In this study, data were collected on 2,029 kidney transplant patients, with a mean age of 47.0 ± 14.2 years, and 62.4% of which were men. A cardiovascular event was suffered by 9.7% of these patients. The accumulated incidence of a cardiovascular event in the presence of competing risks was 5.0% in the first year, 6.6% after three years, 8.1% after five years and 11.9% after ten years. These results are consistent with those published in multicentre studies, including data from transplanted patients from North America, Europe and countries on the Pacific coast, in which the accumulated incidence of coronary events and the accumulated incidence of myocardial infarct after kidney transplant is estimated [13, 14].

Various epidemiological studies have identified the factors associated with an increase in the probability of falling ill or dying owing to cardiovascular disease after kidney transplantation [15–17]. They also test whether the risk factors identified for the general population also increased the risks in kidney transplant patients and to what extent. These studies would suggest that the cardiovascular risk factors for the general population (e.g. high blood pressure, hyperlipidaemia and smoking) are predictive of events in the transplanted population. Diabetes doubles the risk of events in men and triples the risk in women with respect to that estimated for the general population; moreover, it is shown that the episodes of acute rejection during the first year after transplantation are associated with a greater risk [15, 16]. In the study by Weiner, D. E. [17] the factors associated significantly with an increased risk of cardiovascular disease in transplanted patients were old-age, prior cardiovascular disease, diabetes, smoking, systolic and diastolic blood pressure and low BMI and glomerular filtration rates estimated with the CKD-EPI formula lower than 45ml/min/1.73m². Other traditional risk factors in the general population, such as gender, LDL Cholesterol and triglycerides, were not significant.

For cerebrovascular disease (ischaemic cerebrovascular accident, haemorrhagic cerebrovascular accidents and transient ischaemic attacks) after kidney transplantation, the risk factors were age, pre-and post-transplant smoking, diabetes, high blood pressure, obesity and coronary comorbidity [5].

The results of our study are consistent with those in the literature. After performing survival models, we have identified the following as predictors of cardiovascular events: male gender, age of recipient, the presence of prior cardiovascular disease, pre-transplant smoking and post-transplant diabetes. The presence of these factors along with the higher age of the recipient increases the risk of cardiovascular events.

Here we should stress the importance of complications related with immunosuppressant treatment, of which diabetes mellitus is perhaps most important, owing to the vascular problems it gives rise to [18–21]. We believe that the prevention of new-onset diabetes is a key strategy for reducing post-transplant cardiovascular mortality. The individualisation of immunosuppression guidelines (selection of calcineurin inhibitor and the steroid dosage) may be an effective measure for controlling the incidence of post-transplant diabetes and modifying the cardiovascular risk in this patient group.

Among the limitations of the study, we could point out that with a view to minimising the selection bias, information was collected on all transplants performed during the study period. There were no significant differences in information losses among patients presenting the event of interest or not. In order to minimise any information biases, mean values of the baseline measurements closest in time were calculated. Left ventricular hypertrophy was diagnosed by ECG. As opposed to previous studies—based on the retrospective analysis of records, which only contain information prior to transplantation and from short-term follow-up—this study provides long-term information on clinical and analytic parameters and with regard to the treatment of kidney transplant recipients. Although the information was gathered from hospital medical records, which could result in an information bias, the characteristics of these patients mean that they are subject to much more exhaustive follow-up than is habitual, not only during the period immediately after transplantation, but throughout the entire follow-up period. Thus, during the first year after kidney transplantation, patients were seen every 3 months, every 6 months in the 10 years following a transplant, and once a year after the first 10 years of follow-up. The study included a large cohort of patients with a long period of post-transplantation follow-up, which has enabled us to obtain valid results on the long-term results of kidney transplantation. Among the confounding biases, it should be noted that there are risks factors which were not included, information in relation with the heart failure according to the functional state was not available and also the level of HbA1c expressing glicemic control in diabetics was not included. Treatments, which modify the variables studied, were not included. In order to control the confusion, we have employed multivariate regression techniques. The population at risk changes over time in a population of survivors due to the occurrence of competing events. It is possible that the presence of competing events has an impact on the effect estimates over time.

The majority of the studies published in the literature employ the habitual survival analysis techniques, assuming that there is only one event of interest, and that censoring is not informative; i.e., that if we monitored censored patients, they would have the same rate for the event as non-censored patients [22]. In practice these assumptions are not always correct; generally speaking, an individual may experience more than one type of event, or experience a type of event which hinders or modifies the probability of observing the event of interest. Competing risk survival analysis enables us to determine the factors associated with the incidence of a specific event. Fine & Gray [12] modified Cox’s proportional risk model to take into account competing risks.

It is becoming progressively easier to secure comprehensive data sets with full follow-up; consequently, the need to apply mathematical methodologies focused on the precise type of analysis is also increasing. Owing to the foregoing, our principal aim must be to apply specific techniques, such as competing risks, from the design phase of the study up to the interpretation of the results obtained.