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Abstract Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p

BackgroundVirtual patient simulators (VPSs) log all users’ actions, thereby enabling the creation of a multidimensional representation of students’ medical knowledge. This representation can be used to create metrics providing teachers with valuable learning information. ObjectiveThe aim of this study is to describe the metrics we developed to analyze the clinical diagnostic reasoning of medical students, provide examples of their application, and preliminarily validate these metrics on a class of undergraduate medical students. The metrics are computed from the data obtained through a novel VPS embedding natural language processing techniques. MethodsA total of 2 clinical case simulations (tests) were created to test our metrics. During each simulation, the students’ step-by-step actions were logged into the program database for offline analysis. The students’ performance was divided into seven dimensions: the identification of relevant information in the given clinical scenario, history taking, physical examination, medical test ordering, diagnostic hypothesis setting, binary analysis fulfillment, and final diagnosis setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic mean (F1), thereby obtaining a single score evaluating the students’ performance. The 7 metrics were further grouped to reflect the students’ capability to collect and to analyze information to obtain an overall performance score. A methodological score was computed based on the discordance between the diagnostic pathway followed by students and the reference one previously defined by the teacher. In total, 25 students attending the fifth year of the School of Medicine at Humanitas University underwent test 1, which simulated a patient with dyspnea. Test 2 dealt with abdominal pain and was attended by 36 students on a different day. For validation, we assessed the Spearman rank correlation between the performance on these scores and the score obtained by each student in the hematology curricular examination. ResultsThe mean overall scores were consistent between test 1 (mean 0.59, SD 0.05) and test 2 (mean 0.54, SD 0.12). For each student, the overall performance was achieved through a different contribution in collecting and analyzing information. Methodological scores highlighted discordances between the reference diagnostic pattern previously set by the teacher and the one pursued by the student. No significant correlation was found between the VPS scores and hematology examination scores. ConclusionsDifferent components of the students’ diagnostic process may be disentangled and quantified by appropriate metrics applied to students’ actions recorded while addressing a virtual case. Such an approach may help teachers provide students with individualized feedback aimed at filling competence drawbacks and methodological inconsistencies. There was no correlation between the hematology curricular examination score and any of the proposed scores as these scores address different aspects of students’ medical knowledge.

[This corrects the article DOI: 10.1371/journal.pone.0228725.].

Background and Objectives: Knowledge of the incidence and time frames of the adverse events of patients presenting syncope at the ED is essential for developing effective management strategies. The aim of the present study was to perform a meta-analysis of the incidence and time frames of adverse events of syncope patients. Materials and Methods: We combined individual patients’ data from prospective observational studies including adult patients who presented syncope at the ED. We assessed the pooled rate of adverse events at 24 h, 72 h, 7–10 days, 1 month and 1 year after ED evaluation. Results: We included nine studies that enrolled 12,269 patients. The mean age varied between 53 and 73 years, with 42% to 57% females. The pooled rate of adverse events was 5.1% (95% CI 3.4% to 7.7%) at 24 h, 7.0% (95% CI 4.9% to 9.9%) at 72 h, 8.4% (95% CI 6.2% to 11.3%) at 7–10 days, 10.3% (95% CI 7.8% to 13.3%) at 1 month and 21.3% (95% CI 15.8% to 28.0%) at 1 year. The pooled death rate was 0.2% (95% CI 0.1% to 0.5%) at 24 h, 0.3% (95% CI 0.1% to 0.7%) at 72 h, 0.5% (95% CI 0.3% to 0.9%) at 7–10 days, 1% (95% CI 0.6% to 1.7%) at 1 month and 5.9% (95% CI 4.5% to 7.7%) at 1 year. The most common adverse event was arrhythmia, for which its rate was 3.1% (95% CI 2.0% to 4.9%) at 24 h, 4.8% (95% CI 3.5% to 6.7%) at 72 h, 5.8% (95% CI 4.2% to 7.9%) at 7–10 days, 6.9% (95% CI 5.3% to 9.1%) at 1 month and 9.9% (95% CI 5.5% to 17) at 1 year. Ventricular arrhythmia was rare. Conclusions: The risk of death or life-threatening adverse event is rare in patients presenting syncope at the ED. The most common adverse events are brady and supraventricular arrhythmias, which occur during the first 3 days. Prolonged ECG monitoring in the ED in a short stay unit with ECG monitoring facilities may, therefore, be beneficial.

