Patient Information Research Articles

Factors associated with family decision-making self-efficacy among family members of patients with advanced cancer in mainland China.

In Chinese culture, family members are the main decision maker on end-of-life (EoL) issues for patients with advanced cancer. Yet little is known about Chinese families' confidence in making EoL decisions and its associated factors. This study aims to investigate the status and associated factors of Chinese family members' confidence in making EoL decisions for patients with advanced cancer. This cross-sectional study used a convenience sample of 147 family members of patients with stage III or stage IV cancer from a tertiary cancer center in Guangzhou, China. The questionnaires included demographic information of patients and their family members, patients' EoL preferences, and the Chinese version of the Family Decision-Making Self-Efficacy (FDMSE) Scale. A total of145 family members (98.64%) completed the questionnaires. The average score of FDMSE was 3.92 ± 0.53. A multiple regression analysis showed that the factors associated with FDMSE included patients' duration of disease, health insurance, participation in EoL decision-making, the expression of unfilled wishes, and family members' employment status. Chinese family members were not confident enough in making EoL decisions for patients with advanced cancer. It is recommended to develop cultural-tailored advanced care planning models to clarify patient preferences and to enhance the family members' self-efficacy in making EoL decisions with or for patients with advanced cancer.

Risk factors for infliximab-induced infusion reactions based on a retrospective study of medical information.

Infliximab is indicated for a wide range of diseases, and infusion reactions (IRs) have been reported after infliximab administration in 17.6% of patients in clinical practice. This study aimed to identify the risk factors for IRs before the administration of infliximab based on available patient information. We retrospectively analyzed patients treated with infliximab. Data were compared between patients with and without IRs immediately before initiation of infliximab. Elevated C-reactive protein (CRP) (odds ratio (OR), 2.150; 95% confidence interval (CI), 1.329-3.477; p=0.002) before infliximab administration was a significant risk factor for developing an IR. Patients with elevated CRP levels before therapy initiation may require more careful monitoring after infliximab administration.

MASCC 2023 Patient-Centered Antiemetic Guidelines and Education Statements: an evidence-based and consensus resource for patients.

The Multinational Association of Supportive Care in Cancer (MASCC)/European Society of Medical Oncology (ESMO) Patient Antiemetic Guideline Committee aimed to (1) adapt the updated evidence-based, clinical guidelines to patient-centered antiemetic guidelines and (2) develop patient education materials and statements. The MASCC 2023 Patient Antiemetic Guidelines were created and reviewed by antiemetic experts and patient advocates by incorporating the 2023 MASCC/ESMO antiemetic guidelines into patient-friendly language. Patient Education Statements were developed based on current literature and by utilizing an expert modified Delphi consensus (≥ 75% agreement). Patient advocate/focus group input and patient survey results were further integrated into Patient-Centered Antiemetic Guidelines and Education Statements. Patient-Centered Antiemetic Guidelines were created using patient-friendly language and visual slides. Patient-friendly language was also utilized to communicate the Educational Statements. Key content categories identified for the Educational Statements included the following: nausea/vomiting definitions, causes, risk factors, categories, complications, accompanying symptoms, prophylactic antiemetic treatment, general management, when to call/what to ask the healthcare team, what caregivers can do, and available resources. All identified content met the ≥ 75% expert agreement threshold. Fifteen (15) items demonstrated 100% agreement, 11 items achieved ≥ 90% agreement, and three content items demonstrated 80 ~ 82% agreement. The inaugural MASCC 2023 Patient Antiemetic Guidelines can help patients and caregivers understand the prevention of nausea and vomiting related to their cancer treatment. Educational Statements provide further patient information. Educating patients on how to utilize guideline antiemetics and the education statements can contribute improvements in the control of anticancer treatment-related nausea and vomiting.

Meningioma recurrence: Time for an online prediction tool?

Background: Meningioma, the most common brain tumor, traditionally considered benign, has a relatively high risk of recurrence over a patient’s lifespan. In addition, with the emergence of several clinical, radiological, and molecular variables, it is becoming evident that existing grading criteria, including Simpson’s and World Health Organization classification, may not be sufficient or accurate. As web-based tools for widespread accessibility and usage become commonplace, such as those for gene identification or other cancers, it is timely for meningioma care to take advantage of evolving new markers to help advance patient care. Methods: A scoping review of the meningioma literature was undertaken using the MEDLINE and Embase databases. We reviewed original studies and review articles from September 2022 to December 2023 that provided the most updated information on the demographic, clinical, radiographic, histopathological, molecular genetics, and management of meningiomas in the adult population. Results: Our scoping review reveals a large body of meningioma literature that has evaluated the determinants for recurrence and aggressive tumor biology, including older age, female sex, genetic abnormalities such as telomerase reverse transcriptase promoter mutation, CDKN2A deletion, subtotal resection, and higher grade. Despite a large body of evidence on meningiomas, however, we noted a lack of tools to aid the clinician in decision-making. We identified the need for an online, self-updating, and machine-learning-based dynamic model that can incorporate demographic, clinical, radiographic, histopathological, and genetic variables to predict the recurrence risk of meningiomas. Conclusion: Although a challenging endeavor, a recurrence prediction tool for meningioma would provide critical information for the meningioma patient and the clinician making decisions on long-term surveillance and management of meningiomas.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

Pharmaceutical treatment status of patients with COPD in the community based on medical Internet of Things: a real-world study

This study aimed to investigate the real-world standardisation and adherence of medical treatment regimens in patients with chronic obstructive pulmonary disease (COPD) in the community for making future management strategy. The follow-up data and treatment information of patients with COPD, which were collected through the Management Information Center of COPD (MICCOPD) in 21 community health service centres in Songjiang District, a countryside region of Shanghai. Concordance between the pharmaceutical treatment plan and recommendation of 2017 Global Initiative for Chronic Obstructive Lung Disease (GOLD) report during the follow-up management period, as well as the medication adherence by patients,were analysed. Out of the 2044 patients diagnosed with COPD, 814 patients (39.8%) who had an initial record of medication use were found to meet the inclusion criteria. The most common medication regimens were long-acting beta-agonist plus inhaled corticosteroids (35.9%) and oral bronchodilators (41.9%). Among these 814 patients, 45.7%, 38.0%, 31.6% and 14.6% adhered to the treatment after 6, 12, 18 and 24 months of follow-up, respectively. The concordance rate with the regimens recommended by the 2017 GOLD guidelines was 35.5% at baseline, 35.5% at 6 months, 32.7% at 12 months, 35.4% at 18 months and 37% at 24 months. The compliance and guideline consistency rates of patients with COPD in the community under the management of general practitioners need to be improved. Enhancing general practitioner proficiency in the prevention and management of COPD and increasing patient awareness of the condition, are crucial standardising and improving adherence to initial and follow-up COPD treatments.

A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort

Medical coding impacts patient care quality, payor reimbursement, and system reliability through the precision of patient information documentation. Inadequate coding specificity can have significant consequences at administrative and patient levels. Models to identify and/or enhance coding specificity practices are needed. Clinical records are not always available, complete, or homogeneous, and clinically driven metrics to assess medical practices are not logistically feasible at the population level, particularly in non-centralized healthcare delivery systems and/or for those who only have access to claims data. Data-driven approaches that incorporate all available information are needed to explore coding specificity practices. Using N = 487,775 hospitalization records of individuals diagnosed with dementia and discharged in 2022 from a large all-payor administrative claims dataset, we fitted logistic regression models using patient and facility characteristics to explain the coding specificity of principal and secondary diagnoses of dementia. A two-step approach was produced to allow for the flexible clustering of patient-level outcomes. Model outcomes were then used within a Poisson binomial model to identify facilities that over- or under-specify dementia diagnoses against healthcare industry standards across hospitalizations. The results indicate that multiple factors are significantly associated with dementia coding specificity, especially for principal diagnoses of dementia (AUC = 0.727). The practical use of this novel risk-adjusted metric is demonstrated for a sample of facilities and geospatially via a U.S. map. This study’s findings provide healthcare facilities with a benchmark for assessing coding specificity practices and developing quality enhancements to align with healthcare industry standards, ultimately contributing to better patient care and healthcare system reliability.

