Guide Treatment Decisions Research Articles

Chemical Tools for Monitoring and Targeting Collagen Cross-linking.

The formation of collagen, the most abundant protein in mammals, is vital for the integrity of skin, tendons, and tissue in essentially any organ. Excessive collagen formation is, however, characteristic of fibrotic and malignant diseases, which include major global health issues. The diagnosis of abnormal collagen production and deposition is, therefore, critical for disease prognosis and helps guide treatment decisions. Here, we summarize our research on the development of tailored tools for monitoring and targeting excessive collagen crosslinking. We anticipate these tools will provide a deep understanding at the molecular level of collagen formation in normal and disease conditions with applications in imaging and disease treatment.

Patient-specific prostate tumour growth simulation: a first step towards the digital twin

Prostate cancer (PCa) is a major world-wide health concern. Current diagnostic methods involve Prostate-Specific Antigen (PSA) blood tests, biopsies, and Magnetic Resonance Imaging (MRI) to assess cancer aggressiveness and guide treatment decisions. MRI aligns with in silico medicine, as patient-specific image biomarkers can be obtained, contributing towards the development of digital twins for clinical practice. This work presents a novel framework to create a personalized PCa model by integrating clinical MRI data, such as the prostate and tumour geometry, the initial distribution of cells and the vasculature, so a full representation of the whole prostate is obtained. On top of the personalized model construction, our approach simulates and predicts temporal tumour growth in the prostate through the Finite Element Method, coupling the dynamics of tumour growth and the transport of oxygen, and incorporating cellular processes such as proliferation, differentiation, and apoptosis. In addition, our approach includes the simulation of the PSA dynamics, which allows to evaluate tumour growth through the PSA patient’s levels. To obtain the model parameters, a multi-objective optimization process is performed to adjust the best parameters for two patients simultaneously. This framework is validated by means of data from four patients with several MRI follow-ups. The diagnosis MRI allows the model creation and initialization, while subsequent MRI-based data provide additional information to validate computational predictions. The model predicts prostate and tumour volumes growth, along with serum PSA levels. This work represents a preliminary step towards the creation of digital twins for PCa patients, providing personalized insights into tumour growth.

Ejection fraction assessment and left ventricular contractile function: How accurate is this parameter?

Evaluation of the left ventricular (LV) ejection fraction (EF) is a key component for diagnosis and treatment of heart failure (HF). Echocardiography is commonly used to assess LV systolic function and LV EF due to its simplicity and availability. However, it has some limitations, including interand intra-observer variability, dependence on the quality of visualization, etc. HF trials showed that there are some differences in the HF therapy effectiveness depending on the value of LV EF. These results suggest that basic therapy for HF and reduced LVEF is also effective in HF and preserved LVEF if patients have impairment of not only diastolic but also systolic function. Thus, LVEF may not be a reliable parameter to guide treatment decisions andit is necessary to introduce additional markers into clinical practice to assess LV systolic function. The purpose of this review is to discuss the difficulties of assessing LV systolic function using echocardiography and the possibilities of using alternative diagnostic methods, in particular myocardial deformation (speckle tracking echocardiography).

Regression and growth rates in androgen deprivation therapy for advanced castration-sensitive prostate cancer.

No study has compared cancer regression (d) and growth (g) rates in patients with advanced castration-sensitive prostate cancer (CSPC) treated with androgen deprivation therapy. The comparison of d and g rates provides insight into the differential impact of ADT regimens on tumor dynamics, potentially guiding more personalized treatment strategies. Therefore, we aimed to estimate these rates and evaluate their impact on survival outcomes. Sequential prostate-specific antigen (PSA) data was obtained from the KYUCOG-1401 trial including patients with advanced CSPC randomized to gonadotropin-releasing hormone (GnRH) antagonist (group A) and GnRH agonist plus bicalutamide (group B). d and g rates were estimated by applying mathematical models and were compared in subgroups. PSA-progression-free survival (PSA-PFS), radiographic progression-free survival (rPFS), and overall survival (OS) were compared by lower and higher than the median of these rates. Patients with higher PSA and higher extent of disease score at enrollment presented higher d rates (0.03965 vs. 0.03546, p = 0.0006) and (0.03947 vs. 0.03587, p = 0.0113) for groups A and B, respectively. The median d rate was lower for group A than group B (0.03306 vs. 0.039965, respectively [p = 0.0002]). The median g rate was higher for group A than group B (0.00016 vs. 0.00002, respectively [p = 0.0014]). The g rate, but not the d rate discriminated PSA-PFS, rPFS, and OS. Our results suggest that GnRH agonist plus bicalutamide reduced PSA level faster and suppressed PSA rising longer than GnRH antagonist. Moreover, measuring the g rate can predict PSA-PFS, rPFS, and OS in patients with advanced CPSC treated with androgen deprivation therapy. These findings suggest that incorporating g rate measurements into clinical practice could improve prognostic accuracy and guide treatment decisions in advanced CSPC.

