Opportunistic Screening of Chronic Liver Disease with Deep Learning Enhanced Echocardiography.

Importance: Chronic liver disease affects more than 1.5 billion adults worldwide, however the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; but this information is not leveraged. Objective: To develop and evaluate a deep learning algorithm on echocardiography videos to enable opportunistic screening for chronic liver disease. Design: Retrospective observational cohorts. Setting: Two large urban academic medical centers. Participants: Adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests, between July 4, 2012, to June 4, 2022. Exposure: Deep learning model predictions from a deep-learning computer vision pipeline that identifies subcostal view echocardiogram videos and detects the presence of cirrhosis or steatotic liver disease (SLD). Main Outcome and Measures: Clinical diagnosis by paired abdominal ultrasound or magnetic resonance imaging (MRI). Results: A total of 1,596,640 echocardiogram videos (66,922 studies from 24,276 patients) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects the presence of cirrhosis or SLD. In the held-out CSMC test cohort, EchoNet-Liver was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.704 (0.689-0.718) and SLD was detected with an AUC of 0.726 (0.659-0.790). In an external test cohort of 106 patients (n = 5,280 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 - 0.875). Conclusions and Relevance: Deep learning assessment of clinical echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for chronic liver disease.

NMR structures of small molecules bound to a model of an RNA CUG repeat expansion.

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntington's disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG)exp. The RNA folds into a hairpin structure with a periodic array of 1×1 nucleotide UU loops (5'CUG/3'GUC; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5'CUG/3'GUC motifs in complex with three different small molecules, a di-guandinobenzoate (1), a derivative of 1 where the guanidino groups have been exchanged for imidazole (2), and a quinoline with improved drug-like properties (3). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1, 2, and 3 formed stacking and hydrogen bonding interactions with the 5'CUG/3'GUC motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG)exp. As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

Structures of small molecules bound to RNA repeat expansions that cause Huntington's disease-like 2 and myotonic dystrophy type 1.

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntington's disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG)exp. The RNA folds into a hairpin structure with a periodic array of 1 × 1 nucleotide UU loops (5'CUG/3'GUC; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5'CUG/3'GUC motifs in complex with three different small molecules, a di-guandinobenzoate (1), a derivative of 1 where the guanidino groups have been exchanged for imidazole (2), and a quinoline with improved drug-like properties (3). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1, 2, and 3 formed stacking and hydrogen bonding interactions with the 5'CUG/3'GUC motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG)exp. As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

The expanding role of radiation oncology across the prostate cancer continuum.

Radiotherapy is used in the treatment of prostate cancer in a variety of disease states with significant reliance on imaging to guide clinical decision-making and radiation delivery. In the definitive setting, the choice of radiotherapy treatment modality, dose, and fractionation for localized prostate cancer is determined by the patient's initial risk stratification and other clinical considerations. Radiation is also an option as salvage therapy in patients with locoregionally recurrent disease after prior definitive radiation or surgery. In recent years, the role of radiation has expanded for patients with metastatic disease, including prostate-directed radiotherapy in de novo low volume metastatic disease, metastasis-directed therapy for oligorecurrent disease, and palliative management of symptomatic metastases in the advanced setting. Here we review the expanding role of radiation in the treatment of prostate cancer in the definitive, locoregionally recurrent, and metastatic settings, as well as highlight the role of imaging in clinical reasoning, radiation planning, and treatment delivery.

Socioeconomic Disparities and the Prevalence of Antimicrobial Resistance.

BACKGROUND: The increased prevalence of antimicrobial resistant (AMR) infections is a significant global health threat, resulting in increased morbidity, mortality, and costs. The drivers of AMR are complex and potentially impacted by socioeconomic factors. We investigated the relationships between geographic and socioeconomic factors and AMR. METHODS: We collected select patient bacterial culture results from 2015 to 2020 from electronic health records (EHR) of two expansive healthcare systems within the Dallas-Fort Worth, TX (DFW) metropolitan area. Among individuals with EHR records who resided in the four most populus counties in DFW, culture data were aggregated. Case counts for each organism studied were standardized per 1,000 persons per area population. Using residential addresses, the cultures were geocoded and linked to socioeconomic index values. Spatial autocorrelation tests identified geographic clusters of high and low AMR organism prevalence and correlations with established socioeconomic indices. RESULTS: We found significant clusters of AMR organisms in areas with high levels of deprivation, as measured by the Area Deprivation Index (ADI). We found a significant spatial autocorrelation between ADI and the prevalence of AMR organisms, particularly for AmpC and MRSA with 14% and 13%, respectively, of the variability in prevalence rates being attributable to their relationship with the ADI values of the neighboring locations. CONCLUSIONS: We found that areas with a high ADI are more likely to have higher rates of AMR organisms. Interventions that improve socioeconomic factors such as poverty, unemployment, decreased access to healthcare, crowding, and sanitation in these areas of high prevalence may reduce the spread of AMR.

