RESUMO
Left atrial appendage closure (LAAC) can be used to prevent embolic events in patients with atrial fibrillation who cannot tolerate oral anticoagulants. LAAC has not yet been performed in patients with acquired von Willebrand syndrome. A 74-year-old male with von Willebrand disease presents to the emergency department because of palpitations. Atrial fibrillation with congestive heart failure, hypertension, age ≥75, diabetes, stroke, vascular disease, age between 65-74, and female sex (CHA2DS2-VASC) of 4 was diagnosed. Oral anticoagulation was withheld because of a past medical history of major bleeding events despite treatment of the underlying bleeding diathesis. Therefore, LAAC was considered for stroke prevention. However, the procedure was delayed due to abnormal coagulation cascade levels. Because of the ineffectiveness of treatment and persistently low levels of factor VIII and von Willebrand factor (vWF), the von Willebrand disease hypothesis was abandoned, prompting a new diagnosis for the bleeding disorder. Rapid clearance of factor VIII and vWF, the good response to intravenous immunoglobulins, and the presence of monoclonal gammopathy of undetermined significance allowed the diagnosis of acquired von Willebrand syndrome. After administration of immunoglobulins, factor VIII and vWF levels were normalized, and the LAAC was performed. The patient was discharged on low-dose aspirin. At the nine-month follow-up, the patient did not experience bleeding or embolic events. Stroke prevention in patients with atrial fibrillation and increased bleeding risk requires alternatives to oral anticoagulation. LAAC can be safely performed in patients with acquired von Willebrand syndrome and atrial fibrillation.
RESUMO
Bleeding complications after pacemaker implantation pose risks, including infection and prolonged hospital stay. A case involving aortic intramural hematoma (IMH) arising from subclavian vein access during implantation and concomitant acute pulmonary embolism (PE) is presented. In the present case, IMH probably resulted from subclavian artery vasa vasorum trauma during vein puncture and guidewire advancement, leading to IMH and hemothorax. PE possibly stemmed from a prothrombotic state caused by the intervention and the IMH. Conservative management with serial CT scans was chosen due to hemodynamic stability and high surgical risk. IMH and PE resolution was confirmed at follow-up.
RESUMO
Background: Low-voltage area (LVA) ablation, in addition to pulmonary vein isolation (PVI), has been proposed as a new strategy in patients with atrial fibrillation (AF), but clinical trials have shown conflicting results. We performed a systematic review and meta-analysis to assess the impact of LVA ablation in patient undergoing AF ablation (PROSPERO-registered CRD42024537696). Methods: Randomized clinical trials investigating the role of LVA ablation in addition to PVI in patients with AF were searched on PubMed, Embase, and the Cochrane Library from inception to 22 April 2024. Primary outcome was atrial arrhythmia recurrence after the first AF ablation procedure. Secondary endpoints included procedure time, fluoroscopy time, and procedure-related complication rate. Sensitivity analysis including only patients with LVA demonstration at mapping and multiple subgroups analyses were also performed. Results: 1547 patients from 7 studies were included. LVA ablation in addition to PVI reduced atrial arrhythmia recurrence (odds ratio [OR] 0.65, 95% confidence interval [CI] 0.52-0.81, p < 0.001) with a number needed to treat to prevent recurrence of 10. No difference in procedure time (mean difference [MD] -5.32 min, 95% CI -19.01-8.46 min, p = 0.45), fluoroscopy time (MD -1.10 min, 95% CI -2.48-0.28 min, p = 0.12) and complication rate (OR 0.81, 95% CI 0.40-1.61, p = 0.54) was observed. Consistent results were demonstrated when considering only patients with LVA during mapping and in prespecified subgroups for AF type (paroxysmal vs. persistent), multicentric vs. monocentric trial, and ablation strategy in control group. Conclusions: In patients with AF, ablation of LVAs in addition to PVI reduces atrial arrhythmia recurrence without a significant increase in procedure time, fluoroscopy time, or complication rate.
RESUMO
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.
RESUMO
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.
Assuntos
COVID-19 , Aprendizado Profundo , Humanos , COVID-19/diagnóstico , Hospitalização , Prognóstico , Serviço Hospitalar de Emergência , Estudos RetrospectivosRESUMO
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.
RESUMO
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.
RESUMO
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.