Citations

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.

Introduction: Conduction disturbance after transcatheter aortic valve replacement (TAVR) remains a common complication. Predictive tools to identify conduction disturbance requiring pacing are limited. Our main objective was to develop a deep learning model to predict the need for a permanent pacemaker. Hypothesis: An electrocardiogram (ECG) based deep learning algorithm can predict the need for permanent pacemaker post TAVR. Methods: We evaluated patients who had TAVR at Mayo Clinic between June 2010 and May 2021. Digital ECGs done on the TAVR date or within 24 hours post procedure were partitioned into training, validation, and testing datasets. We trained a deep learning algorithm on the training data and optimized hyperparameters against the validation data. All model performance metrics are based on the reserved test data that were estimated once model architecture was optimized. Need for permanent pacemaker, defined as pacemaker placement due to development of conduction disturbance post TAVR and at least 1% ventricular pacing on device interrogation done at least 2 weeks post pacemaker implantation, was the training target. Results: A total of 3021 patients were evaluated for inclusion and 2,407 cases were available for modeling (exclusions: prior pacemaker (n=419), unsuccessful TAVR (n=27) and no ECG within the pre-specified period (n=168)). Overall, the median age was 82 (Q1: 76, Q3: 86) and 41% were female. In the withheld test data, 15% had a permanent pacemaker with at least 1% ventricular pacing. The algorithm detected the need for pacemaker in the test set with an AUC of 0.83 (95% CI: 0.73 to 0.92). Accuracy, sensitivity, specificity, positive and negative predictive were 80%, 72%, 81%, 41% and 94%, respectively. Conclusions: This study demonstrates the ability of a deep learning model based on ECG data to assist in making the decision to implant a pacemaker following TAVR with a high negative predictive value. Larger validation studies are warranted.

Introduction: Obesity is common in heart failure with preserved ejection fraction (HFpEF) and is directly linked with lower levels of physical activity (PA). Visceral adiposity (VAT) is more common in HFpEF than controls and may play a role in its pathogenesis, characterized by impaired exercise capacity (peak VO 2 ). The direct relationship between VAT and PA in HFpEF is unknown. Hypothesis: Compared to controls, HFpEF patients will have excess regional adiposity (VAT, subcutaneous (SAT), and intramuscular (IMAT)), and these fat depots will be associated with lower levels of chronic physical activity. Methods: HFpEF patients (n=51; age 70±9) and non-HFpEF controls (n=49; age 65±5) prospectively underwent body composition measurement by dual-energy x-ray absorptiometry and magnetic resonance imaging. Peak VO 2 was measured by standard cardiopulmonary exercise testing. PA was monitored continuously via accelerometry over two weeks. Data were binned in arbitrary accelerometry units, averaged and stored in 30-second epochs (counts per epoch, CPE). Models were adjusted for age, sex, and ideal body weight. Results: HFpEF were less active overall (3.15±0.80 vs 3.59±0.73 CPE), had less moderate intensity PA (10.13±0.04 vs 10.15±0.03 CPE), and less morning (3.83±1.15 vs 4.24±1.09 CPE) and afternoon (3.84± 1.20 vs 4.53± 0.97 CPE) (p<0.05 for all) PA. Patients with HFpEF spent a greater proportion of time sedentary (0.71±0.08 vs 0.67±0.08 CPE) and less time in moderate (0.04±0.03 vs 0.06±0.03 CPE) and vigorous (0.02±0.01 vs 0.03±0.03 CPE) PA (p<0.01 for all). Chronic PA was significantly associated with regional fat depots (VAT β= -0.003, 95% CI: -0.004-0.001; SAT β= -0.003, 95%CI: -0.004-0.001; IMAT β=-0.02, 95% CI: -0.04—0.01) as a combined cohort (p<0.01 for all). Muscle quality (β=0.28, 95%CI: 0.15-0.40), peak VO 2 (β=0.08, 95% CI: 0.03-0.13), and peak watts (β=0.01, 95% CI: -0.004-0.02) were associated with PA in HFpEF only (p≤0.01 for all). Conclusions: The amount and pattern of PA differs between HFpEF and controls, as do relationships between regional fat depots and PA, and the relationship of PA and peak VO 2 . Further investigation is needed to better understand the temporality and mechanisms related to the pathophysiology and severity of HFpEF.

AbstractThe efficacy of COVID‐19 convalescent plasma (CCP) as a treatment for hospitalized patients with COVID‐19 remains somewhat controversial; however, many studies have not evaluated CCP documented to have high neutralizing antibody titer by a highly accurate assay. To evaluate the correlation of the administration of CCP with titer determined by a live viral neutralization assay with 7‐ and 28‐day death rates during hospitalization, a total of 23 118 patients receiving a single unit of CCP were stratified into two groups: those receiving high titer CCP (>250 50% inhibitory dilution, ID50; n = 13 636) or low titer CCP (≤250 ID50; n = 9482). Multivariable Cox regression was performed to assess risk factors. Non‐intubated patients who were transfused with high titer CCP showed 1.1% and 1.7% absolute reductions in overall 7‐ and 28‐day death rates, respectively, compared to those non‐intubated patients receiving low titer CCP. No benefit of CCP was observed in intubated patients. The relative benefit of high titer CCP was confirmed in multivariable Cox regression. Administration of CCP with high titer antibody content determined by live viral neutralization assay to non‐intubated patients is associated with modest clinical efficacy. Although shown to be only of modest clinical benefit, CCP may play a role in the future should viral variants develop that are not neutralized by other available therapeutics.

AbstractBackgroundWhile COVID‐19 immunization programs attempted to reach targeted rates, cases rose significantly since the emergence of the delta variant. This retrospective cohort study describes the correlation between antispike antibodies and outcomes of hospitalized, breakthrough cases during the delta variant surge.MethodsAll patients with positive SARS‐CoV‐2 polymerase chain reaction hospitalized at Mayo Clinic Florida from 19 June 2021 to 11 November 2021 were considered for analysis. Cases were analyzed by vaccination status. Breakthrough cases were then analyzed by low and high antibody titers against SARS‐CoV‐2 spike protein, with a cut‐off value of ≥132 U/ml. Outcomes included hospital length of stay (LOS), need for intensive care unit (ICU), mechanical ventilation, and mortality. We used 1:1 nearest neighbor propensity score matching without replacement to assess for confounders.ResultsAmong 627 hospitalized patients with COVID‐19, vaccine breakthrough cases were older with more comorbidities compared to unvaccinated. After propensity score matching, the unvaccinated patients had higher mortality (27 [28.4%] vs. 12 [12.6%], p = 0.002) and LOS (7 [1.0–57.0] vs. 5 [1.0–31.0] days, p = 0.011). In breakthrough cases, low‐titer patients were more likely to be solid organ transplant recipients (16 [34.0%] vs. 9 [12.3%], p = 0.006), with higher need for ICU care (24 [51.1%] vs. 22 [11.0%], p = 0.034), longer hospital LOS (median 6 vs. 5 days, p = 0.013), and higher mortality (10 [21.3%] vs. 5 [6.8%], p = 0.025) than high‐titer patients.ConclusionsHospitalized breakthrough cases were more likely to have underlying risk factors than unvaccinated patients. Low‐spike antibody titers may serve as an indicator for poor prognosis in breakthrough cases admitted to the hospital.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman’s life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Abstract IntroductionLiver disease such as cirrhosis is known to cause changes in the composition of volatile organic compounds (VOC) present in patient breath samples. Previous studies have demonstrated the diagnosis of liver cirrhosis from these breath samples, but studies are limited to a handful of discrete, well-characterized compounds.MethodsWe utilized VOC profiles from breath samples from 46 individuals, 35 with cirrhosis and 11 healthy controls. A deep-neural network was optimized to discriminate between healthy controls and individuals with cirrhosis.ResultsA 1D convolutional neural network (CNN) was accurate in predicting which patients had cirrhosis with an AUC of 0.90 (95% CI: 0.75, 0.99). Shapley Additive Explanations characterized the presence of discrete, observable peaks which were implicated in prediction, and the top peaks (based on the average SHAP profiles on the test dataset) were noted.ConclusionCNNs demonstrate the ability to predict the presence of cirrhosis based on a full volatolomics profile of patient breath samples. SHAP values indicate the presence of discrete, detectable peaks in the VOC signal.

Abstract Background To describe response to therapy of small bowel (SB) Crohn’s disease (CD) at CT or MR enterography (CTE/MRE) in patients on vedolizumab. Methods Patients with SB CD who underwent CTE/MRE exams greater than 12 months apart on vedolizumab therapy were included. Length (in cm) and inflammation severity (EMBARK score) of inflamed SB segments were assessed. Changes in inflammation length of 3.4 cm or greater or inflammation severity of 2 EMBARK points or greater was categorized as response or progression, as appropriate, with development of newly inflamed segments, strictures, or penetrating complications also indicating progression. Patients not meeting the criteria for response or progression were categorized as having stable disease. Results Of 36 SB CD patients, the large majority had prior surgery (86%; 31), anti-TNF use (92%; 33), and internal penetrating (78%; 28) disease. Thirty-two patients had paired baseline and follow-up CTE/MRE exams without interval surgery, with clinical response observed in 24/32 (75%). Based on imaging response criteria, 22% (7/32; 95% CI: 9%–40%) had response, 50% (16/32; 95% CI: 32%–68%) were stable, and 28% (9/32; 95% CI: 14%–47%) had disease progression. Fifty-six percent of (18/32; 95% CI: 38%–74%) patients had clinical improvement with response or stable disease by imaging. Patients with stable disease had shorter median baseline lengths of SB inflammation (P = .012). Proportion of patients with colonic inflammation, perianal disease, or penetrating complications did not change. Conclusions Most patients on vedolizumab for over 12 months demonstrated response or stable SB disease when using objective cross-sectional radiologic imaging criteria using CTE/MRE.