BackgroundRisk stratification is challenging in conditions, such as chest pain, shortness of breath and syncope, which can be the manifestation of many possible underlying diseases. In these cases, decision tools are unlikely to accurately identify all the different adverse events related to the possible etiologies. Attribute matching is a prediction method that matches an individual patient to a group of previously observed patients with identical characteristics and known outcome. We used syncope as a paradigm of clinical conditions presenting with aspecific symptoms to test the attribute matching method for the prediction of the personalized risk of adverse events.MethodsWe selected the 8 predictor variables common to the individual-patient dataset of 5 prospective emergency department studies enrolling 3388 syncope patients. We calculated all possible combinations and the number of patients in each combination. We compared the predictive accuracy of attribute matching and logistic regression. We then classified ten random patients according to clinical judgment and attribute matching.ResultsAttribute matching provided 253 of the 384 possible combinations in the dataset. Twelve (4.7%), 35 (13.8%), 50 (19.8%) and 160 (63.2%) combinations had a match size ≥50, ≥30, ≥20 and ConclusionsAttribute matching is a promising tool for personalized and flexible risk prediction. Large databases will need to be used in future studies to test and apply the method in different conditions.

In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.

Regular exercise is recommended to improve the cardiovascular risk profile. However, there is growing evidence that extreme volumes and intensity of long-term exertion may increase the risk of acute cardiac events. The aim of this study is to investigate the after-effects of regular, strenuous physical training on the cardiovascular neural regulation in a group of amateur triathletes compared to age-matched sedentary controls. We enrolled 11 non-elite triathletes (4 women, age 24±4 years), who had refrained from exercise for 72 hours, and 11 age-matched healthy non-athletes (3 women, age 25±2 years). Comprehensive echocardiographic and cardiopulmonary exercise tests were performed at baseline. Electrocardiogram, non-invasive blood pressure, respiratory activity, and muscle sympathetic nerve activity (MSNA) were continuously recorded in a supine position (REST) and during an incremental 15° step-wise head-up tilt test up to 75° (TILT). Blood samples were collected for determination of stress mediators. Autoregressive spectral analysis provided the indices of the cardiac sympathetic (LFRR) and vagal (HFRR) activity, the vascular sympathetic control (LFSAP), and the cardiac sympatho-vagal modulation (LF/HF). Compared to controls, triathletes were characterized by greater LFRR, LF/HF ratio, LFSAP, MSNA, and lower HFRR at REST and during TILT, i.e. greater overall cardiovascular sympathetic modulation together with lower cardiac vagal activity. Cortisol and adrenocorticotropic hormone concentrations were also higher in triathletes. In conclusion, triathletes were characterized by signs of sustained cardiovascular sympathetic overactivity. This might represent a risk factor for future cardiovascular events, given the known association between chronic excessive sympathetic activity and increased cardiovascular risk.

BACKGROUND: Syncope is a clinical event characterized by a transient loss of consciousness, estimated to affect 6.2/1000 person-years, resulting in remarkable health care and social costs. Human pathophysiology suggests that heat may promote syncope during standing. We tested the hypothesis that the increase of air temperatures from January to July would be accompanied by an increased rate of syncope resulting in a higher frequency of Emergency Department (ED) visits. We also evaluated the role of maximal temperature variability in affecting ED visits for syncope. METHODOLOGY/PRINCIPAL FINDINGS: We included 770 of 2775 consecutive subjects who were seen for syncope at four EDs between January and July 2004. This period was subdivided into three epochs of similar length: 23 January-31 March, 1 April-31 May and 1 June-31 July. Spectral techniques were used to analyze oscillatory components of day by day maximal temperature and syncope variability and assess their linear relationship. There was no correlation between daily maximum temperatures and number of syncope. ED visits for syncope were lower in June and July when maximal temperature variability declined although the maximal temperatures themselves were higher. Frequency analysis of day by day maximal temperature variability showed a major non-random fluctuation characterized by a ∼23-day period and two minor oscillations with ∼3- and ∼7-day periods. This latter oscillation was correlated with a similar ∼7-day fluctuation in ED visits for syncope. CONCLUSIONS/SIGNIFICANCE: We conclude that ED visits for syncope were not predicted by daily maximal temperature but were associated with increased temperature variability. A ∼7-day rhythm characterized both maximal temperatures and ED visits for syncope variability suggesting that climate changes may have a significant effect on the mode of syncope occurrence.