Carter, Rickey E

AbstractIn the absence of effective countermeasures, human convalescent plasma has been widely used to treat severe acute respiratory syndrome coronavirus 2, the causative agent of novel coronavirus disease 19 (COVID‐19), including among patients with innate or acquired immunosuppression. However, the association between COVID‐19‐associated mortality in patients with immunosuppression and therapeutic use of convalescent plasma is unknown. We review 75 reports, including one large matched‐control registry study of 143 COVID‐19 patients with hematological malignancies, and 51 case reports and 23 case series representing 238 COVID‐19 patients with immunosuppression. We review clinical features and treatment protocols of COVID‐19 patients with immunosuppression after treatment with human convalescent plasma. We also discuss the time course and clinical features of recovery. The available data from case reports and case series provide evidence suggesting a mortality benefit and rapid clinical improvement in patients with several forms of immunosuppression following COVID‐19 convalescent plasma transfusion. The utility of convalescent plasma or other forms of antibody therapy in immune‐deficient and immune‐suppressed patients with COVID‐19 warrants further investigation.

Background Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

Abstract Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50). Conclusion An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

Aims To investigate the prognostic value of diagnostic scores for heart failure (HF) with preserved ejection fraction (HFpEF). Methods and results Consecutive patients with HFpEF admitted for unequivocal decompensated HF treated with intravenous loop diuretics were evaluated ( n = 443; mean age 78 ± 12 years; 60% women). The HFA‐PEFF and H 2 FPEF scores were calculated for all patients with echocardiography data available within 1 year and the population was stratified according to HFA‐PEFF scores 2–4 ( n = 79), 5 ( n = 93), or 6 ( n = 271) and H 2 FPEF score probabilities <90% ( n = 80), 90–95% ( n = 61), and 96–100% ( n = 293). HF readmission rates (95% confidence intervals) increased from 28.9 (22.7–35.0) per 100 patient‐years in HFA‐PEFF 2–4 to 46.0 (38.5–53.5) in HFA‐PEFF 5 and 45.0 (40.1–49.8) in HFA‐PEFF 6. Similarly, HF readmission rates increased with increasing H 2 FPEF probability: <0.90 [31.8 (25.3–38.2) per 100 patient‐years], 0.90–0.95 [41.5 (32.9–50.1)], and 0.96–1.00 [45.9 (41.2–50.6]. Median survival was 65 months (36–89 months) in HFA‐PEFF score 2–4, 45 months (26–59 months) in HFA‐PEFF score 5, and 28 months (22–42 months) in HFA‐PEFF score 6 ( P < 0.001), while the hazard ratio (95% confidence interval) for all‐cause mortality was 1.16 (1.02–1.32) per 0.10 increase in H 2 FPEF probability. Conclusions Among patients hospitalized with HFpEF, higher HFpEF probability according to diagnostic scores is associated with increased risk of subsequent HF readmissions and all‐cause mortality.

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.