Increasing and Evolving Role of Smart Devices in Modern Medicine

Today is an age of rapid digital integration, yet the capabilities of modern-day smartphones and smartwatches are underappreciated in daily clinical practice. Smartphones are ubiquitous, and smartwatches are very common and on the rise. This creates a wealth of information simply waiting to be accessed, studied and applied clinically, ranging from activity level to various heart rate metrics. This review considers commonly used devices, the validity and accuracy of the data they obtain and potential clinical application of the data

Prospective Observational Study on acute Appendicitis Worldwide (POSAW)

Acute appendicitis (AA) is the most common surgical disease, and appendectomy is the treatment of choice in the majority of cases. A correct diagnosis is key for decreasing the negative appendectomy rate. The management can become difficult in case of complicated appendicitis. The aim of this study is to describe the worldwide clinical and diagnostic work-up and management of AA in surgical departments.info:eu-repo/semantics/publishedVersio

An Unusual Cause of Coronary Occlusion During an Abdominal Aortic Aneurysm Repair: Reperfusion With Diagnostic Angiography Only

We present a unique case of a type I peri-operative myocardial infarction during an extensive abdominal aortic aneurysm repair occurring due to the occlusion of a severe stable ostial plaque stenosis by a small overlying thrombus. During coronary angiography, the thrombus was dislodged by the diagnostic catheter which restored normal flow without stent placement. We demonstrate a care approach that was carefully arrived upon through multidisciplinary management with vascular surgery and anesthesiology colleagues

Diagnostic Performance of High Sensitivity Cardiac Troponin T Strategies and Clinical Variables in a Multisite United States Cohort

Background: European data support the use of low high-sensitivity troponin (hs-cTn) measurements or a 0/1-hour (0/1-h) algorithm for myocardial infarction (MI) or to exclude major adverse cardiac events (MACE) among Emergency Department (ED) patients with possible acute coronary syndrome (ACS). However, modest US data exist to validate these strategies. This study evaluated the diagnostic performance of an initial hs-cTnT measure below the limit of quantification (LOQ: 6 ng/L), a 0/1-h algorithm, and their combination with HEART scores for excluding MACE in a multisite US cohort. Methods: A prospective cohort study was conducted at 8 US sites, enrolling adult ED patients with symptoms suggestive of ACS and without ST-elevation on electrocardiogram. Baseline and 1-hour blood samples were collected and hs-cTnT (Roche, Basel Switzerland) measured. Treating providers blinded to hs-cTnT results prospectively calculated HEART scores. MACE (cardiac death, MI, and coronary revascularization) at 30-days was adjudicated. The proportion of patients with initial hs-cTnT measures Results: Among 1,462 participants with initial hs-cTnT measures, 46.4% (678/1,462) were women and 37.1% (542/1,462) were African American with a mean age of 57.6 (SD±12.9) years. MACE at 30-days occurred in 14.4% (210/1,462). Initial hs-cTnT measures Conclusions: In a prospective multisite US cohort, an initial hs-cTnT 99% for 30-day MACE when used alone or with a HEART score

Performance of the European Society of Cardiology 0/1-Hour Algorithm With High-Sensitivity Cardiac Troponin T Among Patients With Known Coronary Artery Disease

IMPORTANCE: The European Society of Cardiology (ESC) 0/1-hour algorithm is a validated high-sensitivity cardiac troponin (hs-cTn) protocol for emergency department patients with possible acute coronary syndrome. However, limited data exist regarding its performance in patients with known coronary artery disease (CAD; prior myocardial infarction [MI], coronary revascularization, or ≥70% coronary stenosis). OBJECTIVE: To evaluate and compare the diagnostic performance of the ESC 0/1-hour algorithm for 30-day cardiac death or MI among patients with and without known CAD and determine if the algorithm could achieve the negative predictive value rule-out threshold of 99% or higher. DESIGN, SETTING, AND PARTICIPANTS: This was a preplanned subgroup analysis of the STOP-CP prospective multisite cohort study, which was conducted from January 25, 2017, through September 6, 2018, at 8 emergency departments in the US. Patients 21 years or older with symptoms suggestive of acute coronary syndrome without ST-segment elevation on initial electrocardiogram were included. Analysis took place between February and December 2022. INTERVENTIONS/EXPOSURES: Participants with 0- and 1-hour high-sensitivity cardiac troponin T (hs-cTnT) measures were stratified into rule-out, observation, and rule-in zones using the ESC 0/1-hour hs-cTnT algorithm. MAIN OUTCOMES AND MEASURES: Cardiac death or MI at 30 days determined by expert adjudicators. RESULTS: During the study period, 1430 patients were accrued. In the cohort, 775 individuals (54.2%) were male, 826 (57.8%) were White, and the mean (SD) age was 57.6 (12.8) years. At 30 days, cardiac death or MI occurred in 183 participants (12.8%). Known CAD was present in 449 (31.4%). Among patients with known CAD, the ESC 0/1-hour algorithm classified 178 of 449 (39.6%) into the rule-out zone compared with 648 of 981 (66.1%) without CAD (P \u3c .001). Among rule-out zone patients, 30-day cardiac death or MI occurred in 6 of 178 patients (3.4%) with known CAD and 7 of 648 (1.1%) without CAD (P \u3c .001). The negative predictive value for 30-day cardiac death or MI was 96.6% (95% CI, 92.8-98.8) among patients with known CAD and 98.9% (95% CI, 97.8-99.6) in patients without known CAD (P = .04). CONCLUSIONS AND RELEVANCE: Among patients with known CAD, the ESC 0/1-hour hs-cTnT algorithm was unable to safely exclude 30-day cardiac death or MI. This suggests that clinicians should be cautious if using the algorithm in patients with known CAD. The negative predictive value was significantly higher in patients without a history of CAD but remained less than 99%

Machine-learning based prediction of in-hospital death for patients with takotsubo syndrome: the InterTAK-ML model

Aims: Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine-learning (ML) based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results: A Ridge Logistic Regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). 31 clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the receiver-operating characteristic curve (AUC), Sensitivity and Specificity. As secondary endpoint, a K-Medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the ten most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), Sensitivity 0.85 (0.78-0.95) and Specificity 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs 15.5% vs 5.4% vs 0.8% vs 0.5%) which were consistent also in the external cohort. Conclusion: A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death. This article is protected by copyright. All rights reserved