Early prediction of hemodynamic interventions in the intensive care unit using machine learning.

BACKGROUND: Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions. METHODS: We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings. RESULTS: HSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold. CONCLUSIONS: The HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence.

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 s). For this purpose, we combined a signal quality index (SQI) algorithm, to assess noisy instances, and trained densely connected convolutional neural networks to classify ECG recordings. Two convolutional neural network (CNN) models (a main model that accepts 15 s ECG segments and a secondary model that processes shorter 9 s segments) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. The results achieved on the 2017 PhysioNet/Computing in Cardiology challenge test dataset were an overall F1 score of 0.82 (F1 for NSR, AF, and O were 0.91, 0.83, and 0.72, respectively). Compared with 80 challenge entries, this was the third best overall score achieved on the evaluation dataset.

Direct current stimulation boosts synaptic gain and cooperativity in vitro.

KEY POINTS: Direct current stimulation (DCS) polarity specifically modulates synaptic efficacy during a continuous train of presynaptic inputs, despite synaptic depression. DCS polarizes afferent axons and postsynaptic neurons, boosting cooperativity between synaptic inputs. Polarization of afferent neurons in upstream brain regions may modulate activity in the target brain region during transcranial DCS (tDCS). A statistical theory of coincident activity predicts that the diffuse and weak profile of current flow can be advantageous in enhancing connectivity between co-active brain regions. ABSTRACT: Transcranial direct current stimulation (tDCS) produces sustained and diffuse current flow in the brain with effects that are state dependent and outlast stimulation. A mechanistic explanation for tDCS should capture these spatiotemporal features. It remains unclear how sustained DCS affects ongoing synaptic dynamics and how modulation of afferent inputs by diffuse stimulation changes synaptic activity at the target brain region. We tested the effect of acute DCS (10-20 V m-1 for 3-5 s) on synaptic dynamics with constant rate (5-40 Hz) and Poisson-distributed (4 Hz mean) trains of presynaptic inputs. Across tested frequencies, sustained synaptic activity was modulated by DCS with polarity-specific effects. Synaptic depression attenuates the sensitivity to DCS from 1.1% per V m-1 to 0.55%. DCS applied during synaptic activity facilitates cumulative neuromodulation, potentially reversing endogenous synaptic depression. We establish these effects are mediated by both postsynaptic membrane polarization and afferent axon fibre polarization, which boosts cooperativity between synaptic inputs. This potentially extends the locus of neuromodulation from the nominal target to afferent brain regions. Based on these results we hypothesized the polarization of afferent neurons in upstream brain regions may modulate activity in the target brain region during tDCS. A multiscale model of transcranial electrical stimulation including a finite element model of brain current flow, numerical simulations of neuronal activity, and a statistical theory of coincident activity predicts that the diffuse and weak profile of current flow can be advantageous. Thus, we propose that specifically because tDCS is diffuse, weak and sustained it can boost connectivity between co-active brain regions.

Antimicrobial and toxicological studies on fruit pulp of Citrullus colocynthis L.

The methanolic extract of dried fruit pulp of Citrullus colocyn this (Cucurbitaceae) has been studied with respect to antimicrobial and toxicological properties. The antimicrobial profile was investigated against thirty bacterial isolates (10 Gram +ve and 20 Gram-ve) and five fungal species. None of the bacterial or fungal culture used in the study showed sensitivity against the extract. Acute toxicity studies carried out in Albino mice NMRI indicated the highly toxic nature of the colocynth. A very significant decrease in body weight of test animals was noted at P<0.05. The LD(50) was calculated as 1000mg/kg body weight. Within four days of experimentation mortality was 100%. Histopathological studies confirmed the toxic nature of extract. Gross changes in histology of Heart, Liver and Kidneys were noted. Section of spleen did not exhibit any abnormality.

Diagnostic accuracy of ultrasonography in acute appendicitis.

