Clinical-pathological characteristics and short-term follow-up associated with proliferation, apoptosis and angiogenesis in a prospective cohort of patients with colorectal tumours.

We investigate the clinical and pathological features related to variations in colorectal tumour apoptosis, proliferation and angiogenesis and the influence of the latter in short-term mortality (2 years); 551 tumour samples from a prospective cohort of patients with colorectal cancer were examined and tumour biology markers were determined as follows: percentage of apoptotic cells, by the terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling technique; Ki-67 antigen, as a cell proliferation marker and density of microvessels (as a marker of angiogenesis). An increase in the percentage of cellular apoptosis is significantly related to the presence of poorly differentiated tumours, with vascular invasion (p < 0.001). The CD105 angiogenesis marker is not related to any clinical-pathological parameter except that of higher frequency in older patients (p = 0.03). Ki-67 is more frequently expressed in tumours with less nervous invasion (p = 0.05). Neither apoptosis nor angiogenesis present any significant association with short-term survival. The only marker clearly related to 2-year survival is Ki-67, which is shown to be a good prognostic factor in the multivariate analysis (hazard ratio = 0.49; 95% confidence interval = 0.27-0.90). Therefore, in a prospective cohort of colorectal cancer patients, only Ki-67 is a marker of good prognosis in short-term follow-up.

A new approach to categorising continuous variables in prediction models: Proposal and validation.

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points' location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.