Development and validation of risk assessment models for diabetes-related complications based on the DCCT/EDIC data.

AIM: To derive and validate a set of computational models able to assess the risk of developing complications and experiencing adverse events for patients with diabetes. The models are developed on data from the Diabetes Control and Complications Trial (DCCT) and the Epidemiology of Diabetes Interventions and Complications (EDIC) studies, and are validated on an external, retrospectively collected cohort. METHODS: We selected fifty-one clinical parameters measured at baseline during the DCCT as potential risk factors for the following adverse outcomes: Cardiovascular Diseases (CVD), Hypoglycemia, Ketoacidosis, Microalbuminuria, Proteinuria, Neuropathy and Retinopathy. For each outcome we applied a data-mining analysis protocol in order to identify the best-performing signature, i.e., the smallest set of clinical parameters that, considered jointly, are maximally predictive for the selected outcome. The predictive models built on the selected signatures underwent both an interval validation on the DCCT/EDIC data and an external validation on a retrospective cohort of 393 diabetes patients (49 Type I and 344 Type II) from the Chorleywood Medical Center, UK. RESULTS: The selected predictive signatures contain five to fifteen risk factors, depending on the specific outcome. Internal validation performances, as measured by the Concordance Index (CI), range from 0.62 to 0.83, indicating good predictive power. The models achieved comparable performances for the Type I and, quite surprisingly, Type II external cohort. CONCLUSIONS: Data-mining analyses of the DCCT/EDIC data allow the identification of accurate predictive models for diabetes-related complications. We also present initial evidences that these models can be applied on a more recent, European population.

A practice-based survey of patients dying in hospital: can we do more to support end-of-life care at home?

An analysis of patients dying in hospital was carried out in a practice. The research showed that patients with non-malignant disease are overrepresented. Despite good district nurse support, identifying those likely to die and predicting death is more difficult than with cancer. There is a high rate of admission from nursing homes, and a reluctance by patients and relatives to discuss likely death is mirrored by uncertainty among clinicians.

A systematic review of predictive risk models for diabetes complications based on large scale clinical studies.

This work presents a systematic review of long-term risk assessment models for evaluating the probability of developing complications in diabetes patients. Diabetes mellitus can cause many complications if not adequately controlled; risk assessment models can help physicians and patients in identifying the complications most likely to arise and in taking the necessary countermeasures. We identified six large medical studies related to diabetes mellitus upon which current available risk assessment models are built on; all these studies had duration over 5 years and most of them included some common demographic and clinical data strongly related to diabetic complications. The most common predictions for long term diabetes complications are related to cardiovascular diseases and diabetic retinopathy. Our analysis of the literature led us to the conclusion that researchers and medical practitioners should take in account that some limitations undermine the applicability of risk assessment models; for example, it is hard to judge whether results obtained on a specific cohort can be effectively translated to other populations. Nevertheless, all these studies have significantly contributed to identify significant risk factors associated with the major diabetes complications.

A practice-based survey of mortality revisited - what trends in end-of-life care are emerging?

The gathering of mortality data in general practice is useful and important; it provides a vital insight into local health needs, highlights gaps in service provision and educates those working in general practice. This article considers the mortality rates in Chorley Wood Health Centre between 2008 and 2009. The survey results provide an interesting insight into local trends, one key factor being the rate of people being enabled to die at home.

Familial hypercholesterolaemia: different perspectives.

Familial hypercholesterolaemia is a genetic condition characterised by high cholesterol levels in the blood and an increased risk of premature coronary heart disease. In this article, the authors review the clinical features, pathology, epidemiology and clinical management of this often underdiagnosed condition.