Pathophysiological Features in Electronic Medical Records Sustain Model Performance under Temporal Dataset Shift.

Access to real-world data streams like electronic medical records (EMRs) has accelerated the development of supervised machine learning (ML) models for clinical applications. However, few studies investigate the differential impact of particular features in the EMR on model performance under temporal dataset shift. To explain how features in the EMR impact models over time, this study aggregates features into feature groups by their source (e.g. medication orders, diagnosis codes and lab results) and feature categories based on their reflection of patient pathophysiology or healthcare processes. We adapt Shapley values to explain feature groups' and feature categories' marginal contribution to initial and sustained model performance. We investigate three standard clinical prediction tasks and find that while feature contributions to initial performance differ across tasks, pathophysiological features help mitigate temporal discrimination deterioration. These results provide interpretable insights on how specific feature groups contribute to model performance and robustness to temporal dataset shift.

Electronic Phenotyping of Urinary Tract Infections as a Silver Standard Label for Machine Learning.

This study explored the efficacy of electronic phenotyping in data labeling for machine learning with a focus on urinary tract infections (UTIs). We contrasted labels from electronic phenotyping against previously published labels such as urine culture positivity. In comparison, electronic phenotyping showed the potential to enhance specificity in UTI labeling while maintaining similar sensitivity and was easily scaled for application to a large dataset suitable for machine learning, which we used to train and validate a machine learning model. Electronic phenotyping offers a valuable method for machine learning label generation in healthcare, with potential benefits for patient care and antimicrobial stewardship. Further research will expand its application and optimize techniques for increased performance.

Preliminary report of a thoracic duct-to-pulmonary vein lymphovenous anastomosis in swine: A novel technique and potential treatment for lymphatic failure.

OBJECTIVE: The thoracic duct is the largest lymphatic vessel in the body, and carries fluid and nutrients absorbed in abdominal organs to the central venous circulation. Thoracic duct obstruction can cause significant failure of the lymphatic circulation (i.e., protein-losing enteropathy, plastic bronchitis, etc.). Surgical anastomosis between the thoracic duct and central venous circulation has been used to treat thoracic duct obstruction but cannot provide lymphatic decompression in patients with superior vena cava obstruction or chronically elevated central venous pressures (e.g., right heart failure, single ventricle physiology, etc.). Therefore, this preclinical feasibility study sought to develop a novel and optimal surgical technique for creating a thoracic duct-to-pulmonary vein lymphovenous anastomosis (LVA) in swine that could remain patent and preserve unidirectional lymphatic fluid flow into the systemic venous circulation to provide therapeutic decompression of the lymphatic circulation even at high central venous pressures. METHODS: A thoracic duct-to-pulmonary vein LVA was attempted in 10 piglets (median age 80 [IQR 80-83] days; weight 22.5 [IQR 21.4-26.8] kg). After a right thoracotomy, the thoracic duct was mobilized, transected, and anastomosed to the right inferior pulmonary vein. Animals were systemically anticoagulated on post-operative day 1. Lymphangiography was used to evaluate LVA patency up to post-operative day 7. RESULTS: A thoracic duct-to-pulmonary vein LVA was successfully completed in 8/10 (80.0%) piglets, of which 6/8 (75.0%) survived to the intended study endpoint without any complication (median 6 [IQR 4-7] days). Initially, 2/10 (20.0%) LVAs were aborted intraoperatively, and 2/10 (20.0%) animals were euthanized early due to post-operative complications. However, using an optimized surgical technique, the success rate for creating a thoracic duct-to-pulmonary vein LVA in six animals was 100%, all of which survived to their intended study endpoint without any complications (median 6 [IQR 4-7] days). LVAs remained patent for up to seven days. CONCLUSION: A thoracic duct-to-pulmonary vein LVA can be completed safely and remain patent for at least one week with systemic anticoagulation, which provides an important proof-of-concept that this novel intervention could effectively offload the lymphatic circulation in patients with lymphatic failure and elevated central venous pressures.

Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors. DESIGN: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data. SETTING AND CASES: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively. MEASUREMENTS: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts. FINDINGS: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence. CONCLUSIONS: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

Gabapentin as a novel adjunct for postoperative irritability after superior cavopulmonary connection operation in children.

OBJECTIVES: Describing our institution's off-label use of gabapentin to treat irritability after superior cavopulmonary connection surgery and its impact on subsequent opiate and benzodiazepine requirements. METHODS: This is a single-center retrospective cohort study including infants who underwent superior cavopulmonary connection operation between 2011 and 2019. RESULTS: Gabapentin was administered in 74 subjects (74/323, 22.9%) during the observation period, with a median (IQR) starting dose of 5.7 (3.3, 15.0) mg/kg/day and a maximum dose of 10.7 (5.5, 23.4) mg/kg/day. Infants who underwent surgery in 2015-19 were more likely to receive gabapentin compared with those who underwent surgery in 2011-14 (p < 0.0001). Infants prescribed gabapentin were younger at surgery (137 versus 146 days, p = 0.007) and had longer chest tube durations (1.8 versus 0.9 days, p < 0.001), as well as longer postoperative intensive care (5.8 versus 3.1 days, p < 0.0001) and hospital (11.5 versus 7.0 days, p < 0.0001) lengths of stays. The year of surgery was the only predisposing factor associated with gabapentin administration in multivariate analysis. In adjusted linear regression, infants prescribed gabapentin on postoperative day 0-4 (n = 64) had reduced benzodiazepine exposure in the following 3 days (-0.29 mg/kg, 95% CI -0.52 - -0.06, p = 0.01) compared with those not prescribed gabapentin, while no difference was seen in opioid exposure (p = 0.59). CONCLUSIONS: Gabapentin was used with increasing frequency during the study period. There was a modest reduction in benzodiazepine requirements associated with gabapentin administration and no reduction in opioid requirements. A randomised controlled trial could better assess gabapentin's benefits postoperatively in children with congenital heart disease.

Patterns of chemotherapy use with primary radiotherapy for localized bladder cancer in patients 65 or older.

Introduction: Bladder preservation with concurrent chemoradiotherapy after maximum transurethral resection of bladder tumor is an alternative to radical cystectomy in select patients with muscle invasive bladder cancer (MIBC). Concurrent administration of radio-sensitizing chemotherapy and radiation therapy (RT) has been shown to have superior disease control compared with RT alone and can often be administered with modest added toxicity. We sought to describe national patterns of chemotherapy use. Methods: The linked surveillance, epidemiology, and end results (SEER)-Medicare database was used to identify patients with cT2-4, N0/X, M0/X BC who received radiation between 2004 and 2018. Data on demographics, clinicopathologic factors, therapy and outcomes were extracted. Concurrent utilization of chemotherapy with RT was also identified (CRT). Multivariate logistic regression (MVA) models were used to explore factors associated with receipt of chemotherapy and overall survival (OS). Results: 2190 patients met inclusion criteria. Of these, 850 (38.8%) received no chemotherapy. Among those receiving chemotherapy, the most frequent regimens were single agent carboplatin, cisplatin, or gemcitabine. Factors that were independently associated with decreased likelihood of chemotherapy use were increasing age (OR 0.93, CI 0.92 - 0.95), Hispanic race (compared with White, OR 0.62, CI 0.39 - 0.99), cT3 or T4 (compared with cT2, OR 0.70, CI 0.55 - 0.90), and lower National Cancer Institute comorbidity index (OR 0.60, CI 0.51 - 0.70) (p < 0.05). Variables independently associated with increased likelihood of receipt of chemotherapy were married status (OR 1.28, CI 1.06 - 1.54), higher socioeconomic status (OR 1.31, CI 1.06 - 1.64), and later year of diagnosis (OR 1.09, CI 1.06 - 1.12). Receipt of concurrent chemotherapy with RT was associated with superior OS compared with RT alone. Conclusion: Over a third of patients >/65 years old receiving curative-intent RT for MIBC do not receive concurrent chemotherapy. Considering the improvement in oncologic outcomes with CRT over RT alone and more options, such as low dose gemcitabine which can be administered with modest toxicity, efforts are needed to identify barriers to utilization and increase the use of radio-sensitizing chemotherapy.

Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced.

BACKGROUND: Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases. FRAMEWORK: A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis. DESIGN: Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease. RESULTS: When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment. CONCLUSIONS: A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself. HIGHLIGHTS: Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.

