Awareness represents a major modulator for the uptake of preventive measures and healthy life-style choices. Women underestimate the role of cardiovascular diseases as causes of mortality, yet little information is available about their subjective risk awareness. The Berlin Female Risk Evaluation (BEFRI) study included a randomized urban female sample aged 25–74 years, in which 1,066 women completed standardized questionnaires and attended an extensive clinical examination. Subjective estimation was measured by a 3-point Likert scale question asking about subjective perception of absolute cardiovascular risk with a 10 year outlook to be matched to the cardiovascular risk estimate according to the Framingham score for women. An expected linear increase with age was observed for hypertension, hyperlipidemia, obesity, and vascular compliance measured by pulse pressure. Knowledge about optimal values of selected cardiovascular risk factor indicators increased with age, but not the perception of the importance of age itself. Only 41.35% of all the participants correctly classified their own cardiovascular risk, while 48.65% underestimated it, and age resulted as the most significant predictor for this subjective underestimation (OR = 3.5 for age >50 years compared to <50, 95% CI = 2.6–4.8, P <0.0001). Therefore, although socioeconomic factors such as joblessness (OR = 1.9, 95% CI = 1.4–2.6, P <0.0001) and combinations of other social risk factors (low income, limited education, simple job, living alone, having children, statutory health coverage only; OR = 1.5, 95% CI = 1.1–2.1, P = 0.009) also significantly influenced self-awareness, age appeared as the strongest predictor of risk underestimation and at the same time the least perceived cardiovascular risk factor. Less than half of the women in our study population correctly estimated their cardiovascular risk. The study identifies age as the strongest predictor of risk underestimation in urban women and at the same time as the least subjectively perceived cardiovascular risk factor. Although age itself cannot be modified, our data highlights the need for more explicit risk counseling and information campaigns about the cardiovascular relevance of aging while focusing on measures to control coexisting modifiable risk factors.

Research from JAMA Internal Medicine — Predictive Relationship Between Depression and Physical Functioning After Coronary Surgery

Objective: To examine whether the predictive value of gender for health-related quality of life (HRQoL) is independent of clinical health status and depression. Women undergoing coronary bypass surgery generally report a poorer HRQoL than men. Methods: A total of 990 (20% women) patients completed study questionnaires 1 day before coronary bypass surgery and 1 year after surgery. Physical aspects of HRQoL were assessed with the Short Form 36 Health Survey. Depression was measured with the self-reported Patient Health Questionnaire. Propensity score matching was applied to match men and women with respect to 65 clinical variables. Of 198 women, 157 (79.3%) could be matched to a partner, resulting in an excellent balance of clinical variables between the matched groups. Results: At baseline, propensity-matched men and women differed in physical functioning (p < .001) and role functioning (p = .007), but not in bodily pain and general health perception. In both men and women, HRQoL outcomes improved over 1 year. Preoperative depression predicted worse physical HRQoL in all outcomes, except general health perception 1 year after surgery. After adjusting for depression, gender lost its predictive power with respect to physical functioning. However, compared with women, men still reported a better role functioning. Conclusion: Our data suggest that gender is a marker for role functioning, independent of the clinical health status and depression. Rehabilitation measures designed for the specific needs of women might help to improve their HRQoL.

OBJECTIVE To determine the value of particular clinical variables for the preoperative prognostic Cindolo formula (PPCF) to predict disease‐free survival (DFS) of patients with surgically treated renal cell carcinoma (RCC). PATIENTS AND METHODS In all, 771 consecutive patients (T1‐4NxM0) who had radical or partial nephrectomy were reviewed retrospectively. For each patient with RCC, PPCF was constructed according to clinical size and clinical presentation. On the basis of PPCF, patients were divided into Cindolo good prognosis (CGP) and Cindolo poor prognosis (CPP) groups. We also analysed further clinical variables (Eastern Cooperative Oncology Group score, American Society of Anesthesiologists score, body mass index, hepatic dysfunction, night sweat, fever, value of blood platelets, leukocytes, haemoglobin level, gender, age and location). DFS was estimated using the Kaplan‐Meier method. Univariable and multivariable Cox proportional hazard regression models were fitted to determine associations between the PPCF, measured clinical features, and DFS. RESULTS Four of the variables emerged as statistically significant for DFS from the univariable analysis ( P < 0.001), i.e. clinical presentation, clinical tumour size, haemoglobin level and blood platelet count. In the multivariable analysis, only clinical tumour size and blood platelet count remained significant for DFS. By contrast, clinical presentation, used in the PPCF, had no significant influence. According to the PPCF we developed the preoperative Amissah Prognosis Score (PAPS) calculated as (0.19 × clinical size) + (0.492 × platelet count (≤400/nL = 0, >400/nL = 1) with a threshold between the two resulting prognosis groups at 1.76. The multivariable hazard ratio (95% confidence interval, CI) for the PAPS was 2.98 (2.15–4.12) ( P < 0.001) compared to a hazard ratio for the PPCF of 1.36 (0.99–1.87) ( P = 0.061). Furthermore, the predictive ability was greater when using the PAPS (area under the curve 0.721; 95% CI, 0.680–0.763; P ≤ 0.001) than the PPCF (0.690; 0.647–0.734; P ≤ 0.001). CONCLUSIONS Using preoperative prognostic models is reasonable to provide patients with pertinent information about their prognosis, and for tailoring the treatment to each patient’s needs. Applying the PPCF allows a prediction of the outcome of patients with surgically treated RCC on the basis of preoperatively available variables. However, clinical presentation, included in this model, had no significant influence on DFS in the present patients. By contrast, using the PAPS resulted in an improvement in the predictive value and in a greater discrimination between patients subdivided into a good and a poor prognosis group and hence is suitable for preoperative risk assignment.