Recent investigations have suggested an interdependence of lipoprotein(a) [Lp(a)]-related risk for cardiovascular disease with background inflammatory burden. The aim the present analysis was to investigate whether high-sensitive C-reactive protein (hsCRP) modulates the association between Lp(a) and coronary heart disease (CHD) in the general population. Data from 71 678 participants from 8 European prospective population-based cohort studies were used (65 661 without/6017 with established CHD at baseline; median follow-up 9.8/13.8 years, respectively). Fine and Gray competing risk-adjusted models were calculated according to accompanying hsCRP concentration (<2 and ≥2 mg/L). Among CHD-free individuals, increased Lp(a) levels were associated with incident CHD irrespective of hsCRP concentration: fully adjusted sub-distribution hazard ratios [sHRs (95% confidence interval)] for the highest vs. lowest fifth of Lp(a) distribution were 1.45 (1.23–1.72) and 1.48 (1.23–1.78) for a hsCRP group of <2 and ≥2 mg/L, respectively, with no interaction found between these two biomarkers on CHD risk (P_interaction = 0.82). In those with established CHD, similar associations were seen only among individuals with hsCRP ≥ 2 mg/L [1.34 (1.03–1.76)], whereas among participants with a hsCRP concentration <2 mg/L, there was no clear association between Lp(a) and future CHD events [1.29 (0.98–1.71)] (highest vs. lowest fifth, fully adjusted models; P_interaction = 0.024). While among CHD-free individuals Lp(a) was significantly associated with incident CHD regardless of hsCRP, in participants with CHD at baseline, Lp(a) was related to recurrent CHD events only in those with residual inflammatory risk. These findings might guide adequate selection of high-risk patients for forthcoming Lp(a)-targeting compounds. C-reactive protein as a possible modifier of lipoprotein(a)-related risk for coronary heart disease in Europe. CHD, coronary heart disease; CI, confidence interval; Lp(a), lipoprotein(a); FU, follow-up; F, fifth; hsCRP, high-sensitive C-reactive protein; sHR, sub-distribution hazard ratio.

OBJECTIVE: The extent to which psychosocial stress concurs to raise blood pressure is still uncertain. Here the association between job strain and office blood pressure in a pooled analysis of four population samples from northern Italy is assessed.

Introduction: Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain. Methods: A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients. Results: Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered. Conclusions: In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance.

AIMS: To appraise 28-day case-fatality of myocardial infarction (MI) among socio-occupational classes; to assess the consistency of the results for different degrees of MI severity; to explore if such differences are related to differences in management and treatment of the acute attack. METHODS: All coronary events (n = 1077), registered in 1993-1994 among the 35-64 year old male residents in MONICA Area Brianza were investigated and classified into diagnostic categories according to MONICA criteria. For all cases standardized information on acute coronary care and interventions before and during hospitalization was collected. Classification of social classes was derived from the Erikson-Goldthorpe-Portocarero social class scheme, based on information on occupational characteristics widely available in European countries. RESULTS: An increasing rate of case-fatality for decreasing levels of socio-occupational class was found. The social gradient resulted mainly from the higher incidence of out-of-hospital cardiac arrests. Treatment before and during the acute attack cannot explain the observed differences. CONCLUSION: Combining these results with those obtained in other MONICA centres, it appears that further decreases in coronary mortality might be achieved through specific programmes addressed in particular to the reduction of out-of-hospital case-fatality in lower social classes.