Johnson, Patrick W.

Plasma neurofilament light is a reliable, uniform marker to estimate neuroaxonal injury and forecast all-cause mortality across major stroke types.

BACKGROUND: Subjective evaluations to confirm recovery from neuromuscular blockade with a peripheral nerve stimulator (PNS) is inadequate. Quantitative monitors are the only reliable method to confirm adequate recovery of neuromuscular function. Unfortunately, many clinicians are unfamiliar with such devices and there is concern that the introduction of objective monitoring would be exceedingly laborious and could cause workflow delays. This study investigates how long it takes experienced nurse anesthetists to apply various neuromuscular devices as well as their perception regarding the ease of application. METHODS: Twenty nurse anesthetists were consented and participated in an educational session that familiarized them with 3 devices: SunStim Plus PNS (SunMed, Grand Rapids, MI), the acceleromyography-based IntelliVue NMT device (Philips, Amsterdam, the Netherlands), and electromyography-based TetraGraph device (Senzime B.V., Uppsala, Sweden). Participants were timed while placing each monitor on patients in a real-world setting. For the quantitative devices (IntelliVue NMT and TetraGraph), participants were also timed when obtaining calibrated baseline train-of-four (TOF) ratios. Friedman test and pairwise Wilcoxon signed-rank tests were used to evaluate the difference in time to connect different devices. Participants were surveyed about how easy they found it to utilize these devices. RESULTS: After adjusting for multiple comparison, time to connect was significantly less for PNS (median, 29; range, 16–58 seconds) compared to either the TetraGraph device (median, 62.8; range, 32–101 seconds; P < .001) or the IntelliVue NMT device (median, 46; range: 28–90 seconds; P < .001). The difference in time to connect between the TetraGraph device and the IntelliVue NMT device was not statistically significant ( P = .053), but it took significantly less time to calibrate the TetraGraph device than the IntelliVue NMT device (median difference, −16; range, −88 to 49 seconds; P = .002). The participants found applying either the IntelliVue NMT device ( P = .042) or the TetraGraph device ( P = .048) more difficult than applying a PNS while finding it easier to calibrate the TetraGraph device versus the IntelliVue NMT device ( P < .001). CONCLUSIONS: It takes 19 seconds longer to apply a quantitative neuromuscular monitor (the IntelliVue NMT device) than a PNS. While this difference reached significance, this relatively minimal additional time represents an inappropriate barrier to the application of quantitative monitors. Regardless of which quantitative monitor was utilized, these nurse anesthetists found the application and utilization of such devices relatively straightforward.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

e17535 Background: Androgen Deprivation Therapy (ADT) is commonly used to treat prostate cancer (PC), but has been associated with cardiac morbidity and mortality. The exact mechanisms of this association are unclear. We sought to use an artificial intelligence (AI) enabled algorithm to identify ECG changes in PC patients who received ADT compared to PC patients who did not receive ADT. Methods: From 1,000,000 ECGs performed on 210,414 patients between 1993 and 2017 at our institution, a convolutional neural network was developed to detect predictive signatures for cardiac pathologies. During this process, the ability to predict “estimated sex” of the patient was developed, with output values ranging from 0 (female) to 1 (male). We applied this algorithm to 8,619 ECGs performed on 1,057 men age 75 or younger treated with radiation for high-risk or recurrent PC at our institution, and compared estimated sex after receiving ADT (n = 1,065) to ADT-naive ECGs (n = 7,554). We correlated ECG-identified estimated sex with serum testosterone levels using Spearman rank correlation. Results: Patients who had received ADT had a mean (SD) estimated sex value of 0.81 (0.26) compared to 0.92 (0.17) for those who did not (p < 0.001). Difference between estimated sex in post-ADT ECGs and ADT-naive ECGs remained significant across age groups (Table). Decreased serum total testosterone correlated with decreased estimated sex values in men receiving ADT (R = .57, p < 0.001). Conclusions: ADT for prostate cancer is associated with changes in AI-identified ECG parameters, including lower estimated male sex after receiving ADT. Lower ECG male sex estimate was associated with decreased serum testosterone. In this study, we provide preliminary proof of concept for a potential non-invasive means of monitoring treatment effect and physiologic change using ECGs. [Table: see text]