BACKGROUND: The diagnosis of acute appendicitis is mainly clinical and to augment the clinical diagnosis ultrasonography and Computerized Tomographic Scan of the abdomen are also being used to help in diagnosis of the disease; which all carry some inherent limitations. This study was done to establish diagnostic accuracy of Ultrasonography (USG) in acute appendicitis taking histopathology of removed appendix as the gold standard. METHODS: This cross-sectional validation study was conducted in Radiology Department, Military Hospital and Combined Military Hospital Rawalpindi from July 2007 to January 2008. Sixty cases of clinically suspected acute appendicitis were selected on non-probability convenience sampling technique. All of them underwent ultrasound evaluation. Diagnostic accuracy of USG was calculated keeping histopathology of the removed appendix as gold standard whenever appendectomy was carried out. RESULTS: Out of 60 patients whose USG of right lower quadrant was performed, 30 patients were correctly diagnosed as having acute appendicitis on USG out of 34 finally diagnosed cases based on histopathology. Similarly we picked 12 normal appendices out of 26 non-appendicitis patients. This showed that US scan has sensitivity of 88%, specificity of 92%, positive predictive value of 94%, negative predictive value of 86%, and overall accuracy of 90%. The most accurate appendiceal finding for appendicitis was a diameter of 7 mm or larger followed by non- compressibility of inflamed appendix. CONCLUSION: Ultrasonography has high accuracy in diagnosing acute appendicitis and reduces negative appendectomies. Greater than 6-mm diameter of the appendix under compression is the most accurate USG finding with high positive predictive value for the diagnosis of acute appendicitis.

SARS-CoV-2 infection is associated with an increase in new diagnoses of schizophrenia spectrum and psychotic disorder: A study using the US national COVID cohort collaborative (N3C).

Amid the ongoing global repercussions of SARS-CoV-2, it is crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab-negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-19 positive (n = 219,264), and COVID-19 negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-19 negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.

Racecadotril Versus Loperamide in Acute Radiation Enteritis: A Randomized, Double-Masked, Phase 3, Noninferiority Trial.

PURPOSE: There is currently no gold standard for the management of acute radiation enteritis. We compared the efficacy and safety of Racecadotril, an anti-hypersecretory drug, versus Loperamide, an anti-motility agent, in acute radiation enteritis. METHODS AND MATERIALS: We conducted a randomized, double-masked, non-inferiority trial at a single research institute. Patients receiving curative radiation for pelvic malignancies, who developed grade 2 or 3 diarrhea (as per Common Terminology Criteria for Adverse Events, v 4.0) were included in the study. Patients in the intervention arm received Racecadotril and placebo. Patients in the control arm received Loperamide and placebo. The primary outcome was the resolution of diarrhea, 48 hours after the start of treatment. RESULTS: 162 patients were randomized between 2019 and 2022. On intention-to-treat analysis, 68/81 patients, 84%, (95% CI, 74.1%-91.2%) in the Racecadotril arm and 70/81, 86.4%, (95% CI, 77.0%-93.0%) in the Loperamide arm improved from grade 2 or 3 diarrhea to grade 1 or 0, (P= .66, χ2 test). The difference in proportion was 2.4% (95% CI: -8.5% to 13.4%). Since the upper boundary of the 95% CI crossed our non-inferiority margin of 10% (13.4%) we could not prove the non-inferiority of Racecadotril over Loperamide. Rebound constipation was more in the Loperamide arm compared to Racecadotril (17.3% vs 6.2%; P = .028) CONCLUSIONS: The non-inferiority of Racecadotril to Loperamide in acute radiation enteritis could not be demonstrated. However, Racecadotril can be the preferred drug of choice in acute radiation enteritis because Racecadotril does not affect bowel motility, achieved a high clinical success rate similar to that of Loperamide, and was associated with lesser side effects.

Cellular effects of acute direct current stimulation: somatic and synaptic terminal effects.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique to modulate cortical excitability. Although increased/decreased excitability under the anode/cathode electrode is nominally associated with membrane depolarization/hyperpolarization, which cellular compartments (somas, dendrites, axons and their terminals) mediate changes in cortical excitability remains unaddressed. Here we consider the acute effects of DCS on excitatory synaptic efficacy. Using multi-scale computational models and rat cortical brain slices, we show the following. (1) Typical tDCS montages produce predominantly tangential (relative to the cortical surface) direction currents (4-12 times radial direction currents), even directly under electrodes. (2) Radial current flow (parallel to the somatodendritic axis) modulates synaptic efficacy consistent with somatic polarization, with depolarization facilitating synaptic efficacy. (3) Tangential current flow (perpendicular to the somatodendritic axis) modulates synaptic efficacy acutely (during stimulation) in an afferent pathway-specific manner that is consistent with terminal polarization, with hyperpolarization facilitating synaptic efficacy. (4) Maximal polarization during uniform DCS is expected at distal (the branch length is more than three times the membrane length constant) synaptic terminals, independent of and two-three times more susceptible than pyramidal neuron somas. We conclude that during acute DCS the cellular targets responsible for modulation of synaptic efficacy are concurrently somata and axon terminals, with the direction of cortical current flow determining the relative influence.

SARS-CoV-2 Infection is Associated with an Increase in New Diagnoses of Schizophrenia Spectrum and Psychotic Disorder: A Study Using the US National COVID Cohort Collaborative (N3C).

Amid the ongoing global repercussions of SARS-CoV-2, it's crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-positive (n = 219,264), and COVID-negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.

Effect of dipyridamole plus aspirin on hemodialysis graft patency.

BACKGROUND: Arteriovenous graft stenosis leading to thrombosis is a major cause of complications in patients undergoing hemodialysis. Procedural interventions may restore patency but are costly. Although there is no proven pharmacologic therapy, dipyridamole may be promising because of its known vascular antiproliferative activity. METHODS: We conducted a randomized, double-blind, placebo-controlled trial of extended-release dipyridamole, at a dose of 200 mg, and aspirin, at a dose of 25 mg, given twice daily after the placement of a new arteriovenous graft until the primary outcome, loss of primary unassisted patency (i.e., patency without thrombosis or requirement for intervention), was reached. Secondary outcomes were cumulative graft failure and death. Primary and secondary outcomes were analyzed with the use of a Cox proportional-hazards regression with adjustment for prespecified covariates. RESULTS: At 13 centers in the United States, 649 patients were randomly assigned to receive dipyridamole plus aspirin (321 patients) or placebo (328 patients) over a period of 4.5 years, with 6 additional months of follow-up. The incidence of primary unassisted patency at 1 year was 23% (95% confidence interval [CI], 18 to 28) in the placebo group and 28% (95% CI, 23 to 34) in the dipyridamole-aspirin group, an absolute difference of 5 percentage points. Treatment with dipyridamole plus aspirin significantly prolonged the duration of primary unassisted patency (hazard ratio, 0.82; 95% CI, 0.68 to 0.98; P=0.03) and inhibited stenosis. The incidences of cumulative graft failure, death, the composite of graft failure or death, and serious adverse events (including bleeding) did not differ significantly between study groups. CONCLUSIONS: Treatment with dipyridamole plus aspirin had a significant but modest effect in reducing the risk of stenosis and improving the duration of primary unassisted patency of newly created grafts. (ClinicalTrials.gov number, NCT00067119.)

High-resolution modeling assisted design of customized and individualized transcranial direct current stimulation protocols.

OBJECTIVES: Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that delivers low-intensity currents facilitating or inhibiting spontaneous neuronal activity. tDCS is attractive since dose is readily adjustable by simply changing electrode number, position, size, shape, and current. In the recent past, computational models have been developed with increased precision with the goal to help customize tDCS dose. The aim of this review is to discuss the incorporation of high-resolution patient-specific computer modeling to guide and optimize tDCS. METHODS: In this review, we discuss the following topics: 1) The clinical motivation and rationale for models of transcranial stimulation is considered pivotal in order to leverage the flexibility of neuromodulation; 2) the protocols and the workflow for developing high-resolution models; 3) the technical challenges and limitations of interpreting modeling predictions; and 4) real cases merging modeling and clinical data illustrating the impact of computational models on the rational design of rehabilitative electrotherapy. CONCLUSIONS: Though modeling for noninvasive brain stimulation is still in its development phase, it is predicted that with increased validation, dissemination, simplification, and democratization of modeling tools, computational forward models of neuromodulation will become useful tools to guide the optimization of clinical electrotherapy.

