Diagnostic errors and characteristics of patients seen at a general internal medicine outpatient clinic with a referral for diagnosis.

OBJECTIVES: Patients referred to general internal medicine (GIM) outpatient clinics may face a higher risk of diagnostic errors than non-referred patients. This difference in risk is assumed to be due to the differences in diseases and clinical presentations between referred and non-referred patients; however, clinical data regarding this issue are scarce. This study aimed to determine the frequency of diagnostic errors and compare the characteristics of referred and non-referred patients visit GIM outpatient clinics. METHODS: This study included consecutive outpatients who visited the GIM outpatient clinic at a university hospital, with or without referral. Data on age, sex, chief complaints, referral origin, and final diagnosis were collected from medical records. The Revised Safer Dx Instrument was used to detect diagnostic errors. RESULTS: Data from 534 referred and 599 non-referred patients were analyzed. The diagnostic error rate was higher in the referral group than that in the non-referral group (2.2 % vs. 0.5 %, p=0.01). The prevalence of abnormal test results and sensory disturbances was higher in the chief complaints, and the prevalence of musculoskeletal system disorders, connective tissue diseases, and neoplasms was higher in the final diagnoses of referred patients compared with non-referred patients. Among referred patients with diagnostic errors, abnormal test results and sensory disturbances were the two most common chief complaints, whereas neoplasia was the most common final diagnosis. Problems with data integration and interpretation were found to be the most common factors contributing to diagnostic errors. CONCLUSIONS: Paying more attention to patients with abnormal test results and sensory disturbances and considering a higher pre-test probability for neoplasms may prevent diagnostic errors in patients referred to GIM outpatient clinics.

A20 in hepatic stellate cells suppresses chronic hepatitis by inhibiting DCLK1-JNK pathway-dependent chemokines.

Hepatic stellate cells (HSCs) are responsible for liver fibrosis accompanied by its activation into myofibroblasts and the abundant production of extracellular matrix. However, the HSC contribution to progression of liver inflammation has been less known. We aimed to elucidate the mechanism in HSCs underlying the inflammatory response and the function of tumor necrosis factor α-related protein A20 (TNFAIP3). We established A20 conditional knockout (KO) mice crossing Twist2-Cre and A20 floxed mice. Using these mice, the effect of A20 was analyzed in mouse liver and HSCs. The human HSC line LX-2 was also used to examine the role and underlying molecular mechanism of A20. In this KO model, A20 was deficient in >80% of HSCs. Spontaneous inflammation with mild fibrosis was found in the liver of the mouse model without any exogenous agents, suggesting that A20 in HSCs suppresses chronic hepatitis. Comprehensive RNA sequence analysis revealed that A20-deficient HSCs exhibited an inflammatory phenotype and abnormally expressed chemokines. A20 suppressed JNK pathway activation in HSCs. Loss of A20 function in LX-2 cells also induced excessive chemokine expression, mimicking A20-deficient HSCs. A20 overexpression suppressed chemokine expression in LX-2. In addition, we identified DCLK1 in the genes regulated by A20. DCLK1 activated the JNK pathway and upregulates chemokine expression. DCLK1 inhibition significantly decreased chemokine induction by A20-silencing, suggesting that A20 controlled chemokine expression in HSCs via the DCLK1-JNK pathway. In conclusion, A20 suppresses chemokine induction dependent on the DCLK1-JNK signaling pathway. These findings demonstrate the therapeutic potential of A20 and the DCLK1-JNK pathway for the regulation of inflammation in chronic hepatitis.

Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.

Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.

Hospital Use of a Web-Based Clinical Knowledge Support System and In-Training Examination Performance Among Postgraduate Resident Physicians in Japan: Nationwide Observational Study.