Current and Future Directions of Technology in Assessment of Peyronie's Disease.

This review assesses the effectiveness of various imaging modalities for diagnosing and managing Peyronie's disease (PD), exploring their clinical utility and limitations. Ultrasonography, currently the only modality endorsed by the American Urological Association, effectively detects calcifications and assesses vascular status but struggles with identifying non-palpable plaques. Computed tomography and magnetic resonance imaging provide detailed anatomical views but are costly along with other deterring factors. Autophotography and 3-dimensional imaging offer convenient home assessments but with variable accuracy. Elastography improves upon traditional ultrasonography, while emerging AI technologies show promise for automating precise curvature assessments, pending further validation. A combined approach tailored to each individual patient based on their needs enhances diagnostic accuracy. Supplementing ultrasonography with elastography and potentially AI could improve diagnostic outcomes and better guide treatment decisions. Continued research is vital to integrate these advancements into standard clinical practice and develop updated standardized protocols.

Prediction of mortality, future arrhythmia and cardiovascular disease: an artificial intelligence-enhanced electrocardiography platform

Abstract Background Artificial intelligence-enhanced electrocardiography (AI-ECG) can be used to identify existing disease, but could additionally be used to predict occurrence of future disease and death. Purpose We developed an AI-ECG platform that predicts mortality and a wide spectrum of future arrhythmia and cardiovascular disease. Methods The AI-ECG risk estimation platform (AIRE) was developed in a dataset of 1,163,401 ECGs from 189,539 patients from a secondary care setting in the USA. Uniquely, AIRE uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves (Fig 1A). AIRE was fine-tuned to additionally predict the endpoints described below. Results AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776) and can differentiate prognosis in high- and low-risk subjects, even when only cardiologist-defined normal ECGs are considered (Fig 2B). Additionally, in Cox regression analyses, AIRE was superior to conventional risk factors (Fig 1B). AIRE accurately predicted future ventricular arrhythmia (c-index 0.760 (0.756-0.763)), future complete heart block (CHB) (0.809 (0.805-0.814), future atrial fibrillation (0.753 (0.751-0.756), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.789)) . AIRE was superior to left ventricular ejection fraction (LVEF) in predicting risk of future ventricular arrhythmias in subjects with LVEF <50% (C-index 0.649 (0.638 – 0.660) vs 0.615 (0.603 – 0.626) p < 0.0001 ). For CHB prediction, AIRE was able to further risk stratify subjects with bifascicular block and syncope into low (1.9% 5 year CHB), intermediate (8.8%), and high risk (20.7%) groups, and could guide treatment decisions. We externally validated our findings in four diverse, transnational cohorts from Brazil and the UK, including volunteers, primary care subjects and patients with Chagas disease, and found AIRE accurately predicted all-cause mortality in all four external cohorts (Fig 2). Non-mortality endpoints were successful externally validated in the UKB. In explainability analyses, we found features of QRS morphology, low QRS voltage, poor precordial R wave progression, inverted and biphasic T waves and ST segment changes were the most significant morphological features associated with high predicted mortality (Fig 1D). Through phenome- and genome-wide association studies (GWAS), we identified candidate biological pathways including changes in cardiac structure (LV mass, LV trabeculation and left atrial size). GWAS identified variants adjacent to VGLL2, KCNQ1, CCDC91 and TBX3 which have been described in association with QT interval, biological aging and metabolic syndrome (Fig 1E). Conclusion AIRE could be used worldwide across a range of clinical contexts for risk estimation of not only mortality but a wide spectrum of future arrhythmia and cardiovascular diseases.Figure 1Figure 2

Characteristic disease defects in circulating endothelial cells isolated from patients with pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by elevated pulmonary arterial pressures that can lead to right heart failure and death. No cure exists for this disease, but therapeutic advancements have extended its median survival from 2 to 7 years. Mechanistic research in PAH has been limited by factors including that a) animal models do not fully recapitulate the disease or provide insights into its pathogenesis, and b) cellular material from PAH patients is primarily obtained from donor lungs during autopsy or transplantation, which reflect end-stage disease. Therefore, there is a need to identify tools that can elucidate the specific mechanisms of human disease in individual patients, a critical step to guide treatment decisions based on specific pathway abnormalities. Here we demonstrate a simple method to isolate and culture circulating endothelial cells (CECs) obtained at the time of right heart catheterization in PAH patients. We tested these CECs using transcriptomics and found that they have typical traits of PAH, including those involving key treatment pathways, i.e. nitric oxide, endothelin, prostacyclin and BMP/activin pathways. CECs show important gene expression changes in other central PAH disease pathways. In summary, we present a new cellular model for the ex-vivo mechanistic evaluation of critical PAH pathways that participate in the pathogenesis of the disease and may help personalized therapeutic decisions.

Proenkephalin improves risk prediction in acute coronary syndromes: derivation and external validation of the KID-ACS risk score

Abstract Background Early assessment of renal and mortality risk in patients with acute coronary syndromes (ACS) is essential to guide treatment decisions according to international guidelines. Circulating proenkephalin (PENK) is a stable opioid metabolite with fast response to changes in kidney function. Purpose This study examined the predictive value of PENK for acute kidney injury (AKI) and mortality in patients presenting with acute coronary syndromes (ACS). Methods Plasma PENK levels were measured using a validated sandwich immunoassay in a prospective multicentre ACS cohort from Switzerland (n = 4 787) and in validation cohorts from the UK (n = 1 141) and Czechia (n = 920). A biomarker-enhanced risk score for simultaneous prediction of both in-hospital acute kidney injury (AKI) and 30-day death (KID-ACS score) with model coefficients adjusted to the outcome was derived and externally validated. In the development of the in-hospital AKI model, patients from one participating study centre were reserved for external validation (Swiss validation cohort). Results Circulating PENK ranked among the top predictors of renal outcomes and mortality in patients with ACS. Accounting for established risk factors and risk models, circulating PENK levels at presentation were independently associated with in-hospital AKI (per log2 increase: adjusted odds ratio [OR] 1.56, 95% CI 1.14 – 2.12, P = 0.005) and with 30-day mortality (per log2 increase: adjusted OR 2.69, 95% CI 1.89 – 3.84, P < 0.001). Dynamic changes in PENK levels within the first 12-24 hours after presentation were strongly and independently associated with in-hospital AKI (increase vs. decrease: adjusted OR 2.83, 95% CI 1.98 – 4.05, P < 0.001). The simple 6-item KID-ACS risk score for in-hospital AKI and 30-day mortality integrates PENK levels at presentation and showed an AUC of 0.72 (95% CI 0.68 – 0.76) for in-hospital AKI, and of 0.91 (95% CI 0.87 – 0.95) for 30-day mortality in the derivation cohort. Upon external validation, KID-ACS had similarly high performance for in-hospital AKI (Swiss validation cohort: AUC 0.73, 95% CI 0.70 – 0.77; Czech validation cohort: 0.74, 95% CI 0.68 – 0.80) and 30-day mortality (UK validation cohort: AUC of 0.87, 95% CI 0.82 – 0.91; Czech validation cohort: 0.91, 95% CI 0.87 – 0.94). The simple 6-item KID-ACS score was non-inferior to established risk models and significantly outperformed the CA-AKI risk score in predicting in-hospital AKI (Zurich cohort: delta AUC 0.11, 95% CI 0.07 – 0.15, P = 0.001) and the GRACE 2.0 in predicting 30-day mortality (UK cohort: delta AUC 0.05, 95% CI 0.01 – 0.09, P = 0.027). Conclusions Circulating PENK was identified as independent predictor of in-hospital AKI and 30-day mortality in patients with ACS. The simple 6-item KID-ACS risk score integrates circulating PENK levels and provides a novel tool for simultaneous assessment of renal and mortality risk in patients presenting with ACS.Independent Predictive Value PENKPerformance and validation of KID-ACS

Feasibility analysis of using patient-derived tumour organoids for treatment decision guidance in locally advanced head and neck squamous cell carcinoma

BackgroundCurrent treatment for head and neck squamous cell carcinoma (HNSCC) involves surgery, radiotherapy, and chemotherapy. Despite aggressive multimodal approaches, tumour recurrence occurs in 40–60 % of cases, leading to poor survival outcomes. HNSCC lacks common genetic drivers for tailored therapies, and reliable biomarkers for treatment selection are scarce. We investigated the procedural requirements for incorporating drug- and radiosensitivity screens in patient-derived organoids (PDOs) within a clinical trial framework. Patients and methodsFresh tumour samples (N = 198) from 186 HNSCC patients were included. Success rates of organoid establishment were correlated with clinical and procedural parameters. Timelines for establishment of PDO cultures were determined, and their long-term growth potential assessed by serial passaging. Additionally, we conducted whole exome sequencing on matched tumour-organoid pairs. Three PDO models were employed to establish radiosensitivity assays. ResultsIn total, PDO models displaying histomorphological features and genomic alterations of parental tumours were successfully established for 35 % of patient tumours. Success rates rose to 77 % for samples with a tumour cell content of 30 % or higher. Advanced patient age, prior radiotherapy, and delays in tissue processing were identified as negative predictors for engraftment. The estimated time interval needed for screens was compatible with PDO-guided selection of curative-intent radiotherapy regimens. ConclusionsOur findings suggest that with high-quality samples and efficient tissue processing, PDO screens can be successfully performed in 77 % of HNSCC patients. Given the procedural challenges involved, future clinical trials aiming to the utility of PDOs for guiding treatment decisions should consider implementing centralised PDO screening.

Prognostic Value of Sarcopenia in Elderly Patients with Metastatic Non-Small-Cell Lung Cancer Undergoing Radiotherapy

Background: Sarcopenia, a syndrome characterized by age-related loss of muscle mass and function, lacks universally accepted diagnostic criteria, particularly for its role as a prognostic factor in elderly patients with non-small-cell lung cancer (NSCLC). This study aimed to evaluate the prognostic significance of sarcopenia, assessed by psoas muscle size on baseline CT scans, in patients over 70 years of age with metastatic NSCLC. Methods: We retrospectively analyzed 85 elderly patients undergoing palliative radiation therapy between August 2022 and July 2024. Using morphometric analysis of psoas size, we investigated its correlation with overall survival (OS) and progression-free survival (PFS). Results: Our results showed that decreased psoas size was significantly associated with shorter OS and PFS, with median OS of 10 months and PFS of 4 months in sarcopenic patients compared to longer survival times in non-sarcopenic patients. Median survival of non-sarcopenic vs. sarcopenic patients was 21 ± 7 months (muscle area > median) versus 5 ± 2.3 months (muscle area < median). Multivariate analysis confirmed that psoas size, along with ECOG performance status and treatment of primary NSCLC, was a significant predictor of survival. Discussion: These findings suggest that psoas muscle size is a valuable prognostic marker for elderly NSCLC patients, potentially guiding treatment decisions and patient management. Further research is needed to validate these results and refine prognostic models for this population.

HBV biomarkers and their role in guiding treatment decisions.

Over 300 million individuals worldwide are chronically infected with Hepatitis B Virus and at risk for progressive liver disease. Due to the lack of a therapy that reliably achieves viral elimination and the variability of liver disease progression, treatment decisions are guided by the degree of liver disease and viral biomarkers as the viral life-cycle is well characterised and largely conserved between individuals. In contrast, the immunological landscape is much more heterogenous and diverse and the measurement of its components is less well standardized. Due to the lack of a universal and easily measurable set of biomarkers, clinical practice guidelines remain controversial, aiming for a balance between simplifying treatment decisions by reducing biomarker requirements and using all available biomarkers to avoid overtreatment of patients with low risk for disease progression. While approved therapies such as nucleos(t)ide analogues improve patient outcomes, the inability to achieve complete cure highlights the need for novel therapies. Since no treatment candidate has demonstrated universal efficacy, biomarkers will remain important for treatment stratification. Here, we summarize the current knowledge on virological and immunological biomarkers with a specific focus on how they might be beneficial in guiding treatment decisions in chronic Hepatitis B.

