Internal and emergency medicine
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This prospective cohort enrolled all patients above 16 years of age presenting to the in the emergency department (ED) for a reported syncope was designed to test the accuracy of a point-of-care ultrasound (POCUS) integrated approach in risk stratification. The emergency physician responsible for the patient care was asked to classify the syncope risk after the initial clinical assessment and after performing POCUS evaluation. All risk group definitions were based on the 2018 European Society of Cardiology guidelines. ⋯ Positive and negative likelihood ratios were 1.73 (95% CI 0.87-3.44) and 0.84 (95% CI 0.62-1.12) for the clinical evaluation, and 5.93 (95% CI 2.83-12.5) and 0.63 (95% CI 0.45-0.9) for the POCUS-integrated evaluation. The POCUS-integrated approach would reduce the diagnostic error of the clinical evaluation by 4.5 cases/100 patients. This cohort study suggested that the integration of the clinical assessment with POCUS results in patients presenting to the ED for non-high-risk syncope may increase the accuracy of predicting the risk of SFSR outcomes and the usefulness of the clinical assessment alone.
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Multicenter Study
Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.
Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical methods remains unclear. We compared predictive models developed using machine learning (ML) methods to the Canadian Syncope Risk Score (CSRS), a risk-tool developed with logistic regression for predicting serious adverse events (SAE) after emergency department (ED) disposition for syncope. We used the prospective multicenter cohort data collected for CSRS development at 11 Canadian EDs over an 8-year period to develop four ML models to predict 30-day SAE (death, arrhythmias, MI, structural heart disease, pulmonary embolism, hemorrhage) after ED disposition. ⋯ The AUCs and calibration slopes for the ML models and CSRS were similar. Two ML models used fewer predictors than the CSRS but matched its performance. Overall, the ML models matched the CSRS in performance, with some models using fewer predictors.
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Many ST-segment elevation acute coronary syndrome (STEACS) patients fail to activate the Emergency Medical System (EMS), with possible dramatic consequences. Prior studies focusing on barriers to EMS activation included patients with any acute coronary syndrome (ACS) without representation of southern European populations. We aimed to investigate the barriers to EMS call for patients diagnosed for STEACS in Italy. ⋯ Patients who called a private doctor after symptoms onset did not call EMS more frequently than those who did not and 30% of the patients who did not call the EMS would still act in the same way if a new episode occurred. Previous history of cardiovascular disease was the only predictor of EMS call. Information campaigns are urgently needed to increase EMS activation in case of suspected STEACS and should be primary focused on patients without cardiovascular history, on the misperception that a private vehicle is faster than EMS activation, and on the fact that cardiac arrest occurs early and may be prevented by EMS activation.
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Randomized Controlled Trial
Modelling the risk of hospital admission of lab confirmed SARS-CoV-2-infected patients in primary care: a population-based study.
The objectives of this study are to develop a predictive model of hospital admission for COVID-19 to help in the activation of emergency services, early referrals from primary care, and the improvement of clinical decision-making in emergency room services. The method is the retrospective cohort study of 49,750 patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%, respectively). ⋯ Based on the risk score, five risk groups were derived with hospital admission ranging from 2.94 to 51.87%. In conclusion, we propose a classification system for people with COVID-19 with a higher risk of hospitalization, and indirectly with it a greater severity of the disease, easy to be completed both in primary care, as well as in emergency services and in hospital emergency room to help in clinical decision-making. Registration: ClinicalTrials.gov Identifier: NCT04463706.
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The 4S-AF scheme [Stroke risk, Symptom severity, Severity of atrial fibrillation (AF) burden, Substrate severity] was recently proposed to characterize AF patients. In this post hoc analysis we evaluated the agreement between the therapeutic strategy (rate or rhythm control, respectively), as suggested by the 4S-AF scheme, and the actual strategy followed in a patients cohort. Outcomes of interest were as follows: all-cause death, a composite of all-cause death/any thromboembolism/acute coronary syndrome, and a composite of all-cause death, any thrombotic/ischemic event, and major bleeding (net clinical outcome). ⋯ When 4S-AF indicated rhythm control, disagreement was associated with a higher risk of all-cause death (HR 7.59; 95% CI 1.65-35.01), and of the composite outcome (HR 2.69; 95% CI 1.19-6.06). The 4S-AF scheme is a useful tool to comprehensively evaluate AF patients and aid the decision-making process. Disagreement with the rhythm control suggestion of the 4S-AF scheme was associated with adverse clinical outcomes.