Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel multilevel logistic cluster-weighted modeling approach.

Evaluating hospital performance and its relationship to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. This is particularly relevant in areas and situations where the healthcare system must deal with an unexpected surge in hospitalizations, such as heart failure patients in the Lombardy Region of Italy during the COVID-19 pandemic. Motivated by this issue, the paper introduces a novel multilevel logistic cluster-weighted model for predicting 45-day mortality following hospitalization due to COVID-19.

Wavelet-Mixed Landmark Survival Models for the Effect of Short-Term Changes of Potassium in Heart Failure Patients.

Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term care of subjects affected by chronic illnesses, such as potassium in heart failure patients. Particularly in the presence of comorbidities or pharmacological treatments, sudden crises can cause potassium to undergo very abrupt yet transient changes. In the context of the monitoring of potassium, there is a need for a dynamic model that can be used in clinical practice to assess the risk of death related to an observed patient's potassium trajectory.

Real-world effectiveness of influenza vaccination in preventing influenza and influenza-like illness in children.

Background And Objectives: Quadrivalent live attenuated influenza vaccines (LAIV-4) offer an alternative to inactivated influenza vaccines (IIV) for children aged 2-17 years, but data on their comparative effectiveness are limited. This study assessed vaccination rates and real-world effectiveness of LAIV-4 and IIV in preventing influenza and influenza-like illness (ILI) in Italian children during the 2022-2023 and 2023-2024 seasons.

Methods: We conducted a population-based cohort study of children aged 2-14 years from September 2022 to April 2024, using data from Pedianet, a pediatric primary care database of anonymized records from family pediatricians.

CT-radiomics and pathological tumor response to systemic therapy: A predictive analysis for colorectal liver metastases. Development and internal validation of a clinical-radiomic model.

Introduction: The standard treatment of colorectal liver metastases (CRLM) is surgery with perioperative chemotherapy. A tumor response to systemic therapy confirmed at pathology examination is the strongest predictor of survival, but it cannot be adequately predicted in the preoperative setting. This bi-institutional retrospective study investigates whether CT-based radiomics of CRLM and peritumoral tissue provides a reliable non-invasive estimation of the pathological tumor response to chemotherapy.

Cardiovascular safety of assisted reproductive technology: a meta-analysis.

Background And Aims: The increasing use of assisted reproductive technology (ART) has raised concerns regarding its long-term cardiovascular safety due to potential hormonal imbalances and pro-thrombotic states. This study aimed to assess the long-term cardiovascular risk associated with fertility treatments in women.

Methods: Following PRISMA guidelines, a systematic review and meta-analysis was conducted in MEDLINE (via PubMed) from inception to January 2024.

Causal effect of chemotherapy received dose intensity on survival outcome: a retrospective study in osteosarcoma.

Background: This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. Previous research has highlighted discrepancies between planned and actual RDI, even among patients randomized to the same treatment regimen. To mitigate toxic side effects, treatment adjustments, such as dose reduction or delayed courses, are necessary.

Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment.

Purpose: Radiomics has revolutionized clinical research by enabling objective measurements of imaging-derived biomarkers. However, the true potential of radiomics necessitates a comprehensive understanding of the biological basis of extracted features to serve as a clinical decision support. In this work, we propose an end-to-end framework for the in silico simulation of [F]FLT PET imaging process in Pancreatic Ductal Adenocarcinoma, accounting for the biological characterization of tissues (including perfusion and fibrosis) on tracer delivery.

CT-based radiogenomics of intrahepatic cholangiocarcinoma.

Background: Intrahepatic cholangiocarcinoma (ICC) is an aggressive disease with increasing incidence and its genetic alterations could be the target of systemic therapies.

Aims: To elucidate if radiomics extracted from computed tomography (CT) may non-invasively predict ICC genetic alterations.

Methods: All consecutive patients with a diagnosis of a mass-forming ICC (01/2016-06/2022) were considered.

Radiomics of Intrahepatic Cholangiocarcinoma and Peritumoral Tissue Predicts Postoperative Survival: Development of a CT-Based Clinical-Radiomic Model.

Background: For many tumors, radiomics provided a relevant prognostic contribution. This study tested whether the computed tomography (CT)-based textural features of intrahepatic cholangiocarcinoma (ICC) and peritumoral tissue improve the prediction of survival after resection compared with the standard clinical indices.

Methods: All consecutive patients affected by ICC who underwent hepatectomy at six high-volume centers (2009-2019) were considered for the study.

Personalized Timing for Allogeneic Stem-Cell Transplantation in Hematologic Neoplasms: A Target Trial Emulation Approach Using Multistate Modeling and Microsimulation.

Purpose: Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner.

A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals.

Background: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases.

Methods: A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance.

Flexible Approaches Based on Multistate Models and Microsimulation to Perform Real-World Cost-Effectiveness Analyses: An Application to Proprotein Convertase Subtilisin-Kexin Type 9 Inhibitors.

Objectives: This study aims to show the application of flexible statistical methods in real-world cost-effectiveness analyses applied in the cardiovascular field, focusing specifically on the use of proprotein convertase subtilisin-kexin type 9 inhibitors for hyperlipidemia.

Methods: The proposed method allowed us to use an electronic health database to emulate a target trial for cost-effectiveness analysis using multistate modeling and microsimulation. We formally established the study design and provided precise definitions of the causal measures of interest while also outlining the assumptions necessary for accurately estimating these measures using the available data.

Dual adversarial deconfounding autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data.

Medical imaging represents the primary tool for investigating and monitoring several diseases, including cancer. The advances in quantitative image analysis have developed towards the extraction of biomarkers able to support clinical decisions. To produce robust results, multi-center studies are often set up.

Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model.

Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion.

Capturing the variety of clinical pathways in patients with schizophrenic disorders through state sequences analysis.

Background: Care pathways are increasingly being used to enhance the quality of care and optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting, classical statistical tools were insufficient to consider a phenomenon with such high variability adequately and have to be integrated with novel data mining techniques suitable for identifying patterns in complex data structures.

Patients' Radiation Exposure During Endovascular Abdominal Aortic Aneurysm Repair.

Background: To investigate associations between patient characteristics, intraprocedural complexity factors, and radiation exposure to patients during endovascular abdominal aortic aneurysm repair (EVAR).

Methods: Elective standard EVAR procedures between January 2015 and December 2020 were retrospectively analyzed. Patient characteristics and intraprocedural data (i.

Editor's Choice - Comparison of the Reproducibility of Ultrasound Calliper Placement Methods in Abdominal Aortic Diameter Measurements: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

Objective: To assess which ultrasound (US) method of maximum anteroposterior (AP) abdominal aortic diameter measurement can be considered most reproducible.

Data Sources: MEDLINE, Scopus, and Web of Science were searched (PROSPERO ID: 276694). Eligible studies reported intra- and or interobserver agreement according to Bland-Altman analysis (mean ± standard deviation [SD]) for abdominal aortic diameter AP US evaluations with an outer to outer (OTO), inner to inner (ITI), and or leading edge to leading edge (LELE) calliper placement.

Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction.

Image texture analysis has for decades represented a promising opportunity for cancer assessment and disease progression evaluation, evolving in a discipline, i.e., radiomics.

Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible.

Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data.

Radiomics-Based Inter-Lesion Relation Network to Describe [F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer.

Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each "unit" (lesion) as a part of the "entire" (patient).

Learning high-order interactions for polygenic risk prediction.

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g.

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy.