Carter, Rickey E.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Objectives Photon-counting detector (PCD) computed tomography (CT) offers improved spatial and contrast resolution, which can impact quantitative measurements. This work aims to determine in human subjects the effect of dual-source PCD-CT on the quantitation of coronary artery calcification (CAC) compared with dual-source energy-integrating detector (EID) CT in both 1- and 3-mm images. Methods This prospective study enrolled patients receiving a clinical EID-CT CAC examination to undergo a research PCD-CT CAC examination. Axial images were reconstructed with a 512 × 512 matrix, 200-mm field of view, 3-mm section thickness/1.5-mm interval using a quantitative kernel (Qr36). Sharper kernels (Qr56/QIR strength 4 for PCD and Qr49/ADMIRE strength 5 for EID) were used to reconstruct images with 1-mm section thickness/0.5-mm interval. Pooled analysis was performed for all calcifications with nonzero values, and volume and Agatston scores were compared between EID-CT and PCD-CT. A Wilcoxon signed-rank test was performed with P < 0.05 considered statistically significant. Results In 21 subjects (median age, 58 years; range, 50–75 years; 13 male [62%]) with a total of 42 calcified arteries detected at 3 mm and 46 calcified arteries at 1-mm images, EID-CT CAC volume and Agatston scores were significantly lower than those of PCD-CT (P ≤ 0.001). At 3-mm thickness, the mean (standard deviation) volume and Agatston score for EID-CT were 55.5 (63.4) mm3 and 63.8 (76.9), respectively, and 61.5 (69.4) mm3 and 70.4 (85.3) for PCD-CT (P = 0.0001 and P = 0.0013). At 1-mm thickness, the mean (standard deviation) volume and score for EID-CT were 50.0 (56.3) mm3 and 61.1 (69.3), respectively, and 59.5 (63.9) mm3 and 72.5 (79.9) for PCD-CT (P < 0.0001 for both). The applied radiation dose (volume CT dose index) for the PCD-CT scan was 2.1 ± 0.6 mGy, which was 13% lower than for the EID-CT scan (2.4 ± 0.7 mGy, P < 0.001). Conclusions Relative to EID-CT, PCD-CT demonstrated a small but significant increase in coronary artery calcium volume and Agatston score.

BACKGROUND: Sodium-glucose cotransporter-2 inhibitors reduce risk of hospitalization for heart failure in patients who have heart failure with preserved ejection fraction (HFpEF), but the hemodynamic mechanisms underlying these benefits remain unclear. This study sought to determine whether treatment with dapagliflozin affects pulmonary capillary wedge pressure (PCWP) at rest and during exercise in patients with HFpEF. METHODS: This was a single-center, double-blinded, randomized, placebo-controlled trial testing the effects of 10 mg of dapagliflozin once daily in patients with HFpEF. Patients with New York Heart Association class II or III heart failure, ejection fraction ≥50%, and elevated PCWP during exercise were recruited. Cardiac hemodynamics were measured at rest and during exercise using high-fidelity micromanometers at baseline and after 24 weeks of treatment. The primary end point was a change from baseline in rest and peak exercise PCWPs that incorporated both measurements, and was compared using a mixed-model likelihood ratio test. Key secondary end points included body weight and directly measured blood and plasma volumes. Expired gas analysis was performed evaluate oxygen transport in tandem with arterial lactate sampling. RESULTS: Among 38 patients completing baseline assessments (median age 68 years; 66% women; 71% obese), 37 completed the trial. Treatment with dapagliflozin resulted in reduction in the primary end point of change in PCWP at rest and during exercise at 24 weeks relative to treatment with placebo (likelihood ratio test for overall changes in PCWP; P <0.001), with lower PCWP at rest (estimated treatment difference [ETD], −3.5 mm Hg [95% CI, −6.6 to −0.4]; P =0.029) and maximal exercise (ETD, −5.7 mm Hg [95% CI, −10.8 to −0.7]; P =0.027). Body weight was reduced with dapagliflozin (ETD, −3.5 kg [95% CI, −5.9 to −1.1]; P =0.006), as was plasma volume (ETD, −285 mL [95% CI, −510 to −60]; P =0.014), but there was no significant effect on red blood cell volume. There were no differences in oxygen consumption at 20-W or peak exercise, but dapagliflozin decreased arterial lactate at 20 W (−0.70 ± 0.77 versus 0.37 ± 1.29 mM; P =0.006). CONCLUSIONS: In patients with HFpEF, treatment with dapagliflozin reduces resting and exercise PCWP, along with the favorable effects on plasma volume and body weight. These findings provide new insight into the hemodynamic mechanisms of benefit with sodium-glucose cotransporter-2 inhibitors in HFpEF. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04730947.

Background: We developed a deep learning algorithm to predict elevated coronary artery calcium (CAC) score from 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that this CAC-ECG algorithm will independently predict long-term survival. Methods: We leveraged a historical cohort of 43,210 consecutive patients who from the years 1997-2020 underwent clinically indicated ECG-gated unenhanced chest computed tomography (CT) to identify and quantify CAC and had an ECG within 1 year of the CT. Data on cardiovascular risk (CV) factors, and to calculate the Pooled Cohort Equation (PCE) for ASCVD was collected as part of preventive cardiology or general medical evaluations. We used the oldest CAC in record and excluded those taking statins at the time of the CAC. The algorithm was trained in 60% of the cohort, and the association between CAC-ECG and survival was evaluated with multivariate cox proportional hazard models in 40% of the remaining observations. Results: Of the 17,284 evaluated patients, mean ± SD age 55.9 ± 9.9, 33% female, 3,714 (21%) had elevated CAC ≥ 300. During an average of 15±5.9 years follow-up, 848 (5%) patients died. The algorithm’s area under the receiving operating characteristics curve (ROC), sensitivity, specificity, and accuracy to detect a CAC ≥ 300 were 0.83, 0.90%, 0.56%, 0.60%. Those with elevated CAC had a nearly two-fold risk of death, a value similar to those deemed positive by the ECG-CAC algorithm ( Figure A ). Risk of death increased with CAC-ECG probability quartiles ( Figure A ). The CAC-ECG algorithm enhanced predicted capabilities of the PCE across all ASCVD risk subgroups ( Figure B ), as well as in those with no CAC and elevated CAC ( Figure C ), all p for trend <0.001. Conclusions: A deep learning-enabled CAC-ECG algorithm was independently associated with long-term survival and enhanced current risk prediction paradigms. The CAC-ECG algorithm could help identify individuals at risk in primary prevention of CV disease.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.