Large Language Model Influence on Management Reasoning: A Randomized Controlled Trial.

Importance: Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown. Objective: To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources. Design: Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024. Setting: Multi-institutional study from Stanford University, Beth Israel Deaconess Medical Center, and the University of Virginia involving physicians from across the United States. Participants: 92 practicing attending physicians and residents with training in internal medicine, family medicine, or emergency medicine. Intervention: Five expert-developed clinical case vignettes were presented with multiple open-ended management questions and scoring rubrics created through a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT Plus in addition to conventional resources (e.g., UpToDate, Google), or conventional resources alone. Main Outcomes and Measures: The primary outcome was difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case. Results: Physicians using the LLM scored higher compared to those using conventional resources (mean difference 6.5 %, 95% CI 2.7-10.2, p<0.001). Significant improvements were seen in management decisions (6.1%, 95% CI 2.5-9.7, p=0.001), diagnostic decisions (12.1%, 95% CI 3.1-21.0, p=0.009), and case-specific (6.2%, 95% CI 2.4-9.9, p=0.002) domains. GPT-4 users spent more time per case (mean difference 119.3 seconds, 95% CI 17.4-221.2, p=0.02). There was no significant difference between GPT-4-augmented physicians and GPT-4 alone (-0.9%, 95% CI -9.0 to 7.2, p=0.8). Conclusions and Relevance: LLM assistance improved physician management reasoning compared to conventional resources, with particular gains in contextual and patient-specific decision-making. These findings indicate that LLMs can augment management decision-making in complex cases. Trial registration: ClinicalTrials.gov Identifier: NCT06208423; https://classic.clinicaltrials.gov/ct2/show/NCT06208423.

Merlin: A Vision Language Foundation Model for 3D Computed Tomography.

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

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.

Adapted large language models can outperform medical experts in clinical text summarization.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts.

Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy on a diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets and four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not improve results. Further, in a clinical reader study with ten physicians, we show that summaries from our best-adapted LLMs are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis highlights challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and the inherently human aspects of medicine.

Baricitinib Treatment of Coronavirus Disease 2019 Is Associated With a Reduction in Secondary Infections.

We performed a secondary analysis of the National Institutes of Health-sponsored Adaptive COVID-19 Treatment Trial (ACTT-2) randomized controlled trial and found that baricitinib was associated with a 50% reduction in secondary infections after controlling for baseline and postrandomization patient characteristics. This finding provides a novel mechanism of benefit for baricitinib and supports the safety profile of this immunomodulator for the treatment of coronavirus disease 2019.

Characteristics associated with diagnostic yield of imaging for deep venous thrombosis and pulmonary embolism in the emergency department, hospital, and office settings: An Optum Clinformatics claims database study (2015-2019).

BACKGROUND: Different patient characteristics influence the decision to order diagnostic imaging for deep venous thrombosis (DVT) and pulmonary embolism (PE) in different settings (emergency department (ED), hospital, and office). Diagnostic yield is defined as the proportion of tests that report positive results. We hypothesize different patient characteristics are associated with higher or lower diagnostic yield of imaging for DVT and PE in different settings. METHODS: We used Optum Clinformatics™ national claims database (2015-2019) to assess the diagnostic yield of imaging for DVT and PE in three settings: (a) ED discharge, (b) Hospitalized, and (c) Office. We studied the patient characteristics associated with diagnostic yield using logistic regression. RESULTS: Diagnostic imaging for DVT and PE was performed in 1,502,417 and 710,263 visits, respectively. Diagnostic yield for DVT and PE was 9.8 ± 0.1 % and 12.7 ± 0.1 %, respectively in the overall cohort. In the ED discharge, hospitalized, and office settings, diagnostic yield for DVT was 10.4 ± 0.1 %, 16.9 ± 0.1 %, and 6.5 ± 0.1 %, respectively, and that for PE 6.4 ± 0.1 %, 18.7 ± 0.1 %, and 8.8 ± 0.2 %, respectively. Of the patients who underwent imaging for DVT, higher diagnostic yield was more likely with thrombophilia, central venous access, and cancer. Of the patients who underwent imaging for PE, higher diagnostic yield was most likely with thrombophilia, respiratory failure, and heart failure or acute myocardial infarction. CONCLUSIONS: In each setting, different patient characteristics influence the diagnostic yield of imaging for DVT and PE and can inform clinical practice. Judicious use of imaging for DVT and PE could reduce costs and avoid exposure to radiation and contrast.

