The impact of different anticoagulants and antiplatelets regimens on acute epistaxis outcomes.

BACKGROUND: The impact of anticoagulants (AC) and antiplatelets (AP) on the management of acute epistaxis remains unclear. This study investigated the association between AC/AP therapy and treatment outcomes in patients with acute epistaxis. METHODOLOGY: A retrospective analysis of patients presented to the otolaryngology emergency room with acute epistaxis (2014-2022). Patients were categorized based on their regular medications: AP, dual AP therapy (DAPT), new oral anticoagulants (NOAC), vitamin K antagonists (VKA), or no regular AC/AP use (control group). Outcome measures included rates of minor interventions (chemical or electrical cautery, nasal tamponade), major interventions (endoscopic ligation, embolization), recurrent emergency department visits, admission rates, and duration. RESULTS: 786 patients were included with an average follow-up period of 52.56 ± 20.4 months. Compared to the control group, patients on AP, DAPT, or VKA had significantly higher rates of minor interventions (63.1% vs. 74.4%, 79.6%, and 77.3%, respectively, p < 0.05). DAPT users exhibited a higher rate of major interventions than the control (5.6% vs. 1.3%, p = 0.053). NOAC users showed no significant difference in minor interventions compared to control and required no major interventions. Both NOAC and VKA users had significantly higher rates of recurrent epistaxis events and prolonged hospitalization compared to the control (p < 0.01 and p < 0.05, respectively). CONCLUSIONS: NOAC demonstrated more favorable outcomes than VKA in patients with acute epistaxis, and DAPT use was associated with an increased need for major interventions. These findings suggest a more conservative approach in NOAC users than other AC/AP agents.

Do extremely large goiters carry a higher risk of malignancy or complications? A case control study.

INTRODUCTION: The definition of thyroid goiter remains ambiguous, yet size may impact both malignancy rate and surgical complications' rate. METHODS: All patients with thyroid goiter who underwent thyroidectomy between 1/2015-1/2023 were included. Goiter was defined as lobe ≥4 cm. For analysis purpose, goiters measuring 4-8 cm and ≥8 cm were defined as large and extremely large goiters, respectively. For malignancy definition, tumor<1 cm in their largest diameter were excluded from study. Collected data included demographics, cytology, histology and postoperative complication. RESULTS: 144 goiters from 111 patients were included. The most common indication for surgery was symptoms (55 %). Compared with large goiter, extremely large goiters demonstrated a trend for tracheal narrowing on pre-operative CT findings (23 % vs. 45 %, p = 0.07 respectively). Overall differentiated thyroid carcinoma (DTC) rate was 17 % (25/144) without statistical difference between groups (p = 0.89). Within goiters with pre-operative benign cytology, the DTC rate was 17 % (7/43). Follicular variant of papillary thyroid cancer was the most common type for both groups. Nodular hyperplasia was significantly associated with extremely large goiters (53 % vs. 73 %, p = 0.03). No significant difference was found in transient hypocalcemia (48 % [15/31] vs. 41 % [5/12], p = 0.6) and other complications' rate between extremely large goiters and the control group. CONCLUSION: When discussing management options for patients with goiters, the size of the goiter should not regarded as a higher risk for complications or malignancy, yet the relatively high malignancy rate found should be taken under consideration for resection.

Vitamin D Insufficiency is Associated with Higher Incidence of Dementia, a Large Community-Based Retrospective Cohort Study.

INTRODUCTION: Active metabolite of vitamin D has neuro-immunomodulatory and neuroprotective properties. However, there is still a debate about the potential association between low serum levels of hydroxy-vitamin D and increased risk for dementia. OBJECTIVES: To determine an association between hypovitaminosis D and dementia for different 25-hydroxyvitamin-D (25(OH)D) serum level cutoffs. METHODS: Patients were identified utilizing the database of Clalit Health Services (CHS), the largest healthcare provider in Israel. For each subject, all available values of 25(OH)D during the study period, which lasted from 2002 to 2019, were obtained. Rates of dementia were compared across different cutoffs of 25(OH)D levels. RESULTS: Cohort included 4278 patients, of whom 2454 (57%) were women. The mean age at the beginning of follow-up was 53 (±17). During the 17-year study period, a total of 133 patients (3%) were diagnosed with dementia. In a fully adjusted multivariate analysis, the risk for dementia was almost 2-fold higher in patients with an average of vitamin D insufficiency (<75 nmol/l) measurements (OR = 1.8, 95% C.I. = 1.0-3.2) compared to reference values (≥75 nmol/l). Patients with vitamin D deficiency (<50 nmol/l) demonstrated higher rates of dementia (OR = 2.6, 95% C.I. = 1.4-4.8). In our cohort, patients were diagnosed with dementia at a younger age in the deficiency (77 vs. 81 P-value = 0.05) and the insufficiency groups (77 vs. 81 P-value = 0.05) compared to the reference values (≥75 nmol/l). CONCLUSION: Insufficient levels of vitamin D are associated with dementia. Dementia is diagnosed at a younger age in patients with insufficient and deficient vitamin D levels.

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study.

BACKGROUND: The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews. OBJECTIVES: The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers. METHODS: We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms. RESULTS: The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73). CONCLUSION: The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.

Assessing the Performance of a New Artificial Intelligence-Driven Diagnostic Support Tool Using Medical Board Exam Simulations: Clinical Vignette Study.

BACKGROUND: Diagnostic decision support systems (DDSS) are computer programs aimed to improve health care by supporting clinicians in the process of diagnostic decision-making. Previous studies on DDSS demonstrated their ability to enhance clinicians' diagnostic skills, prevent diagnostic errors, and reduce hospitalization costs. Despite the potential benefits, their utilization in clinical practice is limited, emphasizing the need for new and improved products. OBJECTIVE: The aim of this study was to conduct a preliminary analysis of the diagnostic performance of "Kahun," a new artificial intelligence-driven diagnostic tool. METHODS: Diagnostic performance was evaluated based on the program's ability to "solve" clinical cases from the United States Medical Licensing Examination Step 2 Clinical Skills board exam simulations that were drawn from the case banks of 3 leading preparation companies. Each case included 3 expected differential diagnoses. The cases were entered into the Kahun platform by 3 blinded junior physicians. For each case, the presence and the rank of the correct diagnoses within the generated differential diagnoses list were recorded. Each diagnostic performance was measured in two ways: first, as diagnostic sensitivity, and second, as case-specific success rates that represent diagnostic comprehensiveness. RESULTS: The study included 91 clinical cases with 78 different chief complaints and a mean number of 38 (SD 8) findings for each case. The total number of expected diagnoses was 272, of which 174 were different (some appeared more than once). Of the 272 expected diagnoses, 231 (87.5%; 95% CI 76-99) diagnoses were suggested within the top 20 listed diagnoses, 209 (76.8%; 95% CI 66-87) were suggested within the top 10, and 168 (61.8%; 95% CI 52-71) within the top 5. The median rank of correct diagnoses was 3 (IQR 2-6). Of the 91 expected diagnoses, 62 (68%; 95% CI 59-78) of the cases were suggested within the top 20 listed diagnoses, 44 (48%; 95% CI 38-59) within the top 10, and 24 (26%; 95% CI 17-35) within the top 5. Of the 91 expected diagnoses, in 87 (96%; 95% CI 91-100), at least 2 out of 3 of the cases' expected diagnoses were suggested within the top 20 listed diagnoses; 78 (86%; 95% CI 79-93) were suggested within the top 10; and 61 (67%; 95% CI 57-77) within the top 5. CONCLUSIONS: The diagnostic support tool evaluated in this study demonstrated good diagnostic accuracy and comprehensiveness; it also had the ability to manage a wide range of clinical findings.