Data Integration in Cardiac Surgery Health Care Institution: Experience at G. Pasquinucci Heart Hospital

During the last ten years the Hospital Information System (HIS) was developed at the Institute of Clinical Physiology of National Research Council (IFC-CNR), recently reorganized on clinical side into the "Gabriele Monasterio Foundation" (FGM) by joint efforts of CNR, Tuscany Region and Universities. At G.Pasquinucci Heart Hospital (GPH), currently FGM\u27s section in Massa, the HIS was adapted and extended to Cardiac Surgery and Pediatric Cardiology. Data archiving and middleware integration through HIS network, connecting GPH with head institution in Pisa, allowed to achieve full secure access to patient information from any workstation within hospital or outside. PACS was developed using Open Source DICOM utilities. Electronic Medical Record is daily used since 2005 on both inpatients and outpatients. Recently telediagnosis was set up between Balkan countries and GPH in Massa

ADIPONECTIN AND CARDIOVASCULAR RISK PREDICTION: STRATIFICATION OF CHEST PAIN PATIENTS BY A CLUSTER ANALYSIS

Cardiovascular disease (CVD) remains the major cause of death and there is the need to a better stratification of CVD patients. By an unbiased statistical approach we sought to identify clusters of patients to better stratify their risk. 202 patients with chest pain (63% males, age 62?12 yr) undergone to CT coronary angiography (CCTA) were prospectively included and classified using K-means cluster analysis of clinical, imaging and bio-humoral data. The most relevant classification resulted in three phenotypes distinguished according to Framingham score and HMW adiponectin plasma levels. Presence and severity of disease as assessed by CCTA were verified trough these phenotypes. By K-means cluster analysis, we identified CVD phenotypes allowing to stratify patients requiring different diagnostic and therapeutic approach

Morpho-functional imaging of coronary anatomy and left ventricular perfusion obtained by cardiac CT

Volumetric computed tomography (CT) angiography has become a standard non-invasive routine procedure for cardiac imaging and coronary arteries pathology detection. However, before the diagnosis process, a pre-processing task is critical for an accurate examination of the vessels. Specially, the user has to manually remove obscuring structures in order to get an accurate visualization of coronary arteries. Indeed, the coronaries are always hidden by surrounding organs of the heart such as liver, sternum, ribs and lungs which prevent the pathologist from getting a clear view of the heart surface. In this paper, we propose a fast algorithm to automatically isolate the heart anatomy in 3D CT cardiac data sets. Our work eliminates the tedious and time consuming step of the manual delineation and pro- vides a clear and well defined view of the coronary arteries. Consequently, the user can quickly identify suspicious segments on the isolated heart. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization [1]. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object

Monitoring blood biomarkers to predict nivolumab effectiveness in NSCLC patients

Background: We investigated whether early dynamic changes of circulating free (cfDNA) levels as well as the neutrophil to lymphocyte ratio (NLR) could predict nivolumab effectiveness in pretreated patients with advanced non-small cell lung cancer (NSCLC). Methods: A total of 45 patients receiving nivolumab 3 mg/kg every 2 weeks were enrolled. Patients underwent a computed tomography scan and responses were evaluated by the response evaluation criteria in solid tumors. Peripheral blood samples were obtained from the patients and the cfDNA level as well as the NLR were assessed. Time to progression (TTP) and overall survival (OS) were determined. Results: Patients with increased cfDNA >20% at the sixth week reported significantly worse survival outcomes (median OS: 5.7 versus 14.2 months, p 20% (median OS: 8.7 versus 14.6 months, p = 0.035; median TTP: 5.2 versus 10.3 months, p = 0.039). The combined increase of cfDNA and NLR >20% was associated with significantly worse survival outcomes as compared with the remained population (median OS: 5.8 versus 15.5 months, p = 0.012; median TTP: 3.2 versus 11.9 months, p = 0.028). Multivariable analysis identified three significant factors associated with worse OS: combined cfDNA/NLR increase >20% [hazard ratio (HR): 5.16; 95% confidence interval (CI), 1.09–24.29; p = 0.038], liver metastasis (HR: 0.44; 95% CI, 0.20–0.96; p = 0.038), and extra-thoracic disease (HR: 0.33; 95% CI, 0.12–0.89; p = 0.029). Conclusion: An early combined increase of both cfDNA and NLR over the course of the first 6 weeks of nivolumab therapy predicted worse survival in pretreated patients with advanced NSCLC, suggesting a potential role in the real-time monitoring of immunotherapy resistance

Epicardial fat volume assessment in cardiac CT

Epicardial fat, as other visceral fat localizations, is correlated with car- diovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns remain about the method for measuring epi- cardial fat, its regional distribution on the myocardium, as well as the accuracy and reproducibility of such measurements. At present, dedi- cated software procedures to assess epicardial fat are lacking. On the other hand, manual fat segmentation requires a huge and tedious operator intervention, which is expected to cause inaccuracy and large observer- dependent variability. The aim of this study was twofold: (1) the devel- opment of a procedure devoted to assess the volume of epicardial fat, (2) the evaluation of the related intra and inter-observer variability in CT scans, both with and without contrast medium injection