Pain is the leading cause of medical consultations and occurs in 50–70% of emergency department visits. To date, several drugs have been used to manage pain. The clinical use of ketamine began in the 1960s and it immediately emerged as a manageable and safe drug for sedation and anesthesia. The analgesic properties of this drug were first reported shortly after its use; however, its psychomimetic effects have limited its use in emergency departments. Owing to the misuse and abuse of opioids in some countries worldwide, ketamine has become a versatile tool for sedation and analgesia. In this narrative review, ketamine’s role as an analgesic is discussed, with both known and new applications in various contexts (acute, chronic, and neuropathic pain), along with its strengths and weaknesses, especially in terms of psychomimetic, cardiovascular, and hepatic effects. Moreover, new scientific evidence has been reviewed on the use of additional drugs with ketamine, such as magnesium infusion for improving analgesia and clonidine for treating psychomimetic symptoms. Finally, this narrative review was refined by the experience of the Pain Group of the Italian Society of Emergency Medicine (SIMEU) in treating acute and chronic pain with acute manifestations in Italian Emergency Departments.

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.

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.

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.

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.

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.