The timing and magnitude of sex differences in athletic performance during early human development, prior to adulthood, is unknown. Objective To compare swimming velocity of boys and girls for all Olympic‐length freestyle swimming events to determine the age of divergence in swimming performance. Methods We collected the all‐time top 100 U.S. freestyle swimming performance times of boys and girls age 5 to 18 years for the 50m to 1500m events. Results Swimming performance improved with increasing age for boys and girls ( p <0.001) until reaching a plateau, which initiated at a younger age for girls (15 years) than boys (17 years; sex×age; p <0.001). Prior to age 10, the top 5 swimming records for girls were 3% faster than the top boys ( p <0.001). For the 10 th –50 th places, however, there were no sex‐related differences in swimming performance prior to age 10 ( p =0.227). For both the top 5 and 10 th –50 th places, the sex difference in performance increased from age 10 (top 5, 2.5%; 10 th –50 th places, 1.0%) until age 17 (top 5, 7.6%; 10 th –50 th places, 8.0%). For all places, the sex difference in performance at age 18 was larger for sprint events (9.6%; 50–200m) than endurance events (7.1%; 400–1500m; p <0.001). Additionally, the sex‐related difference in performance increased across age and US ranking from 2.4% for 1 st place to 4.3% for 100 th place ( p <0.001), indicating less depth of performance in girls than boys. However, annual participation was ~20% higher in girls than boys for all ages ( p <0.001). Conclusion The top 5 girls demonstrated faster swimming velocities and the 10 th –50 th place girls demonstrated similar swimming velocities than boys (until ~10 years). After age 10, however, boys demonstrated increasingly faster swimming velocities than girls until 17 years. Collectively, these data suggest girls are faster, or at least not slower, than boys prior to the performance‐enhancing effects of puberty. Support or Funding Information This study was supported by the National Institutes of Health R‐35‐HL139854‐02 (MJJ), HL139854‐02S1 (AJC), 5T32DK007352‐40 (JWS), and the Mayo Foundation. Elite swimming performance. Mean swimming velocity from 5 to 18 years of the top 100 fastest US boys (blue circles) and girls (red triangles) for the 50, 100, 200, 400, 800 and 1500m freestyle swimming distances. Figure 1 Sex differences in performance of the top 5 places. The primary plot (heat map) displays the sex differences in swimming velocity (% boy’s swimming velocity) of the top 5 US rankings in each freestyle event distance and age, negative values (red) indicate faster performance of girls. The top displays the mean sex difference across age, and the right plot displays the mean sex difference across swimming event distance. Figure 2

Background: Palliative medicine physicians are challenged by lack of guidance regarding effectiveness and dosing of cannabis products in the setting of their emerging popularity. Objective: The aim of this study was to describe early patterns of tetrahydrocannabinol (THC) and cannabidiol (CBD) use in Florida following passage of the state’s first medical marijuana law. We describe here the perceived benefits, side effects, and beliefs expressed by patients in a single outpatient academic palliative medicine practice. Methods: A cross-sectional survey was performed of a sequential convenience sample of patients who presented to an outpatient academic palliative medicine clinic over a 3-month period. Results: In all, 24% (14/58) of respondents reported THC use, with half using THC on a daily basis. Patients reported improvements in pain, appetite, and nausea. In all, 71% (10/14) began using THC after the diagnosis of their chronic illness, and the most common form of usage was vaping. In all, 24% (14/58) of patients reported CBD use. Patients reported improvements in pain, and the most common form of usage was topical application. None of the patients had used CBD prior to the onset of their chronic illness. In all, 21% (3/14) of THC users and 21% (3/14) of CBD users thought that their substance was helping to cure their illness. Individual reported side effects in both groups were minimal. Conclusions: Approximately a quarter of outpatient palliative care patients use THC or CBD, often on a daily basis. Palliative care providers should be aware of the frequency, diverse usage, and beliefs behind cannabis product use in this patient population.