Risk Categories Research Articles

A Hybrid Fuzzy Evaluation Method for Quantitative Risk Classification of Barrier Lakes Based on an AHP Method Extended by D Numbers

The risk classification of barrier lakes is the key to conducting emergency treatment in a scientific manner. However, risk classification faces difficulties such as a short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly, and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an eight-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lakes and intelligent identification of particles on the surface of barrier bodies were developed, which realized the rapid acquisition of eight-factor evaluation index information in an emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of eight factors. The risk assessment on 15 barrier lakes such as Tangjiashan barrier lake shows that the conclusions drawn for the risk classification method proposed in this paper are basically consistent with those of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss impacts on the risk level of barrier lakes and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

Differences in 6-Minute Walk Distance Across Heart Disease Recurrence Risk Levels in Cardiac Rehab Patients

Background/Objectives: Cardiac rehabilitation (CR) programs are multi-component interventions comprising structured strength and cardiovascular exercise training, psychological support, education, and therapies to promote positive lifestyle changes. This study aimed to determine if there are differences in the 6-minute walk distance (6MWD) across risk groups for recurrent heart disease. Methods: This retrospective cohort study used existing data collected from electronic medical records. The 6-minute walk distance was measured at baseline (pre-6MWD) and upon the completion of the CR program (post-6MWD). Short-term cardiac event recurrence risk was determined using a two-year recurrent coronary heart disease (2yRCHD) risk percentage, calculated according to the Framingham 2yRCHD calculator. Risk was then stratified into (1) low, (2) moderate, and (3) high-risk groups. Demographic variables (e.g., age, sex, racial/ethnic group) and clinical variables (e.g., BMI, lipid panels, fasting glucose levels, comorbidities) were collected to describe the study participants and identify potential confounders. An ANOVA and ANCOVA were performed to examine the differences in 6MWD across the 2yRCHD risk groups. Results: A total of 394 CR participants’ data were included in this analysis. Ninety-nine percent of the female participants were classified as low risk for recurrent heart disease, resulting in an extremely small sample size in the moderate-risk (n = 1) and no representation (n = 0) in the high-risk group. This lack of representation made it impossible to conduct comparative analyses across all the participants or to analyze female participants separately by risk category. Consequently, only male participant data were included in the final analysis. The study showed that pre- and post-6MWD measurements were significantly different across the three 2yRCHD risk groups (p = 0.006 for pre-6MWD; p = 0.002 for post-6MWD). The ANCOVA indicated that these differences were independent of the selected covariates. Post hoc analyses revealed significant differences in 6MWDs between the low- and high-risk groups and between the moderate- and high-risk groups, but not between the low- and moderate-risk groups, for both pre- and post-6MWD measurements. Compared to the CR participants in the high-risk group, those in the low- and moderate-risk groups achieved significantly longer distances in the 6-minute walk tests. Conclusions: The observed differences in the 6MWD across short-term cardiac recurrence risk levels suggest its potential as a simple, accessible tool for assessing cardiac recurrence risk levels in community settings. Further research is needed to generalize these findings to more diverse populations and to support aging in place for older adults living with heart disease.

O PAPEL DO ENFERMEIRO PARA OS CUIDADOS DO DIABETES MELLITUS GESTACIONAL EM USF DO MUNICÍPIO DE FEIRA DE SANTANA – BAHIA

During pregnancy, women will be susceptible to the occurrence of several hormonal adaptations that can interfere with carbohydrate metabolism, which may result in the triggering of GDM. In this sense, the objective was to describe nursing care for quality care for women with GDM in primary health care. This study is a descriptive literature review, selecting articles indexed in the VHL and LILACS databases, using the descriptors: Diabetes; Enfermagem; Gravidez and its correspondents in the English language between the years 2017 and 2023, excluding those that did not fit the proposed theme. The results of this review demonstrate the need to understand the prevention and control of GDM to humanize care and make the services provided increasingly beneficial to pregnant women. Therefore, promoting, curing and improving health, developing strategies to mitigate complications and proposing ways to prevent this metabolic disorder, referring pregnant women to appropriate levels of monitoring based on their risk classification are the responsibilities of nursing professionals to pregnant women with GDM.

Determinant Factors of Microbial Drinking Water Quality at the Point of Use in Rural Ethiopia: A Case Study of the South Gondar Zone

Access to safe drinking water is a fundamental human need for health and well-being implemented globally by the United Nations under Sustainable Development Goal (SDG) 6. Storing drinking water is common in rural areas of Ethiopia due to off-premises water sources and intermittent piped water supply. However, this practice can lead to further contamination during collection, transport, and storage, posing a risk to public health. The objective of this study was to identify the determinant factors of drinking water quality at the point of use in the rural setting of northwestern Ethiopia, South Gondar zone. A questionnaire survey was conducted, and water samples from 720 households were collected during the wet and dry seasons. The determinant factors were identified using the multivariable logistic regression model. About 39.2% of the surveyed households had basic water supply services, 41.9% were using unimproved sources, and 8.3% were using surface water. Only 9.4% were using basic sanitation services, and 57.2% were practicing open defecation. Safe water storage was practiced by 84.3% of households, while only 2% engaged in household water treatment. About 14% of dry and 8% of wet season samples from the storage were free from fecal coliform bacteria. Furthermore, 52.9% of dry and 62.2% of wet season samples fell under the high microbial health risk category. The season of the year, the water source type, storage washing methods, and the socioeconomic status of the household were identified as key predictors of household drinking water fecal contamination using the multivariable logistic regression model. It was observed that the drinking water in households had a high load of fecal contamination, posing health risks to consumers. To tackle these problems, our study recommends that stakeholders should enhance access to improved water sources, implement source-level water treatment, increase access to improved sanitation facilities, advocate for safe household water management practices, and endorse household water treatment methods.

Hybrid Machine Learning Approach for Early Stroke Prediction in Elderly People

Stroke represents a significant global health challenge, often leading to severe disability and mortality. The timely prediction and intervention of stroke are paramount in enhancing patient outcomes and reducing healthcare burdens. This project proposes a machine learning-based approach to predict stroke risk using multi-modal biosignals, specifically electrocardiogram (ECG) data. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system aims to classify patient health data into critical risk categories such as Normal, Abnormal, Ischemic, and Hemorrhagic. The study utilizes a comprehensive dataset consisting of ECG signals and incorporates techniques for data preprocessing, class balancing, and feature extraction. The predictive model is trained and validated using robust evaluation metrics, including accuracy, precision, recall, and F1-score. The findings underscore the efficacy of the proposed system in providing real-time stroke risk assessments, offering a cost-effective alternative to traditional diagnostic methods. Furthermore, this research explores the integration of wearable technology with machine learning, highlighting its potential for continuous patient monitoring and early detection of stroke symptoms. By creating a user-friendly interface for healthcare professionals and patients, the system aims to facilitate prompt decision-making and intervention, ultimately improving the overall quality of care for individuals at risk of stroke

Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: aretrospective study.

The aim of this study was to develop and assess aradiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). An analysis was performed retrospectively on agroup of 121 patients who received ahistological diagnosis of GIST. They were then divided into two sets, with85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing arandom forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with apathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct aprognostic prediction model. The clinical model (ModelC) displayed limited predictive efficacy in the context of GIST. Conversely, aradiomics model (ModelR) incorporating five parameters exhibited robust discriminative capabilities across both the training and validation sets, yielding area under the ROC curve (AUC) values of 0.893 (95% confidence interval [CI]: 0.807-0.949) and 0.855 (95% CI: 0.732-0.978), respectively. The F1max scores derived from the P‑R curves were 0.741 and 0.842 for the training and validation sets, respectively. Noteworthy was the exclusion of the two-dimensional tumor diameter and tumor location when constructing ahybrid model (ModelCR) that amalgamated radiomics and clinical features. ModelR demonstrated asubstantially enhanced discriminative capacity in the training set compared with ModelC (p < 0.005). The net reclassification improvement (NRI) corroborated the superior performance of ModelR over ModelC, thereby enhancing diagnostic accuracy and clinical applicability. Patients in the high-risk group had significantly worse recurrence-free survival (RFS, p < 0.001) and overall survival (OS, p = 0.004), and the radiomics signature is an independent risk factor for RFS. The extended model incorporating the radiomics signature outperformed the baseline model in terms of risk assessment accuracy (p < 0.001). Our investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.

Contemporary treatment of metastatic clear cell renal cell carcinoma

Renal cell carcinoma is one of the mostcommon malignant tumors in Germany with an increasing incidence. Drug therapy is indicated in relapsed or metastatic disease. The article is based on the content of the recent guidelines and aselective literature search. Combination therapies based on acheckpoint inhibitor are the current standard in first-line treatment of metastatic renal cell carcinoma. The median overall survival could thus be extended to > 50months. The International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) score is used for risk classification. When selecting asuitable combination therapy, it is important to consider the advantages and disadvantages for each individual patient. There is currently no standard for follow-up therapies. So far, combination therapies have not shown any significant advantage in second-line treatment. It is recommended to switch to asubstance that has not yet been used. Currently, one purely immuno-oncology combination and four combinations of one immune checkpoint inhibitor and one tyrosine kinase inhibitor (TKI) are approved for first-line therapy in Germany. The added value of further intensification of therapy, in particular through triple combinations or further combination therapy in the second line, has not yet been proven.

Long-Term Disease-Free Survival and Health-Related Quality of Life Results of High-Dose-Rate Brachytherapy as Monotherapy for Low and Intermediate-Risk Prostate Cancer Treated in a Community Cancer Centre

To determine the long-term disease-free survival, long-term toxicity, and effect on health-related quality of life of a two-fraction regimen of high-dose-rate (HDR) prostate brachytherapy. Patients with low- or intermediate-risk prostate cancer were treated with CT-planned HDR brachytherapy as monotherapy in two implants of 13.5 Gy in one community cancer center. Prostate-specific antigen (PSA), International Prostate Symptom Score (IPSS) and Expanded Prostate Index Composite (EPIC) questionnaires were evaluated at each follow-up visit. Biochemical recurrence (Phoenix definition) was evaluated to determine disease-free survival at 5 and 7 years. Proportion of patients in each IPSS category (mild = 0-7, moderate = 8-18, severe = 19+) was evaluated at each follow-up interval. Thirty patients were accrued to the study between 2014 and 2016. Median prostate-specific antigen was 8,7 (range 4.1-17.5). T stages were T1c = 65%, T2a = 21%, and T2b = 14%. Twenty-seven percent of patients had a Gleason score of 6 and 73% had a Gleason score of 7. 13% were in low risk category and 87% in intermediate risk category. Median follow-up was 84 months. There were no deaths at 84 months after brachytherapy. Disease-free survival was 90% at 5 years and 86% at 7 years. Two patients experienced late Grade 3 GU toxicity (6.6%). This study serves as proof of concept that HDR monotherapy can be performed successfully with excellent long-term outcomes in a community cancer center with adequate training and experience. The long-term disease-free survival rate and the health-related quality of life seem acceptable as compared to other treatment modalities. Further study is ongoing with regard to the optimal dosing regimen for HDR monotherapy.

Outcomes of Urgent Coronary Artery Bypass Graft Surgery Following Non-ST Elevation Myocardial Infarction

Background: Non-ST elevation myocardial infarction (NSTEMI) significantly reduces patient survival, a condition that is essential for the optimization of results and the restoration of effective vascular flow multivessel coronary artery disease, Emergency coronary artery bypass grafting (CABG) is frequently contemplated. In the aftermath of NSTEMI, the objective of this investigation is to assess the results of urgent CABG, to prioritize risk stratification through the use of the Global Registry of Acute Coronary Events (GRACE) score Methods: An analysis of 60 consecutive patients who underwent emergent CABG following NSTEMI was conducted retrospectively. Data on patients were obtained from a prospectively compiled database, and the GRACE score was implemented to evaluate the probability of mortality. Patients were divided into three risk categories: low (&lt;10%), intermediate (10-19%), and high (≥20%), based on their predicted mortality percentages. Results: Patients in the high-risk group (Group 3) exhibited significantly lower Euro Scores and ejection fractions (EF) than those in the low and intermediate groups, as demonstrated by the study. Additionally, the cross-clamp time was notably longer in Group 3, highlighting the urgency of intervention in this population The postoperative complications' overall incidence did not differ significantly between the groups. Even though Group 3 exhibited an increased in-hospital mortality rate. Conclusions: The findings underscore the importance of timely intervention and tailored management strategies for NSTEMI patients, more specifically, the individuals who have been classified as high-risk by the GRACE score. This study contributes to the growing body of literature supporting urgent CABG as a lifesaving procedure and emphasizes the need for further research to optimize outcomes in this vulnerable patient population. The results advocate for a multidisciplinary approach to enhance perioperative care and improve survival rates among NSTEMI patients undergoing CABG.

Cumulative Psychosocial and Health Disparities in US Adolescent Cigarette Smoking, 2002 to 2019.

Understanding disparities in adolescent cigarette smoking is important for effective prevention. We investigated disparities in adolescent smoking based on cumulative reported psychosocial/health risk among respondents ages 12-17 years in the US National Survey of Drug Use and Health from 2002 to 2019. Multivariable regression estimated associations of cumulative risk, survey years, and their interaction predicting past-month and daily smoking. Eleven psychosocial/health variables associated with youth smoking formed composite measures of cumulative risk, categorizing risk as Low (0-2), Moderate (3-4), or High (5+). The main outcomes were weighted past-month and daily smoking by cumulative risk and time, examining prevalence and proportional change across years. Among 244,519 adolescents, greater cumulative risk predicted higher smoking prevalence across all outcomes. Compared to the Low-risk category, past-month smoking odds (adjusted odds ratio, 95%CI) were 9.14 (8.58-9.72) and 46.15 (43.38-49.10) times greater in the Moderate- and High-risk categories. For daily smoking, odds were 14.11 (11.92-16.70) and 97.32 (83.06-114.03) times greater among the Moderate- and High-risk categories. Regarding proportional change, the Low-risk category exhibited the steepest decline in past-month smoking from 2002-2003 to 2018-2019 (-85.1%), followed by the Moderate- (-79.2%) and High-risk (-65.7%) categories. Daily smoking declined more steeply among the Low- (-96.5%) and Moderate- (-90.5%) than High-risk category (-86.4%). Cumulative risk is a robust predictor of adolescent smoking. While record-setting reductions in adolescent smoking extend across risk categories, disparities favoring youth with fewer risks are evident throughout. Recognizing cumulative risk can inform the development of more targeted and effective prevention efforts.

Baseline characteristics, management patterns and outcome in patients with pulmonary embolism and malignancy: Insights from a single-centre study.

Acute pulmonary embolism (PE) is one of the main causes of death in patients with active cancer. In this study, we evaluated the impact of malignancy on the treatment choices, and short- and long-term clinical outcomes in patients with acute PE. In this study, 872 acute PE patients (age 61.6 ± 16.8 years, female 57.5 %) from different risk and treatment categories were retrospectively analyzed and divided into two groups according to the presence of active malignancy. Active malignancy was documented in 129 (14.8 %) out of the 872 patients. Ultrasound-assisted-thrombolysis (USAT), rheolytic-thrombectomy (RT), systemic-thrombolysis (ST) and anticoagulation-alone therapies were noted in 27.3 %, 16.6 %, 6.4 % and 49.7 % of overall PE patients. RT and anticoagulation therapies were more frequent in patients with malignancy whereas ST and USAT were more frequently used in the other group. Regardless of the presence of malignancy and the treatment modality chosen, significant improvements were achieved in all treatment targets (p < 0.001 for all). Bleeding rates were similar in both groups, while in-hospital and long-term mortality was higher in the malignancy cohort. Active malignancy was found to be an independent predictor for composite of 60-day mortality and PE-related rehospitalization (adjusted OR: 2.43; 95 % CI: 1.32-4.47, p = 0.04) and long-term mortality (adjusted HR: 2.25, 95 % CI: 1.29-3.91, p = 0.004). Concomitant malignancy adversely affects both short- and long-term outcomes in patients with acute PE. Although these patients are more vulnerable, it is possible to achieve satisfactory treatment success with acceptable bleeding rates with the inclusion of catheter-based methods as treatment option.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear. Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n=1319) and an in-house dataset (n=308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up. The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

Abstract 4137927: Physicians’ Misrecognition of Stroke Risk in Patients with Atrial Fibrillation

Background: Current clinical practice guidelines for atrial fibrillation (AF) recommend stroke risk stratification and the use of oral anticoagulants (OACs) for patients at risk. However, physicians’ recognition of patients’ stroke risk may differ from the calculated risk, and the effect of this discrepancy on subsequent care remains unknown. Aims: We aimed to document treating physicians’ estimations of individual patients' stroke risk and assess its association with OAC utilization. Methods: A multicenter, prospective cohort study was conducted in two outpatient practices in Tokyo, Japan, between 2018 to 2020. Participants included patients with newly diagnosed AF or those referred for initial treatment for AF. Treating physicians were asked to document the patient's estimated risk of stroke in numbers. The estimations were categorized as low risk (&lt;1.0%/year), intermediate risk (1.0%≤ to &lt;2.0%/ year), and high risk (2.0%≤/year). We then calculated patients’ baseline CHA 2 DS 2 -VASc scores and divided them into three risk groups: 0 or 1 point as low risk, 2 points as intermediate risk, and 3 or more points as high risk. Higher risk categorization by physicians was defined as "overestimation," while lower risk categorization was defined as "underestimation." The independent association of physician-patient risk concordance with OAC use was explored by multivariable Logistic regression models. Results: Among the 285 patients enrolled in this study (mean age 68±12 years, male 72.6%), the mean CHA 2 DS 2 -VASc score was 2.3±1.6. Physicians correctly estimated stroke risk in 147 patients (51.6%), underestimated it in 25 patients (8.8%) and overestimated it in 113 patients (39.6%). OACs were used in 89.8% of patients whose stroke risk was correctly estimated, 72.0% of those whose stroke risk was underestimated, and 84.1% of patients whose health status was overestimated. After multivariable adjustment, the underestimation of stroke risk was independently associated with less use of OACs (adjusted odds ratio 0.23, 95% CI 0.061-0.85, P=0.028). Conclusions: In this cohort study, physician underestimation of stroke risk was not rare and associated with less use of OACs.

Abstract 4112869: Development of a Sitosterolemia Risk Prediction Scale (SRPS): A Screening Tool

Introduction: Sitosterolemia, a hereditary disorder marked by elevated plant sterol levels, presents diagnostic challenges due to its similarity to other lipid disorders. The development of the Sitosterolemia Risk Prediction Scale (SRPS) aims to address this by synthesising genetic, clinical, and dietary data into a coherent risk assessment model. Research Question: We propose that a structured risk scale, integrating diverse factors known to affect sitosterolemia, can significantly improve the accuracy of predicting the disorder. The SRPS is hypothesised to facilitate early detection and inform targeted interventions. Aim: The primary aim is to conceptualise and outline the SRPS, which categorises individuals into risk categories based on a point system reflecting genetic predispositions, clinical symptoms, dietary habits, and response to treatments. This scale seeks to enhance the clinical identification of sitosterolemia, promoting timely and personalised management strategies. Methods: A detailed table was generated to present the SRPS, categorising risk factors into genetic, clinical, dietary, and response to treatment. This innovative method allowed for the efficient synthesis and visualisation of complex data. Results: The SRPS table methodically organizes risk factors into low (0-2 points), moderate (3-5 points), and high (6+ points) categories. This stratification guides further diagnostic actions, ranging from exploring alternative causes of hyperlipidemia to necessitating comprehensive genetic and lipid analyses. Conclusion: The SRPS represents an innovative framework for assessing sitosterolemia risk, highlighting the potential benefits of integrating genetic, clinical, and dietary information. It further underscores the importance of a multifactorial approach in the early detection and management of sitosterolemia.

Abstract 4135517: Echocardiographic Biomarkers Enhance Mortality Predictions in Cirrhosis Patients

Introduction: Cirrhosis, a late-stage liver disease, is associated with numerous systemic complications, making long-term mortality prediction challenging. Traditional models like Child-Pugh and MELD are primarily designed for short-term (3-month) mortality. This study evaluates the impact of incorporating echocardiographic (echo) variables in predicting long-term mortality in cirrhosis patients. Methods: A retrospective cohort study was conducted using data from 60 patients with clinically diagnosed cirrhosis from Hippokration Hospital, Greece, spanning 2018 to 2022. Patients were followed for up to 52 months. Exclusion criteria included pre-existing cardiovascular diseases and significant co-morbidities. Patients were categorized into survived to end of study (n=31) and died during study (n=29) groups. Outcomes were analyzed using multivariate logistic regression and Cox proportional hazards models, and include parameters for age, blood pressure, lab-based biomarkers (potassium, pro-brain natriuretic peptide, hemoglobin, platelet count, aspartate transaminase, gamma-glutamyl transferase, uric acid), and echo/ecg measurements (QT interval, left ventricular (LV) ejection fraction, LV global longitudinal strain (GLS), left atrial GLS, annulus velocities, and E/A ratio). Results: The multivariate logistic regression model (MELD+) that included echo variables showed a 10% improvement in area under the curve (AUC: 0.901) compared to the MELD model alone (AUC: 0.817). MELD+ improved risk classification accuracy by nearly 10% with a conservative threshold. The Cox proportional hazards model with MELD+ also demonstrated superior performance (AUC: 0.923; C-index: 0.875; Brier Score: 0.097) compared to the MELD model alone (AUC: 0.892; C-index: 0.831; Brier Score: 0.134). The MELD+ model showed less variation in hazard ratio confidence intervals, indicating more consistent risk estimation. Conclusion: Incorporating echocardiographic measurements enhances the accuracy of long-term mortality predictions in cirrhosis patients. These findings suggest that cardiac imaging should be considered in risk assessments for cirrhosis patients, particularly for predicting survival beyond three months.

Iron deficiency and phlebotomy in patients with polycythemia vera.

Polycythemia vera (PV) is a myeloproliferative neoplasm that is associated with an elevated risk of thrombosis. Treatment strategies are based on thrombosis risk classification. Phlebotomy is a commonly recommended treatment for all patients with PV, regardless of their risk classification, and reduces the incidence of thrombosis by lowering hematocrit levels. However, patients with PV frequently present with iron deficiency at diagnosis due to increased erythropoiesis, which repeated phlebotomy can exacerbate. This can produce symptoms that diminish quality of life, such as fatigue, lethargy, and impaired concentration. Recently, hepcidin mimetics have been developed to suppress iron utilization in erythropoiesis. Among them, rusfertide has been shown to control hematocrit levels without requiring phlebotomy. Further studies are needed to identify new treatment strategies for PV that also consider iron deficiency.

Implementing Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Risk Assessment of Drug Interactions

Novel drug development is time-consuming, difficult, and often unsuccessful. This has made combining therapies increasingly common and profitable in recent years. Healthcare professionals in the pharmaceutical sector are interested in the combination, but we must instantly solve drug-drug interactions. In few cases, single-perspective DDI evaluations are insufficient. Pharmacological therapy and patient safety depend on accurate drug interaction risk assessments. This work uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess and predict medication interaction hazards. The proposed method uses neural networks' generative skills and fuzzy logic to construct a robust decision-making framework that can handle drug interaction scenarios and their unpredictability. Fuzzy rules provide all the necessary risk assessment components. This includes dose, patient health issues, drug interaction profiles, and pharmacological properties. Accuracy, specificity, and Mean Squared Error evaluate the model after training with known drug interactions. These metrics evaluate model performance. These measures are essential for machine learning evaluation. The ANFIS-based model outperforms previous risk assessment methods in risk classification and prediction. This study found that the ANFIS architecture may improve medication management security and prevent dangerous drug interactions.

A population-based study using nomograms to predict overall and cancer-specific survival in HPV-associated CSCC.

Constructing and validating two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) in cutaneous squamous cell carcinoma (CSCC) correlated with human papillomavirus (HPV) infection was the main goal of this study. We constructed predictive models for OS and CSS incidence in HPV infection-associated CSCC using information from 2238 patients in the Surveillance, Epidemiology, and End Results (SEER) database and screened the variables by LASSO regression, Cox univariate regression, and Cox multifactorial regression models, which were calibrated and validated by internal and external cohorts. Finally, all patients were categorized into intermediate-risk, low-risk, and high-risk groups based on the optimal threshold calculated from the total score. Multivariate analysis showed that HPV infection status, marital status, tumor metastatic stage, surgical status, radiotherapy status, lymph node biopsy, local lymph node dissection, primary tumor status, and bone metastasis were risk factors for OS and CSS. The C index, the time-dependent area under the receiver-operating characteristic curve, and the column-line diagrams of the calibration plot were among the excellent-performance metrics that were effectively displayed. Moreover, the decision curve analysis of the two nomograms consistently revealed their favorable net benefits spanning 1, 2, and 3 years. In addition, the survival curves indicate that each of the two risk classification systems clearly differentiates high, medium, and low risk groups. These meticulously crafted nomograms stand poised to serve as indispensable instruments in clinical practice, empowering clinicians to adeptly communicate with patients regarding their prognostic outlook over the forthcoming 1, 2, and 3 years.

The EU's Artificial Intelligence Act and copyright

AbstractThe European Union's (EU's) Artificial Intelligence Act (AI Act), published on 12 July 2024, seeks to establish a consistent legal framework for AI systems within the EU, promoting trustworthy and human‐centric AI while safeguarding various fundamental rights. The Act classifies AI applications into three risk categories: unacceptable risk, high risk, general purpose AI models with systemic risk and low or no risk, each with corresponding regulatory measures. Although initially not focused on copyright issues, the rise of generative AI led to specific provisions addressing general purpose AI models. These provisions include transparency obligations, particularly regarding the technical documentation and content used for training AI models, and policies to respect EU copyright laws. The Act aims to balance the interests of copyright holders and AI developers, ensuring compliance while fostering innovation and protecting rights.

Risk stratification in the clinical application of minimal residual disease assessment in acute myeloid leukemia.

In acute myeloid leukemia (AML), further investigation is warranted to integrate measurable residual disease (MRD) with genetic characteristics for formulating a dynamic prognostic system for predicting response and selecting appropriate postremission therapeutic strategies. The authors incorporated MRD with genetic risk classification and assessed its impact on transplantation decision making within different risk cohorts, comprising 769 patients with newly diagnosed AML across three clinical trials. Only patients who achieved complete remission (CR) within two courses of chemotherapy were selected. In the favorable-risk and intermediate-risk groups, patients who underwent transplantation according to the protocol experienced significant 3-year overall survival (OS) benefits compared with those who did not (favorable-risk group: hazard ratio [HR], 0.38; 95% confidence interval [CI], 0.20-0.73l p=.004; intermediate-risk group: HR, 0.53; 95% CI, 0.33-0.85; p=.008). In the intermediate-risk group, early detection of MRD positivity, even after the initial course of chemotherapy, was associated with a significantly elevated cumulative incidence of relapse (47.2% vs. 36.0%; p=.009) and a notable extension of OS with allogeneic hematopoietic stem cell transplantation (HR, 0.47; 95% CI, 0.28-0.79; p=.004). Conversely, patients who achieved MRD negativity at either of the two time points had comparable OS in the favorable-risk and intermediate-risk groups, regardless of whether they underwent transplant or not. In the adverse-risk group, allogeneic hematopoietic stem cell transplantation led to improvements in OS irrespective of MRD status (HR, 0.51; 95% CI, 0.38-0.69; p<.001). Early clearance of MRD demonstrated significant prognostic value, particularly for patients in the favorable-risk and intermediate-risk groups. Positive MRD status after two courses of intensive chemotherapy were associated with a higher relapse rate and inferior OS, necessitating allogeneic hematopoietic stem cell transplantation.