Mitral Valve Replacement in Infants and Children: Five Year Outcomes of the HALO Clinical Trial.

BACKGROUND: Repair is preferable for children with mitral valve disease, but mitral valve replacement (MVR) is occasionally necessary. This report presents the results of a multiinstitutional Investigational Device Exemption trial of the 15-mm St Jude (SJM) mechanical mitral valve (Abbott Structural Heart). METHODS: From May 2015 to March 2017, 23 children aged 0.4 to 27.4 months (mean, 7.8 months; 85% <1 year) weighing 2.9 to 10.9 kg (mean, 5.5 kg) at 15 centers underwent MVR with a 15-mm SJM mechanical mitral valve (intraannular, 45%; supraannular, 55%). A total of 21 (91%) of the children had undergone previous cardiac operations. Follow-up until death, valve explantation, or 5 years postoperatively was 100% complete. RESULTS: There were 6 deaths, all in the first 12 months; no death was valve related. Four patients required a pacemaker (2 supraannular, 2 intraannular). Three patients had thrombosis requiring valve explantation at 13, 21, and 35 days postoperatively. Two of these 3 patients were receiving low-molecular-weight heparin for anticoagulation, and the third had factor V Leiden deficiency. There were 5 nonfatal bleeding complications within 4 months of MVR (1-year freedom from bleeding, 71.0%). The 1- and 5-year freedom from death or valve explantation was 71.0%. CONCLUSIONS: In small children with severe mitral valve disease requiring MVR, the 15-mm SJM mechanical mitral valve provides satisfactory hemodynamics. Mortality and complications in these patients are not trivial. Low-molecular-weight heparin likely should be avoided as primary anticoagulation. Eventual valve replacement is inevitable.

Single-cell transcriptomic analyses reveal distinct immune cell contributions to epithelial barrier dysfunction in checkpoint inhibitor colitis.

Immune checkpoint inhibitor (ICI) therapy has revolutionized oncology, but treatments are limited by immune-related adverse events, including checkpoint inhibitor colitis (irColitis). Little is understood about the pathogenic mechanisms driving irColitis, which does not readily occur in model organisms, such as mice. To define molecular drivers of irColitis, we used single-cell multi-omics to profile approximately 300,000 cells from the colon mucosa and blood of 13 patients with cancer who developed irColitis (nine on anti-PD-1 or anti-CTLA-4 monotherapy and four on dual ICI therapy; most patients had skin or lung cancer), eight controls on ICI therapy and eight healthy controls. Patients with irColitis showed expanded mucosal Tregs, ITGAEHi CD8 tissue-resident memory T cells expressing CXCL13 and Th17 gene programs and recirculating ITGB2Hi CD8 T cells. Cytotoxic GNLYHi CD4 T cells, recirculating ITGB2Hi CD8 T cells and endothelial cells expressing hypoxia gene programs were further expanded in colitis associated with anti-PD-1/CTLA-4 therapy compared to anti-PD-1 therapy. Luminal epithelial cells in patients with irColitis expressed PCSK9, PD-L1 and interferon-induced signatures associated with apoptosis, increased cell turnover and malabsorption. Together, these data suggest roles for circulating T cells and epithelial-immune crosstalk critical to PD-1/CTLA-4-dependent tolerance and barrier function and identify potential therapeutic targets for irColitis.

Updated Pseudo-seq Protocol for Transcriptome-Wide Detection of Pseudouridines.

Pseudouridine (Ψ), the most prevalent modified base in cellular RNAs, has been mapped to numerous sites not only in rRNAs, tRNAs, and snRNAs but also mRNAs. Although there have been multiple techniques to identify Ψs, due to the recent development of sequencing technologies some reagents are not compatible with the current sequencer. Here, we show the updated Pseudo-seq, a technique enabling the genome-wide identification of pseudouridylation sites with single-nucleotide precision. We provide a comprehensive description of Pseudo-seq, covering protocols for RNA isolation from human cells, library preparation, and detailed data analysis procedures. The methodology presented is easily adaptable to any cell or tissue type with high-quality mRNA isolation. It can be used for discovering novel pseudouridylation sites, thus constituting a crucial initial step toward understanding the regulation and function of this modification. Key features • Identification of Ψ sites on mRNAs. • Updated Pseudo-seq provides precise positional and quantitative information of Ψ. • Uses a more efficient library preparation with the latest, currently available materials.