Temporal Improvements in COVID-19 Outcomes for Hospitalized Adults: A Post Hoc Observational Study of Remdesivir Group Participants in the Adaptive COVID-19 Treatment Trial.

BACKGROUND: The COVID-19 standard of care (SOC) evolved rapidly during 2020 and 2021, but its cumulative effect over time is unclear. OBJECTIVE: To evaluate whether recovery and mortality improved as SOC evolved, using data from ACTT (Adaptive COVID-19 Treatment Trial). DESIGN: ACTT is a series of phase 3, randomized, double-blind, placebo-controlled trials that evaluated COVID-19 therapeutics from February 2020 through May 2021. ACTT-1 compared remdesivir plus SOC to placebo plus SOC, and in ACTT-2 and ACTT-3, remdesivir plus SOC was the control group. This post hoc analysis compared recovery and mortality between these comparable sequential cohorts of patients who received remdesivir plus SOC, adjusting for baseline characteristics with propensity score weighting. The analysis was repeated for participants in ACTT-3 and ACTT-4 who received remdesivir plus dexamethasone plus SOC. Trends in SOC that could explain outcome improvements were analyzed. (ClinicalTrials.gov: NCT04280705 [ACTT-1], NCT04401579 [ACTT-2], NCT04492475 [ACTT-3], and NCT04640168 [ACTT-4]). SETTING: 94 hospitals in 10 countries (86% U.S. participants). PARTICIPANTS: Adults hospitalized with COVID-19. INTERVENTION: SOC. MEASUREMENTS: 28-day mortality and recovery. RESULTS: Although outcomes were better in ACTT-2 than in ACTT-1, adjusted hazard ratios (HRs) were close to 1 (HR for recovery, 1.04 [95% CI, 0.92 to 1.17]; HR for mortality, 0.90 [CI, 0.56 to 1.40]). Comparable patients were less likely to be intubated in ACTT-2 than in ACTT-1 (odds ratio, 0.75 [CI, 0.53 to 0.97]), and hydroxychloroquine use decreased. Outcomes improved from ACTT-2 to ACTT-3 (HR for recovery, 1.43 [CI, 1.24 to 1.64]; HR for mortality, 0.45 [CI, 0.21 to 0.97]). Potential explanatory factors (SOC trends, case surges, and variant trends) were similar between ACTT-2 and ACTT-3, except for increased dexamethasone use (11% to 77%). Outcomes were similar in ACTT-3 and ACTT-4. Antibiotic use decreased gradually across all stages. LIMITATION: Unmeasured confounding. CONCLUSION: Changes in patient composition explained improved outcomes from ACTT-1 to ACTT-2 but not from ACTT-2 to ACTT-3, suggesting improved SOC. These results support excluding nonconcurrent controls from analysis of platform trials in rapidly changing therapeutic areas. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.

Association between Obesity and Length of COVID-19 Hospitalization: Unexpected Insights from the American Heart Association National COVID-19 Registry.

Background: The mechanism for possible association between obesity and poor clinical outcomes from Coronavirus Disease 2019 (COVID-19) remains unclear. Methods: We analyzed 22,915 adult COVID-19 patients hospitalized from March 2020 to April 2021 to non-intensive care using the American Heart Association National COVID Registry. A multivariable Poisson model adjusted for age, sex, medical history, admission respiratory status, hospitalization characteristics, and laboratory findings was used to calculate length of stay (LOS) as a function of body mass index (BMI). We similarly analyzed 5,327 patients admitted to intensive care for comparison. Results: Relative to normal BMI subjects, overweight, class I obese, and class II obese patients had approximately half-day reductions in LOS (-0.469 days, P<0.01; -0.480 days, P<0.01; -0.578 days, P<0.01, respectively). Conclusion: The model identified a dose-dependent, inverse relationship between BMI category and LOS for COVID-19, which was not seen when the model was applied to critically ill patients.

