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Limitations of Cox Proportional Hazards Analysis in Mortality Prediction of Patients with Acute Coronary Syndrome


The aim of this study was to evaluate the possibility of incorrect assessment of mortality risk factors in a group of patients affected by acute coronary syndrome, due to the lack of hazard proportionality in the Cox regression model. One hundred and fifty consecutive patients with acute coronary syndrome (ACS) and no age limit were enrolled. Univariable and multivariable Cox proportional hazard analyses were performed. The proportional hazard assumptions were verified using Schoenfeld residuals, χ2 test and rank correlation coefficient t between residuals and time. In the total group of 150 patients, 33 (22.0%) deaths from any cause were registered in the follow-up time period of 64 months. The non-survivors were significantly older and had increased prevalence of diabetes and erythrocyturia, longer history of coronary artery disease, higher concentrations of serum creatinine, cystatin C, uric acid, glucose, C-reactive protein (CRP), homocysteine and B-type natriuretic peptide (NT-proBNP), and lower concentrations of serum sodium. No significant differences in echocardiography parameters were observed between groups. The following factors were risk of death factors and fulfilled the proportional hazard assumption in the univariable model: smoking, occurrence of diabetes and anaemia, duration of coronary artery disease, and abnormal serum concentrations of uric acid, sodium, homocysteine, cystatin C and NT-proBNP, while in the multivariable model, the risk of death factors were: smoking and elevated concentrations of homocysteine and NT-proBNP. The study has demonstrated that violation of the proportional hazard assumption in the Cox regression model may lead to creating a false model that does not include only time-independent predictive factors.

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Application of Normalized Compression Distance and Lempel-Ziv Jaccard Distance in Micro-electrode Signal Stream Classification for the Surgical Treatment of Parkinson’s Disease

Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets. Frontiers in Computational Neuroscience , 10 :58. doi: 10.3389/fncom.2016.00058 Trevathan, J. K., Yousefi, A., Park, H. O., Bartoletta, J. J., Ludwig, K. A., Lee, K. H., & Lujan, J. L. (2017). Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release. ACS Chemical Neuro-science , 8 (2), 394–410. doi: 10.1021/acschemneuro.6b00319 Valsky, D

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Entrepreneurial Behaviour on the New Silk Road: Space for Improvement?

References Acs Z.J., How is entrepreneurship good for economic growth? Innovations, 1(1)/2006, pp. 97–107. Acs, Z.J., Desai, S., Hessels, J., Entrepreneurship, economic development and institutions. Small Business Economics, 31(3)/2008, pp. 219–234. Allen I.E., Langowitz N., Minniti M., Global Entrepreneurship Monitor. 2007 Report on Women and Entrepreneurship, Wellesley: Babson College 2008. Audretsch D.B., Entrepreneurship: a Survey of the Literature, Luxembourg: Publications Office of the European Union 2011. Calinski T., Harabasz

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AI Case Studies: Potential for Human Health, Space Exploration and Colonisation and a Proposed Superimposition of the Kubler-Ross Change Curve on the Hype Cycle

discovery with recurrent neural networks, ACS Central. Science 4, 2018, 120-131. 42. Hessler, G., Baringhaus, K.-H. Artificial intelligence in drug design, Molecules 23, 2018, 2520. 43. Benhenda, M. ChenGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv 1708.08227, 2017. 44. Popova, M., Isayev, O., Tropsha, A. Deep reinforcement for drug design, Science Advances 4, 2018, eaap7855. 45. Fleming, N. How artificial intelligence is changing drug discovery, Nature 557, 2018, pp. S55-S57. 46. Mak, K

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