Background: The relationship between educational outcomes and the use of web-based clinical knowledge support systems in teaching hospitals remains unknown in Japan. A previous study on this topic could have been affected by recall bias because of the use of a self-reported questionnaire. Objective: We aimed to explore the relationship between the use of the Wolters Kluwer UpToDate clinical knowledge support system in teaching hospitals and residents' General Medicine In-Training Examination (GM-ITE) scores. In this study, we objectively evaluated the relationship between the total number of UpToDate hospital use logs and the GM-ITE scores. Methods: This nationwide cross-sectional study included postgraduate year-1 and -2 residents who had taken the examination in the 2020 academic year. Hospital-level information was obtained from published web pages, and UpToDate hospital use logs were provided by Wolters Kluwer. We evaluated the relationship between the total number of UpToDate hospital use logs and residents' GM-ITE scores. We analyzed 215 teaching hospitals with at least 5 GM-ITE examinees and hospital use logs from 2017 to 2019. Results: The study population consisted of 3013 residents from 215 teaching hospitals with at least 5 GM-ITE examinees and web-based resource use log data from 2017 to 2019. High-use hospital residents had significantly higher GM-ITE scores than low-use hospital residents (mean 26.9, SD 2.0 vs mean 26.2, SD 2.3; P=.009; Cohen d=0.35, 95% CI 0.08-0.62). The GM-ITE scores were significantly correlated with the total number of hospital use logs (Pearson r=0.28; P<.001). The multilevel analysis revealed a positive association between the total number of logs divided by the number of hospital physicians and the GM-ITE scores (estimated coefficient=0.36, 95% CI 0.14-0.59; P=.001). Conclusions: The findings suggest that the development of residents' clinical reasoning abilities through UpToDate is associated with high GM-ITE scores. Thus, higher use of UpToDate may lead physicians and residents in high-use hospitals to increase the implementation of evidence-based medicine, leading to high educational outcomes.

Performance evaluation of ChatGPT in detecting diagnostic errors and their contributing factors: an analysis of 545 case reports of diagnostic errors.

BACKGROUND: Manual chart review using validated assessment tools is a standardised methodology for detecting diagnostic errors. However, this requires considerable human resources and time. ChatGPT, a recently developed artificial intelligence chatbot based on a large language model, can effectively classify text based on suitable prompts. Therefore, ChatGPT can assist manual chart reviews in detecting diagnostic errors. OBJECTIVE: This study aimed to clarify whether ChatGPT could correctly detect diagnostic errors and possible factors contributing to them based on case presentations. METHODS: We analysed 545 published case reports that included diagnostic errors. We imputed the texts of case presentations and the final diagnoses with some original prompts into ChatGPT (GPT-4) to generate responses, including the judgement of diagnostic errors and contributing factors of diagnostic errors. Factors contributing to diagnostic errors were coded according to the following three taxonomies: Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC) and Generic Diagnostic Pitfalls (GDP). The responses on the contributing factors from ChatGPT were compared with those from physicians. RESULTS: ChatGPT correctly detected diagnostic errors in 519/545 cases (95%) and coded statistically larger numbers of factors contributing to diagnostic errors per case than physicians: DEER (median 5 vs 1, p<0.001), RDC (median 4 vs 2, p<0.001) and GDP (median 4 vs 1, p<0.001). The most important contributing factors of diagnostic errors coded by ChatGPT were 'failure/delay in considering the diagnosis' (315, 57.8%) in DEER, 'atypical presentation' (365, 67.0%) in RDC, and 'atypical presentation' (264, 48.4%) in GDP. CONCLUSION: ChatGPT accurately detects diagnostic errors from case presentations. ChatGPT may be more sensitive than manual reviewing in detecting factors contributing to diagnostic errors, especially for 'atypical presentation'.

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.

Humoral immune response against SARS-CoV-2 and polyethylene glycol elicited by anti-SARS-CoV-2 mRNA vaccine, and effect of pre-existing anti-polyethylene glycol antibody in patients with hematological and autoimmune diseases.