Anti-nucleocapsid antibody levels and pulmonary comorbid conditions are linked to post-COVID-19 syndrome.

BACKGROUNDProlonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODSAdult SARS-CoV-2 reverse transcription PCR-positive (RT-PCR-positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1-3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTSOur cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSIONWe found that all disease severities had a similar risk of developing post-COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATIONClinicalTrials.gov, NCT04373148.FUNDINGNIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

Asthma phenotypes, associated comorbidities, and long-term symptoms in COVID-19.

BACKGROUND: It is unclear whether asthma and its allergic phenotype are risk factors for hospitalization or severe disease from SARS-CoV-2. METHODS: All patients over 28 days old testing positive for SARS-CoV-2 between March 1 and September 30, 2020, were retrospectively identified and characterized through electronic analysis at Stanford. A sub-cohort was followed prospectively to evaluate long-term COVID-19 symptoms. RESULTS: 168,190 patients underwent SARS-CoV-2 testing, and 6,976 (4.15%) tested positive. In a multivariate analysis, asthma was not an independent risk factor for hospitalization (OR 1.12 [95% CI 0.86, 1.45], p = .40). Among SARS-CoV-2-positive asthmatics, allergic asthma lowered the risk of hospitalization and had a protective effect compared with non-allergic asthma (OR 0.52 [0.28, 0.91], p = .026); there was no association between baseline medication use as characterized by GINA and hospitalization risk. Patients with severe COVID-19 disease had lower eosinophil levels during hospitalization compared with patients with mild or asymptomatic disease, independent of asthma status (p = .0014). In a patient sub-cohort followed longitudinally, asthmatics and non-asthmatics had similar time to resolution of COVID-19 symptoms, particularly lower respiratory symptoms. CONCLUSIONS: Asthma is not a risk factor for more severe COVID-19 disease. Allergic asthmatics were half as likely to be hospitalized with COVID-19 compared with non-allergic asthmatics. Lower levels of eosinophil counts (allergic biomarkers) were associated with a more severe COVID-19 disease trajectory. Recovery was similar among asthmatics and non-asthmatics with over 50% of patients reporting ongoing lower respiratory symptoms 3 months post-infection.

Cost-Effectiveness of Baricitinib Compared With Standard of Care: A Modeling Study in Hospitalized Patients With COVID-19 in the United States.

PURPOSE: In the Phase III COV-BARRIER (Efficacy and Safety of Baricitinib for the Treatment of Hospitalised Adults With COVID-19) trial, treatment with baricitinib, an oral selective Janus kinase 1/2 inhibitor, in addition to standard of care (SOC), was associated with significantly reduced mortality over 28 days in hospitalized patients with coronavirus disease-2019 (COVID-19), with a safety profile similar to that of SOC alone. This study assessed the cost-effectiveness of baricitinib + SOC versus SOC alone (which included systemic corticosteroids and remdesivir) in hospitalized patients with COVID-19 in the United States. METHODS: An economic model was developed to simulate inpatients' stay, discharge to postacute care, and recovery. Costs modeled included payor costs, hospital costs, and indirect costs. Benefits modeled included life-years (LYs) gained, quality-adjusted life-years (QALYs) gained, deaths avoided, and use of mechanical ventilation avoided. The primary analysis was performed from a payor perspective over a lifetime horizon; a secondary analysis was performed from a hospital perspective. The base-case analysis modeled the numeric differences in treatment effectiveness observed in the COV-BARRIER trial. Scenario analyses were also performed in which the clinical benefit of baricitinib was limited to the statistically significant reduction in mortality demonstrated in the trial. FINDINGS: In the base-case payor perspective model, an incremental total cost of 17,276 US dollars (USD), total QALYs gained of 0.6703, and total LYs gained of 0.837 were found with baricitinib + SOC compared with SOC alone. With the addition of baricitinib, survival was increased by 5.1% and the use of mechanical ventilation was reduced by 1.6%. The base-case incremental cost-effectiveness ratios were 25,774 USD/QALY gained and 20,638 USD/LY gained; a "mortality-only" scenario analysis yielded similar results of 26,862 USD/QALY gained and 21,433 USD/LY gained. From the hospital perspective, combination treatment with baricitinib + SOC was more effective and less costly than was SOC alone in the base case, with an incremental cost of 38,964 USD per death avoided in the mortality-only scenario. IMPLICATIONS: In hospitalized patients with COVID-19 in the United States, the addition of baricitinib to SOC was cost-effective. Cost-effectiveness was demonstrated from both the payor and the hospital perspectives. These findings were robust to sensitivity analysis and to conservative assumptions limiting the clinical benefits of baricitinib to the statistically significant reduction in mortality demonstrated in the COV-BARRIER trial.

