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Displaying 651 - 660 of 7611 in ACP Online
ACP National Abstract Competition FAQs
Do I need to be an ACP member to submit an Abstract?Yes. The first author (also known as the submitting author) must be an ACP member in good standing—meaning dues are paid—at the time of submission. Acceptable membership categories include Medical Student, Resident/Fellow, Early Career Physician, or Transitional Medical Graduate (for Resident/Fellow competitions only).
Clinical Vignettes: Medical Student Oral Presentations
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Clinical Research: Medical Student Oral Presentations
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ACP Medical Student Member Winning Presentations for the 2021 National Abstracts Competition
ACP highlights 2021 virtual presentations for winning abstracts.
Quality Improvement-Patient Safety: Medical Student Oral Presentations
Social Isolation within an Urban Safety Net Hospital Patient Population Agnes Premkumar, ACP California Southern 1 Chapter
High Value Care: Medical Student Oral Presentations
Influence of Default Order Sentence Standardization on the Prescribing Patterns of Hydrocodone-Acetaminophen Combination Products Andrew Mudreac, ACP Illinois Northern Chapter
Displaying 651 - 660 of 6848 in Annals of Internal Medicine
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The Regulatory Repercussions of Approving Muscular Dystrophy Medications on the Basis of Limited Evidence
The U.S. Food and Drug Administration (FDA) approved eteplirsen (Exondys 51) for Duchenne muscular dystrophy in 2016 via its accelerated approval program on the basis of a study of 12 boys. After a contentious review process and a high-profile meeting of an external advisory committee, FDA leaders concluded that very small increases in treated patients’ levels of dystrophin, a muscle protein, were reasonably likely to predict clinical benefit. The eteplirsen approval, which was followed by approvals of other drugs in the same class via the same pathway, has been controversial because of the questionable evidence underlying these decisions, delays in mandated postapproval testing, and high U.S. prices. Questions remain about the effectiveness and long-term safety of these products. Although the FDA initially set a November 2020 deadline for eteplirsen’s manufacturer to complete a clinical trial determining whether the drug has clinical benefit, the company will not complete the trial until 2024 or later. The relationship between levels of truncated dystrophin, the muscle protein studied in eteplirsen’s pivotal trial, and clinical outcomes remains uncertain. Despite recent legislative and regulatory changes to the FDA’s accelerated approval pathway, the history of eteplirsen and similar drugs points to the need for additional reforms to better balance evidence generation with patient safety and access to promising medications. Lawmakers and regulators should take further action to limit excessive spending on unproven therapies and ensure that drug sponsors conduct robust and timely confirmatory trials after receiving accelerated approval.
Efficacy of Acupuncture for Chronic Spontaneous Urticaria: A Randomized Controlled Trial: Annals of Internal Medicine: Vol 176, No 12
Background: The effectiveness of acupuncture for patients with chronic spontaneous urticaria (CSU), reported in a few small-scale studies, is not convincing. Objective: To investigate whether acupuncture leads to better effects on CSU than sham acupuncture or waitlist control. Design: A multicenter, randomized, sham-controlled trial. (Chinese Clinical Trial Registry: ChiCTR1900022994) Setting: Three teaching hospitals in China from 27 May 2019 to 30 July 2022. Participants: 330 participants diagnosed with CSU. Intervention: Participants were randomly assigned in a 1:1:1 ratio to receive acupuncture, sham acupuncture, or waitlist control over an 8-week study period (4 weeks for treatment and another 4 weeks for follow-up). Measurements: The primary outcome was the mean change from baseline in the Weekly Urticaria Activity Score (UAS7) at week 4. Secondary outcomes included itch severity scores, self-rated improvement, and Dermatology Life Quality Index scores. Results: The mean change in UAS7 (range, 0 to 42) for acupuncture from baseline (mean score, 23.5 [95% CI, 21.8 to 25.2]) to week 4 (mean score, 15.3 [CI, 13.6 to 16.9]) was −8.2 (CI, −9.9 to −6.6). The mean changes in UAS7 for sham acupuncture and waitlist control from baseline (mean scores, 21.9 [CI, 20.2 to 23.6] and 22.1 [CI, 20.4 to 23.8], respectively) to week 4 (mean scores, 17.8 [CI, 16.1 to 19.5] and 20.0 [CI, 18.3 to 21.6], respectively) were −4.1 (CI, −5.8 to −2.4) and −2.2 (CI, −3.8 to −0.5), respectively. The mean differences between acupuncture and sham acupuncture and waitlist control were −4.1 (CI, −6.5 to −1.8) and −6.1 (CI, −8.4 to −3.7), respectively, which did not meet the threshold for minimal clinically important difference. Fifteen participants (13.6%) in the acupuncture group and none in the other groups reported adverse events. Adverse events were mild or transient. Limitation: Lack of complete blinding, self-reported outcomes, limited generalizability because antihistamine use was disallowed, and short follow-up period. Conclusion: Compared with sham acupuncture and waitlist control, acupuncture produced a greater improvement in UAS7, although the difference from control was not clinically significant. Increased adverse events were mild or transient. Primary Funding Source: The National Key R&D Program of China and the Science and Technology Department of Sichuan Province.
Effect of Complementary Interventions to Redesign Care on Teamwork and Quality for Hospitalized Medical Patients: A Pragmatic Controlled Trial: Annals of Internal Medicine: Vol 176, No 11
Background: Multiple challenges impede interprofessional teamwork and the provision of high-quality care to hospitalized patients. Objective: To evaluate the effect of interventions to redesign hospital care delivery on teamwork and patient outcomes. Design: Pragmatic controlled trial. Hospitals selected 1 unit for implementation of interventions and a second to serve as a control. (ClinicalTrials.gov: NCT03745677) Setting: Medical units at 4 U.S. hospitals. Participants: Health care professionals and hospitalized medical patients. Intervention: Mentored implementation of unit-based physician teams, unit nurse–physician coleadership, enhanced interprofessional rounds, unit-level performance reports, and patient engagement activities. Measurements: Primary outcomes were teamwork climate among health care professionals and adverse events experienced by patients. Secondary outcomes were length of stay (LOS), 30-day readmissions, and patient experience. Difference-in-differences (DID) analyses of patient outcomes compared intervention versus control units before and after implementation of interventions. Results: Among 155 professionals who completed pre- and postintervention surveys, the median teamwork climate score was higher after than before the intervention only for nurses (n = 77) (median score, 88.0 [IQR, 77.0 to 91.0] vs. 80.0 [IQR, 70.0 to 89.0]; P = 0.022). Among 3773 patients, a greater percentage had at least 1 adverse event after compared with before the intervention on control units (change, 1.61 percentage points [95% CI, 0.01 to 3.22 percentage points]). A similar percentage of patients had at least 1 adverse event after compared with before the intervention on intervention units (change, 0.43 percentage point [CI, −1.25 to 2.12 percentage points]). A DID analysis of adverse events did not show a significant difference in change (adjusted DID, −0.92 percentage point [CI, −2.49 to 0.64 percentage point]; P = 0.25). Similarly, there were no differences in LOS, readmissions, or patient experience. Limitation: Adverse events occurred less frequently than anticipated, limiting statistical power. Conclusion: Despite improved teamwork climate among nurses, interventions to redesign care for hospitalized patients were not associated with improved patient outcomes. Primary Funding Source: Agency for Healthcare Research and Quality.
Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings: A Simulation Study: Annals of Internal Medicine: Vol 176, No 10
Background: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. Objective: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. Design: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. Setting: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). Patients: 130 000 critical care admissions across both health systems. Intervention: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. Measurements: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. Results: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. Limitations: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. Conclusion: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. Primary Funding Source: National Center for Advancing Translational Sciences.