2 (TxA2) and soluble CD-40-ligand (sCD40L). DESIGN, SETTING, AND PARTICIPANTS: This was a nested case-control study of ARDS cases matched to non-ARDS controls. Blood samples were collected from a cohort of 500 patients undergoing thoracic, aortic vascular, or cardiac surgery that placed them at high-risk of developing postoperative ARDS. MAIN OUTCOMES AND MEASURES: TxA2 and sCD40L were analyzed at baseline (prior to surgical incision) as well as 2 hours and 6 hours after the key intraoperative events believed to be associated with increased risk of postoperative ARDS. RESULTS: Of 500 patients enrolled, 20 ARDS cases were matched 1:2 to non-ARDS controls based on age, sex, surgical procedure, and surgical lung injury prediction score. Those who developed ARDS had longer surgeries, greater fluid administration, and higher peak inspiratory pressures. There were no significant differences in levels of TxA2 or sCD40L at baseline, at 2 hours, or at 6 hours. There was also no difference in the change in biomarker concentration between baseline and 2 hours or baseline and 6 hours. CONCLUSIONS: Two novel platelet-associated biologic markers (TxA2 and sCD40L) were not elevated in patients who developed ARDS in a postoperative ARDS model. Although limited by the relatively small study size, these results do not support a clear role for platelets in the early pathogenesis of postoperative ARDS.

Objective The aim of this study was to evaluate the clinical impact of a higher spatial resolution, full field-of-view investigational photon-counting detector computed tomography (PCD-CT) on radiologist confidence in imaging findings and diagnosis of usual interstitial pneumonia (UIP) compared with conventional energy-integrating detector CT (EID-CT). Materials and Methods Patients suspected of interstitial lung disease were scanned on a PCD-CT system after informed consent and a clinically indicated EID-CT. In 2 sessions, 3 thoracic radiologists blinded to clinical history and scanner type evaluated CT images of the right and left lungs separately on EID- or PCD-CT, reviewing each lung once/session, rating confidence in imaging findings of reticulation, traction bronchiectasis, honeycombing, ground-glass opacities (GGOs), mosaic pattern, and lower lobe predominance (100-point scale: 0–33, likely absent; 34–66, indeterminate; 67–100, likely present). Radiologists also rated confidence for the probability of UIP (0–20, normal; 21–40, inconsistent with UIP; 41–60, indeterminate UIP; 61–81; probable UIP; 81–100, definite UIP) and graded image quality. Because a confidence scale of 50 represented completely equivocal findings, magnitude score (the absolute value of confidence scores from 50) was used for analysis (higher scores were more confident). Image noise was measured for each modality. The magnitude score was compared using linear mixed effects regression. The consistency of findings and diagnosis between 2 scanners were evaluated using McNemar test and weighted κ statistics, respectively. Results A total of 30 patients (mean age, 68.8 ± 11.0 years; M:F = 18:12) underwent conventional EID-CT (median CTDIvol, 7.88 mGy) and research PCD-CT (median CTDIvol, 6.49 mGy). The magnitude scores in PCD-CT were significantly higher than EID-CT for imaging findings of reticulation (40.7 vs 38.3; P = 0.023), GGO (34.4 vs 31.7; P = 0.019), and mosaic pattern (38.6 vs 35.9; P = 0.013), but not for other imaging findings (P ≥ 0.130) or confidence in UIP (34.1 vs 22.2; P < 0.059). Magnitude score of probability of UIP in PCD-CT was significantly higher than EID-CT in one reader (26.0 vs 21.5; P = 0.009). Photon-counting detector CT demonstrated a decreased number of indeterminate GGO (17 vs 26), an increased number of unlikely GGO (74 vs 50), and an increased number of likely reticulations (140 vs 130) relative to EID-CT. Interobserver agreements among 3 readers for imaging findings and probability of UIP were similar between PCD-CT and EID-CT (intraclass coefficient: 0.507–0.818 vs 0.601–0.848). Photon-counting detector CT had higher scores in overall image quality (4.84 ± 0.38) than those in EID-CT (4.02 ± 0.40; P < 0.001) despite increased image noise (mean 85.5 vs 36.1 HU). Conclusions Photon-counting detector CT provided better image quality and improved the reader confidence for presence or absence of imaging findings of reticulation, GGO, and mosaic pattern with idiosyncratic improvement in confidence in UIP presence.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Abstract Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined &gt;8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P &lt; 0.001). The rate of increase prior to AF was higher for age &gt;50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.