Suture passer combined with two-hole laparoscopic peritoneal dialysis catheterization in patients undergoing peritoneal dialysis

Background Laparoscopic techniques are being widely applied for peritoneal dialysis (PD) catheter (PDC) placement. The suture passer is a novel fixation tool that aims to reduce catheter migration. We compared the clinical value of the suture passer combined with two-hole laparoscopic PDC placement to open surgical placement by evaluating preoperative and postoperative conditions, as well as the onset of complications in both groups. Methods A retrospective study was conducted including 169 patients who underwent PDC placement surgery from January 2021 to May 2023. Based on the method employed, patients were divided into two groups: the suture passer combined with a two-hole laparoscopy group (SLG) and the open surgical group (SG). Comprehensive patient information, including general data, preoperative and postoperative indicators, peritoneal function after surgery, and the incidence rate of complications, were collected and analyzed. Results The SLG showed a statistically significant decrease in operative time, intraoperative blood loss, and 6-month postoperative drift rate compared to the SG (p < 0.05). No statistically significant differences were observed between the two groups in terms of sex, age, primary disease, hospitalization time, hospitalization costs, preoperative and postoperative examination indicators, peritonitis, and omental wrapping. Conclusions Suture passer combined with two-hole laparoscopic PDC placement, characterized by simplicity and facilitating secure catheter fixation, was deemed safe and effective for patients undergoing PD. It reduces the catheter migration rate and improved surgical comfort. Overall, this technique demonstrates favorable outcomes in clinical practice.

Psychometric validation of the short version of the Information Needs in Cardiac Rehabilitation scale through a first global assessment.

Tailored education is recommended for cardiac patients, yet little is known about information needs in areas of the world where it is most needed. This study aims to assess (i) the measurement properties of the Information Needs in Cardiac Rehabilitation short version (INCR-S) scale and (ii) patient's information needs globally. In this cross-sectional study, English, simplified Chinese, Portuguese, or Korean versions of the INCR-S were administered to in- or out-patients via Qualtrics (January 2022-November 2023). Members of the International Council of Cardiovascular Prevention and Rehabilitation community facilitated recruitment. Importance and knowledge sufficiency of 36 items were rated. Links to evidence-based lay education were provided where warranted. A total of 1601 patients from 19 middle- and high-income countries across the world participated. Structural validity was supported upon factor analysis, with five subscales extracted: symptom response/medication, heart diseases/diagnostic tests/treatments, exercise and return-to-life roles/programmes to support, risk factors, and healthy eating/psychosocial management. Cronbach's alpha was 0.97. Construct validity was supported through significantly higher knowledge sufficiency ratings for all items and information importance ratings for all subscales in cardiac rehabilitation (CR) enrolees vs. non-enrolees (all P < 0.001). All items were rated as very important-particularly regarding cardiac events, nutrition, exercise benefits, medications, symptom response, risk factor control, and CR-but more so in high-income countries in the Americas and Western Pacific. Knowledge sufficiency ranged from 30.0 to 67.4%, varying by region and income class. Ratings were highest for medications and lowest for support groups, resistance training, and alternative medicine. Identification of information needs using the valid and reliable INCR-S can inform educational approaches to optimize patients' health outcomes across the globe.

DNA Steganography: Embedding the Secret Messages Using Glass Stack Method and Detecting Errors Made by Different Attacks

Cyber threats pose a significant challenge to protecting cloud-based health data, including DNA sequences and patient information. Steganography is a well-known solution to this problem. Recent studies have limitations, such as not reconstructing the cover or enlarging the original sequence, increasing personal data security risks. In this study, we provide a novel data-hiding technique that uses a secret key, bit padding, and exclusive OR (XOR) operation to insert secrets into a pre-processed DNA sequence. The message encryption and pre-processing of the cover are performed using a new glass stack pattern technique. The message encryption and pre-processing processes using the new glass stack pattern method increase the security and robustness of the system. The method embeds 3 bits per nucleotide which is greater than the existing schemes. It has a relatively small DNA expansion which is 50% and has a low risk of breaking security. The method also preserves the biological properties of DNA sequences. Additionally, a parity check approach further improves security by thwarting attacks like man-in-the-middle, chosen stego, and modification attacks that alter the stego DNA. These advances improve the system's overall security capabilities. The experimental findings demonstrate that the suggested method reconstructs the cover, healthcare data, and DNA sequence properly and error-free, and outperforms competing algorithms on all performance metric dimensions. Highlights The scheme implants a maximum of 3 bits per nucleotide with a small expansion. Its cover pre-processing and message encryption using a glass stack pattern makes strong security. The scheme can detect and fix changed bits caused by attacks. A very low cracking probability shows strong security and robustness. The biological properties of the stego DNA sequence are preserved by this scheme.

Headache in systemic lupus erythematosus: The LUNA registry cross-sectional study

Objectives This study investigated the clinically relevant factors for headaches in patients with systemic lupus erythematosus (SLE) using a registry from a Japanese multicenter cohort. Methods This cross-sectional study analysed the clinical information of patients with SLE who experienced headache episodes using the Migraine Disability Assessment (MIDAS) questionnaire. Significant findings in the comparisons between patients with headache (HA patients) and those without headache (non-HA patients) and in the comparisons depending on the grades of headache-induced disability in daily life based on the MIDAS scores were evaluated. Multivariate logistic regression analyses were performed to identify the relevant factors for headache. Results We analyzed 369 patients (median age, 45 years; female, 90.8%), including 113 HA patients who were significantly younger than non-HA patients ( p &lt; .005). HA patients had significantly higher frequencies of photosensitivity, rashes, and mucosal ulcers than non-HA patients ( p &lt; .05). Age and photosensitivity were significantly associated with headache (odds ratio (OR) 0.93, 95% confidence interval (CI) 0.95–0.99; OR 2.11, 95% CI 1.29–3.49, respectively). In the HA patients, hypocomplementemia was significantly associated with a disability of more than mild grade (OR 2.89, 95% CI 1.14–7.74), while rash was significantly observed in those presenting with moderate and severe disability. Conclusion This study suggests that photosensitivity is a relevant manifestation of headache in patients with SLE. Persistent hypocomplementemia can contribute to headache-induced disability in daily life, whereas a rash may be a dominant manifestation in patients presenting with moderate/severe headache-induced disability.

Wearable sensors in patient acuity assessment in critical care

Acuity assessments are vital for timely interventions and fair resource allocation in critical care settings. Conventional acuity scoring systems heavily depend on subjective patient assessments, leaving room for implicit bias and errors. These assessments are often manual, time-consuming, intermittent, and challenging to interpret accurately, especially for healthcare providers. This risk of bias and error is likely most pronounced in time-constrained and high-stakes environments, such as critical care settings. Furthermore, such scores do not incorporate other information, such as patients’ mobility level, which can indicate recovery or deterioration in the intensive care unit (ICU), especially at a granular level. We hypothesized that wearable sensor data could assist in assessing patient acuity granularly, especially in conjunction with clinical data from electronic health records (EHR). In this prospective study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for estimating acuity. Accelerometry data were collected from 87 patients wearing accelerometers on their wrists in an academic hospital setting. The data was evaluated using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (Sequential Organ Failure Assessment, SOFA) used as a baseline when predicting acuity state (for ground truth we labeled as unstable patients if they needed life-supporting therapies, and as stable otherwise), particularly regarding the precision, sensitivity, and F1 score. The results demonstrate that integrating accelerometer data with demographics and clinical variables improves predictive performance compared to traditional scoring systems in healthcare. Deep learning models consistently outperformed the SOFA score baseline across various scenarios, showing notable enhancements in metrics such as the area under the receiver operating characteristic (ROC) Curve (AUC), precision, sensitivity, specificity, and F1 score. The most comprehensive scenario, leveraging accelerometer, demographics, and clinical data, achieved the highest AUC of 0.73, compared to 0.53 when using SOFA score as the baseline, with significant improvements in precision (0.80 vs. 0.23), specificity (0.79 vs. 0.73), and F1 score (0.77 vs. 0.66). This study demonstrates a novel approach beyond the simplistic differentiation between stable and unstable conditions. By incorporating mobility and comprehensive patient information, we distinguish between these states in critically ill patients and capture essential nuances in physiology and functional status. Unlike rudimentary definitions, such as equating low blood pressure with instability, our methodology delves deeper, offering a more holistic understanding and potentially valuable insights for acuity assessment.

Clinical characteristics and prognosis of patients with incidentally discovered chest wall sarcoma compared with those of symptomatic patients.

Sarcomas of the bone and soft tissues are detected after the onset of pain, detectable mass and related symptoms in the absence of a standardized screening examination. However, primary chest wall sarcomas can be incidentally detected upon chest X-ray or computed tomography. Previous studies of incidental primary chest wall sarcomas lack prognosis and disease-specific clinical data. This study aimed to investigate the prognoses of patients with incidental chest wall sarcomas and compare them with those of symptomatic patients. This study included 18 patients diagnosed with primary chest wall sarcoma between 2010 and 2023. Patient information such as age, sex, tumour diameter, tumour location, symptoms, treatment, time to treatment initiation, pathological diagnosis and outcome were retrospectively analysed. Among the 18 patients, the sarcomas were incidentally detected in five by chest X-ray and computed tomography in three and two patients, respectively. The pathological diagnoses of the patients were Ewing sarcoma, Chondrosarcoma grade 1, grade 2, periosteal osteosarcoma and malignant peripheral nerve sheath tumour. The patients had no symptoms at the first visit to our hospital, and no lesions in other organs were detected at the time of the initial examination. At the final follow-up, the patients remained disease-free after radical treatment. The tumour sizes of the five patients were significantly smaller than those of patients with symptoms (P=0.003). The incidental detection of chest wall sarcomas and consequent early detection and treatment of tumours improves patient prognosis relative to that of symptomatically diagnosed patients.

Thirst Distress in Palliative Care Patients.

Objective: It has been reported that chronically critical patients and patients at high risk of death have moderate to high levels of thirst distress. It was planned as a descriptive and cross-sectional study to determine thirst distress in palliative care patients. Methods: A Patient Information Form, the Thirst Severity Form, and the Thirst Distress Scale were used for data collection. The research was carried out between March 2023 and July 2023 with 157 patients hospitalized in the palliative care services of a state hospital and a training and research hospital in Istanbul, Turkey. Results: Of the patients, 51.6% were female. The mean thirst distress score of the patients was 23.96 ± 5.096. Of the patients, 0.6% had no thirst distress; 7.6% had mild, 48.4% had moderate, 38.9% had high, and 4.5% had severe thirst distress. Thirst scores of patients who were illiterate and did not smoke or drink alcohol were high (P < .05). The thirst distress score of patients fed with percutaneous endoscopic gastrostomy was higher than that of patients fed with total parenteral nutrition. There was a positive weak correlation between age and thirst distress score, a negative very weak correlation between fluid balance and thirst distress score, and a positive moderate correlation between Visual Analog Scale and thirst distress. Conclusion: Thirst distress was found to be at moderate and high levels in palliative care patients. In patients, thirst should be routinely assessed, and necessary interventions should be planned.

Applications of ChatGPT in Otolaryngology-Head Neck Surgery: A State of the Art Review.

To review the current literature on the application, accuracy, and performance of Chatbot Generative Pre-Trained Transformer (ChatGPT) in Otolaryngology-Head and Neck Surgery. PubMED, Cochrane Library, and Scopus. A comprehensive review of the literature on the applications of ChatGPT in otolaryngology was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. ChatGPT provides imperfect patient information or general knowledge related to diseases found in Otolaryngology-Head and Neck Surgery. In clinical practice, despite suboptimal performance, studies reported that the model is more accurate in providing diagnoses, than in suggesting the most adequate additional examinations and treatments related to clinical vignettes or real clinical cases. ChatGPT has been used as an adjunct tool to improve scientific reports (referencing, spelling correction), to elaborate study protocols, or to take student or resident exams reporting several levels of accuracy. The stability of ChatGPT responses throughout repeated questions appeared high but many studies reported some hallucination events, particularly in providing scientific references. To date, most applications of ChatGPT are limited in generating disease or treatment information, and in the improvement of the management of clinical cases. The lack of comparison of ChatGPT performance with other large language models is the main limitation of the current research. Its ability to analyze clinical images has not yet been investigated in otolaryngology although upper airway tract or ear images are an important step in the diagnosis of most common ear, nose, and throat conditions. This review may help otolaryngologists to conceive new applications in further research.

Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

The Effect of Rational Drug Use On Disease Adaptation And Quality Of Life In Geriatric Patients With Heart Failure

Aim: Due to chronic diseases that occur with advancing age, the number of medications used in geriatric individuals increases dramatically. This increment necessitates the rational use of drugs within a proper program. Reasonable drug use can affect individuals' adaptation to the diseases and their quality of life. This research was undertaken to explore the impact of rational drug use on disease adaptation and life Quality in geriatric patients with heart failure. Materials and Method: The research involved a cohort of 322 individuals aged 65 and above. Participants completed a patient information form, including the Rational Drug Use Scale, the Chronic Disease Adjustment Scale, and the Elderly Quality of Life Scale (CASP-19). Statistical comparisons utilized t-tests, Mann-Whitney U tests, and ANOVA tests, while Pearson correlation analysis was employed to assess the associations between scale scores. Significance was determined for conditions with p-values less than 0.05. Results: The study revealed a notable correlation between the patients' levels of rational drug use and their adaptation to the disease (r=0.226; p0.05). However, only in the group with polypharmacy was the total score of the physical adaptation sub-dimension of the ACIS significantly lower (p=0.011). Conclusion and Suggestions: The research findings indicated that rational drug use among geriatric individuals positively influenced their ability to adapt to the disease, albeit resulting in a comparatively modest impact on the sub-dimensions of quality of life. Further studies are needed to evaluate rational drug use and to investigate its effect on symptom control, patient satisfaction, and quality of life in patients with heart failure.

Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression.

A deep learning classifier that improves the accuracy of colposcopic impression. Colposcopy images taken 56 seconds after acetic acid application were processed by a cervix detection algorithm to identify the cervical region. We optimized models based on the SegFormer architecture to classify each cervix as high-grade or negative/low-grade. The data were split into histologically stratified, random training, validation, and test subsets (80%-10%-10%). We replicated a 10-fold experiment to align with a prior study utilizing expert reviewer analysis of the same images. To evaluate the model's robustness across different cameras, we retrained it after dividing the dataset by camera type. Subsequently, we retrained the model on a new, histologically stratified random data split and integrated the results with patients' age and referral data to train a Gradient Boosted Tree model for final classification. Model accuracy was assessed by the receiver operating characteristic area under the curve (AUC), Youden's index (YI), sensitivity, and specificity compared to the histology. Out of 5,485 colposcopy images, 4,946 with histology and a visible cervix were used. The model's average performance in the 10-fold experiment was AUC = 0.75, YI = 0.37 (sensitivity = 63%, specificity = 74%), outperforming the experts' average YI of 0.16. Transferability across camera types was effective, with AUC = 0.70, YI = 0.33. Integrating image-based predictions with referral data improved outcomes to AUC = 0.81 and YI = 0.46. The use of model predictions alongside the original colposcopic impression boosted overall performance. Deep learning cervical image classification demonstrated robustness and outperformed experts. Further improved by including additional patient information, it shows potential for clinical utility complementing colposcopy.

Implementing a shared decision-making intervention to support treatment decisions for patients following an anterior cruciate ligament rupture — a protocol for the POP-ACLR feasibility study

BackgroundTreatment for anterior cruciate ligament (ACL) rupture may follow a surgical or nonsurgical pathway. At present, there is uncertainty around treatment choice. Two shared decision-making tools have been codesigned to support patients to make a decision about treatment following an ACL rupture. The shared decision-making tools include a patient information leaflet and an option grid. We report the protocol for a mixed-methods feasibility study, with nested qualitative interviews, to understand feasibility, acceptability, indicators of effectiveness and implementation factors of these shared decision-making tools (combined to form one shared decision-making intervention).MethodsA single-centre non-randomised feasibility study will be conducted with 20 patients. Patients diagnosed with an ACL rupture following magnetic resonance imaging will be identified from an orthopaedic clinic. The shared decision-making intervention will be delivered during a clinical consultation with a physiotherapist. The primary feasibility outcomes include the following: recruitment rate, fidelity, acceptability and follow-up questionnaire completion. The secondary outcome is the satisfaction with decision scale. The nested qualitative interview will explore experience of using the shared decision-making intervention to understand acceptability, implementation factors and areas for further refinement.DiscussionThis study will determine the feasibility of using a newly developed shared decision-making intervention designed to support patients to make a decision about treatment of their ACL rupture. The acceptability and indicators of effectiveness will also be explored. In the long term, the shared decision-making intervention may improve service and patient outcomes and ensure cost-effectiveness for the NHS; ensuring those most likely to benefit from surgical treatment proceed along this pathway.Trial registrationPending registration on ISRCTN.

Short-term revision rate of Rigicon Testi10TM testicular prosthesis in adolescents and adults: a retrospective chart review.

Testicular prosthesis implantation is a valuable solution for the physical, cosmetic, and psychological challenges associated with testicular loss which may affect males of any age. We evaluated the safety and reliability of the new Rigicon Testi10TM testicular prosthesis in adults and adolescents by performing an IRB-approved retrospective study of data drawn from Patient Information Forms (PIFs). A total of 427 patients (382 adults and 45 adolescents) had at least one testicular prosthesis implanted. Only one adult patient required revision surgery due to rupture of the Rigicon Testi10 TM saline-filled prosthesis. A 40-year-old patient was found to have a leaking prosthesis approximately one week postoperatively, which was suspected to be due to inadvertently punctured by the surgeon during the sterile saline filling process. There were no post-implantation revisions required for adolescent patients. According to our results, Kaplan-Meier calculation of survival from removal or revision was 99.8% for all patients at 54 months (99.7% for adults and 100% for adolescents). The complication rates among patients in this study are lower than those reported in previous published studies. Our study underscores the generally safe nature of testicular prosthesis implantation, as well as the very rare incidence of revision surgery for this new device.