Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

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 < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department.

Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58-83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient's initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.

Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing-Based Virtual Patient Simulator: Case Study.

BACKGROUND: Virtual 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. OBJECTIVE: The 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: Different 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.

Comparison between standard and ultrasound-integrated approach for risk stratification of syncope in the emergency department.

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. Thirty days after the index event, all participants were followed up to assess the frequency of short-term serious outcomes as defined in the San Francisco Syncope Rule (SFSR) cohorts. We estimated the accuracy of clinical and POCUS-integrated evaluation in predicting SFSR outcomes. Between February 2016 and January 2018, 196 patients were enrolled [109 women (55.6%)]. Median age was 64 years (interquartile range 31 years). After a follow-up of 30 days, 19 patients experienced 20 SFSR outcomes. 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.

Syncope Time Frames for Adverse Events after Emergency Department Presentation: An Individual Patient Data Meta-Analysis.

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.

Correction: Personalized risk stratification through attribute matching for clinical decision making in clinical conditions with aspecific symptoms: The example of syncope.

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

Multicentre external validation of the Canadian Syncope Risk Score to predict adverse events and comparison with clinical judgement.

BACKGROUND: The Canadian Syncope Risk Score (CSRS) has been proposed for syncope risk stratification in the emergency department (ED). The aim of this study is to perform an external multicenter validation of the CSRS and to compare it with clinical judgement. METHODS: Using patients previously included in the SyMoNE database, we enrolled subjects older than 18 years who presented reporting syncope at the ED. For each patient, we estimated the CSRS and recorded the physician judgement on the patients' risk of adverse events. We performed a 30-day follow-up. RESULTS: From 1 September 2015 to 28 February 2017, we enrolled 345 patients; the median age was 71 years (IQR 51-81), 174 (50%) were men and 29% were hospitalised. Serious adverse events occurred in 43 (12%) of the patients within 30 days. The area under the curve of the CSRS and clinical judgement was 0.75 (95% CI 0.68 to 0.81) and 0.68 (95% CI 0.61 to 0.74), respectively. The risk of adverse events of patients at low risk according to the CSRS and clinical judgement was 6.7% and 2%, with a sensitivity of 70% (95% CI 54% to 83%) and 95% (95% CI 84% to 99%), respectively. CONCLUSION: This study represents the first validation analysis of CSRS outside Canada. The overall predictive accuracy of the CSRS is similar to the clinical judgement. However, patients at low risk according to clinical judgement had a lower incidence of adverse events as compared with patients at low risk according to the CSRS. Further studies showing that the adoption of the CSRS improve patients' outcomes is warranted before its widespread implementation.

Machine Learning and Syncope Management in the ED: The Future Is Coming.

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.

A Natural Language Processing-Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study.

BACKGROUND: Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators. OBJECTIVE: The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. METHODS: We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. RESULTS: We developed a VPS called Hepius that allows students to gather clinical information from the patient's medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance. CONCLUSIONS: By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.

Psychogenic Pseudosyncope: Clinical Features, Diagnosis and Management.

Psychogenic Pseudosyncope (PPS) is the appearance of Transient Loss of Consciousness (TLOC) in which movements are absent, but there are no hemodynamic and electroencephalographic modifications as are induced by gravitational challenges which characterize syncope and true loss of consciousness. For younger and adult populations, a detailed history is crucial for the diagnosis. Clinical clues that should raise the suspicion for PPS include prolonged duration of the LOC, eye closure during the episode, unusual triggers, no recognizable prodromes and the high frequency of attacks. The presence of an established diagnosis of syncope should not deter from the concomitant diagnosis of PPS. The gold standard for a proper diagnosis of PPS is the documentation by a tilt test of normal hemodynamic and electroencephalographic parameters, when recorded during an attack. Treatment of PPS, based on the clear and empathetic communication of the diagnosis, can lead to an immediate reduction of attack frequency and lower the need to call on emergency services. Pharmacological treatment of associated psychiatric disorders and psychological interventions may be beneficial in patients with PPS. Cognitive-behavioural therapy holds the most reliable evidence of efficacy. In the present review, we aimed to address PPS with historical aspects, main clinical features and diagnostic tests, current diagnostic classification, underlying neurobiological abnormalities, management and therapy.

Work Ability Assessment and Its Relationship with Cardiovascular Autonomic Profile in Postural Orthostatic Tachycardia Syndrome.

Postural orthostatic tachycardia syndrome (POTS) negatively impacts quality of life. The excessive increase in cardiac sympathetic modulation during standing, which characterizes POTS patients, leads to many symptoms and signs of orthostatic intolerance. Little is known about the consequences of the disease on work performance and its relationship with individual autonomic profiles. Twenty-two POTS patients regularly engaged in working activity (20 females, age 36 ± 12 years) and 18 gender- and age-matched controls underwent a clinical evaluation and filled out the Work Ability Index (WAI) questionnaire. POTS patients completed the Composite Autonomic Symptom Score (COMPASS31) questionnaire, underwent continuous electrocardiogram, blood pressure and respiratory activity recordings while supine and during a 75° head-up tilt (HUT). A power spectrum analysis provided the index of cardiac sympatho-vagal balance (LF/HF). WAI scores were significantly reduced in POTS patients (29.84 ± 1.40) compared to controls (45.63 ± 0.53, p < 0.01). A significant inverse correlation was found between individual WAI and COMPASS31 scores (r = -0.46; p = 0.03), HUT increase in heart rate (r = -0.57; p = 0.01) and LF/HF (r = -0.55; p = 0.01). In POTS patients, the WAI scores were inversely correlated to the intensity of autonomic symptoms and to the excessive cardiac sympathetic activation induced by the gravitational stimulus.

Time Course of Autonomic Symptoms in Postural Orthostatic Tachycardia Syndrome (POTS) Patients: Two-Year Follow-Up Results.

Postural orthostatic tachycardia syndrome (POTS) is a multifactorial condition capable of chronically reducing the quality of life and the work ability of patients. The study aim was to assess the burden of autonomic symptoms in a cohort of POTS patients over 2 years. Patients' clinical profiles were assessed by the 31-item Composite Autonomic Symptom Score questionnaire (COMPASS 31) and a visual analog scale (VAS). One-way ANOVA for repeated measures followed by Dunnett's post-hoc test were used to compare symptoms at baseline and at 1 and 2 years. Out of 42 enrolled patients, 25 had a 1-year follow-up and 12 had a 2-year follow-up. At baseline, the reported burden of autonomic symptoms was high (overall COMPASS 31 = 49.9 ± 14.3 /100). Main complaints were related to orthostatic intolerance according to both COMPASS 31 and VAS. Fourteen patients were rendered inactive because of symptoms. At 1-year follow-up, a statistically significant improvement in pupillomotor function and overall score was detected by the COMPASS 31. These findings were confirmed at 2 years, together with a significant reduction in quality of life impairment, assessed by VAS. However, these improvements did not change patients' occupational status. Awareness of POTS diagnosis, patient monitoring, and tailored therapies can help to improve patients' condition.

Personalized risk stratification through attribute matching for clinical decision making in clinical conditions with aspecific symptoms: The example of syncope.

BACKGROUND: Risk 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. METHODS: We 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. RESULTS: Attribute 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 <10 patients, respectively. The AUC for the attribute matching and the multivariate model were 0.59 and 0.74, respectively. CONCLUSIONS: Attribute 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.

Predictive Accuracy of Electrocardiographic Monitoring of Patients With Syncope in the Emergency Department: The SyMoNE Multicenter Study.