New-onset IgG autoantibodies in hospitalized patients with COVID-19.

COVID-19 is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. Here we develop three protein arrays to measure IgG autoantibodies associated with connective tissue diseases, anti-cytokine antibodies, and anti-viral antibody responses in serum from 147 hospitalized COVID-19 patients. Autoantibodies are identified in approximately 50% of patients but in less than 15% of healthy controls. When present, autoantibodies largely target autoantigens associated with rare disorders such as myositis, systemic sclerosis and overlap syndromes. A subset of autoantibodies targeting traditional autoantigens or cytokines develop de novo following SARS-CoV-2 infection. Autoantibodies track with longitudinal development of IgG antibodies recognizing SARS-CoV-2 structural proteins and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins.

Comparison of Adverse Events Among Home- vs Facility-Administered Biologic Infusions, 2007-2017.

Importance: Infusion reactions occur in 7% to 20% of patients receiving biologics. Home infusions are convenient and incur lower costs but may be associated with more adverse events; the safety of receiving biologic infusions for immune-mediated diseases at home remains unclear. Objective: To assess whether patients receiving home biologic infusions have increased adverse events requiring emergency department (ED) or hospital admission compared with patients receiving facility infusions. Design, Setting, and Participants: This retrospective cohort study used administrative claims data from a large national insurer for adult patients who received biologic infusions for immune-mediated disease between January 2007 and December 2017. Patients with hematologic malignant neoplasms or bone marrow transplantation were excluded. Data were analyzed from August 2019 to October 2020. Main Outcomes and Measures: ED or hospital admission on the same or next day after administration of a biologic infusion at home vs at a facility; secondary outcomes included discontinuation of the biologic after an ED or hospital admission and postinfusion mortality. Results: Of a total of 57 220 patients (mean [SD] age, 50.1 [14.8] years; 512 314 [68.1%] women) who received 752 150 biologic infusions (34 078 home infusions [4.5%] to 3954 patients and 718 072 facility infusions [95.5%] to 54 770 patients), patients who received home infusions were younger (mean [SD] age, 43.2 [13.2] vs 51.3 [14.8] years), more likely to be men (14 031 [41.2%] vs 225 668 [31.4%]), and had a lower Charlson comorbidity score compared with patients who received facility infusions (mean [SD] score, 0.5 [1.0] vs 1.1 [1.3]). Home infusions were associated with 25% increased odds of ED or hospital admission on the same or next day after the infusion (odds ratio [OR], 1.25; 95% CI, 1.09-1.44; P = .002) and 28% increased odds of discontinuation of the biologic after the ED or hospital admission (OR, 1.28; 95% CI, 1.08-1.51; P = .005). There was no difference in postinfusion mortality between home or facility infusions. The rates of adverse events were highest with home infusions of tocilizumab (48 of 481 infusions [10.0%]), vedolizumab (150 of 2681 infusions [5.6%]), and infliximab (1085 of 20 653 infusions [5.3%]), although the number of tocilizumab and vedolizumab infusions was low. Conclusions and Relevance: In this study, biologic infusions administered at home, compared with those administered at a facility, were associated with increased adverse events requiring escalation of care. Because the number of home infusions has increased and is expected to continue to rise, the safety implications of administering biologic infusions at home needs to be further assessed.

Merits of Surgical Comanagement of Patients With Hip Fracture by Dedicated Orthopaedic Hospitalists.

