Expert Physicians Research Articles

P0493 Patient satisfaction with IBD undergoing colonoscopy: a multicenter cross-sectional study

Abstract Background Colonoscopy is essential in managing inflammatory bowel disease (IBD), facilitating disease activity assessment, mucosal healing monitoring, and colorectal cancer prevention. However, its invasive nature and associated discomfort often compromise patient satisfaction. This study evaluated patient satisfaction with colonoscopy and its determinants, including perceived stress, disease activity, procedural characteristics, and physician expertise. Methods This multicentric cross-sectional study was conducted between December 2023 and September 2024 at three endoscopy centres: Fondazione Policlinico Gemelli IRCCS, Azienda Sanitaria Universitaria Friuli Centrale and La Maddalena Cancer Center. Patients with IBD were consecutively recruited during routine colonoscopy appointments. Patient satisfaction was assessed using the Endoscopy Customer Satisfaction Questionnaire (ECSQ), and perceived stress was measured using the Perceived Stress Scale (PSS-10). Additional demographic and clinical data, including age, gender, prior colonoscopy experience, and disease activity, were collected via pre- and post-procedure questionnaires. Statistical analyses included linear regression to identify satisfaction determinants and Kruskal-Wallis tests to evaluate differences among patient subgroups. Results A total of 444 IBD patients were included (54% male; median age 45 years, interquartile range 38–53). Most patients (60%) had prior colonoscopy experience, and 70% were in clinical remission during the procedure. Average satisfaction measures with ECSQ scores indicated a moderate level. Higher satisfaction was associated with remission status (β = 1.705, p = 0.021), prior colonoscopy experience (β = 0.459, p = 0.041), and physician expertise (β = 2.111, p = 0.013). Severe disease activity negatively impacted satisfaction (β = −3.872, p < 0.001). Conclusion Improving patient satisfaction requires targeted strategies addressing disease activity, procedural experience, and stress management. Tailored interventions for specific patient subgroups could enhance adherence to colonoscopy surveillance, improve disease outcomes, and reduce cancer risk in IBD populations.

Comparative outcomes of ultrasound-guided radiofrequency ablation vs. microwave ablation for patients with T1N0M0 papillary thyroid carcinoma: a retrospective cohort study.

To compare the clinical outcomes between radiofrequency ablation (RFA) and microwave ablation (MWA) for the treatment of T1N0M0 papillary thyroid carcinoma (PTC) in a large cohort. This retrospective study included 1111 patients with solitary T1N0M0 PTC treated with RFA (n = 894) or MWA (n = 215) by experienced physicians. A propensity score matching was used to compare disease progression, including lymph node metastases (LNM), recurrent tumors and persistent tumors, recurrence-free survival (RFS), volume reduction ratio (VRR), and complications between the RFA and MWA groups. The subgroup outcomes of T1a and T1b tumors treated with RFA and MWA were also compared. During a mean follow-up period of 41.7 ± 12.6 months, the overall incidence rates of disease progression, LNM, recurrent tumors, and persistent tumors were 2.1%, 0.7%, 1.2%, and 0.2%, respectively. Eight patients (0.7%) experienced transient voice change and recovered within 3 months. After 1:1 matching, there were no significant differences in disease progression (3.3% vs. 3.7%, p = 0.793), LNM (1.4% vs. 1.9%, p = 1.000), recurrent tumor (1.4% vs. 1.9%, p = 1.000), persistent tumors (0.5% vs. 0%, p = 0.317), RFS rates (96.7% vs. 96.3%, p = 0.821), VRR (99.3 ± 3.0% vs. 98.3 ± 8.5%, p = 0.865), tumor disappearance rate (87.9% vs. 81.4%, p = 0.061) and complications (1.4% vs. 1.9%, p = 1.000) between the RFA and MWA groups. For T1a and T1b tumors, clinical outcomes were comparable between RFA and MWA. RFA and MWA did not show significant differences in clinical outcomes for T1N0M0 PTC. The choice between RFA and MWA could depend on physician expertise and resources of local center resources. A qualified and experienced physician with extensive knowledge of the modalities is recommended when thermal ablation is performed. Question With radiofrequency ablation (RFA) and microwave ablation (MWA) used worldwide for T1N0M0 papillary thyroid carcinoma (PTC), is one of them superior to the others? Findings There were no significant differences in disease progression, recurrence-free survival, volume reduction ratio, tumor disappearance, or complications between techniques for solitary T1N0M0 papillary thyroid carcinoma. Clinical relevance The choice between RFA and MWA could depend on physician expertise and resources of the local center resources. A qualified and experienced physician with extensive knowledge of the modalities is recommended when thermal ablation is performed.

Outcomes of bile duct cannulation using a novel contrast-enhanced catheter: A single-center, retrospective cohort study.

Endoscopic retrograde cholangiopancreatography is a challenging procedure involving bile duct cannulation. Despite the development of several cannulation devices, none have effectively facilitated the procedure. To evaluate the efficacy of a recently developed catheter for bile duct cannulation. We retrospectively examined 342 patients who underwent initial cholangiopancreatography. We compared the success rate of bile duct cannulation and the incidence of complications between the groups using existing and novel catheters. The overall success rates of bile duct cannulation were 98.3% and 99.1% in the existing and novel catheter groups, respectively (P = 0.47). The bile duct cannulation rate using the standard technique was 73.0% and 82.1% in the existing and novel catheter groups, respectively (P = 0.042). Furthermore, when catheterization was performed by expert physicians, the bile duct cannulation rate was significantly higher in the novel catheter group (81.3%) than in the existing catheter group (65.2%) (P = 0.017). The incidence of difficult cannulation was also significantly lower in the novel catheter group (17.4%) than in the existing catheter group (33.0%) (P = 0.019). The novel catheter improved the bile duct cannulation rate using the standard technique and reduced the frequency of difficult cannulation cases, valuable tool in endoscopic retrograde cholangiopancreatography procedures performed by experts.

Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence.

Current tools to review focused abdominal sonography for trauma (FAST) images for quality have poorly defined grading criteria or are developed to grade the skills of the sonographer and not the examination. The purpose of this study is to establish a grading system with substantial agreement among coders, thereby enabling the development of an automated assessment tool for FAST examinations using artificial intelligence (AI). Five coders labeled a set of FAST clips. Each coder was responsible for a different subset of clips (10% of the clips were labeled in triplicate to evaluate intercoder reliability). The clips were labeled with a quality score from 1 (lowest quality) to 5 (highest quality). Clips of 3 or greater were considered passing. An AI training model was developed to score the quality of the FAST examination. The clips were split into a training set, a validation set, and a test set. The predicted scores were rounded to the nearest quality level to distinguish passing from failing clips. A total of 1,514 qualified clips (1,399 passing and 115 failing clips) were evaluated in the final data set. This final data set had a 94% agreement between pairs of coders on the pass/fail prediction, and the set had a Krippendorff α of 66%. The decision threshold can be tuned to achieve the desired tradeoff between precision and sensitivity. Without using the AI model, a reviewer would, on average, examine roughly 25 clips for every 1 failing clip identified. In contrast, using our model with a decision threshold of 0.015, a reviewer would examine roughly five clips for every one failing clip - a fivefold reduction in clips reviewed while still correctly identifying 85% of passing clips. Integration of AI holds significant promise in improving the accurate evaluation of FAST images while simultaneously alleviating the workload burden on expert physicians. Diagnostic Test/Criteria; Level II.

Artificial Intelligence vs. Physician Expertise in Appendicular Skeleton Fracture Detection: A Scoping Review

Background Fracture types, resulting from both acute and chronic conditions, vary widely, including closed, open, stress, and pathological fractures. Trauma, falls, and sports injuries are leading causes, particularly among younger males, while factors like age, gender, and bone density significantly influence fracture susceptibility. Artificial intelligence (AI), through machine learning (ML) and convolutional neural networks (CNN), has emerged as a powerful tool in fracture detection, improving diagnostic accuracy and reducing errors in interpretation. Radiological AI algorithms are increasingly used in clinical settings, offering reliable diagnostic performance on par with trained clinicians. . Purpose/Hypothesis This review examines the role of AI in fracture detection, focusing on its accuracy compared to physician diagnoses and its potential to enhance clinical outcomes across different anatomical regions of the appendicular skeleton. Study/Design The exploration includes the AI's role in enhancing clinical care, its comparative accuracy with trained clinicians, and its potential in minimizing diagnostic disparities during trauma-related scenarios. Methods A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language between January 1, 2018 to December 31, 2022. Inclusion criteria targeted studies utilizing artificial intelligence modalities, radiographic images of pediatric or adult fractures, and the participation of orthopedic surgeons or radiologists for comparison of diagnostic accuracy with AI. Results A total of 754 articles were screened, with 36 meeting inclusion criteria for this review. These studies focused on AI-based fracture detection compared directly with physician diagnoses, specifically for appendicular skeleton fractures. Among the included articles, 13 compared AI to radiologists, 3 to orthopedic surgeons, and 1 to ER physicians, with 19 involving unspecified or mixed specialties. AI models were categorized by type, with 12 using Convolutional Neural Networks (CNNs), 14 using Deep Convolutional Neural Networks (DCNNs), and 10 employing unspecified deep learning models. Regarding fracture locations, 5 studies focused on the distal radius, 4 on wrist bones, 2 each on ankle and humerus fractures, 4 on the scaphoid, 6 on the hip, and 3 on the femur, while 7 evaluated fractures across the appendicular skeleton. Most studies were retrospective cohort designs (n=35), with one prospective study included. A general population was examined in 28 studies, while 5 focused on pediatric fractures, and 1 excluded pediatric images. X-rays were the primary imaging modality in 35 studies, with 1 study using MRI and CT. Comparative results indicated that AI outperformed physicians in 7 studies, matched their performance in 10, and was outperformed by physicians in 1. Physician performance improved with AI assistance in 11 studies. Additionally, 8 studies compared AI to “ground-truth” diagnoses to assess sensitivity and specificity. This categorization highlights the range of AI model types, specialties, and comparison methods across the studies. Conclusions This review evaluates the application of AI in radiological fracture detection. While current AI algorithms demonstrate promising potential, our findings indicate a need for further improvement in predictive accuracy before broad clinical implementation. Multidisciplinary collaboration appears crucial for optimizing diagnostic outcomes and improving patient care.

Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis.

To date, no studies have evaluated Chat Generative Pre-Trained Transformer (ChatGPT) as a large language model chatbot in optical applications for digestive endoscopy images. This study aimed to weigh the performance of ChatGPT-4 in assessing bowel preparation (BP) quality for colonoscopy. ChatGPT-4 analysed 663 anonymised endoscopic images, scoring each according to the Boston BP scale (BBPS). Expert physicians scored the same images subsequently. ChatGPT-4 deemed 369 frames (62.9%) to be adequately prepared (i.e., BBPS > 1) compared to 524 frames (89.3%) assessed by human assessors. The agreement was slight (κ: 0.099, p = 0.0001). The raw human BBPS score was higher at 3 (2-3) than that of ChatGPT-4 at 2 (1-3), demonstrating moderate concordance (W: 0.554, p = 0.036). ChatGPT-4 demonstrates some potential in assessing BP on colonoscopy images, but further refinement is still needed.

Abstract 4128519: Performance of a Popular Large Language Model in Answering Cardiovascular Related Queries: A Systematic Review and Pooled-Analysis

Background: The integration of large language models (LLMs) such as ChatGPT into healthcare can have significant implications for patient education and clinical decision-making. Aims: This systematic review and pooled analysis aim to evaluate the accuracy of ChatGPT 3.5 and 4 in answering simple queries across cardiovascular (CV) medicine disciplines. Methods: Literature searches were conducted in PubMed, Embase, and Cochrane Central in May 2024. Keywords included “ChatGPT”, “LLMs”, and “Chat-based artificial intelligence models”. Cross-sectional, peer-reviewed studies published in 2023 and 2024 investigating ChatGPT’s performance in CV medicine-related queries (Table/Figure) were extracted and included. All queries were evaluated by expert physicians in the corresponding fields within each study (and not by our readjudication), and a standardized grading system was employed for pooled analysis using an "accurate" and "inaccurate" grading scale for each answer. Results: Out of 127 identified and screened peer-reviewed studies, fourteen studies involving 542 CV-related queries were included. Pooled analysis revealed an overall accuracy of 84.5% (458/542) (95% CI [81.5, 87.6]). Stratification by model (ChatGPT-4 vs. ChatGPT-3.5) did not show a significant difference in accuracy (p=0.32). Furthermore, no significant differences in accuracies were seen between answers in 2023 and 2024 (p=0.07). The accuracies across the various topics were statistically comparable, except in the field of cardio-oncology, which showed significantly lower accuracy at 68% (p=0.02). Detailed performances per topic are included in the table and figure. Conclusion: ChatGPT demonstrated consistently high accuracy in answering CV-related queries with no significant differences across model versions or years. These results support the potential use of online-chat based LLMs as an informational tool in cardiology.

A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.

Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics. We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models. Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign ( P < 0.001) and cortical thickening ( P < 0.001). In contrast, AI models were sensitive to abnormal gyration ( P < 0.01) or gray-white matter blurring ( P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs ( P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) ( P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17). This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.

Abstract 4120657: A Personal Risk Assessment Device in Patients with Chest Pain

Introduction: Early prehospital chest pain assessment improves acute coronary syndrome (ACS) outcomes and can reduce unnecessary Emergency Room (ER) visits but requires medical personnel and electrocardiographic (ECG) equipment. Objective: To evaluate the feasibility of a personal credit-card sized chest pain self-assessment device (HeartBeam, Santa Clara, CA, USA) in diagnosing ACS. Methods: ACS risk was estimated by serial likelihood ratio analysis of 3 components: pre-existing coronary heart disease (CHD) risk (pre-calculated based on pooled ASCVD equation), chest pain characteristics (interactive proprietary questionnaire), and integrated near-orthogonal 3-lead self-recorded vectorcardiogram (VCG) with optional comparison to baseline using portable handheld HeartBeam recorder. Final ACS risk was graded High/Intermediate/Low by the cloud-based proprietary algorithm. High risk was considered positive for ACS. Results: 184 ER patients with chest pain (Age 57 +/- 7years, 90 Male, 43 (23%) ACS) comprised learning (n=96) and test (n=88) sets. ACS sensitivity was 28/28 (100%) in learning and 15/16 (94%) in test set with specificity of 43% and 42% respectively. In the subset of patients with pain-free portable VCG recorded 9 months later used as a “reverse baseline” (n=110) sensitivity was 17/17 (100%) and 11/12 (92%), specificity 54% and 58% in learning and test set, respectively (p&gt;0.1 with no baseline). Single patient missed by the analysis had known coronary disease with usual anginal episode resulting in minor troponin leak. In “reverse baseline” analysis 41/110 (37%) patients were classified as Low risk compared to 18/184 (10%) without baseline (p&lt;0.001), all negative for ACS. Based on the analysis of CHD risk factors, chest pain characteristics, and 12-lead ECG, a consensus of 3-member ER chest pain unit physicians panel had sensitivity/specificity of 94/54% for the whole and 93/48% for the “reverse baseline” groups (p&gt;0.1 with HeartBeam device). Conclusion: HeartBeam chest pain self-assessment device provides accurate ACS detection comparable to expert ER physicians using 12 lead ECG. The presence of baseline recording reduces the number of false positive results. If confirmed in larger studies, it can be used to facilitate outpatient chest pain assessment and triage by remote physicians and patients themselves.

Barriers to opioid use in the Arab world.

The Arab world consists of 22 countries, representing 5.5% of the world's population. Morphine consumption accounts for less than 1% of the world's consumption. This is the first study to identify barriers to opioid use in the Arab world. This study aimed to investigate the barriers to opioid use in pain management in the Arab world. Online survey was developed to investigate barriers to governance, prescribing, distributing, dispensing and administering, as well as educational barriers in the Arab world. The questionnaire was sent via email and a mobile app to one expert physician in pain management and one licensed pharmacist from each Arab country. With the exception of Tunisia, Djibouti and Comoros, 34(77%) participants from 19 Arab countries answered the survey. Most countries lack local opioid production, necessitate special licenses for physicians, and restrict opioid prescribing to medical specialists. Special prescription forms are mandated, and pharmacists lack the authority to correct prescription errors or accept refill or verbal orders on opioids. Storage requirements for empty ampoules and prescriptions are enforced. Nurses are not allowed to carry opioids during home visits, and only first degree relatives can collect opioids for patients. Furthermore, the integration of palliative care and pain management curricula into pharmacies and medical schools is lacking. There is a wide range of regulatory and other barriers to opioid use in the Arab world. There is a substantial need for regulatory review and reform, as well as for educational initiatives, in most Arab countries.

Management of Adult Patients with Newly Diagnosed or Relapsed Primary Immune Thrombocytopenia in Eastern Austria.

Treatment sequence in primary immune thrombocytopenia (ITP) is based on national and international recommendations, treatment availability, and physician expertise. This article aimed to provide real-world data on treatment sequence and responses to first- and second-line treatments in newly diagnosed and relapsed adult ITP patients. We analyzed a cohort of 46 adult ITP patients from the Vienna ITP Biobank, who started first-line therapy within 1 week before their first study visit between February 2016 and March 2023. We investigated clinical patient characteristics and patient management in our specialized center and examined the impact of the international ASH guidelines on ITP treatment. Forty-six primary ITP patients, 27 (58.7%) with newly diagnosed ITP and 19 (41.3%) with relapsed ITP, were investigated. Most patients were female (65.2%) with a median platelet count of 9 × 109/L, and 31 patients (67.4%) had bleeding symptoms. All patients received first-line treatment with oral prednisolone; 15 patients received oral prednisolone combined with intravenous immunoglobulins (IVIGs), which were more commonly administered in newly diagnosed than in relapsed ITP patients. First-line therapy resulted an overall response in 82.6% of patients after a median (interquartile range [IQR]) time of 10 (5-25) days. There was no difference in treatment responses between newly diagnosed and relapsed ITP patients, but newly diagnosed patients had a shorter time to response (median [IQR]: 8 [5-14] and 14 [8-27], p = 0.02). Twenty-three (50%) of the patients (11/27 newly diagnosed [40.7%], 12/19 relapsed [63.2%]) required second-line ITP therapy. Thrombopoietin-receptor agonists (TPO-RAs) were the most commonly used second-line therapy with a response rate of 73.7%, and a median (IQR) time to treatment response of 15 (12-20) days. Overall response rates to TPO-RA treatment did not differ between newly diagnosed and relapsed ITP. Following the publication of novel guidelines in 2019, the median (IQR) duration of corticosteroid treatment shortened (100-52 days, p = 0.01), as did the time to second-line treatment (160-47 days, p = 0.01), and the median number of first-line therapies decreased from 2 (1-3) to 1 (1-2). Initial treatment with corticosteroids was effective in the majority of newly diagnosed and relapsed ITP. Response rates to initial corticosteroid treatment in ITP patients are consistent with previous data, but only 50% achieve sustained remission. TPO-RAs, which are well tolerated and effective, are the most commonly used second-line therapy in our study population. International guidelines have led to faster treatment transitions and reduced splenectomy rates. Integration of real-life experience, expert consensus, and guidelines optimizes ITP patient management.

Artificial Intelligence Versus Expert Physician Assessments of Real-World Hematology Cases: Implications for Clinical Practice

Artificial Intelligence Versus Expert Physician Assessments of Real-World Hematology Cases: Implications for Clinical Practice

Effectiveness of King's Theory of Goal Attainment in Blood Glucose Management for Newly Diagnosed Patients With Type 2 Diabetes: Randomized Controlled Trial.

Diabetes poses a significant public health challenge in China and globally, with the number of patients expected to reach 592 million by 2035, notably in Asia. In China alone, an estimated 140 million individuals are living with diabetes, and a significant portion is nonadherent to medications, underscoring the urgency of effective management strategies. Recognizing the necessity of early and comprehensive management for newly diagnosed patients with type 2 diabetes, this study leverages an online teach-back method and "Internet + Nursing" platform based on King's Theory of Goal Attainment. The approach aims to enhance glycemic control and reduce fear and misconceptions about the disease, addressing both the educational and emotional needs of the patients. The primary aim of this study was to assess the effectiveness of King's Goal Attainment Theory in the management of newly diagnosed patients with type 2 diabetes. This research sought to develop a collaborative model for blood glucose management, integrating the expertise and roles of physicians, nurses, and patients. The model is designed to enhance the synergy in health care provision, ensuring a comprehensive approach to diabetes management. In this study conducted at Changzhou Traditional Chinese Medicine Hospital between January 2022 and February 2023, eligible patients were randomized into a control group or an online feedback group. The control group received standard care, while the online feedback group participated in a King's Theory of Goal Attainment-based online teach-back program, enhanced by "Internet + Nursing" strategies. This included an interactive platform for goal planning, video content sharing, comprehension assessment, misconception correction, and patient-driven recaps of disease information. Health monitoring was facilitated through the "Internet + Nursing" platform. The study focused on comparing changes in glucose metabolism and emotional disorder symptoms between the groups to evaluate the intervention's effectiveness. Following a 24-week intervention, we observed significant differences in key metrics between the online feedback group and the control group, each comprising 60 participants. The online feedback group demonstrated significant reductions in fasting plasma glucose, 2-hour postprandial glucose, and hemoglobin A1c (P<.05). Additionally, there was a notable decrease in hypoglycemia-related anxiety and alexithymia within this group. Conversely, the control group maintained relatively higher values for these metrics at the same time point (P<.05). These findings underscore the efficacy of online feedback in managing glycemic control and reducing psychological distress associated with hypoglycemia. The online teaching-back method, guided by King's Theory of Goal Attainment, effectively enhances glycemic control, reducing fasting plasma glucose, 2-hour postprandial glucose, and hemoglobin A1c levels in newly diagnosed patients with type 2 diabetes. Simultaneously, it alleviates hypoglycemia-related anxiety and mitigates alexithymia. This approach merits widespread promotion and implementation in clinical settings. Chinese Clinical Trial Registry ChiCTR2400079547; https://www.chictr.org.cn/showproj.html?proj=208223.

Consensus Statement: Postoperative Management After Balloon Dilation of the Eustachian Tube.

Balloon dilation of the Eustachian tube (BDET) is widely recognized as a minimally invasive treatment for obstructive Eustachian tube dysfunction (ETD). We employed a Delphi consensus methodology to develop recommendations for the clinical management of BDET in cases of obstructive ETD. A Delphi panel consisting of 26 expert physicians specializing in otology participated in two rounds of anonymous, iterative questionnaires. Consensus was defined as agreement from ≥70% of the panelists on a recommendation, while disagreement was defined as <70% agreement. The responses from the Delphi study were analyzed using both the content validity ratio and Kendall's coefficient of concordance. The panel finally evaluated 26 topics, reaching agreement on 9 and failing to reach consensus on 17 after two rounds. While consensus was not achieved regarding the postoperative follow-up period, a duration of 12 months was most commonly adopted. The Valsalva maneuver and questionnaire responses were identified as the most agreed-upon postoperative assessment tools following BDET. Consensus was reached on several recommendations for managing BEDT in obstructive ETD. This agreement will guide future research aimed at defining standard postoperative management for BEDT.

VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network.

Traditional rehabilitation training for stroke patients with ankle joint issues typically relies on the expertise of physicians. However, when confronted with complex challenges, such as online decision-making or assessing rehabilitation progress, even seasoned experts may not anticipate all potential hurdles. A novel approach is necessary-one that effectively addresses these complexities without solely leaning on expert experience. Previous studies have introduced a rehabilitation assessment method based on fuzzy neural networks. This paper proposes a novel approach, which is a VR-aided ankle rehabilitation decision-making model based on a convolutional gated recurrent neural network. This model takes various inputs, including ankle dorsiflexion range of motion, angular velocity, jerk, and motion performance scores, gathered from wearable motion inertial sensors during virtual reality rehabilitation. To overcome the challenge of limited data, data augmentation techniques are employed. This allows for the simulation of five stages of rehabilitation based on the Brunnstrom staging scale, providing tailored control parameters for virtual training scenarios suited to patients at different stages of recovery. Experiments comparing the classification performance of convolutional neural networks and long short-term memory networks were conducted. The results were compelling: the optimized convolutional gated recurrent neural network outperformed both alternatives, boasting an average accuracy of 99.16% and a Macro-F1 score of 0.9786. Importantly, it demonstrated a strong correlation (correlation coefficient r > 0.9) with the assessments made by clinical rehabilitation experts, showing its effectiveness in real-world applications.

The Dutch practice of euthanasia and assisted suicide in patients suffering from psychiatric disorders: a qualitative case review study.

Euthanasia or assisted suicide (EAS) in patients suffering from a psychiatric disorder (PD) is a controversial topic worldwide. In the Netherlands, this practice is regulated by law. All cases of EAS have to be reported and are assessed by the Regional Euthanasia Review Committees (RTEs), who publish a selection of all cases on their website. To provide insight into the Dutch practice of EAS in patients suffering from a psychiatric disorder. We performed a retrospective case review study in which all published cases of EAS in patients suffering from a PD between 2017 and 2022 were analyzed. Not applicable. Characteristics of patients who died by EAS because of suffering from a PD, characteristics of the reporting physician and consultant(s) and the RTEs assessment of published cases. Of the 72 cases studied, the majority of patients were female (n=48, 67%), suffered from 3 or more conditions (n=38, 53%) and died by euthanasia instead of assistance in suicide (n=56, 78%). In 63% of cases (n=45), the life termination was performed by a physician from the Euthanasia Expertise center (EE). The RTEs' judgement that the case did not meet the due care criteria (n=11) was in all cases related to issues regarding the (advice of the) independent physician or psychiatric expert. This qualitative study shows that the RTEs attach great importance to a careful evaluation procedure of physicians handling EAS requests and to the physician demonstrating ability to reflect on his views, especially when the independent consultant evaluates the case different than the physician. Training for physicians and more transparency in the assessment of EAS requests in patients with a PD may lower the threshold for physicians to handle requests of these patients themselves. Not applicable.

Empowering Healthcare: TinyML for Precise Lung Disease Classification

Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.

The Digitized Memory Clinic.

Several major challenges, including an ageing population and declining workforce and the implementation of recent breakthrough therapies for Alzheimer disease, are prompting a necessary rethink of how people with neurodegenerative dementias are diagnosed and medically managed. Digital health technologies could play a pivotal part in this transformation, with new advances enabling the collection of millions of data points from a single individual. Possible applications include unobtrusive monitoring that aids early detection of disease and artificial intelligence-based health advice. To translate these advances to meaningful benefits for people living with a disease, technologies must be implemented within a system that retains the physician expert as a central figure in decision-making. This Perspective presents a new framework, termed the Digitized Memory Clinic, for the diagnostic pathway of neurodegenerative dementias that incorporates digital health technologies with currently available assessment tools, such as fluid and imaging biomarkers, in an interplay with the physician. The Digitized Memory Clinic will manage people across the entire disease spectrum, from the detection of risk factors for cognitive decline and the earliest symptoms to dementia, and will replace the present paradigm of a pure 'brick-and-mortar' memory clinic. Important ethical, legal and societal barriers associated with the implementation of digital health technologies in memory clinics need to be addressed. The envisioned Digitized Memory Clinic aims to improve diagnostics and enable precise disease-tracking prognostication for individuals with memory disorders and to open new possibilities, such as precision medicine for prevention and treatment.