BACKGROUND: Arrhythmia is one of the most worrisome causes of syncope. Electrocardiographic (ECG) monitoring is crucial for the management of non-low-risk patients in the emergency department (ED). However, its diagnostic accuracy and optimal duration are unknown. We aimed to assess the diagnostic accuracy of ECG monitoring in non-low-risk patients with syncope in the ED. METHODS: This prospective multicenter observational study included adult patients presenting to the ED after syncope. Patients without an obvious etiology after ED evaluation who were classified by ED physicians as being at non-low risk of adverse events underwent ECG monitoring. We assessed sensitivity, specificity, and diagnostic yield (defined as the proportion of patients with true-positive ECG monitoring findings) of ECG monitoring in the identification of 7- and 30-day adverse and arrhythmic events according to monitoring duration. RESULTS: Of 242 patients included in the study, 29 patients had 7-day serious outcomes. Ten additional patients had serious outcomes at 30 days. The overall sensitivity, specificity, and diagnostic yield of ECG monitoring in the identification of 7-day adverse events were 0.55 (95% confidence interval [CI] = 0.36 to 0.74], 0.93 (95% CI = 0.89 to 0.96), and 0.07 (95% CI = 0.04 to 0.10), respectively. The sensitivity, specificity, and diagnostic yield of >12-hour ECG monitoring in the identification of 7-day adverse events were 0.89 (95% CI = 0.65 to 0.99), 0.78 (95% CI = 0.67 to 0.87), and 0.18 (95% CI = 0.12 to 0.28), respectively. Similar results were observed for 30-day adverse events. The median (interquartile range) ECG monitoring time was 6.5 (6 to 15) hours. ECG monitoring findings were positive in 31 patients. CONCLUSIONS: Although the overall diagnostic accuracy of ECG monitoring is fair, its sensitivity at >12 hours' duration is substantially higher. These results suggest that prolonged (>12 hours) monitoring is a safe alternative to hospital admission in the management of non-low-risk patients with syncope in the ED.

Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records.

BACKGROUND: Enrollment of large cohorts of syncope patients from administrative data is crucial for proper risk stratification but is limited by the enormous amount of time required for manual revision of medical records. AIM: To develop a Natural Language Processing (NLP) algorithm to automatically identify syncope from Emergency Department (ED) electronic medical records (EMRs). METHODS: De-identified EMRs of all consecutive patients evaluated at Humanitas Research Hospital ED from 1 December 2013 to 31 March 2014 and from 1 December 2015 to 31 March 2016 were manually annotated to identify syncope. Records were combined in a single dataset and classified. The performance of combined multiple NLP feature selectors and classifiers was tested. Primary Outcomes: NLP algorithms' accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F3 score. RESULTS: 15,098 and 15,222 records from 2013 and 2015 datasets were analyzed. Syncope was present in 571 records. Normalized Gini Index feature selector combined with Support Vector Machines classifier obtained the best F3 value (84.0%), with 92.2% sensitivity and 47.4% positive predictive value. A 96% analysis time reduction was computed, compared with EMRs manual review. CONCLUSIONS: This artificial intelligence algorithm enabled the automatic identification of a large population of syncope patients using EMRs.

Cardiac and Vascular Sympathetic Baroreflex Control during Orthostatic Pre-Syncope.

We hypothesized that sympathetic baroreflex mediated uncoupling between neural sympathetic discharge pattern and arterial pressure (AP) fluctuations at 0.1 Hz during baroreceptor unloading might promote orthostatic pre-syncope. Ten volunteers (32 ± 6 years) underwent electrocardiogram, beat-to-beat AP, respiratory activity and muscle sympathetic nerve activity (MSNA) recordings while supine (REST) and during 80° head-up tilt (HUT) followed by -10 mmHg stepwise increase of lower body negative pressure until pre-syncope. Cardiac and sympathetic baroreflex sensitivity were quantified. Spectrum analysis of systolic and diastolic AP (SAP and DAP) and calibrated MSNA (cMSNA) variability assessed the low frequency fluctuations (LF, ~0.1 Hz) of SAP, DAP and cMSNA variability. The squared coherence function (K2) quantified the coupling between cMSNA and DAP in the LF band. Analyses were performed while supine, during asymptomatic HUT (T1) and at pre-syncope onset (T2). During T2 we found that: (1) sympathetic baroreceptor modulation was virtually abolished compared to T1; (2) a progressive decrease in AP was accompanied by a persistent but chaotic sympathetic firing; (3) coupling between cMSNA and AP series at 0.1 Hz was reduced compared to T1. A negligible sympathetic baroreceptor modulation during pre-syncope might disrupt sympathetic discharge pattern impairing the capability of vessels to constrict and promote pre-syncope.

Can strenuous exercise harm the heart? Insights from a study of cardiovascular neural regulation in amateur triathletes.

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.

Mechanical somatosensory stimulation decreases blood pressure in patients with Parkinson's disease.

OBJECTIVE: The current study aimed to assess the effects of five cycles of automated mechanical somatosensory stimulation (AMSS) of the fore-feet on blood pressure (BP) and cardiovascular autonomic control in Parkinson's Disease patients. METHODS: Out of 23 patients, 16 underwent an AMSS session every 72 h, for a total of five sessions per patient. Electrocardiogram, noninvasive beat-to-beat blood pressure and respiratory activity were recorded for 20 min in supine position at baseline and after the AMSS sessions. Main outcomes were the changes in SBP and DBP, in the spectral indices of cardiac sympathetic (LFRRn.u.) and vagal (HFRR) modulatory activities, cardiac sympathovagal relationship (LF/HF), vascular sympathetic modulation (LFSAP) and arterial baroreflex sensitivity (sequence technique). Symbolic analysis of heart rate variability provided additional indices of cardiac sympathetic (0V%) and vagal (2UV%) modulation to the sinoatrial node. RESULTS: After five AMSS trials a reduction in SBP (baseline: 131.2 ±â€Š15.5 mmHg; post-AMSS: 122.4 ±â€Š16.2 mmHg; P = 0.0004) and DBP (baseline: 73.2 ±â€Š6.1 mmHg; post-AMSS: 68.9 ±â€Š6.2 mmHg; P = 0.008) was observed. Post-AMSS, spectral and symbolic indices of cardiovascular sympathetic control decreased and arterial baroreflex sensitivity increased (baseline: 5.7 ±â€Š1.3 ms/mmHg; post-AMSS: 11.27 ±â€Š2.7 ms/mmHg). CONCLUSION: AMSS sessions were effective in reducing BP, increasing baroreflex sensitivity and decreasing cardiovascular sympathetic modulation in Parkinson's disease patients. AMSS might be useful to control supine hypertension in Parkinson's disease.

Syncope in a Working-Age Population: Recurrence Risk and Related Risk Factors.

Syncope in a worker undertaking risky tasks may result in fatalities for the individual or for third parties. We aimed at assessing the rate of syncope recurrence and the risk factors underlying the likelihood of syncope relapse in a working-age population. A prospective cohort of all patients aged 18⁻65 years consecutively admitted to the Emergency Department for syncope was enrolled. Risk of syncope relapse was assessed at a six-month, 1-year, and 5-year follow-up. Predictors of syncope recurrence have been evaluated at six months and 1 year from the syncope index by a multivariable logistic regression analysis. 348 patients were enrolled. Risk of syncope relapse was 9.2% at 6 months, 11.8% at 1 year, and 23.4% at 5 years. At 6-month follow-up, predictor of syncope recurrence was ≥3 prior lifetime syncope episodes. At 1-year, ≥3 prior lifetime syncope episodes, diabetes mellitus, and anaemia were risk factors for syncope relapse. There was an exceeding risk of recurrence in the first 6 months and a reduced risk of 3.5% per year after the first year. Anaemia, diabetes mellitus, and prior lifetime syncope burden are of importance when giving advice about the resumption of "high risk" jobs following a syncope episode.

Effects of Clockwise and Counterclockwise Job Shift Work Rotation on Sleep and Work-Life Balance on Hospital Nurses.

Rotational shift work is associated with sleep disturbances, increased risk of cardiovascular and psychological disorders, and may negatively impact work⁻life balance. The direction of shift rotation (Clockwise, CW or counterclockwise, CCW) and its role in these disorders are poorly understood. The aim of the study was to investigate the effect of the shift schedule direction on sleep quantity and quality, alertness and work performance, and on work⁻life balance on hospital nurses. One-hundred female nurses, working a continuous rapid shift schedule in hospitals in the north of Italy, participated in this cross-sectional study. Fifty worked on CW rotation schedule (Morning: 6 a.m.⁻2 p.m., Afternoon: 2 p.m.⁻10 p.m., Night: 10 p.m.⁻6 a.m., 2 rest days) and fifty on CCW rotation (Afternoon, Morning, Morning, Night, 3 rest days). Data were collected by ad hoc questionnaire and daily diary. During the shift cycle CW nurses slept longer (7.40 ± 2.24 h) than CCW (6.09 ± 1.73; p < 0.001). CW nurses reported less frequently than CCW awakening during sleep (40% vs. 80%; p < 0.001), attention disturbance during work (20% vs. 64%; p < 0.001), and interference with social and family life (60% vs. 96% and 20% vs. 70%, respectively; p < 0.001). CCW rotating shift schedule seems to be characterized by higher sleep disturbances and a worse work⁻life balance.