Eosinopenia as a diagnostic marker of bloodstream infection in a general internal medicine setting: a cohort study.

BACKGROUND: Little is known about the potential use of the eosinophil count as a predictive marker of bloodstream infection. In this study, we aimed to assess the reliability of eosinopenia as a predictive marker of bloodstream infection. METHODS: This retrospective cohort study was performed in the outpatient department and general internal medicine department of a tertiary university hospital in Japan. A total of 189 adult patients with at least 2 sets of blood cultures obtained during the period January 1-December 31, 2018, were included; those with the use of antibiotic therapy within 2 weeks prior to blood culture, steroid therapy, or a history of haematological cancer were excluded. The diagnostic accuracies of each univariate variable and the multivariable logistic regression models were assessed by calculating the areas under the receiver operating characteristic curves (AUROCs). The primary outcome was a positive blood culture indicating bloodstream infection. RESULTS: Severe eosinopenia (< 24.4 cells/mm3) alone yielded small but statistically significant overall predictive ability (AUROC: 0.648, 95% confidence interval (CI): 0.547-0.748, P < 0.05), and only moderate sensitivity (68, 95% CI: 46-85%) and specificity (62, 95% CI: 54-69%). The model comprising baseline variables (age, sex), the C-reactive protein level, and neutrophil count yielded an AUROC of 0.729, and further addition of eosinopenia yielded a slight improvement, with an AUROC of 0.758 (P < 0.05) and a statistically significant net reclassification improvement (NRI) (P = 0.003). However, the integrated discrimination index (IDI) (P = 0.284) remained non-significant. CONCLUSIONS: Severe eosinopenia can be considered an inexpensive marker of bloodstream infection, although of limited diagnostic accuracy, in a general internal medicine setting.

Enhancing clinical reasoning with Chat Generative Pre-trained Transformer: a practical guide.

OBJECTIVES: This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicians. METHODS: We conducted a comprehensive exploration of the capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of ChatGPT, with a focus on application and language selection, input methodology, and output verification; and (2) specific strategies to bolster clinical reasoning using ChatGPT, including self-learning via simulated clinical case creation and engagement with published case reports. RESULTS: Effective AI-based clinical reasoning development requires a clear delineation of both system roles and user needs. All outputs from the system necessitate rigorous verification against credible medical resources. When used in self-learning scenarios, capabilities of ChatGPT in clinical case creation notably enhanced disease comprehension. CONCLUSIONS: The efficient use of generative AIs, as exemplified by ChatGPT, can impressively enhance clinical reasoning among medical professionals. Adopting these cutting-edge tools promises a bright future for continuous advancements in clinicians' diagnostic skills, heralding a transformative era in digital healthcare.

The potential, limitations, and future of diagnostics enhanced by generative artificial intelligence.

OBJECTIVES: This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics. METHODS: This commentary reviews current applications and advancements in GAI, particularly focusing on its integration into medical diagnostics. It examines the role of GAI in supporting medical interviews, assisting in differential diagnosis, and aiding clinical reasoning through the lens of dual-process theory. The discussion is supported by recent examples and theoretical frameworks to illustrate the practical and potential uses of GAI in medicine. RESULTS: GAI shows significant promise in enhancing diagnostic processes by supporting the translation of patient descriptions into visual formats, providing differential diagnoses, and facilitating complex clinical reasoning. However, limitations such as the potential for generating medical misinformation, known as hallucinations, exist. Furthermore, the commentary highlights the integration of GAI with both intuitive and analytical decision-making processes in clinical diagnostics, demonstrating potential improvements in both the speed and accuracy of diagnoses. CONCLUSIONS: While GAI presents transformative potential for medical diagnostics, it also introduces risks that must be carefully managed. Future advancements should focus on refining GAI technologies to better align with human diagnostic reasoning, ensuring GAI enhances rather than replaces the medical professionals' expertise.

Can ChatGPT-4 evaluate whether a differential diagnosis list contains the correct diagnosis as accurately as a physician?

OBJECTIVES: The potential of artificial intelligence (AI) chatbots, particularly the fourth-generation chat generative pretrained transformer (ChatGPT-4), in assisting with medical diagnosis is an emerging research area. While there has been significant emphasis on creating lists of differential diagnoses, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in these lists. This short communication aimed to assess the accuracy of ChatGPT-4 in evaluating lists of differential diagnosis compared to medical professionals' assessments. METHODS: We used ChatGPT-4 to evaluate whether the final diagnosis was included in the top 10 differential diagnosis lists created by physicians, ChatGPT-3, and ChatGPT-4, using clinical vignettes. Eighty-two clinical vignettes were used, comprising 52 complex case reports published by the authors from the department and 30 mock cases of common diseases created by physicians from the same department. We compared the agreement between ChatGPT-4 and the physicians on whether the final diagnosis was included in the top 10 differential diagnosis lists using the kappa coefficient. RESULTS: Three sets of differential diagnoses were evaluated for each of the 82 cases, resulting in a total of 246 lists. The agreement rate between ChatGPT-4 and physicians was 236 out of 246 (95.9 %), with a kappa coefficient of 0.86, indicating very good agreement. CONCLUSIONS: ChatGPT-4 demonstrated very good agreement with physicians in evaluating whether the final diagnosis should be included in the differential diagnosis lists.

Evaluating ChatGPT-4's Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases.

BACKGROUND: The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists. OBJECTIVE: This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series. METHODS: We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4's evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician. RESULTS: The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4's evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians' evaluations. CONCLUSIONS: GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

Evaluating ChatGPT-4's Diagnostic Accuracy: Impact of Visual Data Integration.

BACKGROUND: In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear. OBJECTIVE: This study aims to assess the impact of adding image data on ChatGPT-4's diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics. Specifically, this study endeavored to compare the diagnostic accuracy between ChatGPT-4V, which processed both text and image data, and its counterpart, ChatGPT-4, which only uses text data. METHODS: We identified a total of 557 case reports published in the American Journal of Case Reports from January 2022 to March 2023. After excluding cases that were nondiagnostic, pediatric, and lacking image data, we included 363 case descriptions with their final diagnoses and associated images. We compared the diagnostic accuracy of ChatGPT-4V and ChatGPT-4 without vision based on their ability to include the final diagnoses within differential diagnosis lists. Two independent physicians evaluated their accuracy, with a third resolving any discrepancies, ensuring a rigorous and objective analysis. RESULTS: The integration of image data into ChatGPT-4V did not significantly enhance diagnostic accuracy, showing that final diagnoses were included in the top 10 differential diagnosis lists at a rate of 85.1% (n=309), comparable to the rate of 87.9% (n=319) for the text-only version (P=.33). Notably, ChatGPT-4V's performance in correctly identifying the top diagnosis was inferior, at 44.4% (n=161), compared with 55.9% (n=203) for the text-only version (P=.002, χ2 test). Additionally, ChatGPT-4's self-reports showed that image data accounted for 30% of the weight in developing the differential diagnosis lists in more than half of cases. CONCLUSIONS: Our findings reveal that currently, ChatGPT-4V predominantly relies on textual data, limiting its ability to fully use the diagnostic potential of visual information. This study underscores the need for further development of multimodal generative AI systems to effectively integrate and use clinical image data. Enhancing the diagnostic performance of such AI systems through improved multimodal data integration could significantly benefit patient care by providing more accurate and comprehensive diagnostic insights. Future research should focus on overcoming these limitations, paving the way for the practical application of advanced AI in medicine.

Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial.

Background: The utility of a clinical decision support system using a machine learning (ML) model for simultaneous cardiac and pulmonary auscultation is unknown. Objective: This study aimed to develop and evaluate an ML system's utility for cardiopulmonary auscultation. Methods: First, we developed an ML system for cardiopulmonary auscultation, using cardiopulmonary sound files from our previous study. The technique involved pre-processing, feature extraction, and classification through several neural network layers. After integration, the output class was categorized as "normal," "abnormal," or "undetermined." Second, we evaluated the ML system with 24 junior residents in an open-label randomized controlled trial at a university hospital. Participants were randomly assigned to the ML system group (intervention) or conventional auscultation group (control). During training, participants listened to four cardiac and four pulmonary sounds, all of which were correctly classified. Then, participants classified a series of 16 simultaneous cardiopulmonary sounds. The control group auscultated the sounds using noise-cancelling headphones, while the intervention group did so by watching recommendations from the ML system. Results: The total scores for correctly identified normal or abnormal cardiopulmonary sounds in the intervention group were significantly higher than those in the control group (366/384 [95.3%] vs. 343/384 [89.3%], P = 0.003). The cardiac test score in the intervention group was better (111/192 [57.8%] vs. 90/192 [46.9%], P = 0.04); there was no significant difference in pulmonary auscultation. Conclusions: The ML-based system improved the accuracy of cardiopulmonary auscultation for junior residents. This result suggests that the system can assist early-career physicians in accurate screening.

Comparative Evaluation of Diagnostic Accuracy Between Google Bard and Physicians.

BACKGROUND: In this study, we evaluated the diagnostic accuracy of Google Bard, a generative artificial intelligence (AI) platform. METHODS: We searched published case reports from our department for difficult or uncommon case descriptions and mock cases created by physicians for common case descriptions. We entered the case descriptions into the prompt of Google Bard to generate the top 10 differential-diagnosis lists. As in previous studies, other physicians created differential-diagnosis lists by reading the same clinical descriptions. RESULTS: A total of 82 clinical descriptions (52 case reports and 30 mock cases) were used. The accuracy rates of physicians were still higher than Google Bard in the top 10 (56.1% vs 82.9%, P < .001), the top 5 (53.7% vs 78.0%, P = .002), and the top differential diagnosis (40.2% vs 64.6%, P = .003). Even within the specific context of case reports, physicians consistently outperformed Google Bard. When it came to mock cases, the performances of the differential-diagnosis lists by Google Bard were no different from those of the physicians in the top 10 (80.0% vs 96.6%, P = .11) and the top 5 (76.7% vs 96.6%, P = .06), except for those in the top diagnoses (60.0% vs 90.0%, P = .02). CONCLUSION: While physicians excelled overall, and particularly with case reports, Google Bard displayed comparable diagnostic performance in common cases. This suggested that Google Bard possesses room for further improvement and refinement in its diagnostic capabilities. Generative AIs, including Google Bard, are anticipated to become increasingly beneficial in augmenting diagnostic accuracy.

Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study.

The diagnostic accuracy of differential diagnoses generated by artificial intelligence (AI) chatbots, including the generative pretrained transformer 3 (GPT-3) chatbot (ChatGPT-3) is unknown. This study evaluated the accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical vignettes with common chief complaints. General internal medicine physicians created clinical cases, correct diagnoses, and five differential diagnoses for ten common chief complaints. The rate of correct diagnosis by ChatGPT-3 within the ten differential-diagnosis lists was 28/30 (93.3%). The rate of correct diagnosis by physicians was still superior to that by ChatGPT-3 within the five differential-diagnosis lists (98.3% vs. 83.3%, p = 0.03). The rate of correct diagnosis by physicians was also superior to that by ChatGPT-3 in the top diagnosis (53.3% vs. 93.3%, p < 0.001). The rate of consistent differential diagnoses among physicians within the ten differential-diagnosis lists generated by ChatGPT-3 was 62/88 (70.5%). In summary, this study demonstrates the high diagnostic accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical cases with common chief complaints. This suggests that AI chatbots such as ChatGPT-3 can generate a well-differentiated diagnosis list for common chief complaints. However, the order of these lists can be improved in the future.

The utility of phonocardiograms in real-time remote cardiac auscultation using an internet-connected electronic stethoscope: Open-label randomized controlled pilot trial.

Background: We have shown classical cardiac auscultation was superior to remote auscultation. We developed a phonocardiogram system to visualize sounds in remote auscultation. Objective: This study aimed to evaluate the effect of phonocardiograms on the diagnostic accuracy in remote auscultation using a cardiology patient simulator. Methods: In this open-label randomized controlled pilot trial, we randomly assigned physicians to the real-time remote auscultation group (control group) or the real-time remote auscultation with the phonocardiogram group (intervention group). Participants attended a training session in which they auscultated 15 sounds with the correct classification. After that, participants attended a test session where they had to classify 10 sounds. The control group auscultated the sounds remotely using an electronic stethoscope, an online medical program and a 4-K TV speaker without watching the TV screen. The intervention group performed auscultation like the control group but watched the phonocardiogram on the TV screen. The primary and secondary outcomes were the total test scores and each sound score, respectively. Results: A total of 24 participants were included. The total test score in the intervention group (80/120, 66.7%) was higher than that in the control group (66/120, 55.0%), although the difference was statistically insignificant (P = .06). The correct answer rates of each sound were not different. Valvular/irregular rhythm sounds were not misclassified as normal sounds in the intervention group. Conclusions: Using a phonocardiogram improved the total correct answer rate by more than 10% in remote auscultation, although statistically insignificant. The phonocardiogram could help physicians screen valvular/irregular rhythm sounds from normal sounds. Trial registration: UMIN-CTR UMIN000045271; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051710.

Effect of Prior Antibiotic Treatment on Blood Culture in an Outpatient Department of General Internal Medicine: A Retrospective Case-Control Analysis.

Purpose: The effect of antibiotics administered before blood cultures performed in general internal medicine outpatient settings is not well known. Patients and Methods: We conducted a retrospective case-control study including adult patients who underwent blood cultures in the general internal medicine outpatient department of a Japanese university hospital between 2016 and 2022. Patients with positive blood cultures were included as cases and matched patients with negative blood cultures were included as controls. Univariable and multivariable logistic regression analyses were performed. Results: A total of 200 patients and 200 controls were included. Antibiotics were administered prior to blood culture in 20% of patients (79/400). Oral antibiotics were prescribed to 69.6% of the prior antibiotics (55/79). Prior antibiotic use was significantly lower among patients with positive than negative blood cultures (13.5% vs 26.0%, p = 0.002) and was an independent predictive factor in univariable (odds ratio, 0.44; 95% confidence interval, 0.26-0.73; p = 0.002) and multivariable (adjusted odds ratio, 0.31; 95% confidence interval, 0.15-0.63; p = 0.002) logistic regression models for positive blood culture. The area under the receiver operating characteristic (AUROC) curve of the multivariable model for predicting positive blood cultures was 0.86. Conclusion: There was a negative correlation between prior antibiotic use and positive blood cultures in the general internal medicine outpatient department. Therefore, physicians should interpret the negative results of blood cultures performed after the administration of antibiotics with care.

ChatGPT-Generated Differential Diagnosis Lists for Complex Case-Derived Clinical Vignettes: Diagnostic Accuracy Evaluation.

BACKGROUND: The diagnostic accuracy of differential diagnoses generated by artificial intelligence chatbots, including ChatGPT models, for complex clinical vignettes derived from general internal medicine (GIM) department case reports is unknown. OBJECTIVE: This study aims to evaluate the accuracy of the differential diagnosis lists generated by both third-generation ChatGPT (ChatGPT-3.5) and fourth-generation ChatGPT (ChatGPT-4) by using case vignettes from case reports published by the Department of GIM of Dokkyo Medical University Hospital, Japan. METHODS: We searched PubMed for case reports. Upon identification, physicians selected diagnostic cases, determined the final diagnosis, and displayed them into clinical vignettes. Physicians typed the determined text with the clinical vignettes in the ChatGPT-3.5 and ChatGPT-4 prompts to generate the top 10 differential diagnoses. The ChatGPT models were not specially trained or further reinforced for this task. Three GIM physicians from other medical institutions created differential diagnosis lists by reading the same clinical vignettes. We measured the rate of correct diagnosis within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and the top diagnosis. RESULTS: In total, 52 case reports were analyzed. The rates of correct diagnosis by ChatGPT-4 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 83% (43/52), 81% (42/52), and 60% (31/52), respectively. The rates of correct diagnosis by ChatGPT-3.5 within the top 10 differential diagnosis lists, top 5 differential diagnosis lists, and top diagnosis were 73% (38/52), 65% (34/52), and 42% (22/52), respectively. The rates of correct diagnosis by ChatGPT-4 were comparable to those by physicians within the top 10 (43/52, 83% vs 39/52, 75%, respectively; P=.47) and within the top 5 (42/52, 81% vs 35/52, 67%, respectively; P=.18) differential diagnosis lists and top diagnosis (31/52, 60% vs 26/52, 50%, respectively; P=.43) although the difference was not significant. The ChatGPT models' diagnostic accuracy did not significantly vary based on open access status or the publication date (before 2011 vs 2022). CONCLUSIONS: This study demonstrates the potential diagnostic accuracy of differential diagnosis lists generated using ChatGPT-3.5 and ChatGPT-4 for complex clinical vignettes from case reports published by the GIM department. The rate of correct diagnoses within the top 10 and top 5 differential diagnosis lists generated by ChatGPT-4 exceeds 80%. Although derived from a limited data set of case reports from a single department, our findings highlight the potential utility of ChatGPT-4 as a supplementary tool for physicians, particularly for those affiliated with the GIM department. Further investigations should explore the diagnostic accuracy of ChatGPT by using distinct case materials beyond its training data. Such efforts will provide a comprehensive insight into the role of artificial intelligence in enhancing clinical decision-making.

The Clinical and Biological Manifestations in Women with Iron Deficiency Without Anemia Compared to Iron Deficiency Anemia in a General Internal Medicine Setting: A Retrospective Cohort Study.

Purpose: The clinical presentation of iron deficiency is not well understood. We aimed to identify the clinical manifestations of iron deficiency without anemia in women. Patients and Methods: We conducted a retrospective cohort study of women who visited the general internal medicine outpatient department of a university hospital in Japan between 2016 and 2022. Women who were prescribed iron supplements were included in the study. Anemia was defined as hemoglobin levels below 12 g/dl. Iron deficiency was defined as serum ferritin levels < 30.0 µg/l. The primary outcome was the difference in symptoms between patients with iron deficiency with and without anemia. The secondary outcome was the ratio of symptom, hemoglobin, and serum ferritin improvement (levels > 30.0 µg/l after treatment), comparing the measurements at the beginning and after supplementation. Results: A total of 147 women were included in the final analysis. There were no significant differences in the initial symptoms and the ratio of symptom improvement between the groups. Compared to patients with iron deficiency anemia, patients with iron deficiency without anemia had high initial serum ferritin levels (14.8 vs 7.1 µg/l, p<0.001), and hemoglobin (13.2 vs 9.9 g/dl, p<0.001). Iron supplements significantly improved the serum ferritin level in two groups and the hemoglobin in iron deficiency anemia. After treatment, iron deficiency without anemia still had high serum ferritin levels (37.7 vs 28.2 µg/l, p=0.017) and hemoglobin (13.3 vs 12.3 g/dl, p < 0.001). Conclusion: There were no differences in any of the investigated symptoms and the ratio of the symptom improvement depending on the anemic state in iron deficiency. After iron supplementation, the serum ferritin levels in the iron deficiency without anemia group improved. Hemoglobin and serum ferritin in iron deficiency without anemia were still highly comparable to that of iron deficiency anemia.

Bandemia as an Early Predictive Marker of Bacteremia: A Retrospective Cohort Study.

This single-center retrospective observational study aimed to verify whether a diagnosis of bandemia could be a predictive marker for bacteremia. We assessed 970 consecutive patients (median age 73 years; male 64.8%) who underwent two or more sets of blood cultures between April 2015 and March 2016 in both inpatient and outpatient settings. We assessed the value of bandemia (band count > 10%) and the percentage band count for predicting bacteremia using logistic regression models. Bandemia was detected in 151 cases (15.6%) and bacteremia was detected in 188 cases (19.4%). The incidence of bacteremia was significantly higher in cases with bandemia (52.3% vs. 13.3%; odds ratio (OR) = 7.15; 95% confidence interval (CI) 4.91-10.5). The sensitivity and specificity of bandemia for predicting bacteremia were 0.42 and 0.91, respectively. The bandemia was retained as an independent predictive factor for the multivariable logistic regression model (OR, 6.13; 95% CI, 4.02-9.40). Bandemia is useful for establishing the risk of bacteremia, regardless of the care setting (inpatient or outpatient), with a demonstrable relationship between increased risk and bacteremia. A bandemia-based electronic alert for blood-culture collection may contribute to the improved diagnosis of bacteremia.

Evaluation of Internet-Connected Real-Time Remote Auscultation: An Open-Label Randomized Controlled Pilot Trial.

The utility of remote auscultation was unknown. This study aimed to evaluate internet-connected real-time remote auscultation using cardiopulmonary simulators. In this open-label randomized controlled trial, the physicians were randomly assigned to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). After the training session, the participants had to classify the ten cardiopulmonary sounds in random order as the test session. In both sessions, the intervention group auscultated with an internet-connected electronic stethoscope. The control group performed direct auscultation using a classical stethoscope. The total scores for correctly identified normal or abnormal cardiopulmonary sounds were 97/100 (97%) in the intervention group and 98/100 (98%) in the control group with no significant difference between the groups (p > 0.99). In cardiac auscultation, the test score in the control group (94%) was superior to that in the intervention group (72%, p < 0.05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. The utility of real-time remote cardiopulmonary auscultation using an internet-connected electronic stethoscope was comparable to that of classical auscultation. Classical cardiac auscultation was superior to real-time remote auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds.

The Utility of Real-Time Remote Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial.