Background: The effects of vaccination are modified by hematological and autoimmune diseases and/or treatment. Anti-SARS-CoV-2 mRNA vaccine contains polyethylene glycol (PEG), it is largely unknown whether PEG influences the effects of vaccination or induces a humoral response. This study examined whether anti-PEG antibodies before vaccination (pre-existing) influenced the acquisition of SARS-CoV-2 antibodies and evaluated the relationship between the development of anti-SARS-CoV-2 antibodies and anti-PEG antibodies after SARS-CoV-2 vaccination in hematological and autoimmune diseases. Methods: Anti-SARS-CoV-2 antibody IgG, anti-PEG IgG, and IgM titers were evaluated in patients with hematological and autoimmune diseases after the second dose of BNT162B2. Anti-PEG IgG and IgM titers were also measured before vaccination to examine changes after vaccination and the relationship with vaccine efficacy. Results: In patients with hematological (n = 182) and autoimmune diseases (n = 96), anti-SARS-CoV-2 and anti-PEG antibody titers were evaluated after a median of 33 days from 2nd vaccination. The median anti-SARS-CoV-2 antibody titers were 1901 AU/mL and 3832 AU/mL in patients with hematological and autoimmune disease, respectively. Multiple regression analysis showed that age and days from 2nd vaccination were negatively associated with anti-SARS-CoV-2 antibody titers. Anti-CD20 antibody treatment was negatively correlated with anti-SARS-CoV-2 antibody titers in hematological disease, and C-reactive protein (CRP) was positively correlated with anti-SARS-CoV-2 antibody titers in autoimmune disease. Baseline anti-PEG antibody titers were significantly higher in patients with autoimmune disease but were not correlated with anti-SARS-CoV-2 antibody titers. Patients with increased anti-PEG IgG acquired higher anti-SARS-CoV-2 antibody titers in patients with autoimmune disease. Conclusions: Anti-SARS-CoV-2 antibody acquisition was suboptimal in patients with hematological disease, but both anti-SARS-CoV-2 antibody and anti-PEG IgG were acquired in patients with autoimmune disease, reflecting robust humoral immune response. Pre-existing anti-PEG antibody titers did not affect anti-SARS-CoV-2 antibody acquisition.

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.

BACKGROUND: Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited. OBJECTIVE: This study aimed to assess the longitudinal changes in the accuracy of differential diagnosis lists created by an AI-based symptom checker used in the real world. METHODS: This was a single-center, retrospective, observational study. Patients who visited an outpatient clinic without an appointment between May 1, 2019, and April 30, 2022, and who were admitted to a community hospital in Japan within 30 days of their index visit were considered eligible. We only included patients who underwent an AI-based symptom checkup at the index visit, and the diagnosis was finally confirmed during follow-up. Final diagnoses were categorized as common or uncommon, and all cases were categorized as typical or atypical. The primary outcome measure was the accuracy of the differential diagnosis list created by the AI-based symptom checker, defined as the final diagnosis in a list of 10 differential diagnoses created by the symptom checker. To assess the change in the symptom checker's diagnostic accuracy over 3 years, we used a chi-square test to compare the primary outcome over 3 periods: from May 1, 2019, to April 30, 2020 (first year); from May 1, 2020, to April 30, 2021 (second year); and from May 1, 2021, to April 30, 2022 (third year). RESULTS: A total of 381 patients were included. Common diseases comprised 257 (67.5%) cases, and typical presentations were observed in 298 (78.2%) cases. Overall, the accuracy of the differential diagnosis list created by the AI-based symptom checker was 172 (45.1%), which did not differ across the 3 years (first year: 97/219, 44.3%; second year: 32/72, 44.4%; and third year: 43/90, 47.7%; P=.85). The accuracy of the differential diagnosis list created by the symptom checker was low in those with uncommon diseases (30/124, 24.2%) and atypical presentations (12/83, 14.5%). In the multivariate logistic regression model, common disease (P<.001; odds ratio 4.13, 95% CI 2.50-6.98) and typical presentation (P<.001; odds ratio 6.92, 95% CI 3.62-14.2) were significantly associated with the accuracy of the differential diagnosis list created by the symptom checker. CONCLUSIONS: A 3-year longitudinal survey of the diagnostic accuracy of differential diagnosis lists developed by an AI-based symptom checker, which has been implemented in real-world clinical practice settings, showed no improvement over time. Uncommon diseases and atypical presentations were independently associated with a lower diagnostic accuracy. In the future, symptom checkers should be trained to recognize uncommon conditions.

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.

Spinal Deformity Manifested by Urinary Tract Infection: A Case of Acute-on-Chronic Mobility Dysfunction.

A 53-year-old man with diabetes mellitus presented to the emergency department with a fever and impaired mobility. A preliminary diagnosis of urinary tract infection was made based on dysuria and pyuria. History-taking revealed a history of gait disturbance and difficult urination. A thorough physical examination suggested a spinal abnormality. MRI scan revealed a narrow spinal canal due to ossification of the posterior longitudinal ligament and diffuse idiopathic skeletal hyperostosis. Throughout the diagnostic process, we employed both vertical tracing to investigate the causes of urinary tract infection and horizontal tracing to explore comorbidities such as diabetes. Additionally, we introduced appropriate social security and support systems under the name of diagnostic excellence.

Analysis of prognosis and background liver disease in non-advanced hepatocellular carcinoma in two decades.

BACKGROUND/AIM: Antiviral hepatitis and systemic therapies for hepatocellular carcinoma (HCC) remarkably progressed in the recent 10 years. This study aimed to reveal the actual transition and changes in the prognosis and background liver disease in non-advanced HCC in the past 20 years. METHODS: This retrospectively recruited 566 patients who were diagnosed with non-advanced HCC from February 2002 to February 2022. The prognosis was analyzed by subdividing according to the diagnosis date (period I: February 2002-April 2009 and period Ⅱ: May 2009-February 2022). RESULTS: Patients in period II (n = 351) were significantly older, with lower albumin-bilirubin (ALBI) scores and alpha-fetoprotein (AFP) and more anti-viral therapy, systemic therapy, and hepatic arterial infusion chemotherapy as compared with those in period I (n = 215). The etiology ratio of the background liver disease revealed decreased hepatitis C virus from 70.6% to 49.0% and increased non-B, non-C from 17.7% to 39.9% from periods I to Ⅱ. The multivariate analysis revealed older age and higher ALBI score in Barcelona Clinic Liver Cancer (BCLC) 0/A stage, AFP of >20 ng/mL, and higher ALBI score in BCLC B stage as independent prognosis factors. Fine-Gray competing risk model analysis revealed that liver-related deaths significantly decreased in period II as compared to period I, especially for BCLC stage 0/A (HR: 0.656; 95%CI: 0.442-0.972, P = 0.036). CONCLUSION: The characteristics of patients with non-advanced HCC have changed over time. Appropriate background liver management led to better liver-related prognoses in BCLC 0/A.

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.

Definitions and Measurements for Atypical Presentations at Risk for Diagnostic Errors in Internal Medicine: Protocol for a Scoping Review.

BACKGROUND: Atypical presentations have been increasingly recognized as a significant contributing factor to diagnostic errors in internal medicine. However, research to address associations between atypical presentations and diagnostic errors has not been evaluated due to the lack of widely applicable definitions and criteria for what is considered an atypical presentation. OBJECTIVE: The aim of the study is to describe how atypical presentations are defined and measured in studies of diagnostic errors in internal medicine and use this new information to develop new criteria to identify atypical presentations at high risk for diagnostic errors. METHODS: This study will follow an established framework for conducting scoping reviews. Inclusion criteria are developed according to the participants, concept, and context framework. This review will consider studies that fulfill all of the following criteria: include adult patients (participants); explore the association between atypical presentations and diagnostic errors using any definition, criteria, or measurement to identify atypical presentations and diagnostic errors (concept); and focus on internal medicine (context). Regarding the type of sources, this scoping review will consider quantitative, qualitative, and mixed methods study designs; systematic reviews; and opinion papers for inclusion. Case reports, case series, and conference abstracts will be excluded. The data will be extracted through MEDLINE, Web of Science, CINAHL, Embase, Cochrane Library, and Google Scholar searches. No limits will be applied to language, and papers indexed from database inception to December 31, 2023, will be included. Two independent reviewers (YH and RK) will conduct study selection and data extraction. The data extracted will include specific details about the patient characteristics (eg, age, sex, and disease), the definitions and measuring methods for atypical presentations and diagnostic errors, clinical settings (eg, department and outpatient or inpatient), type of evidence source, and the association between atypical presentations and diagnostic errors relevant to the review question. The extracted data will be presented in tabular format with descriptive statistics, allowing us to identify the key components or types of atypical presentations and develop new criteria to identify atypical presentations for future studies of diagnostic errors. Developing the new criteria will follow guidance for a basic qualitative content analysis with an inductive approach. RESULTS: As of January 2024, a literature search through multiple databases is ongoing. We will complete this study by December 2024. CONCLUSIONS: This scoping review aims to provide rigorous evidence to develop new criteria to identify atypical presentations at high risk for diagnostic errors in internal medicine. Such criteria could facilitate the development of a comprehensive conceptual model to understand the associations between atypical presentations and diagnostic errors in internal medicine. TRIAL REGISTRATION: Open Science Framework; www.osf.io/27d5m. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56933.

How an Antimicrobial Stewardship Team Treated a Nocardia farcinica-Associated Brain Abscess: A Case Report.

Nocardia species, which are ubiquitous in the environment, form lesions primarily in immunocompromised patients via oral or cutaneous infection. Some of these Nocardia species, such as N. farcinica, also infect the central nervous system via hematogenous dissemination, which rarely results in brain abscesses. Notably, N. farcinica is resistant to numerous antimicrobial drugs used in empirical therapy, necessitating the intervention of an infectious disease specialist. To date, no case of antimicrobial stewardship teams (ASTs) playing a central role in community hospitals without an infectious disease specialist has been reported. Here, we describe a case of N. farcinica-associated brain abscess in a small-to-medium-sized hospital with no infectious disease department or specialist, in which the AST assisted in the identification of the causative organism and in selecting appropriate therapeutic agents, ultimately leading to a cure. The patient was an 88-year-old man with a high fever. He had been taking prednisolone (10-15 mg/day) for approximately 1 year for pemphigoid. Considering the possibility of fever owing to bacteremia of cutaneous origin, ampicillin/sulbactam antimicrobial therapy at 6 g/day was initiated. A subsequent close examination led to the diagnosis of a brain abscess. Emergency abscess drainage was performed by a neurosurgeon, and postoperative antimicrobial combination therapy comprising ceftriaxone (4 g/day), vancomycin (2 g/day), and metronidazole (1,500 mg/day) was commenced. The AST suspected Nocardia infection earlier, but further testing was difficult to perform at this facility. Therefore, by requesting assistance from Nagoya University Hospital, we performed early bacterial identification by mass spectrometry and appropriate antimicrobial susceptibility testing by a custom panel on day 11. The patient was non-responsive to all the previously used antibiotics at the time of admission. On day 13 after admission, the patient was successfully treated with trimethoprim-sulfamethoxazole (TMP-SMX) and imipenem/cilastatin sodium, and the patient was cured. The AST can be as effective as an infectious disease specialist when a strong working relationship is established between the team and clinicians. Further, the activities of the AST can improve patient survival via active medical support in collaboration with attending physicians.

Distribution of internal medicine rotations among resident physicians in Japan: a nationwide, multicenter, cross-sectional study.

BACKGROUND: In Japan, postgraduate clinical training encompasses a 2-year residency program, including at least 24 weeks of internal medicine (IM) rotations. However, the fragmented structure of these rotations can compromise the training's quality and depth. For example, a resident might spend only a few weeks in cardiology before moving to endocrinology, without sufficient time to deepen their understanding or have clinical experience. This study examined current patterns and lengths of IM rotations within the Japanese postgraduate medical system. It scrutinized the piecemeal approach-whereby residents may engage in multiple short-term stints across various subspecialties without an overarching, integrated experience-and explored potential consequences for their clinical education. METHODS: This nationwide, multicenter, cross-sectional study used data from self-reported questionnaires completed by participants in the 2022 General Medicine In-Training Examination (GM-ITE). Data of 1,393 postgraduate year (PGY) one and two resident physicians who participated in the GM-ITE were included. We examined the IM rotation duration and number of IM subspecialties chosen by resident physicians during a 2-year rotation. RESULTS: Approximately half of the participants chose IM rotation periods of 32-40 weeks. A significant proportion of participants rotated in 5-7 internal medicine departments throughout the observation period. Notable variations in the distribution of rotations were observed, characterized by a common pattern where resident physicians typically spend 4 weeks in each department before moving to the next. This 4-week rotation is incrementally repeated across different subspecialties without a longer, continuous period in any single area. Notably, 39.7% of participants did not undertake general internal medicine rotations. These results suggest a narrowed exposure to medical conditions and patient care practices. CONCLUSIONS: Our study highlights the need to address the fragmented structure of IM rotations in Japan. We suggest that short, specialized learning periods may limit the opportunity to gain broad in-depth knowledge and practical experience. To improve the efficacy of postgraduate clinical education, we recommend fostering more sustained and comprehensive learning experiences.

How Do We Establish the Utility and Evidence of General Medicine in Japan?

Hospital Medicine in the United States has achieved significant progress in the accumulation of evidence. This development has influenced the increasing societal demand for General Medicine in Japan. Generalists in Japan actively engage in a wide range of interdisciplinary clinical practices, education, and management. Furthermore, Generalists have also contributed to advances in research. However, there is limited evidence regarding the benefits of General Medicine in Japan in all these areas, with most of the evidence derived from single-center studies. In Japan, the roles of Generalists are diverse, and the comprehensive definition of General Medicine makes it difficult to clearly delineate its scope. This results in an inadequate accumulation of evidence regarding the benefits of General Medicine, potentially making it less attractive to the public and younger physicians. Therefore, it is necessary to categorize General Medicine and collect clear evidence regarding its benefits.

Peritoneal B Cells Play a Role in the Production of Anti-polyethylene Glycol (PEG) IgM against Intravenously Injected siRNA-PEGylated Liposome Complexes.

Polyethylene glycol (PEG)-modified (PEGylated) cationic liposomes are frequently used as delivery vehicles for small interfering RNA (siRNA)-based drugs because of their ability to encapsulate/complex with siRNA and prolong the circulation half-life in vivo. Nevertheless, we have reported that subsequent intravenous (IV) injections of siRNA complexed with PEGylated cationic liposomes (PLpx) induces the production of anti-PEG immunoglobulin M (IgM), which accelerates the blood clearance of subsequent doses of PLpx and other PEGylated products. In this study, it is interesting that splenectomy (removal of spleen) did not prevent anti-PEG IgM induction by IV injection of PLpx. This indicates that B cells other than the splenic version are involved in anti-PEG IgM production under these conditions. In vitro and in vivo studies have shown that peritoneal cells also secrete anti-PEG IgM in response to the administration of PLpx. Interleukin-6 (IL-6) is a glycoprotein that is secreted by peritoneal immune cells and has been detected in response to the in vivo administration of PLpx. These observations indicate that IV injection of PLpx stimulates the proliferation/differentiation of peritoneal PEG-specific B cells into plasma cells via IL-6 induction, which results in the production of anti-PEG IgM from the peritoneal cavity of mice. Our results suggest the mutual contribution of peritoneal B cells as a potent anti-PEG immune response against PLpx.

Development of a Clinical Simulation Video to Evaluate Multiple Domains of Clinical Competence: Cross-Sectional Study.

BACKGROUND: Medical students in Japan undergo a 2-year postgraduate residency program to acquire clinical knowledge and general medical skills. The General Medicine In-Training Examination (GM-ITE) assesses postgraduate residents' clinical knowledge. A clinical simulation video (CSV) may assess learners' interpersonal abilities. OBJECTIVE: This study aimed to evaluate the relationship between GM-ITE scores and resident physicians' diagnostic skills by having them watch a CSV and to explore resident physicians' perceptions of the CSV's realism, educational value, and impact on their motivation to learn. METHODS: The participants included 56 postgraduate medical residents who took the GM-ITE between January 21 and January 28, 2021; watched the CSV; and then provided a diagnosis. The CSV and GM-ITE scores were compared, and the validity of the simulations was examined using discrimination indices, wherein ≥0.20 indicated high discriminatory power and >0.40 indicated a very good measure of the subject's qualifications. Additionally, we administered an anonymous questionnaire to ascertain participants' views on the realism and educational value of the CSV and its impact on their motivation to learn. RESULTS: Of the 56 participants, 6 (11%) provided the correct diagnosis, and all were from the second postgraduate year. All domains indicated high discriminatory power. The (anonymous) follow-up responses indicated that the CSV format was more suitable than the conventional GM-ITE for assessing clinical competence. The anonymous survey revealed that 12 (52%) participants found the CSV format more suitable than the GM-ITE for assessing clinical competence, 18 (78%) affirmed the realism of the video simulation, and 17 (74%) indicated that the experience increased their motivation to learn. CONCLUSIONS: The findings indicated that CSV modules simulating real-world clinical examinations were successful in assessing examinees' clinical competence across multiple domains. The study demonstrated that the CSV not only augmented the assessment of diagnostic skills but also positively impacted learners' motivation, suggesting a multifaceted role for simulation in medical education.