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Objective: To evaluate the serum levels of tumour-associated antigens (TAAs) in patients with SSc and interstitial lung disease (ILD) and to define whether their levels mirror the severity and the progression of lung damage. Methods: Data from 80 SSc patients with ILD were collected at baseline and after 2 years as well as from 40 SSc controls without ILD. The occurrence of any malignancy was recorded. Results: At baseline, an increase of at least one TAA was present in 35 SSc patients with ILD compared with 6 SSc patients without ILD (P < 0.0001); this was associated with lower forced vital capacity (FVC) and higher interstitial and alveolar scores. Levels of carbohydrate antigen 15-3 and carcinoembryonic antigen inversely correlated with FVC and directly correlated with alveolar and interstitial scores and their levels were higher in patients who presented a progression of lung damage after 2 years. During 4 years of follow-up, a malignancy was detected in seven patients who already had an increase of at least one TAA. Values of TAAs increased over time in patients who developed cancer, while their trend remained stable in the others. At multivariate analysis, to have three or more TAAs emerged as a strong independent predictor of the development of malignancies [relative risk 24.1 (95% CI 1.8, 315.0), P = 0.02]. Conclusion: TAAs can be elevated in the sera of SSc patients and correlate with the degree of lung damage, suggesting a role as severity biomarkers. Close follow-up is necessary in SSc patients because of the increased cancer risk overall in patients with increased TAAs.

Background: Arrhythmias are frequent in Systemic Sclerosis (SSc) and portend a bad prognosis, accounting alone for 6% of total deaths. Many of these patients die suddenly, thus prevention and intensified risk-stratification represent unmet medical needs. The major goal of this study was the definition of ECG indexes of poor prognosis. Methods: We performed a prospective cohort study to define the role of 24h-ECG-Holter as an additional risk-stratification technique in the identification of SSc-patients at high risk of life-threatening arrhythmias and sudden cardiac death (SCD). One-hundred SSc-patients with symptoms and/or signs suggestive of cardiac involvement underwent 24h-ECG-Holter. The primary end-point was a composite of SCD or need for implantable cardioverter defibrillator (ICD). Results: Fifty-six patients (56%) had 24h-ECG-Holter abnormalities and 24(24%) presented frequent ventricular ectopic beats (VEBs). The number of VEBs correlated with high-sensitive cardiac troponin T (hs-cTnT) levels and inversely correlated with left-ventricular ejection fraction (LV-EF) on echocardiography. During a mean follow-up of 23.1±16.0 months, 5 patients died suddenly and two required ICD-implantation. The 7 patients who met the composite end-point had a higher number of VEBs, higher levels of hs-cTnT and NT-proBNP and lower LV-EF (p = 0.001 for all correlations). All these 7 patients had frequent VEBs, while LV-EF was not reduced in all and its range was wide. At ROC curve, VEBs>1190/24h showed 100% of sensitivity and 83% of specificity to predict the primary end-point (AUROC = 0.92,p1190/24h had lower LV-EF and higher hs-cTnT levels and, at multivariate analysis, the presence of increased hs-cTnT and of right bundle branch block on ECG emerged as independent predictors of VEBs>1190/24h. None of demographic or disease-related characteristics emerged as predictors of poor outcome. Conclusions: VEBS>1190/24h identify patients at high risk of life-threatening arrhythmic complications. Thus, 24h-ECG-Holter should be considered a useful additional risk-stratification test to select SSc-patients at high-risk of SCD, in whom an ICD-implantation could represent a potential life-saving intervention.

Purpose: Pulmonary embolism (PE), a life-threatening emergency is underdiagnosed because of a non-specific presentation.First-level exams (clinical exhamination, electrocardiography, blood gas analysis and laboratory tests)have low sensitivity and specificity.Clinical prediction rulers (CPRs)such as Wells and Geneva Revised, have been derived from different combinations of these exams. Our aim was to perform a comparison between the two score systems in our population and to derive a new CPR using an Artificial Neural Network (ANN). Methods: We enrolled 755 consecutive outpatients with suspect of PE (351 males, mean age 71±14years) and analyzed 24 clinical,instrumental and laboratoristic variables and Wells and Geneva scores.Logistic regression with ROC curves was used to evaluate the the diagnostic reliability of the scores. To derive the new classifier,the dataset was first split (in supervised classification step)into a train and a test subset containing 2/3 ad 1/3 of the patients' dataset,respectively.To find the optimal configuration of the new classifier we tested two different ANNs:a non-linear feed-forward ANN with back-propagation and a Levenberg-Marquardt network.For both we fixed the topological configurations of the network (one hidden layer,one output neuron)and stressed the system to find the optimal number of neurons in the hidden layer for the best configuration among highest AUC with the highest number of hit in the validation process and the minimum epochs.We repeated this study changing the dimension of the input dataset in two ways:excluding interactively some features or performing the reduction of the dimensionality of the feature space with principal component analysis. The application of the trained ANN to a "map set" gave,for each patient,the probability of belonging to the "pathological" or "healthy" class, obtaining the new CPR.Automatic classifications were compared with the manual ones, calculating the Jaccard coefficient, giving a measure of the quality. The system was implemented in Matlab using Neural Network toolboxes and PRTools. Results: In our population,Wells performed better than Revised Geneva (AUC 0.75%,0.63%,respectively),while our CPR (feed-forward ANN with back-propagation) obtained an average AUC of 0.86% from the train set and Jaccard coefficient 0.86 from the map set.The optimal ANN configuration was with 3 neurons in the hidden layer.The difference among the three ROC curves resulted statistically significant. Conclusions: An ANN-based CPR performs better in the clinical prediction of PE than classical rulers without increasing the number of items required for the analysis.