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A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for estimating pairwise conditional independence relationships among mixed-type, i.e. continuous and discrete, variables. The number of edges is a tuning parameter in any graphical model estimator and there is no obvious number that constitutes a good choice. Stability Selection helps choosing this parameter with respect to a bound on the expected number of false positives (error control). The performance of GRaFo is evaluated and compared with various other methods for p = 50, 100, and 200 possibly mixed-type variables while sample size is n = 100 (n = 500 for maximum likelihood). Furthermore, GRaFo is applied to data from the Swiss Health Survey in order to evaluate how well it can reproduce the interconnection of functional health components, personal, and environmental factors, as hypothesized by the World Health Organization's International Classification of Functioning, Disability and Health (ICF). Finally, GRaFo is used to identify risk factors which may be associated with adverse neurodevelopment of children who suffer from trisomy 21 and experienced open-heart surgery. GRaFo performs well with mixed data and thanks to Stability Selection it provides an error control mechanism for false positive selection., Comment: The authors report no conflict of interest. There was no external funding

OBJECTIVE: In view of ethical considerations and the limited resources in intensive care medicine, the present investigation aims to give a descriptive overview of the prognosis and therapeutic activity for the oldest age group of elderly patients admitted to an intensive care unit (ICU) in comparison with younger ICU patients. PATIENTS AND METHODS: 3069 patients admitted to the ICU during a seven-year period were categorized into four age groups: under 65 years (48%), 65 to 74 years (26%), 75 to 85 years (22%) and 85 years or older (5%). Type and reason for ICU admission, length of ICU stay, severity of illness as measured by the simplified acute physiology score (SAPS)-II, level of provided care as measured by the simplified therapeutic intervention scoring system (TISS)-28, and vital status at the date of ICU discharge were recorded. RESULTS: The ICU mortality rate of patients aged 85 years or older was significantly higher than in patients under 65 (OR of mortality: 1.8, p < 0.001). Non-survivors had higher SAPS II levels (even when excluding age points) in all age groups, but higher daily average TISS points only in patients under 85. The daily average TISS score was negatively correlated to age (r = −0.03; p < 0.001) and was significantly lower in the oldest group when compared with all the younger groups (p < 0.001). The oldest patients had a significantly shorter length of stay (median: 2; interquartile range [IQR] 1–3, p < 0.001) than the younger patient groups. CONCLUSIONS: Within the very elderly population, age is an important and independent predictor of mortality, but acute severity of illness is even more strongly associated with mortality. Consequently, age alone may be an inappropriate criterion for allocation of ICU resources.

Purpose: Kruppel-like factor (KLF5) is a cell growth mediator in various epithelial cells. Higher KLF5 increases cell growth rate and leads to transformed phenotypes. Because tumor cell proliferation is tightly associated with tumor progression, and consequently, with survival of cancer patients, we wanted to examine the prognostic value of KLF5 gene expression for patients with breast cancer. Experimental Design: The gene expression levels of KLF5, ER, PR, HER2, and MKI67 were quantified in the tumor tissues of 90 patients with breast cancer and correlated with disease-free survival and overall survival of the patients. The correlations of gene expression between KLF5 and ER, PR, HER2, and MKI67 were analyzed. In addition, KLF5 expression was also compared with clinical data and age of patients. Results: Statistically significant correlations were found between gene expression of KLF5 and both disease-free survival (univariate analysis) and overall survival (univariate and multivariate analysis). Patients with higher KLF5 expression had shorter disease-free survival and overall survival time, whereas patients with lower KLF5 expression had better survival. Moreover, KLF5 was also found to be positively correlated with HER2 and MKI67, and negatively correlated with age of the patients at diagnosis. Conclusion: The gene expression of KLF5 is directly correlated with cell proliferation in vivo and is a prognostic factor for patients with breast cancer. Patients with higher KLF5 expression have shorter disease-free survival and overall survival than patients with lower KLF5 expression. In addition, KLF5 has higher expression in patients ages ≤50 years old than in patients >50 years old.