BACKGROUND: Rotating medical consultants, hospitalists or geriatricians, are involved in the care of patients with hip fracture, often after medical complications have already occurred. In August 2012, we implemented a unique surgical comanagement (SCM) model in which the same Internal Medicine hospitalists are dedicated year-round to the orthopaedic surgery service. We examine whether this SCM model was associated with a decrease in medical complications, length of stay, and inpatient mortality in patients with hip fracture admitted at our institution, compared with the previous model. METHODS: We included 2,252 admissions to the orthopaedic surgery service with a hip fracture between 2009 and 2018 (757 pre-SCM and 1495 post-SCM). We adjusted for age, Charlson comorbidity score, and operating time in all regression analyses. RESULTS: Mean Charlson comorbidity score (1.6 versus 1.2) and median case mix index (2.1 versus 1.9) were higher in the post-SCM group. A 32% decrease was observed in the odds of having ≥1 medical complication(s) (odds ratio, 0.68 [95% confidence interval, 0.50 to 0.91], P = 0.009) post-SCM. No change was observed in length of stay or inpatient mortality despite an increase in medical complexity post-SCM. CONCLUSION: Having dedicated orthopaedic hospitalists may contribute to fewer medical complications in patients with hip fracture.

Handheld Ultrasound Device Usage and Image Acquisition Ability Among Internal Medicine Trainees: A Randomized Trial.

BACKGROUND: There is insufficient knowledge about how personal access to handheld ultrasound devices (HUDs) improves trainee learning with point-of-care ultrasound (POCUS). OBJECTIVE: To assess whether HUDs, alongside a yearlong lecture series, improved trainee POCUS usage and ability to acquire images. METHODS: Internal medicine intern physicians (n = 47) at a single institution from 2017 to 2018 were randomized 1:1 to receive personal HUDs (n = 24) for patient care/self-directed learning vs no-HUDs (n = 23). All interns received a repeated lecture series on cardiac, thoracic, and abdominal POCUS. Main outcome measures included self-reported HUD usage rates and post-intervention assessment scores using the Rapid Assessment of Competency in Echocardiography (RACE) scale between HUD and no-HUD groups. RESULTS: HUD interns reported performing POCUS assessments on patients a mean 6.8 (SD 2.2) times per week vs 6.4 (SD 2.9) times per week in non-HUD arm (P = .66). There was no relationship between the number of self-reported examinations per week and a trainee's post-intervention RACE score (rho = 0.022, P = .95). HUD interns did not have significantly higher post-intervention RACE scores (median HUD score 17.0 vs no-HUD score 17.8; P = .72). Trainee confidence with cardiac POCUS did not correlate with RACE scores. CONCLUSIONS: Personal HUDs without direct supervision did not increase the amount of POCUS usage or improve interns' acquisition abilities. Interns who reported performing more examinations per week did not have higher RACE scores. Improved HUD access and lectures without additional feedback may not improve POCUS mastery.

New-Onset IgG Autoantibodies in Hospitalized Patients with COVID-19.

Coronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. We developed three different protein arrays to measure hallmark IgG autoantibodies associated with Connective Tissue Diseases (CTDs), Anti-Cytokine Antibodies (ACA), and anti-viral antibody responses in 147 hospitalized COVID-19 patients in three different centers. Autoantibodies were identified in approximately 50% of patients, but in <15% of healthy controls. When present, autoantibodies largely targeted autoantigens associated with rare disorders such as myositis, systemic sclerosis and CTD overlap syndromes. Anti-nuclear antibodies (ANA) were observed in ∼25% of patients. Patients with autoantibodies tended to demonstrate one or a few specificities whereas ACA were even more prevalent, and patients often had antibodies to multiple cytokines. Rare patients were identified with IgG antibodies against angiotensin converting enzyme-2 (ACE-2). A subset of autoantibodies and ACA developed de novo following SARS-CoV-2 infection while others were transient. Autoantibodies tracked with longitudinal development of IgG antibodies that recognized SARS-CoV-2 structural proteins such as S1, S2, M, N and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. COVID-19 patients with one or more autoantibodies tended to have higher levels of antibodies against SARS-CoV-2 Nonstructural Protein 1 (NSP1) and Methyltransferase (ME). We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins.