Clinical Decision Research Articles

How is gout currently managed, and is there interest in changing the way we deliver care? A qualitative exploratory study.

This study aimed to understand how gout is currently managed in Australian primary care and to assess the level of interest in changing the delivery of care for gout. This pragmatic qualitative study was conducted among Australian general practitioners (GPs), pharmacists and adults living with gout. Semi-structured interviews were conducted and analysed using thematic analysis. The key theme identified was that chronic gout has low priority compared to managing other conditions, and management is often responsive to patient action. Lack of confidence was expressed about medication regimens for multimorbidities. Regarding changing care delivery, there was widespread interest in enhancing pharmacists' role in providing medication reviews and guidance, but there were conflicting views between some pharmacists and GPs about clinical decisions and prescribing arrangements. Interpreting findings based on Wagner's chronic care model, it is apparent that there are multiple potential opportunities to change practice that might improve goutmanagement.

Pulmonary nodules and the psychological harm they can cause: A scoping review

More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black-box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time-series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.

Comparative Cardiovascular Risks of Radical Prostatectomy and External Beam Radiation Therapy in Early-Stage Prostate Cancer: A Comprehensive Retrospective Analysis.

The risk of cardiac disease mortality has recently become a focal point of concern within the medical community for patients with prostate cancer (PCa). Given that radical prostatectomy (RP) and external beam radiation therapy (EBRT) are the main treatment modalities for localized PCa, their specific impact on cardiovascular-specific mortality (CSM) remains unclear. This study explored the specific effects of RP and EBRT on CSM risk to guide clinical treatment decisions. Data from patients aged 45-74years, who were diagnosed with T1-2N0M0 stage PCa from the SEER database (2010-2015), were used. Multivariate statistical methods, including propensity score matching (PSM), competing risk regression, COX regression analysis, and Fine-Gray testing, were applied to assess the impact of RP and EBRT on CSM risk. Among 146,082 T1-2 stage PCa patients, cardiac disease emerged as the primary cause of death, surpassing PCa itself. Multifactorial COX regression and competing risk regression analyses indicated that local treatments do not increase CSM risk. Further analysis revealed a significant increase in CSM risk for patients undergoing only EBRT compared with those undergoing only RP (hazard ratio [HR] = 2.71, 95% confidence interval [CI] 1.96-3.74, P < 0.001), with subsequent PSM adjustment, further confirming a significantly reduced risk in the RP treatment group (HR 0.23, 95% CI 0.13-0.40, P < 0.001). T1-2 stage PCa patients face a significant risk of CSM, with RP offering a potential advantage over EBRT in reducing this risk. These findings encourage clinicians to comprehensively consider the potential impact on cardiac health when formulating treatment plans, providing crucial guidance for optimizing treatment strategies.

Cost-effectiveness of falls prevention strategies for older adults: protocol for a living systematic review

IntroductionOne-third of adults aged 65+ fall annually. Injuries from falls can be devastating for individuals and account for 1.5% of annual healthcare spending. With the growing ageing population, falls place...

Prospective study of the real impact of fusion centered genomic assays in patient management in a national collaborative group: the GETHI-XX-16 study.

Precision medicine represents a paradigm shift in oncology. Access to genetic testing and targeted therapies is frequently limited. Assays based on DNA sequencing can miss druggable alterations. We aimed to determine the impact of a free access program to RNA tests in patient management. We designed a multicenter prospective observational study within the Spanish National Group for Translational Oncology and Rare and Orphan Tumors (GETTHI). Eligible patients were adults with solid cancers that had progressed on standard therapies. Tumor samples were analyzed using two RNA sequencing assays (Trailblaze PharosTM and Archer FusionPlex Solid TumorTM). A central committee evaluated the actionability of genetic alterations and reported the findings to attending physicians, who made the final clinical management decisions. Between November 2016 and April 2019, 395 patients with 41 different tumors across 30 hospitals were included. Molecular analysis revealed actionable genetic alterations in 57 individuals (14.4%). Targeted therapies were advised for 23 and seven received a matched targeted therapy: two lung cancers (EML4-ALK and CD74-ROS1 fusion), three glioblastomas (EGFR point mutations), one oligodendroglioma (FGFR3-TACC3 fusion) and a prostate cancer (SND1-BRAF fusion). The outcomes included two tumor responses, one disease stabilization, one early withdrawal due to toxicity, one progression, and one unknown. Despite the growing knowledge of cancer biology and its translation to drug development, the overall impact of personalized treatments remains low. Access to comprehensive molecular tests covering properly all known actionable alterations and programs for a wide access to targeted therapies seem to be critical steps.

Concurrent meningioma and intracranial aneurysm: Insights from an updated systematic review and a case report

Background: The concurrent presentation of meningioma and intracranial aneurysm (IA) poses diagnostic and therapeutic challenges, with no standardized management protocol available. This study aims to address this through an updated systematic review, delineating optimal strategies for managing this dual pathology. Methods: A systematic review was conducted across PubMed, Web of Science, and Embase databases. Articles were screened independently by two reviewers. Treatment strategies and patient outcomes were comprehensively analyzed to formulate a treatment framework based on several characteristics. In addition, one concurrent meningioma and IA case from our institution was presented. Results: A total of 69 articles comprising 115 patients were included in the study. The cohort exhibited a female predominance (80%) with a mean age of 56 (±13) years. Meningiomas were primarily localized to the frontotemporal and sellar regions, while aneurysms favored the anterior circulation – notably, 16.5% of cases presented with ruptured aneurysms. Management strategies varied based on the spatial relationship between lesions and aneurysm rupture status. In unruptured cases, 34% underwent a single craniotomy for simultaneous resection of both pathologies, while endovascular intervention was favored when the IA originated from an artery feeding the meningioma (73%). Remarkably, postoperative aneurysm rupture occurred in 33% of cases managed solely through tumor resection (range 0–30 days postop). Conclusion: This study proposes a comprehensive treatment algorithm to guide neurosurgeons in managing concurrent meningioma and IA cases. By considering individual patient intricacies, the feasibility of simultaneous management, aneurysm rupture risk, and symptomatology, this framework is a valuable tool for clinical decision-making in these complex scenarios.

Local Neuronal Activity and the Hippocampal Functional Network Can Predict the Recovery of Consciousness in Individuals With Acute Disorders of Consciousness Caused by Neurological Injury.

There is limited research on predicting the recovery of consciousness in patients with acute disorders of consciousness (aDOC). The purpose of this study is to investigate the altered characteristics of the local neuronal activity indicated by the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) of the hippocampus network in patients with aDOC caused by neurological injury and to explore whether these characteristics can predict the recovery of consciousness. Thirty-seven patients with aDOC were included, all of whom completed resting-state functional magnetic resonance imaging (rsfMRI) scans. The patients were divided into two groups based on prognosis of consciousness recovery, 24 patients were in prolonged disorders of consciousness (pDOC) and 13 in emergence from minimally conscious state (eMCS) at 3 months after neurological injury. Univariable and multivariate logistic regression analyses were used to investigate the clinical indicators affecting patients' recovery of consciousness. The ALFF values and FC of the hippocampal network were compared between patients with pDOC and those with eMCS. Additionally, we employed the support vector machine (SVM) method to construct a predictive model for prognosis of consciousness based on the ALFF and FC values of the aforementioned differential brain regions. The accuracy (ACC), area under the curve (AUC), sensitivity, and specificity were used to evaluate the efficacy of the model. The FOUR score at onset and the length of mechanical ventilation (MV) were found to be significant influential factors for patients who recovered to eMCS at 3 months after onset. Patients who improved to eMCS showed significantly increased ALFF values in the right calcarine gyrus, left lingual gyrus, right middle temporal gyrus, and right precuneus compared to patients in a state of pDOC. Furthermore, significant increases in FC values of the hippocampal network were observed in the eMCS group, primarily involving the right lingual gyrus and bilateral precuneus, compared to the pDOC group. The predictive model constructed using ALFF alone or ALFF combined with FC values from the aforementioned brain regions demonstrated high accuracies of 83.78% and 81.08%, respectively, with AUCs of 95% and 94%, sensitivities of 0.92 for both models, and specificities of 0.92 for both models in predicting the recovery of consciousness in patients with aDOC. The present findings demonstrate significant differences in the local ALFF and FC values of the hippocampus network between different prognostic groups of patients with aDOC. The constructed predictive model, which incorporates ALFF and FC values, has the potential to provide valuable insights for clinical decision-making and identifying potential targets for early intervention.

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input–output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

Predictive nomograms and an algorithm for managing patients with probable Meniere's disease

PurposeTo explore the efficacy of diagnostic tests in accurately reclassifying patients initially diagnosed with probable Meniere's disease (MD) into either definite or non-MD categories. Materials and methodsA retrospective cohort study was conducted at a neurotology clinic between 1/2016 and 5/2022. Patients underwent a battery of tests, from which sensitivity, specificity, positive and negative predictive values, as well as positive and negative likelihood ratios, were calculated. Additionally, prediction nomograms were developed. ResultsOf the 69 patients, 25 (36.2 %) were initially classified as definite MD, 21 (30.4 %), probable MD, and 23 (33.4 %) non-MD. The mean follow-up was 3.5 years. The sensitivity of electrocochleography (ECochG) was the highest (92 %), with a negative likelihood ratio of 15 %. Magnetic resonance imaging (MRI) with MD-protocol had the highest specificity (100 %), with a positive likelihood ratio of 100 %. Videonystagmography, video head impulse test, and cervical vestibular-evoked myogenic potentials, had lower sensitivity and specificity. We were able to reclassify 18 (86 %) patients with probable MD: 12 (57 %) were diagnosed with definite MD, and 6 (29 %) were diagnosed with non-MD, consistent with their clinical course. ConclusionsThe combination of ECochG and MRI with MD-protocol provides the most reliable approach to reclassify patients with Probable MD, ensuring a precise and accurate diagnosis. Vestibular tests express the functional status of the labyrinth and may not be reliable. Our findings provide valuable insights into clinical decision-making for patients with Probable MD and raise the consideration of additional diagnostic tests as supplementary to the existing clinical-only diagnosis criteria.

Prediction of Postoperative Delirium in Older Adults from Preoperative Cognition and Occipital Alpha Power from Resting-State Electroencephalogram.

Postoperative delirium is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications. In this prospective observational study, we used machine learning to evaluate whether baseline (preoperative) cognitive function and resting-state EEG could be used to identify patients at risk for postoperative delirium. Preoperative resting-state EEGs and the Montreal Cognitive Assessment were collected from 85 patients (age = 73 +- 6.4 years, 12 cases of delirium) undergoing elective surgery. The model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (age = 68 +- 5.2 years, 6 cases of delirium) undergoing elective surgery. Occipital alpha powers have higher f1-score than frontal alpha powers and EEG spectral slowing in the training cohort. Occipital alpha powers were able to predict postoperative delirium with AUC, specificity and accuracy all >90%, and sensitivity >80%, in the validation cohort. Notably, models incorporating transformed alpha powers and cognitive scores outperformed models incorporating occipital alpha powers alone or cognitive scores alone. While requiring prospective validation in larger cohorts, these results suggest that strong prediction of postoperative delirium may be feasible in clinical settings using simple and widely available clinical tools. Additionally, our results suggested that the thalamocortical circuit exhibits different EEG patterns under different stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.

The effect of median household income on the prognosis of lung adenocarcinoma: A SEER analysis

Background: Lung cancer is the main cause of death related to malignant tumors. Since cause-specific mortality can guide clinical decision-making, this study employed the Fine–Gray model based on the Surveillance, Epidemiology, and End Results (SEER) database to identify significantly how socio-economic status influences initial treatment decisions and survival outcomes in patients with lung adenocarcinoma. Objective: The aim of this study was to identify the predictors of lung adenocarcinoma. Methods: The U.S. Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients diagnosed with lung adenocarcinoma between 2000 and 2018. Seventeen thousand four hundred forty-one patients with lung adenocarcinoma were subdivided in four socio-economic tertiles, based on median household income. Cox regression modeling explored the relationship between race, surgery, grading, and median household income on survival outcomes. The study also assessed patient demographic characteristics, age at diagnosis, and surgical interventions. Results: Among 17 441 patients with primary lung adenocarcinoma, the age distribution was as follows: less than 45 years (n=202, 1.16%), between 45 and 54 years (n=1121, 6.43%), 55 and 64 years (n=4252, 24.38%), 65 and 74 years (n=6357, 36.45%), 75 and 84 years (n=4426, 25.38%), and more than 84 years (n=1083, 6.2%). The adjusted hazard ratio (aHR) with 95% CI for ages 65–74 years, 75–84 years, and older than or equal to 85 years were 0.25 (0.11,1.29), 0.40 (0.11,1.50), and 0.72 (0.11,2.05), respectively. Multifactorial Cox regression indicated that the aHR for tumor metastasis was 0.93 (0.03, 2.54), and for patients who did not undergo surgery, it was 1.46 (0.03, 4.31). Grade IV patients exhibited the lowest survival rate [0.66(0.11, 1.93)]. A notable correlation existed between median household income and survival, with distinctly lower survival rates observed in low-income groups. Conclusion: Older patients, especially those who did not undergo surgery and had a higher tumor grade, had significantly reduced survival. Moreover, survival rates for black patients in lower-income brackets were worse than those for white patients in the same financial category. Implications for practice: The quality of life of lung cancer patients is affected by low-income family.

Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients

BackgroundThe hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence.MethodsAdult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves.ResultsA total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence.ConclusionsNomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

Using Patient Reported Outcomes Measurement Information System (PROMIS®) Measures in Rheumatoid Arthritis Clinical Care, Research, and Trials

Patient-reported outcomes (PROs) are reports coming directly from patients about how they feel and function. PROs in the Rheumatoid Arthritis (RA) Core Set include the patient global assessment, and self-ratings of pain and physical function. As a result, these outcomes are widely collected in trials. The patient global assessment is a component included in several RA disease activity indices including the clinical disease activity index (CDAI) and the disease activity score (DAS28), which guide clinical decision-making for the initiation, maintenance, and change of disease modifying anti-rheumatic drugs (DMARDs).

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.

Moving Beyond Systemic Inflammatory Response Syndrome and Bacteremia: Are Modern Critical Care Calculators Useful in Predicting Debridement, Antibiotics, and Implant Retention Treatment Outcomes in Periprosthetic Joint Infection?

BackgroundIn critically ill periprosthetic joint infection (PJI) patients, surgeons need to balance the need for aggressive, definitive treatment against the health state of a potentially unstable patient. A clear understanding of the association between treatment outcomes and assessment scores for sepsis would benefit clinical decision-making in these urgent cases. The current study evaluates the effect of critical illness on debridement, antibiotics, and implant retention (DAIR) outcomes, as defined by systemic inflammatory response syndrome (SIRS) and, for the first time, by contemporary markers quick Sequential Organ Failure Assessment (qSOFA) and Modified Early Warning Score (MEWS). MethodsWe retrospectively identified 253 patients who underwent DAIR for PJI at a single institution between 2017 and 2021. The SIRS, qSOFA, and MEWS scores were calculated based on variables on admission. A DAIR treatment failure, defined as reoperation or mortality, was measured at 90 days and two years. Univariate analysis was used to determine the association between elevated critical care scores and DAIR failure. ResultsThe DAIR treatment success was 59% at two years, with hip procedures and Charlson comorbidity index (CCI) ≥ 1 independently associated with higher odds of DAIR failure. There were 43 patients (16%) who presented with SIRS, however, only four (2%) had positive qSOFA scores. Neither SIRS nor qSOFA were predictive of DAIR failure. For knees only, elevated MEWS scores were predictive of 90-day DAIR failure (P = 0.019). ConclusionOver one in six patients undergoing DAIR for PJI presented with SIRS, while only one in 50 had a positive qSOFA. The SIRS and qSOFA scores were not predictive of DAIR failure. Elevated MEWS scores were associated with DAIR failure at 90 days postoperatively in knee PJIs only, and should be confirmed in a larger cohort. Our results suggest that SIRS is not predictive of DAIR outcomes, possibly because it overestimates the proportion of critically ill patients.

Lower Incidence of Hepatocellular Carcinoma with Tenofovir Alafenamide in Chronic Hepatitis B: Evidence from a Large-Scale Cohort

Background & AimsThe newer tenofovir alafenamide (TAF) lacks extensive research regarding its impact on hepatocellular carcinoma (HCC). This study aims to evaluate and compare the effects of TAF, tenofovir disoproxil fumarate (TDF), and entecavir (ETV) on HCC incidence using nationwide claim data. MethodsA total of 75,816 treatment-naïve chronic hepatitis B patients were included and divided into TAF (n=25,680), TDF (n=26,954), and ETV (n=23,182) groups after exclusions. Propensity score matching (1:1:1) resulted in 17,537 patients per group, and HCC incidence rates were compared. ResultsBefore matching, the incidence of HCC was significantly lower in the TAF group compared to the TDF and ETV groups (11.47 vs. 15.04 and 14.24 per 1,000 person-years). The incidence rate ratio for TDF was 1.31 (1.19-1.44) and for ETV was 1.24 (1.12-1.37). Before matching, the TAF group had a significantly lower HCC compared to TDF and ETV in both cirrhotic and non-cirrhotic patients. After matching, the TAF group had a lower HCC incidence compared to TDF (12.38 vs. 15.39, incidence rate ratio 1.24, p<0.001) but not ETV (incidence rate ratio 1.08, p=0.219). In cirrhotic patients, TAF had lower HCC incidence than TDF and ETV (30.25 vs. 39.56 and 38.51). In non-cirrhotic patients, TAF had lower HCC incidence than TDF (incidence rate ratio 1.19, p=0.030) but not ETV (incidence rate ratio 0.85, p=0.066). Cox regression analysis showed TAF had a significantly lower HCC incidence compared to TDF (hazard ratio 1.335, p<0.001) and ETV (hazard ratio 1.162, p=0.011), after adjusting for age, gender, and cirrhosis status. ConclusionsThe TAF group consistently demonstrated a lower incidence of HCC compared to the TDF and ETV groups, especially in patients with cirrhosis. IMPACT and IMPLICATIONSThe scientific justification for this work lies in its potential to fill the knowledge gap regarding the comparative efficacy of Tenofovir Alafenamide (TAF), Tenofovir Disoproxil Fumarate (TDF), and Entecavir (ETV) in reducing the incidence of hepatocellular carcinoma (HCC) in chronic hepatitis B patients. The study's findings are particularly crucial for healthcare providers and policymakers, as they highlight the significantly lower incidence of HCC associated with TAF, especially in patients with cirrhosis. These results suggest that TAF could be a preferable antiviral therapy option to mitigate HCC risk, thus influencing clinical decision-making and healthcare guidelines. Practically, these findings can guide physicians in prescribing more effective treatments, assist researchers in designing further studies to explore the mechanisms behind TAF's effectiveness, and inform policymakers in crafting healthcare policies that optimize patient outcomes while considering potential limitations such as the observational nature of the study and residual confounding factors.

Mutations of ARID1B, PIK3C2B, KMT2B, and FAT1 genes influence clinical outcome in newly diagnosed myeloma

The study aimed to elucidate the mutational profile of patients with newly diagnosed multiple myeloma to understand correlations of alterations with clinical outcomes. A cohort of 20 patients was enrolled, and mutational analysis was conducted using the TruSight Oncology 500 DNA Kit. Identified genetic alterations were related to clinicopathologic features and treatment outcomes. A total of 724 high-quality variants were validated. All patients harbored mutations associated with the RTK-RAS pathway, with over half having alterations in PI3K, NOTCH, and WNT pathways. Several gene mutations were associated with specific clinical characteristics and prognostic indicators, revealing a complex interplay between genetic alterations and myeloma type, standard prognostic indicators, biochemical parameters, and renal function. Genetic alterations significantly influencing progression-free survival concerned PIK3C2B, ARID1B genes, and concomitant mutations in KMT2B, FAT1, and ARID1B. The findings underscore the potential of gene mutation-based prognostic tools in enhancing clinical decision-making and suggest that further exploration of identified genetic markers could pave the way for improved prognostic stratification and targeted therapeutic interventions in multiple myeloma.

Machine Learning Model for Early Prediction of Survival in Gallbladder Adenocarcinoma: A comparison Study

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70%) and validation (30%) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.

Circulating tumor DNA status and dynamics predict recurrence in patients with resected extrahepatic cholangiocarcinoma

Background & AimsSurgery is the only curative therapeutic option for resectable extrahepatic cholangiocarcinoma (eCCA), but recurrence is common, and prognosis is poor. There is an unmet clinical need for improved decision-making regarding adjuvant chemotherapy (ACT). Here we evaluated the usefulness of monitoring longitudinal circulating tumor DNA (ctDNA) for minimal residual disease (MRD) in patients of the STAMP trial, which compares the efficacy of adjuvant capecitabine (CAP) versus gemcitabine plus cisplatin (GemCis). MethodsBetween July 2017 and November 2020, 101 patients were randomized 1:1 to receive GemCis (n=50) or CAP (n=51). Efficacy outcomes were analyzed with an extended follow-up of 19 months from the previous report. From a biomarker cohort of 89 patients, longitudinal plasma samples (n=254) were prospectively collected post-surgery before adjuvant chemotherapy (ACT), and on-ACT at 12 and 24 weeks from cycle 1 day 1 (C1D1). ctDNA was evaluated using a personalized, tumor-informed, 16-plex PCR-NGS assay and was correlated with clinical outcomes. ResultsIn the extended follow-up analysis, median disease-free survival (DFS) and overall survival (OS) did not significantly differ between the CAP and GemCis groups. Significantly inferior DFS was associated with ctDNA-positivity before ACT (HR, 1.8; p=0.029), on-ACT at 12 weeks from C1D1 (HR, 7.72; p<0.001), on-ACT at 24 weeks from C1D1 (HR, 5.24; p<0.001), and anytime post-surgery (HR, 3.81; p<0.001). Analysis of pre-treatment to on-treatment ctDNA dynamics revealed that serially ctDNA-negative patients exhibited a significantly longer DFS compared to those with sustained ctDNA-positivity (HR, 6.7; p<0.001) or who turned ctDNA-positive (HR, 5.8; p<0.001). ConclusionIn patients with resected eCCA, ctDNA status and dynamics predicted recurrence during adjuvant therapy, and may help optimize clinical decision-making. Impact and implicationsThe findings from this study highlight the critical role of ctDNA as a prognostic biomarker and monitoring tool for patients with resected extrahepatic cholangiocarcinoma (eCCA). By demonstrating the superiority of ctDNA to predict disease recurrence compared to conventional biomarkers such as CA 19-9 and CEA, this study underscores its potential in guiding decision-making during adjuvant chemotherapy. These results may be crucial to refine post-surgical treatment strategies and improve patient outcomes. The practical application of ctDNA monitoring could lead to more personalized treatment approaches, enabling timely interventions based on minimal residual disease (MRD) status. Clinical Trial Registration numberNCT03079427