Treatment Decisions For Patients Research Articles

Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.

Several guidelines recommend the use of different classifiers to determine the risk of recurrence (ROR) and treatment decisions in patients with HR+HER2- breast cancer. However, data are still lacking for their usefulness in Latin American (LA) patients. Our aim was to evaluate the comparative prognostic and predictive performance of different ROR classifiers in a real-world LA cohort. The Molecular Profile of Breast Cancer Study (MPBCS) is an LA case-cohort study with 5-year follow-up. Stages I and II, clinically node-negative HR+HER2- patients (n = 340) who received adjuvant hormone therapy and/or chemotherapy, were analyzed. Time-dependent receiver-operator characteristic-area under the curve, univariate and multivariate Cox proportional hazards regression (CPHR) models were used to compare the prognostic performance of several risk biomarkers. Multivariate CPHR with interaction models tested the predictive ability of selected risk classifiers. Within this cohort, transcriptomic-based classifiers such as the recurrence score (RS), EndoPredict (EP risk and EPClin), and PAM50-risk of recurrence scores (ROR-S and ROR-PC) presented better prognostic performances for node-negative patients (univariate C-index 0.61-0.68, adjusted C-index 0.77-0.80, adjusted hazard ratios [HR] between high and low risk: 4.06-9.97) than the traditional classifiers Ki67 and Nottingham Prognostic Index (univariate C-index 0.53-0.59, adjusted C-index 0.72-0.75, and adjusted HR 1.85-2.54). RS (and to some extent, EndoPredict) also showed predictive capacity for chemotherapy benefit in node-negative patients (interaction P = .0200 and .0510, respectively). In summary, we could prove the clinical validity of most transcriptomic-based risk classifiers and their superiority over clinical and immunohistochemical-based methods in the heterogenous, real-world node-negative HR+HER2- MPBCS cohort.

Investigating Combination Therapy: The Role of Lutetium-177 PSMA-617 Radioligand Therapy and Androgen Receptor Pathway Inhibitors in Metastatic Castration-Resistant Prostate Cancer

Background: The combination of Lutetium-177 (Lu-177) PSMA-617 radioligand therapy (RLT) with androgen receptor pathway inhibitors (ARPIs) has shown promise in metastatic castration-resistant prostate cancer (mCRPC). However, real-world data on the efficacy and safety of this combination are limited. This study aimed to evaluate the impact of combination therapy with Lu-177 PSMA-617 RLT and ARPIs on progression-free survival (PFS) and overall survival (OS) in patients with mCRPC. Methods: In this retrospective study, 104 mCRPC patients receiving Lu-177 PSMA-617 RLT at our institution between December 2017 and January 2024 were divided into the following two groups those receiving Lu-177 PSMA-617 RLT plus ARPI (n = 34) and those receiving Lu-177 PSMA-617 RLT alone (n = 70). Patients received 150 to 200 millicuries Lu-177 PSMA-617 RLT in each cycle. PFS and zOS were assessed using Kaplan–Meier analysis and Cox proportional hazard models. Results: The combination therapy significantly prolonged median PFS compared to Lu-177 PSMA-617 RLT alone (11 vs. 5.6 months; HR, 0.47; 95% CI, 0.28–0.79; p < 0.01). A trend towards improved OS was also observed in the combination group (20.3 vs. 15.9 months; HR, 0.58; 95% CI, 0.33–1.02; p = 0.06). Age was a significant predictor of OS (21.2 vs. 12.4 months for younger vs. older patients; p < 0.01), while Gleason score and visceral involvement did not significantly impact PFS. The safety profile indicated that adverse effects were generally comparable between the two groups, with no statistically significant differences in the incidence of anemia, neutropenia, thrombocytopenia, nephrotoxicity, or hepatotoxicity. Conclusions: This study provides evidence that combining Lu-177 PSMA-617 RLT with ARPIs may significantly improve PFS in mCRPC patients. The potential OS benefit warrants further investigation in larger prospective trials. Age should be considered when making treatment decisions for mCRPC patients.

Influence of Social Media and Internet on Treatment Decisions in Adult Female Acne Patients: A Cross-Sectional Survey Study

Introduction: Adult female acne is a chronic condition that significantly impacts quality of life. The content on social media can influence patients’ perception of their disease and serve as a channel through which they may seek or obtain treatment options. Objectives: This study aims to evaluate the impact of social media usage habits on treatment decisions among adult female acne patients. Methods: A cross-sectional, multicenter survey study involved 358 females aged 25 or above, diagnosed with acne. Sociodemographic data were collected, and social media behavior, treatment choices, outcomes, and motivation were explored. Results: Among 358 participants, 95.3% used at least 1 social media platform; 72.1% sought acne information online. Top platforms used to seek acne information were Google (75.6%), Instagram (72.3%), YouTube (60%), and TikTok (29.4%). For advice, 67.4% consulted doctor accounts, 53.5% non-medical influencers, 53.5% patient accounts, and 36.1% product promotion accounts. Commonly followed advice included skincare products (88%), dietary changes (42.3%), home remedies (38.8%), exercise (30.3%), topical medications (25.2%), and dietary supplements (17.4%). Notably, 20.9% were willing to alter prescribed treatment by their physician for acne based on social media advice. Patient motivations included quick information access (84.1%) and difficulty in securing dermatologist appointments (54.3%). Conclusion: The study reveals widespread social media use among adult female acne patients, highlighting concerns about potentially misleading information. Dermatologists can enhance the impact of social media by providing reliable sources for patients.

Desmoid tumors of rectus abdominis: A case report and literature review.

Desmoid tumor (DT) is a rare soft tissue tumor that can occur anywhere in the body. Abdominal wall DT presents unique clinical challenges due to its distinctive manifestations, treatment modalities, and the lack of biomarkers for diagnosis and recurrence prediction, making clinical decisions exceedingly complex. A 32-year-old female who underwent radical resection combined with patch reinforcement for rectus abdominis DT, successfully alleviating abdominal discomfort, with no recurrence during the 6-month follow-up after surgery. Based on the imaging studies and medical history, the patient underwent radical surgical resection. Histopathology reveals that the tumor cells predominantly composed of proliferative fibroblasts with local collagen deposition. The lesional cells show positive staining for β-catenin, indicating a diagnosis of DT. The patient underwent radical surgical resection with patch reinforcement to repair the abdominal wall defect. Pathology confirmed negative margins, achieving an R0 resection, and genetic testing identified a T41A mutation in CTNNB1. Consequently, no additional adjuvant therapy was administered postoperatively. The patient was discharged with the incision healing well after 3 days postoperation. Upon reexamination 6 months later, no recurrence or adverse complications were observed. Abdominal wall DT treatment requires personalized plans from multidisciplinary team discussions. Genetic testing plays a crucial role in identifying novel biomarkers for abdominal wall DT. We have once again demonstrated the significant clinical significance of CTNNB1 mutations in the diagnosis and progression of abdominal wall DT. Additionally, genes such as CCND1, CYP3A4, SLIT1, RRM1, STIM1, ESR2, UGT1A1, among others, may also be closely associated with the progression of abdominal wall DT. Future research should delve deeper into and systematically evaluate the precise impact of these genetic mutations on treatment selection and prognosis for abdominal wall DT, in order to better guide patient management and treatment decisions.

The relationship between TNF-α, IL-1, IL-12, IL-17, IL-23, IL-36 expression and treatment response in psoriasis histopathologically and immunohistochemically

Aim There is no marker that can predict whether there is resistance to treatment in patients with psoriasis. In this study, we investigated the relationship between the staining rates of TNF-α, IL-1, IL-12, IL-17, IL-23, and IL-36 markers immunohistochemically from cutaneous biopsy and the treatment success. Methods The patients who were followed up in the dermatology clinic with the diagnosis of plaque-type psoriasis vulgaris and received biological treatment and previously had cutaneous biopsy were included in the study. The cutaneous biopsies of the cases that met the conditions were re-sectioned and subjected to immunohistochemical examination for TNF-α, IL-1, IL-12, IL-17, IL-23, and IL-36. Results Comparing the staining scores with psoriasis area severity index (PASI); A statistically significant positive correlation was found between PASI and TNF-α staining score (p = 0.034). A statistically significant positive correlation was found between PASI and IL-17 staining score (p = 0.004). When the staining scores and PASI response rates of psoriasis treatment were evaluated in terms of correlation; there was a positive correlation between TNF-α, IL-17, and IL-23 immunohistochemical staining rates and PASI response rates. Conclusions In line with the data obtained from our study, we think that making immunohistochemical scoring before the biological treatment decision in psoriasis patients will be beneficial in treatment selection. In this respect, our study may open a new era in the selection of biological treatments for psoriasis.

Feasibility Study on Detecting Breast Cancer Lymph Node Metastasis and Optimizing Nursing Workflow.

This study explores using AI for detecting lymph node metastasis in breast cancer, aiming to analyze extensive medical imaging data. AI assists clinicians in identifying metastases and formulating precise treatment plans. Employing image analysis and machine learning, the research assesses AI's potential to recognize patterns within medical imaging data. The study evaluates the feasibility of integrating AI-based detection into diagnostic workflows. Collaborative efforts with hospitals include collecting annotated breast cancer lymph node data, optimizing workflows, and clinically validating results. Anticipated outcomes aim to highlight AI's crucial role in enhancing early detection and treatment decisions for breast cancer patients. Anticipated results aim to underscore the crucial role of AI in improving early detection and treatment decision-making for breast cancer patients while optimizing efficiency in the operating room nursing workflow.

Innovative regression model-based decision support tool for optimizing radiotherapy techniques in thoracic esophageal cancer.

Modern radiotherapy exemplified by intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), has transformed esophageal cancer treatment. Facing challenges in treating thoracic esophageal cancer near vital organs, this study introduces a regression model-based decision support tool for the optimal selection of radiotherapy techniques. We enrolled 106 patients diagnosed with locally advanced thoracic esophageal cancer in this study and designed individualized IMRT and VMAT radiotherapy plans for each patient. Detailed dosimetric analysis was performed to evaluate the differences in dose distribution between the two radiotherapy techniques across various thoracic regions. Single-factor and multifactorial logistic regression analyses were employed to establish predictive models (P1 and P2) and factors such as TLV/PTV ratio. These models were used to predict the compliance and potential advantages of IMRT and VMAT plans. External validation was performed in a validation group of 30 patients. Using predictive models, we developed a data-driven decision support tool. For upper thoracic cases, VMAT plans were recommended; for middle/lower thoracic cases, the tool guided VMAT/IMRT choices based on TLV/PTV ratio. Models P1 and P2 assessed IMRT and VMAT compliance. In validation, the tool showed high specificity (90.91%) and sensitivity (78.95%), differentiating IMRT and VMAT plans. Balanced performance in compliance assessment demonstrated tool reliability. In summary, our regression model-based decision support tool provides practical guidance for selecting optimal radiotherapy techniques for thoracic esophageal cancer patients. Despite a limited sample size, the tool demonstrates potential clinical benefits, alleviating manual planning burdens and ensuring precise, individualized treatment decisions for patients.

Knowledge, attitudes, and practices of caregivers with children diagnosed with epilepsy attending a pediatric outpatient clinic: a descriptive, cross-sectional, questionnaire-based study in Addis Ababa, Ethiopia

BackgroundCaregivers’ knowledge and attitudes influence help-seeking behavior and treatment decisions of patients with epilepsy, which in turn significantly impacts epilepsy care. In Ethiopia, epilepsy is often misunderstood, associated with misconceptions and accompanied by persistent negative attitudes. The objective of this study is to assess the knowledge, attitude, and practice of caregivers of children with epilepsy.MethodsWe conducted a hospital-based survey at the Yekatit 12 Hospital Pediatric Neurology Clinic, Addis Ababa, Ethiopia, between May and July 2022. We invited caregivers of children with epilepsy taking one or more daily anti-seizure medications to participate. Caregivers were invited to complete a structured questionnaire with guidance from a trained nurse to estimate knowledge and attitudes towards epilepsy and its treatment. Knowledge and attitudes were categorized as “good” and “favorable” (correct answers to ≥ 50% of questions) or “bad” and “unfavorable” (< 50% correct answers), respectively. Attitudes towards standard care versus non-standard (e.g., spiritual) care were also estimated.ResultsA total of 120 caregivers completed the questionnaire. Many caregivers were familiar with the term ‘epilepsy’, with more than half (51.7%) having heard or read about it previously. The reported causes of epilepsy varied, with birth injury being the most common cause (44 out of 120 caregivers). Notably, there was association between the caregiver’s gender and their knowledge score, with a p-value = 0.05. Caregivers exposed to information about epilepsy through hearing or reading demonstrated significantly higher levels of knowledge, with a p-value < 0.001. Additionally, knowing someone with epilepsy other than the index child was significantly associated with higher knowledge scores (p-value < 0.001). The study also revealed negative attitudes toward epilepsy: for example, 56.7% of surveyed caregivers believed it is unlikely that a child with epilepsy has normal cognitive abilities and 39.1% believed they should never be allowed to attend regular school. Additionally, a high proportion of caregivers (70%) sought alternative treatments (e.g., spiritual help) alongside standard medical care.ConclusionsA significant knowledge gap was identified among caregivers, revealing prevalent misconceptions and negative attitudes. Improving epilepsy awareness, attitudes, and practices among caregivers will potentially contribute to overall improved quality of life for children with epilepsy.

A Decision Tree Model to Help Treatment Decision-Making for Unruptured Intracranial Aneurysms: A Multi-center, Long-Term Follow-up Study in a Large Chinese Cohort.

Chinese population have a high prevalence of unruptured intracranial aneurysm (UIA). Clinical and imaging risk factors predicting UIA growth or rupture are poorly understood in the Chinese population due to the lack of large-scale longitudinal studies, and the treatment decision for UIA patients was challenging. Develop a decision tree (DT) model for UIA instability, and validate its performance in multi-center studies. Single-UIA patients from two prospective, longitudinal multicenter cohort studies were analyzed, and set as the development cohort and validation cohort. The primary endpoint was UIA instability (rupture, growth, or morphological change). A DT was established within the development cohort and validated within the validation cohort. The performance of clinicians in identifying unstable UIAs before and after the help of the DT was compared using the area under curve (AUC). The development cohort included 1270 patients with 1270 UIAs and a follow-up duration of 47.2 ± 15.5months. Aneurysm instability occurred in 187 (14.7%) patients. Multivariate Cox analysis revealed hypertension (hazard ratio [HR], 1.54; 95%CI, 1.14-2.09), aspect ratio (HR, 1.22; 95%CI, 1.17-1.28), size ratio (HR, 1.31; 95%CI, 1.23-1.41), bifurcation configuration (HR, 2.05; 95%CI, 1.52-2.78) and irregular shape (HR, 4.30; 95%CI, 3.19-5.80) as factors of instability. In the validation cohort (n = 106, 12 was unstable), the DT model incorporating these factors was highly predictive of UIA instability (AUC, 0.88 [95%CI, 0.79-0.97]), and superior to existing UIA risk scales such as PHASES and ELAPSS (AUC, 0.77 [95%CI, 0.67-0.86] and 0.76 [95%CI, 0.66-0.86], P < 0.001). Within all 1376 single-UIA patients, the use of the DT significantly improved the accuracy of junior neurosurgical clinicians to identify unstable UIAs (AUC from 0.63 to 0.82, P < 0.001). The DT incorporating hypertension, aspect ratio, size ratio, bifurcation configuration and irregular shape was able to predict UIA instability better than existing clinical scales in Chinese cohorts. CLINICAL TRIAL REGISTRATION: IARP-CP cohort were included (unique identifier: ChiCTR1900024547. Published July 15, 2019. Completed December 30, 2020), with 100-Project phase-I cohort (unique identifier: NCT04872842, Published May 5, 2021. Completed November 8, 2022) as the development cohort. The 100-Project phase-II cohort (unique identifier: NCT05608122. Published November 8, 2022) as the validation cohort.

Clinical efficacy of multigene panels in the management of congenital hypothyroidism with gland in situ.

Congenital hypothyroidism (CHT) is a diverse condition with various genetic etiologies. This study aimed to investigate the utility of next-generation sequencing (NGS) analysis in guiding treatment decisions and predicting prognosis for CHT patients with gland in situ (GIS). A retrospective analysis was conducted on 33 CHT patients with GIS who underwent NGS analysis at a single institution between 2018 and 2023. Patients were classified as having permanent (PCH), transient congenital hypothyroidism, or ambiguous congenital hypothyroidism (ACH) CHT based on their response to levothyroxine discontinuation at 3 years of age. Among the 33 patients, genetic variants were identified in 26, with the most prevalent variants found in DUOX2 (26.92%), TSHR (30.77%), TG (19.35%), and DUOXA2 (19.23%). Patients with high initial thyroid-stimulating hormone levels (>50 mIU/L) and low free thyroxine levels (<0.89 ng/dL) at diagnosis tended to have compound heterozygous or homozygous variants in DUOX2, DUOXA2, and TG, and were more likely to develop PCH. In contrast, patients with heterozygous variants in these genes often exhibited ACH. TSHR variants were associated with diverse clinical manifestations, ranging from PCH to ACH, and were more common in patients with initial thyroid-stimulating hormone levels <50 mIU/L. The study highlights the potential utility of NGS analysis in predicting the clinical course and guiding treatment decisions for CHT patients with GIS. Genetic analysis may aid in determining the appropriate duration of levothyroxine therapy and monitoring strategies, particularly in cases where traditional clinical indicators are inconclusive.

Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective

Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection.

Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer.

Various prognostic factors are expected to refine the American Thyroid Association (ATA) recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making. We developed two predictive models for structural incomplete response (SIR) at the one-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (i.e., number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis. Both Model 1 and Model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (Model 1: IDI=0.02, event NRI=42.31%; Model 2: IDI=0.07, event NRI=53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, Model 2 maintained similar performance while Model 1 did not significantly improve correct reclassification. The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.

Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer.

Various prognostic factors are expected to refine the American Thyroid Association (ATA) recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making. We developed two predictive models for structural incomplete response (SIR) at the one-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (i.e., number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis. Both Model 1 and Model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (Model 1: IDI=0.02, event NRI=42.31%; Model 2: IDI=0.07, event NRI=53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, Model 2 maintained similar performance while Model 1 did not significantly improve correct reclassification. The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.

On leveraging self-supervised learning for accurate HCV genotyping

Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool.

AC-ASPECTS, ACh-ASPECTS, and H-ASPECTS: new imaging scales to assess territorial and total cerebral hemispheric ischemic injury.

The extent of ischemic injury in acute stroke is assessed in clinical practice using the Acute Stroke Prognosis Early CT Score (ASPECTS) rating system. However, current ASPECTS semi-quantitative topographic scales assess only the middle cerebral artery (MCA) (original ASPECTS) and posterior cerebral (PC-ASPECTS) territories. For treatment decision-making in patients with anterior cerebral artery (ACA) occlusions and internal carotid artery (ICA) occlusions with large ischemic cores, measures of all hemispheric regions are desirable. In this cohort study, anatomic rating systems were developed for the anterior cerebral (AC-ASPECTS, 3 points) and anterior choroidal artery (ACh-ASPECTS, 1 point) territories. In addition, a total supratentorial hemisphere (H-ASPECTS, 16 points) score was calculated as the sum of the MCA ASPECTS (10 regions), supratentorial PC-ASPECTS (2 regions), AC-ASPECTS (3 regions), and ACh-ASPECTS (1 region). Three raters applied these scales to initial and 24 h CT and MR images in consecutive patients with ischemic stroke (IS) due to ICA, M1-MCA, and ACA occlusions. Imaging ratings were obtained for 96 scans in 50 consecutive patients with age 74.8 (±14.0), 60% female, NIHSS 15.5 (9.25-20), and occlusion locations ICA 34%; M1-MCA 58%; and ACA 8%. Treatments included endovascular thrombectomy +/- thrombolysis in 72%, thrombolysis alone in 8%, and hemicraniectomy in 4%. Among experienced clinicians, inter-rater reliability for AC-, ACh-, and H-ASPECTS scores was substantial (kappa values 0.61-0.80). AC-ASPECTS abnormality was present in 14% of patients, and ACh-ASPECTS abnormality in 2%. Among patients with ACA and ICA occlusions, H-ASPECTS scores compared with original ASPECTS scores were more strongly associated with disability level at discharge, ambulatory status at discharge, discharge destination, and combined inpatient mortality and hospice discharge. AC-ASPECTS, ACh-ASPECTS, and H-ASPECTS expand the scope of acute IS imaging scores and increase correlation with functional outcomes. This additional information may enhance prognostication and decision-making, including endovascular thrombectomy and hemicraniectomy.

P-460 Study on the current status and influencing factors of emotional suppression in women undergoing in vitro fertilization-embryo transfer

Abstract Study question Exploring the Current Levels of Emotional Suppression in Women Undergoing In Vitro Fertilization-Embryo Transfer and Analyzing its Influencing Factors. Summary answer Factors influencing emotional suppression in infertile women: residence, treatment costs, infertility causes, miscarriage history, personality type, marital adjustment, self-pity, and medical coping. What is known already Emotional suppression refers to the conscious suppression of negative emotional expression and the behavior of hiding negative emotions. Emotional suppression often hides beneath a cheerful exterior and isn’t easily detected by medical professionals. If left unchecked, it can easily lead to the breakdown of a patient’s psychological defenses. Long-term emotional suppression can result in severe anxiety and depression. Clinical studies have found that couples seeking in vitro fertilization (IVF) treatment exhibit significant levels of stress, depression, and anxiety, which often indicate poor pregnancy outcomes. it’s necessary to understand the levels of emotional suppression and influencing factors in women undergoing IVF-ET. Study design, size, duration Cross-sectional survey was conducted in Hunan Province from May 1, 2023, to September 30, using a random sampling method to select a reproductive specialized hospital According to Sun’s book “Medical Statistics” (2014), the sample size for a multifactorial study should be 10-20 times the number of variables. With 36 variables in this study and accounting for a 10% rate of invalid questionnaires, the required sample size was 396-792. 810 questionnaires were distributed, and 791 were collected. Participants/materials, setting, methods Inclusion criteria: Infertility diagnosed per WHO standards; Eligible for assisted pregnancy via IVF-ET and enrolled in a cycle. Exclusion criteria:History of mental illness or psychiatric disorders;Hearing, visual, language impairments The surveys utilized in this study included the Emotional Suppression Scale, Locke-Wollance Marital Adjustment Questionnaire, Self-Pity Scale, and Medical Coping Style Questionnaire. Statistical analyses, such as descriptive analysis, t-tests, analysis of variance, Mann-Whitney U test, Pearson correlation analysis, and multiple linear regression analysis, were conducted using SPSS 26. Main results and the role of chance The average emotional suppression score of 791 women undergoing IVF-ET was 27.97±6.39. Correlation analysis indicated a negative relationship between emotional suppression and marital adaptation, self-pity, and medical coping styles, while a positive association was observed with the production dimension of medical coping styles. Multiple linear regression analysis revealed that factors such as place of residence, infertility cost, infertility factors, history of miscarriage, personality type, marital adaptation, self-pity, and confrontation and yielding dimensions of medical coping styles significantly influenced emotional suppression in women. The independent variables in the regression equation explained 50.1% of the variance in emotional suppression and the equation was statistically significant. Women exhibited a moderate level of emotional suppression, with the emotional camouflage dimension scoring the highest. Healthcare professionals need to assess emotional suppression in patients and provide personalized psychological counseling interventions. Encouraging positive coping strategies, involving patients in treatment decisions, providing education, and facilitating communication with successful pregnancy cases can help patients face their condition more effectively. Mindfulness interventions and meditation can also be recommended to enhance self-compassion and acceptance. Additionally, comprehensive health education for both patients and their partners, promoting open emotional communication, and fostering mutual support can contribute to reducing emotional suppression in infertility patients. Limitations, reasons for caution This study is a cross-sectional survey. Future research could adopt a longitudinal design to further explore the levels of emotional suppression in women at different stages. This study only investigated women in Hunan Province, which may introduce bias. The emotional suppression levels of infertile couples across China could be investigated. Wider implications of the findings Medical professionals should prioritize addressing the emotional distress of introverted women residing in rural areas, who have spent over 10,000 yuan, undergone three or more abortions, and are experiencing female-factor infertility. Patients can be encouraged to adopt positive coping mechanisms by enhancing their self-compassion levels and adjusting their marital relationships. Trial registration number 2023372

Mandibular movement monitor provides faster, yet accurate diagnosis for obstructive sleep apnoea: A randomised controlled study

Many patients with obstructive sleep apnoea (OSA) remain undiagnosed and thus untreated, and in part this relates to delay in diagnosis. Novel diagnostic strategies may improve access to diagnosis. In a multicentre, randomised study, we evaluated time to treatment decision in patients referred for suspected OSA, comparing a mandibular movement (MM) monitor to respiratory polygraphy, the most commonly used OSA detection method in the UK. Adults with high pre-test probability OSA were recruited from both northern Scotland and London. 40 participants (70 % male, mean±SD age 46.8 ± 12.9 years, BMI 36.9 ± 7.5 kg/m2, ESS 14.9 ± 4.1) wore a MM monitor and respiratory polygraphy simultaneously overnight and were randomised (1:1) to receive their treatment decision based on results from either device. Compared to respiratory polygraphy, MM monitor reduced time to treatment decision by 6 days (median(IQR): 13.5 (7.0–21.5) vs. 19.5 (13.7–35.5) days, P = 0.017) and saved an estimated 29 min of staff time per patient.

Current status of percutaneous coronary interventions in diabetics with multivessel disease - is it time to challenge FREEDOM?

Diabetes mellitus (DM) and coronary artery disease (CAD) are the leading causes of death in the world. Over the last two decades, clinical trials have indicated that DM patients with CAD have poorer cardiac outcomes than non-diabetic patients with CAD. The pivotal findings of the FREEDOM trial greatly impacted the way clinicians approached revascularisation in diabetic patients with multivessel disease (MVD). However, since the publication of the FREEDOM trial, much has changed both in percutaneous coronary intervention (PCI) technology, as well as in the management of diabetes. This review provides insights into advancements in stent technology, enhanced patient management strategies, improved clinical outcomes with newer hypoglycaemic agents, current approaches to antiplatelet therapy, and advances in lipid management in diabetic patients. The influence of patient-specific factors such as comorbidities and anatomical complexities on treatment decisions in diabetic patients with MVD is also discussed. The ongoing TUXEDO-2 India trial was designed to primarily compare the clinical outcomes of PCI with the new-generation ultrathin-strut Supraflex Cruz stent, compared to the second-generation XIENCE stent in the setting of contemporary optimal medical therapy in Indian diabetic patients with MVD. The secondary objective of this study is to compare clinical outcomes in the combined group from both study arms against a performance goal derived from the coronary artery bypass grafting (CABG) arm of the FREEDOM trial (historical cohort). The tertiary objective is to compare the efficacy and safety of ticagrelor versus prasugrel in diabetic patients with MVD. In view of recent advances in PCI and medical therapy since the FREEDOM trial, now is an appropriate time to revisit the results of CABG versus PCI in diabetic patients with MVD.

How Do Exercise, Sleep and Depression Affect Disease Scores and Quality of Life in Primary Sjögren's Syndrome Patients?

Primary Sjögren's syndrome (PSS) has many effects such as fatigue, pain, physical activity limitation and sleep disturbance, which limit patient's daily and social lives. The aim of our study was to assess fatigue, depression, physical activity status and quality of life in patients with PSS, and to determine the relationship between these data and disease-related parameters. This study was conducted with 117 primary Sjögren's syndrome patients. Demographic and anthropometric characteristics, disease activity (ESSDAI), quality of life scale (SF36), depression (Beck Depression Scale), physical activity status (International Physical Activity Questionnaire Short Form (IPAQ) score) and sleep status (Pittsburgh Sleep Quality Scale) of PSS patients were evaluated and relationships have been examined. According to the results of our study, we found that sleep disorders are common in PSS patients (74.4%). Overweight patients, particularly higher lean mass sleep better (r:-0.201, p:0.043). Poor sleep causes fatigue (p=0.062) and depression (p=0.030). Sleep disturbance could not be explained by depressive state alone. However, after controlling for depression, the effect of sleep on fatigue seriously decreases (p=0.311). Exercise did not improve sleep quality (p=0.35) and the rate of poor sleep was higher among who exercised (p=0.192). Based on the results of our study, we believe that, treating depression in PSS patients is crucial for reducing fatigue. Patients need education on performing the correct exercises and weight gain should done in a professional manner. Gaining a deeper understanding of the multisystem involvement of the disease and the impact of exercise on the disease, will have positive effects on patient care and treatment decisions.

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes. For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an invitro assay using primary human muscle cells to interrogate serum-induced complement formation. This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay. Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice. CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).