BACKGROUND: The urgent need for telemedicine has become clear in the COVID-19 pandemic. To facilitate telemedicine, the development and improvement of remote examination systems are required. A system combining an electronic stethoscope and Bluetooth connectivity is a promising option for remote auscultation in clinics and hospitals. However, the utility of such systems remains unknown. OBJECTIVE: This study was conducted to assess the utility of real-time auscultation using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation, using lung and cardiology patient simulators. METHODS: This was an open-label, randomized controlled trial including senior residents and faculty in the department of general internal medicine of a university hospital. The only exclusion criterion was a refusal to participate. This study consisted of 2 parts: lung auscultation and cardiac auscultation. Each part contained a tutorial session and a test session. All participants attended a tutorial session, in which they listened to 15 sounds on the simulator using a classic stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group) for test sessions. In the test sessions, participants had to classify a series of 10 lung sounds and 10 cardiac sounds, depending on the study part. The intervention group listened to the sounds remotely using the electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the sounds directly using a traditional stethoscope. The primary outcome was the test score, and the secondary outcomes were the rates of correct answers for each sound. RESULTS: In total, 20 participants were included. There were no differences in age, sex, and years from graduation between the 2 groups in each part. The overall test score of lung auscultation in the intervention group (80/110, 72.7%) was not different from that in the control group (71/90, 78.9%; P=.32). The only lung sound for which the correct answer rate differed between groups was that of pleural friction rubs (P=.03); it was lower in the intervention group (3/11, 27%) than in the control group (7/9, 78%). The overall test score for cardiac auscultation in the intervention group (50/60, 83.3%) was not different from that in the control group (119/140, 85.0%; P=.77). There was no cardiac sound for which the correct answer rate differed between groups. CONCLUSIONS: The utility of a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope, except for classification of pleural friction rubs. This means that most of the real world's essential cardiopulmonary sounds could be classified by a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope. TRIAL REGISTRATION: UMIN-CTR UMIN000040828; https://tinyurl.com/r24j2p6s and UMIN-CTR UMIN000041601; https://tinyurl.com/bsax3j5f.

Safety and efficacy of outpatient follow-up for referred patients with undiagnosed fever: A retrospective cohort study.

Fever is one of the most common symptoms seen in patients. The work-up and follow-up of fever in an outpatient-only setting is a reasonable option for stable patients referred for unexplained fever; however, the safety and efficacy of outpatient follow-up for those patients remain unclear. We conducted this study to evaluate the safety and efficacy of outpatient follow-up for referred patients with unexplained fever.This study was a retrospective cohort study. We included patients referred to the outpatient department of the diagnostic medicine of our university hospital for unexplained fever between October 2016 and September 2017. Exclusion criteria were recurrent fever or admission for fever evaluation prior to referral. Main outcomes of interest were the rate of admission without diagnosis, rate of remission of fever, and the total duration of fever in undiagnosed patients.Among 84 patients included in this study, 17 (20%) were diagnosed during outpatient follow-up, 6 (7%) were admitted due to worsened condition, 5 (6%) were lost to follow-up, and 56 (67%) were followed up as outpatients without a diagnosis. Among the 56 undiagnosed patients, fever resolved in 53 during outpatient follow-up with or without treatment (95%). The total duration of resolved fever in undiagnosed patients was within 8 weeks.Follow-up of patients referred for unexplained fever in an outpatient setting is safe and effective.

Metoclopramide-induced Serotonin Syndrome.

A 40-year-old woman with bipolar disorder who was taking mirtazapine presented with mydriasis, abnormal diaphoresis, myoclonus and muscle rigidity after taking metocloplamide. Her medical history, which included the use of serotonergic agents, and the presence of symptoms including myoclonus and muscle rigidity were consistent with a diagnosis of serotonin syndrome (SS) according to the Hunter criteria. The symptoms diminished following three days of treatment with oral lorazepam and cyproheptadine and a reduced dose of mirtazapine. Metoclopramide is frequently used to various gastric symptom. Metoclopramide is not widely known to induce SS. This potentially fatal condition should be avoided by exercising care in the use of drugs that have the potential to cause drug-drug interactions.

Left pleural effusion caused by pancreaticopleural fistula with a pancreatic pseudocyst.

Pancreaticopleural fistula is an uncommon complication of chronic pancreatitis. The authors described a case of a man with medical history of alcohol-related chronic pancreatitis, presented with dyspnoea. The roentgenogram showed a massive left pleural effusion. Additional work-up revealed a pancreaticopleural fistula and amylase-rich pleural effusion. His respiratory state improved after the insertion of chest drainage tube. During his admission, conservative and endoscopic therapy was required for the treatment of his complication of mediastinal abscess and arterial aneurysm in the pancreatic pseudocyst.