Polyhydroxyalkanoate quantification in organic wastes and pure cultures using a single-step extraction and 1H NMR analysis.

In this study, a proton nuclear magnetic resonance (1H NMR) method was developed to quantitatively analyze polyhydroxyalkanoate (PHA) content in Cupriavidus necator H16, Azotobacter vinelandii AvOP, and mixed microbial cultures from the effluent of an agricultural waste treatment anaerobic digester. In contrast to previous methods, a single-step PHA extractive method using deuterated chloroform was established, thereby facilitating direct 1H NMR analysis. The accuracy of the method was verified through comparison with well-established gas chromatography (GC) methanolysis techniques. Nile blue fluorescence staining was also carried out to serve as an independent and qualitative indicator of intracellular PHA content. The results indicate that the 1H NMR method is appropriate for rapid and non-destructive quantification of overall PHA content and determination of PHA copolymer composition in a variety of cultures. Notably, this technique was effective in measuring PHA content in full-strength waste samples where high concentrations of background impurities and organic compounds are present. The straightforward procedures minimize error-introducing steps, require less time and materials, and result in an accurate method suitable for routine analyses.

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared prediction model without moving the data outside their secure infrastructure. To do so, after having sent model updates to a central server, an update aggregation is performed, and the model is sent back to the sites for further training. Although widely applied on neural networks, the deployment of FL architectures is lacking scalability and support for machine learning techniques such as decision tree-based models. The latter, when embedded in FL, suffer from costly encryption techniques applied for sharing sensitive information such as the splitting decisions within the trees. In this work, we focus on predicting hemodynamic instability on ICU patients by enabling distributed gradient boosting in FL. We employ a clinical dataset from 25 hospitals generated based on the Philips eICU database and we design a FL pipeline that supports neural-based boosting models as well as conventional neural networks. This enhancement enables decision tree models in FL, which represent the state-of-the-art approach for classification tasks involving tabular clinical data. Comparable performances in terms of accuracy, precision, recall and F1 score have been reached when detecting hemodynamic instability in FL, and in a centralized setup. In summary, we demonstrate the feasibility of a scalable FL for detecting hemodynamic instability in ICU data, which preserves privacy and holds the deployment benefits of a neural-based architecture.

Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing.

Clinical effects of transcranial electrical stimulation with weak currents are remarkable considering the low amplitude of the electric fields acting on the brain. Elucidating the processes by which small currents affect ongoing brain activity is of paramount importance for the rational design of noninvasive electrotherapeutic strategies and to determine the relevance of endogenous fields. We propose that in active neuronal networks, weak electrical fields induce small but coherent changes in the firing rate and timing of neuronal populations that can be magnified by dynamic network activity. Specifically, we show that carbachol-induced gamma oscillations (25-35 Hz) in rat hippocampal slices have an inherent rate-limiting dynamic and timing precision that govern susceptibility to low-frequency weak electric fields (<50 Hz; <10 V/m). This leads to a range of nonlinear responses, including the following: (1) asymmetric power modulation by DC fields resulting from balanced excitation and inhibition; (2) symmetric power modulation by lower frequency AC fields with a net-zero change in firing rate; and (3) half-harmonic oscillations for higher frequency AC fields resulting from increased spike timing precision. These underlying mechanisms were elucidated by slice experiments and a parsimonious computational network model of single-compartment spiking neurons responding to electric field stimulation with small incremental polarization. Intracellular recordings confirmed model predictions on neuronal timing and rate changes, as well as spike phase-entrainment resonance at 0.2 V/m. Finally, our data and mechanistic framework provide a functional role for endogenous electric fields, specifically illustrating that modulation of gamma oscillations during theta-modulated gamma activity can result from field effects alone.

Diagnostic accuracy of magnetic resonance imaging in meniscal injuries of knee joint and its role in selection of patients for arthroscopy.

BACKGROUND: Magnetic Resonance Imaging (MRI) is frequently advised to evaluate clinically suspected cases of meniscal injuries in our setup. The objective was to determine the diagnostic accuracy of MRI in meniscal injuries of knee joint and its effectiveness in selection of patients for arthroscopy. METHODS: A Cross-sectional comparative study was conducted at Radiology Department Military Hospital (MH) Rawalpindi in collaboration with Orthopaedic Department Combined Military Hospital (CMH) Rawalpindi from 31 Jan 2007 to 1 Aug 2007. Fifty-seven patients with clinical suspicion of meniscal injuries were subjected to MRI. Arthroscopy was done only in 34 patients while 23 were excluded on the basis of MRI findings. MRI findings were compared with arthroscopic findings. Medial and lateral menisci were considered separately in each case. RESULTS: Among 57 patients only 30 showed significant tear on MRI. Arthroscopy was done in these cases. Arthroscopy was considered on clinical grounds only in 4 patients who did not show significant tear on MRI. MRI showed Medial Meniscus (MM) injury in 23 patients and Lateral Meniscus (LM) injury in 10 patients. Arthroscopy confirmed MM injury in 17 patients and LM injury in 7 patients. MRI missed two MM and one LM injuries. This showed that MRI has sensitivity of 89.4% and specificity of 62% in diagnosing injuries of MM, while sensitivity of 87% and specificity of 88% in diagnosing injuries of LM. Diagnostic accuracy of MRI in MM and LM injuries was 76.4% and 88.2% respectively. CONCLUSION: MRI is accurate in diagnosing meniscal injuries of knee joint and is effective in selection of patients for arthroscopy.

Direct current stimulation boosts hebbian plasticity in vitro.

BACKGROUND: There is evidence that transcranial direct current stimulation (tDCS) can improve learning performance. Arguably, this effect is related to long term potentiation (LTP), but the precise biophysical mechanisms remain unknown. HYPOTHESIS: We propose that direct current stimulation (DCS) causes small changes in postsynaptic membrane potential during ongoing endogenous synaptic activity. The altered voltage dynamics in the postsynaptic neuron then modify synaptic strength via the machinery of endogenous voltage-dependent Hebbian plasticity. This hypothesis predicts that DCS should exhibit Hebbian properties, namely pathway specificity and associativity. METHODS: We studied the effects of DCS applied during the induction of LTP in the CA1 region of rat hippocampal slices and using a biophysical computational model. RESULTS: DCS enhanced LTP, but only at synapses that were undergoing plasticity, confirming that DCS respects Hebbian pathway specificity. When different synaptic pathways cooperated to produce LTP, DCS enhanced this cooperation, boosting Hebbian associativity. Further slice experiments and computer simulations support a model where polarization of postsynaptic pyramidal neurons drives these plasticity effects through endogenous Hebbian mechanisms. The model is able to reconcile several experimental results by capturing the complex interaction between the induced electric field, neuron morphology, and endogenous neural activity. CONCLUSIONS: These results suggest that tDCS can enhance associative learning. We propose that clinical tDCS should be applied during tasks that induce Hebbian plasticity to harness this phenomenon, and that the effects should be task specific through their interaction with endogenous plasticity mechanisms. Models that incorporate brain state and plasticity mechanisms may help to improve prediction of tDCS outcomes.

Delivering multi-disease screening to migrants for latent TB and blood-borne viruses in an emergency department setting: A feasibility study.

BACKGROUND: Screening for latent tuberculosis infection (LTBI) in migrants is important for elimination of tuberculosis in low-incidence countries, alongside the need to detect blood-borne infections to align with new guidelines on migrant screening for multiple infections in European countries. However, feasibility needs to be better understood. METHODS: We did a feasibility study to test an innovative screening model offering combined testing for LTBI (QuantiFERON), HIV, hepatitis B/C in a UK emergency department, with two year follow-up. RESULTS: 96 economic migrants, asylum seekers and refugees from 43 countries were screened (46 [47.9%] women; mean age 35.2 years [SD 11.7; range 18-73]; mean time in the UK 4.8 years [SD 3.2; range 0-10]). 14 migrants (14.6%) tested positive for LTBI alongside HIV [1], hepatitis B [2], and hepatitis C [1] Of migrants with LTBI, 5 (35.7%) were successfully engaged in treatment. 74 (77.1%) migrants reported no previous screening since migrating to the UK. CONCLUSION: Multi-disease screening in this setting is feasible and merits being further tested in larger-scale studies. However, greater emphasis must be placed on ensuring successful treatment outcomes. We identified major gaps in current screening provision; most migrants had been offered no prior screening despite several years since migration, which holds relevance to policy and practice in the UK and other European countries.

Randomized Controlled Trial of Simulation vs. Standard Training for Teaching Medical Students High-quality Cardiopulmonary Resuscitation.

INTRODUCTION: Most medical schools teach cardiopulmonary resuscitation (CPR) during the final year in course curriculum to prepare students to manage the first minutes of clinical emergencies. Little is known regarding the optimal method of instruction for this critical skill. Simulation has been shown in similar settings to enhance performance and knowledge. We evaluated the comparative effectiveness of high-fidelity simulation training vs. standard manikin training for teaching medical students the American Heart Association (AHA) guidelines for high-quality CPR. METHODS: This was a prospective, randomized, parallel-arm study of 70 fourth-year medical students to either simulation (SIM) or standard training (STD) over an eight-month period. SIM group learned the AHA guidelines for high-quality CPR via an hour session that included a PowerPoint lecture with training on a high-fidelity simulator. STD group learned identical content using a low-fidelity Resusci Anne® CPR manikin. All students managed a simulated cardiac arrest scenario with primary outcome based on the AHA guidelines definition of high-quality CPR (specifies metrics for compression rate, depth, recoil, and compression fraction). Secondary outcome was time to emergency medical services (EMS) activation. We analyzed data via Kruskal-Wallis rank sum test. Outcomes were performed on a simulated cardiac arrest case adapted from the AHA Advanced Cardiac Life Support (ACLS) SimMan® Scenario manual. RESULTS: Students in the SIM group performed CPR that more closely adhered to the AHA guidelines of compression depth and compression fraction. Mean compression depth was 4.57 centimeters (cm) (95% confidence interval [CI] [4.30-4.82]) for SIM and 3.89 cm (95% CI [3.50-4.27]) for STD, p=0.02. Mean compression fraction was 0.724 (95% CI [0.699-0.751]) for SIM group and 0.679 (95% CI [0.655-0.702]) for STD, p=0.01. There was no difference for compression rate or recoil between groups. Time to EMS activation was 24.7 seconds (s) (95% CI [15.7-40.8]) for SIM group and 79.5 s (95% CI [44.8-119.6]) for STD group, p=0.007. CONCLUSION: High-fidelity simulation training is superior to low-fidelity CPR manikin training for teaching fourth-year medical students implementation of high-quality CPR for chest compression depth and compression fraction.

Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is the most common sustained cardiac arrhythmia that is associated with an increased morbidity and mortality, its timely diagnosis is clinically desirable. The main aim of this study as our contribution to the PhysioNet/CinC Challenge 2017 was to develop an automatic algorithm for classification of normal sinus rhythm (NSR), AF, other rhythm (O), and noise using a short single-channel ECG. Furthermore, the impact of changing labels/annotations on performance of the proposed algorithm was studied in this article. APPROACH: The challenge training dataset (8528 ECG recordings) and a complementary dataset (6312 ECG recordings) from other sources were used for algorithm development. Version 3 (v3), which is an updated version of the annotations at the official phase of the challenge (v2), was used in this study. In the proposed algorithm, densely connected convolutional networks were combined with feature-based post-processing after initial signal quality analysis for the classification of ECG recordings. MAIN RESULTS: The F1 scores for classification of NSR, AF, and O were 0.91, 0.83, and 0.72, respectively, which led to a F1 of 0.82. There was a small or no performance difference between the top entries in the official phase of the challenge and our proposed method. An increase of 2.5% in F1 score was observed when the same annotations for training and test was used (using v3 annotations) compared to using different annotations (v2 annotations for training and v3 annotations for the test). SIGNIFICANCE: Our promising results suggest that the availability of more data with improved labeling along with improvement in signal quality analysis make our algorithm suitable for practical clinical applications.