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.

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.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Unnecessary ED visits and transfers to hand clinics raise treatment costs and patient burden at trauma centers. In the present COVID-19 pandemic, needless transfers can increase patients’ risk of viral exposure. Therefore, this review analyzes different aspects of the remote diagnosis and triage of traumatic hand injuries. The most common file was photography, with the most common devices being cell phone cameras. Treatment, triage, diagnosis, cost, and time outcomes were assessed, showing concordance between teleconsultation and face-to-face patient evaluations. We conclude that photography and video consultations are feasible surrogates for ED visits in patients with traumatic hand injuries. These technologies should be leveraged to decrease treatment costs and potentially decrease the time to definitive treatment after initial evaluation.

Importance Reduced heart rate during exercise is common and associated with impaired aerobic capacity in heart failure with preserved ejection fraction (HFpEF), but it remains unknown if restoring exertional heart rate through atrial pacing would be beneficial. Objective To determine if implanting and programming a pacemaker for rate-adaptive atrial pacing would improve exercise performance in patients with HFpEF and chronotropic incompetence. Design, Setting, and Participants Single-center, double-blind, randomized, crossover trial testing the effects of rate-adaptive atrial pacing in patients with symptomatic HFpEF and chronotropic incompetence at a tertiary referral center (Mayo Clinic) in Rochester, Minnesota. Patients were recruited between 2014 and 2022 with 16-week follow-up (last date of follow-up, May 9, 2022). Cardiac output during exercise was measured by the acetylene rebreathe technique. Interventions A total of 32 patients were recruited; of these, 29 underwent pacemaker implantation and were randomized to atrial rate responsive pacing or no pacing first for 4 weeks, followed by a 4-week washout period and then crossover for an additional 4 weeks. Main Outcomes and Measures The primary end point was oxygen consumption (V̇ o 2 ) at anaerobic threshold (V̇ o 2,AT ); secondary end points were peak V̇ o 2 , ventilatory efficiency (V̇ e /V̇ co 2 slope), patient-reported health status by the Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS), and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Results Of the 29 patients randomized, the mean age was 66 years (SD, 9.7) and 13 (45%) were women. In the absence of pacing, peak V̇ o 2 and V̇ o 2 at anaerobic threshold (V̇ o 2,AT ) were both correlated with peak exercise heart rate ( r = 0.46-0.51, P &amp;lt; .02 for both). Pacing increased heart rate during low-level and peak exercise (16/min [95% CI, 10 to 23], P &amp;lt; .001; 14/min [95% CI, 7 to 21], P &amp;lt; .001), but there was no significant change in V̇ o 2,AT (pacing off, 10.4 [SD, 2.9] mL/kg/min; pacing on, 10.7 [SD, 2.6] mL/kg/min; absolute difference, 0.3 [95% CI, −0.5 to 1.0] mL/kg/min; P = .46), peak V̇ o 2 , minute ventilation (V̇ e) /carbon dioxide production (V̇ co 2 ) slope, KCCQ-OSS, or NT-proBNP level. Despite the increase in heart rate, atrial pacing had no significant effect on cardiac output with exercise, owing to a decrease in stroke volume (−24 mL [95% CI, −43 to −5 mL]; P = .02). Adverse events judged to be related to the pacemaker device were observed in 6 of 29 participants (21%). Conclusions and Relevance In patients with HFpEF and chronotropic incompetence, implantation of a pacemaker to enhance exercise heart rate did not result in an improvement in exercise capacity and was associated with increased adverse events. Trial Registration ClinicalTrials.gov Identifier: NCT02145351

Abstract Aims The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines. Methods and results The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.