A proof-of-concept study of the effectiveness of a removable device for offloading in patients with neuropathic ulceration of the foot: the Ransart boot

AIM

To undertake a proof-of-concept study to determine whether a removable offloading device (the Ransart boot) for the management of diabetic foot ulcers (DFU) was as effective as reports of non-removable devices.

RESEARCH DESIGN AND METHODS

This observational study used the Ransart boot for patients with DFU, in seven specialist centres. If a patient had two or more ulcers, one was selected as the index ulcer. Ulcers were classified by the University of Texas (UT) system.

RESULTS

There were 135 patients (mean age 60.3 +/- 11.4 years); 96 (71.1%) male. Median ulcer duration at presentation was 90 [interquartile range (IQR) 30-1825] days. Seven were lost to follow-up, seven developed other major illnesses and four died; outcomes were documented in the remaining 117. Eighty-two (70.1% of 117) healed, after a median (IQR) 60 (43-99) days, while 22 (18.8%) ulcers were resolved by amputation (one major). The remaining 13 (11.1%) patients were judged non-compliant. There was a close correlation between ulcer classification at baseline and both time to healing (P < 0.001 chi(2)-test) and amputation (P < 0.001; Spearman's rank correlation coefficient). There was a positive correlation between ulcer duration at presentation and time to healing (P < 0.02), UT class (P < 0.01), glycated haemoglobin (P < 0.02) and amputation (P < 0.04).

CONCLUSIONS

Time to healing and incidence of amputation were comparable with those previously reported for non-removable devices. Given that a removable device is much more acceptable to the patient, the effectiveness, cost and acceptability of the removable devices, such as the Ransart boot, need to be compared with a non-removable device in a randomized trial. Diabet. Med. 26, 778-782 (2009).

Health and nutritional promotion program for patients with dementia (NutriAlz Study): design and baseline data

BACKGROUND

There is a lack of data on global weight loss prevention programs for patients with dementia or clear evidence about their impact on a functional level, caregiver burden or the use of healthcare and social resources. NutriAlz is a socio-educative and nutritional intervention program to prevent weight loss and loss of function in dementia patients.

STUDY DESIGN AND METHODS

A cluster randomized multi-centre study, which will allow the comparison of a group benefiting from the intervention with a control group after a year of monitoring. Patients were recruited from 11 hospitals in the ambulatory diagnostic units and day care centres. The baseline interview include: sociodemographic and socioeconomic variables (age, gender, educational level, marital status); diagnostic, treatments, MMS, a list of comorbid conditions; activities of daily living (ADL, IADL), Zarit Scale, brief-NPI, Cornell scale and nutritional status as measured by the Mini Nutritional Assessment. All participants or their family signed the inform consent form.

BASELINE CHARACTERISTICS

Total of 946 patients were included, with a mean (+/- SD) of 79 +/- 7.3 year of age; 68,1 % were women; 44,9% lives with their partner, only 3% lives alone; 79.8% had Alzheimer's dementia, 5.25 +/- 3.0 years since symptoms of dementia and 2.8 +/- 2.11 years since diagnosis. Mean MMSE score was 15.4 +/- 6.2; mean weight was 64.4 +/- 12.5 kg; mean BMI was 27.0 +/- 4.5 (with 3% below 19, 5% between 19-21, 10% between 21-23, and 82% above 23). Mean ADL without difficulties was 3.2 +/- 2.1; mean IADL without difficulties was 0.7 +/- 1.6; mean number of symptoms in the NPI was 4.4 +/- 2.59, with severity score of 7.9 +/- 5.9 and distress score of 11.3 +/- 9.0; mean Zarit scale was 27.4 +/- 15.5; mean MNA was 23.2 +/- 3.5 with 5 % as malnourished, 32 % at risk of malnutrition, and 63 % with adequate nutritional status.

CLUM: a cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so call path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.