The Emergence of Candida auris is Not Associated with Changes in Antifungal Prescription at Hospitals.

Purpose: This study describes the emergence of Candida auris in Hong Kong, focusing on the incidence and trends of different Candida species over time. Additionally, the study analyzes the relationship between C. auris and antifungal prescription, as well as the impact of outbreaks caused by C. auris. Patients and Methods: Data were collected from 43 public hospitals across seven healthcare networks (A to G) in Hong Kong, including Candida species culture and antifungal prescription information. Among 150,267 patients with 206,405 hospitalization episodes, 371,653 specimens tested positive for Candida species. Trends in Candida species and antifungal prescription were analyzed before (period 1: 2015 1Q to 2019 1Q) and after (period 2: 2019 2Q to 2023 2Q) the emergence of C. auris in Hong Kong. Results: Candida albicans was the most prevalent species, accounting for 57.1% (212,163/371,653) of isolations, followed by Candida glabrata (13.1%, 48,666), Candida tropicalis (9.2%, 34,261), and Candida parapsilosis (5.3%, 19,688). C. auris represented 2.0% of all Candida species isolations. Comparing period 2 to period 1, the trend of C. albicans remained stable, while C. glabrata, C. tropicalis, and C. parapsilosis demonstrated a slower increasing trend in period 2 than in period 1. Other species, including C. auris, exhibited a 1.1% faster increase in trend during period 2 compared to period 1. Network A, with the highest antifungal prescription, did not experience any outbreaks, while networks F and G had 40 hospital outbreaks due to C. auris in period 2. Throughout the study period, healthcare networks B to G had significantly lower antifungal prescription compared to network A, ranging from 54% to 78% less than that of network A. Conclusion: There is no evidence showing correlation between the emergence of C. auris and antifungal prescription in Hong Kong. Proactive infection control measures should be implemented to prevent nosocomial transmission and outbreak of C. auris.

Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study.

Importance: Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning. Objective: To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources. Design: Multi-center, randomized clinical vignette study. Setting: The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions. Participants: Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine. Interventions: Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams. Main Outcomes and Measures: The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis. Results: 50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group. Conclusions and Relevance: In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

Genomic investigation of Lactococcus formosensis, Lactococcus garvieae, and Lactococcus petauri reveals differences in species distribution by human and animal sources.

Lactococcus garvieae is a fish pathogen that can cause diseases in humans and cows. Two genetically related species, Lactococcus formosensis and Lactococcus petauri, may be misidentified as L. garvieae. It is unclear if these species differ in host specificity and virulence genes. This study analyzed the genomes of 120 L. petauri, 53 L. formosensis, and 39 L. garvieae isolates from various sources. The genetic diversity and virulence gene content of these isolates were compared. The results showed that 77 isolates previously reported as L. garvieae were actually L. formosensis or L. petauri. The distribution of the three species varied across different collection sources, with L. petauri being predominant in human infections, human fecal sources, and rainbow trout, while L. formosensis was more common in bovine isolates. The genetic diversity of isolates within each species was high and similar. Using a genomic clustering method, L. petauri, L. formosensis, and L. garvieae were divided into 45, 22, and 13 clusters, respectively. Most rainbow trout and human isolates of L. petauri belonged to different clusters, while L. formosensis isolates from bovine and human sources were also segregated into separate clusters. In L. garvieae, most human isolates were grouped into three clusters that also included isolates from food or other sources. Non-metric multidimensional scaling ordination revealed the differential association of 15 virulence genes, including 14 adherence genes and a bile salt hydrolase gene, with bacterial species and certain collection sources. In conclusion, this work provides evidence of host specificity among the three species. IMPORTANCE: Lactococcus formosensis and Lactococcus petauri are two newly discovered bacteria, which are closely related to Lactococcus garvieae, a pathogen that affects farmed rainbow trout, as well as causes cow mastitis and human infections. It is unclear whether the three bacteria differ in their host preference and the presence of genes that contribute to the development of disease. This study shows that L. formosensis and L. petauri were commonly misidentified as L. garvieae. The three bacteria showed different distribution patterns across various sources. L. petauri was predominantly found in human infections and rainbow trout, while L. formosensis was more commonly detected in cow mastitis. Fifteen genes displayed a differential distribution among the three bacteria from certain sources, indicating a genetic basis for the observed host preference. This work indicates the importance of differentiating the three bacteria in diagnostic laboratories for surveillance and outbreak investigation purposes.