Rare Head and Neck Cancers and Pathological Diagnosis Challenges: A Comprehensive Literature Review

Head and neck cancers (HNCs) arise from anatomically adjacent sites and subsites, with varying etiological factors, diagnostic strategies, prognoses, and treatment approaches. While conventional squamous cell carcinoma (SCC) is the most common histology in the head and neck district, HNCs encompass a variety of rare histopathological entities, categorized into epithelial tumors such as salivary gland cancers, sinonasal tumors, neuroendocrine tumors, malignant odontogenic tumors, and SCC variants versus non-epithelial tumors including soft tissue sarcomas, mucosal melanomas, and hematological malignancies. Rare HNCs (R-HNCs) represent a diagnostic and clinical challenge, requiring histopathological expertise, the availability of peculiar molecular analysis, and the personalization of local and systemic treatments, all guided by a multidisciplinary tumor board. Here, we provide a comprehensive literature review on R-HNCs, emphasizing key histopathological and molecular characteristics that are crucial for guiding treatment decisions. An insight about the latest developments in systemic treatments is also reported.

Guiding treatment decisions in renal cell carcinoma: the role of biomarkers and clinical factors.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for metastatic renal cell carcinoma (mRCC), significantly improving overall survival and achieving durable responses. This review is timely due to the increasing number of ICI-based regimens now considered standard care for RCC. There is an urgent need to identify reliable biomarkers that can predict therapeutic responses and resistance, a key challenge in current research. While tumor-specific factors such as pathological characteristics, genomic mutations, and transcriptional profiles have been extensively studied, no definitive predictive biomarker has yet emerged. Additionally, advanced technologies are being explored to address tumor heterogeneity. Recent research has focused on novel areas such as the microbiome, radiomics, and spatial transcriptomics, which show promise as potential biomarkers. The translation of these emerging biomarker findings into clinical practice is essential to improving personalized treatment strategies for RCC. Until reliable biomarkers are clinically available, clinical factors may play a pivotal role in guiding individualized treatment decisions to optimize patient outcomes.

Bringing Hope to Improve Treatment in Pancreatic Ductal Adenocarcinoma—A New Tool for Molecular Profiling of KRAS Mutations in Tumor and Plasma Samples

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) incidence is rising, and prognosis remains poor due to late diagnosis and limited effective therapies. Currently, patients are treated based on TNM staging, without molecular tumor characterization. This study aimed to validate a technique that combines the amplification refractory mutation system (ARMS) with high-resolution melting analysis (HRMA) for detecting mutations in codon 12 of KRAS in tumor and plasma, and to assess its prognostic value. Methods: Prospective study including patients with newly diagnosed PDAC with tumor and plasma samples collected before treatment. Mutations in codon 12 of KRAS (G12D, G12V, G12C, and G12R) were detected using ARMS–HRMA and compared to Sanger sequencing (SS). Univariate and multivariate analyses were used to evaluate the prognostic significance of these mutations. Results: A total of 88 patients, 93% with ECOG-PS 0–1, 57% with resectable disease. ARMS–HRMA technique showed a higher sensitivity than SS, both in tumor and plasma (77% vs. 51%; 25 vs. 0%, respectively). The most frequent mutation was G12D (n = 32, 36%), followed by G12V (n = 22, 25%). On multivariate analysis, patients with G12D and/or G12C mutations, either in tumor or plasma, had lower PFS (HR 1.792, 95% CI 1.061–3.028, p = 0.029; HR 2.081, 95% CI 1.014–4.272, p = 0.046, respectively) and lower OS (HR 1.757, 95% CI 1.013–3.049, p = 0.045; HR 2.229, 95% CI 1.082–4.594, p = 0.030, respectively). Conclusions: ARMS–HRMA is a rapid and cost-effective method for detecting KRAS mutations in PDAC patients, offering the potential for stratifying prognosis and guiding treatment decisions. The presence of G12D and G12C mutations in both tumor and plasma is associated with a poorer prognosis.

The Neoangiogenic Transcriptomic Signature Impacts Hepatocellular Carcinoma Prognosis and Can Be Triggered by Transarterial Chemoembolization Treatment.

Background/Objectives: We evaluated the relationship between the neoangiogenic transcriptomic signature (nTS) and clinical symptoms, treatment outcomes, and survival in hepatocellular carcinoma (HCC) patients. Methods: This study prospectively followed 328 patients in the derivation and 256 in the validation cohort (with a median follow-up of 31 and 22 months, respectively). The nTS was associated with disease presentation, treatments administered, and overall survival rates. Additionally, this study investigated how multiple treatments influenced changes in nTS status and alterations in microRNA expression. Results: The nTS was identified in 27.4% of patients, linked to aggressive features like multifocality and elevated alpha-fetoprotein (AFP), a pattern consistent with that of the validation cohort. Most patients in both cohorts received treatment for HCC. nTS+ patients had limited access to, and benefited less from, liver transplantation or radiofrequency ablation (RFA) compared to nTS- patients. By the end, 78.9% had died, with nTS- patients showing better median survival and response to treatments than their nTS+ counterparts, who had lower survival across all treatment types. Among those who received transarterial chemoembolization (TACE), 31.2% (21/80 patients after the initial treatment and another four following a second TACE) transitioned from an nTS- to an nTS+ status. This shift was associated with lower survival and alterations in microRNA expressions related to oncogenic pathways. Conclusions: The nTS markedly influences treatment eligibility and survival in patients with HCC. Notably, the nTS can develop after repeated TACE procedures, significantly impacting patient survival and altering oncogenic microRNA expression patterns. These findings highlight the critical role of the nTS in guiding treatment decisions and prognostication in HCC management.

Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor.

Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved. The Guidance for Unbiased predictive Information for healthcare Decision-making and Equity (GUIDE) provides expert guidelines for model developers and health system administrators on the transparent use of race in CPMs and mitigation of algorithmic bias across contexts developed through a 5-round, modified Delphi process from a diverse 14-person technical expert panel (TEP). Deliberations affirmed that race is a social construct and that the goals of prediction are distinct from those of causal inference, and emphasized: the importance of decisional context (e.g., shared decision-making versus healthcare rationing); the conflicting nature of different anti-discrimination principles (e.g., anticlassification versus antisubordination principles); and the importance of identifying and balancing trade-offs in achieving equity-related goals with race-aware versus race-unaware CPMs for conditions where racial identity is prognostically informative. The GUIDE, comprising 31 key items in the development and use of CPMs in healthcare, outlines foundational principles, distinguishes between bias and fairness, and offers guidance for examining subgroup invalidity and using race as a variable in CPMs. This GUIDE presents a living document that supports appraisal and reporting of bias in CPMs to support best practice in CPM development and use.

Risk of Recurrent Venous Thromboembolism in Patients with Cancer: An Individual Patient Data Meta-analysis and Development of a Prediction Model.

About 7% of patients with cancer-associated venous thromboembolism (CAT) develop a recurrence during anticoagulant treatment. Identification of high-risk patients may help guide treatment decisions. To identify clinical predictors and develop a prediction model for on-treatment recurrent CAT. For this individual patient data meta-analysis, we used data from four randomized controlled trials evaluating low-molecular-weight heparin or direct oral anticoagulants (DOACs) for CAT (Hokusai VTE Cancer, SELECT-D, CLOT, and CATCH). The primary outcome was adjudicated on-treatment recurrent CAT during a 6-month follow-up. A clinical prediction model was developed using multivariable logistic regression analysis with backward selection. This model was validated using internal-external cross-validation. Performance was assessed by the c-statistic and a calibration plot. After excluding patients using vitamin K antagonists, the combined dataset comprised 2,245 patients with cancer and acute CAT who were treated with edoxaban (23%), rivaroxaban (9%), dalteparin (47%), or tinzaparin (20%). Recurrent on-treatment CAT during the 6-month follow-up occurred in 150 (6.7%) patients. Predictors included in the final model were age (restricted cubic spline), breast cancer (odds ratio [OR]: 0.42; 95% confidence interval [CI]: 0.20-0.87), metastatic disease (OR: 1.44; 95% CI: 1.01-2.05), treatment with DOAC (OR: 0.66; 95% CI: 0.44-0.98), and deep vein thrombosis only as an index event (OR: 1.72; 95% CI: 1.31-2.27). The c-statistic of the model was 0.63 (95% CI: 0.54-0.72) after internal-external cross-validation. Calibration varied across studies. The prediction model for recurrent CAT included five clinical predictors and has only modest discrimination. Prediction of recurrent CAT at the initiation of anticoagulation remains challenging.

Prediction model for early diagnosis of late-onset sepsis in preterm newborns.

Late-onset neonatal sepsis has a high mortality rate in premature infants. To date, no single test in the evaluation of neonatal sepsis has been demonstrated to be both sensitive and specific enough to assist in timely decision making. The aim of our study is to develop a predictive model that can be applied to all premature babies, using clinical and laboratory findings in premature babies, to recognize late-onset neonatal sepsis. 65 premature patients diagnosed with culture-proven late-onset neonatal sepsis and hospitalized in Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital neonatal intensive care unit between January 2018 and December 2020, and 65 premature newborns of similar age and gender who did not have sepsis were included in the study retrospectively. In our study, feeding difficulties, worsening in clinical appearance and fever were found to be significant among clinical findings, while thrombocytopenia and high C-reactive protein among laboratory findings are the strongest data supporting late-onset neonatal sepsis. In multiple regression analysis, thrombocytopenia, mean platelet volume, C-reactive protein, lymphocyte count and feeding difficulties had the highest odds ratio (p < 0.05). By converting these data into a scoring system, a nomogram was created that can be easily used by all clinicians. In our study, we developed a scoring system that can be easily applied to all premature patients by evaluating the clinical and laboratory findings in late-onset neonatal sepsis. We think that it will help in recognizing late-onset neonatal sepsis and strengthening the treatment decision. Predicting the individual probability of sepsis in preterm newborns may provide benefits for uninfected newborns to be exposed to less antibiotics, not to be separated from mother and baby, and to reduce healthcare system expenditures. The nomogram can be used to assess the likelihood of sepsis and guide treatment decision.

Liver metastases in high-grade neuroendocrine neoplasms: A comparative study of hepatic tumor volume and biochemical findings in NET G3 versus NEC.

Abnormal liver blood tests and liver tumor burden are known prognostic factors in neuroendocrine neoplasms (NEN). However, the relationship between biochemical liver parameters and hepatic tumor load is largely unknown in NEN and in high-grade NEN (G3) specifically. The primary objective of this study was to correlate the biochemical parameters and liver tumor volume of patients with neuroendocrine tumors grade 3 (NET G3) or neuroendocrine carcinomas (NEC). We wanted to investigate whether patients with NET G3 with extensive liver involvement had less severely elevated laboratory liver parameters than NEC patients. In total, 46 patients with NEN were included, 31 had NEC and 15 NET G3. All patients had distant metastatic disease, with liver metastases being the most common (n = 39). Both laboratory results and semiautomatic volumetric measurements of liver tumor burden were obtainable for 34 patients at baseline and 26 patients at follow-up. Alkaline phosphatase (AP), gamma-GT (gGT), and lactate dehydrogenase (LDH) increased significantly between the two time periods (p < .01). In a regression model, liver tumor burden significantly affected several blood parameters, for example, increasing AP, gGT, LDH, and aspartate aminotransferase (ASAT) by a factor of 1.02-1.04 per unit increase (1% tumor burden; all p < .001). AP, gGT, and LDH were significantly lower in NET G3 (factor of 0.43-0.68) than in NEC. Here, we found that liver chemistries changed over the NEN disease course, correlated with hepatic tumor burden, and differed by histologic subtype. The current data can potentially guide treatment decisions, for example, with regard to integration of liver-directed therapies.

Anterior circulation location-specific results for stent-assisted coiling – carotid versus distal aneurysms: 1-year outcomes from the Neuroform Atlas Stent Pivotal Trial

BackgroundThe Neuroform Atlas Stent System is an established treatment modality for unruptured anterior and posterior circulation intracranial aneurysms. Location-specific results are needed to guide treatment decision-making. However, it is unclear...