RESUMO
Peritoneal dialysis (PD) is a self-administered chronic renal replacement therapy. It is a home-based therapy, and thus subject to the risk of discrepancy between prescribed dose and effective dialysis delivery. Till now automated peritoneal dialysis (APD) cyclers have recorded the dialysis treatments on a card that patients bring to the hospital for consultation in the PD unit. This card contains the operative parameters of each APD session. Recently, Baxter Healthcare developed a cloud-based tool for remote patient and treatment management. The new platform named Sharesource® embedded into the cycler HOMECHOICE CLARIA® allows to overcome the problems related to poor compliance and feeling of uncertainty by the patient, reducing the number of hospital visits and the workload for physician and nurses of the PD Unit. This new system uploads all treatment information to a secure cloud-based software. The 2-way communication platform gives remote visibility to patient's treatment and allows for feedback and correction of inadequate treatment program. Remote patient management (RPM) allows to visualize the course of home PD day after day, evaluating adherence to prescription, possible alarms during treatment, drainage times, and ultrafiltration amount. The evaluation of all the data can be done by the physician at his desk in the Hospital in front of the computer. RPM allows a patient's dialytic management in real time and enables the nephrologist to remotely modify treatment operative parameters, leaving the patient at home saving kilometers, money and time. In this chapter, we describe a simple algorithm used in our unit to define alarm thresholds and to describe actions to be instituted to correct any possible problem occurring during APD.
RESUMO
BACKGROUND & AIMS: Body hydration changes continuously in hemodialysis patients. The Subjective Global Assessment (SGA) is used for the nutritional evaluation but it does not allow a direct evaluation of hydration. Bioelectrical impedance vector analysis (BIVA) is very sensitive to hydration. The potential of the combined evaluation of hydration and nutrition with SGA and BIVA is still lacking. METHODS: Observational cross-sectional study on 130 (94 Male) uremic patients undergoing chronic hemodialysis three times a week. Nutritional status was evaluated with the SGA. Each subject was classified as SGA-A (normal nutritional status), SGA-B (moderate malnutrition), or SGA-C (severe malnutrition). Body hydration was evaluated with BIVA. The two vector components resistance (R) and reactance (Xc) were normalized by the subject's height and standardized as bivariate Z-score, i.e. Z(R) and Z(Xc). RESULTS: Undernutrition influenced impedance vector distribution both before and after a dialysis session. In pre-dialysis, the mean vector of SGA A was inside the 50% tolerance ellipse. In SGA B and C, Z(R) was increased and Z(Xc) decreased, indicating a progressive loss of soft tissue mass. Fluid removal with dialysis increased both Z(R) and Z(Xc) in SGA A and B but not in C. With ROC curve analysis on the slope of increase, we found the cutoff value of 27.8° below which undernutrition was present, either moderate or severe. The area under the ROC curve was 77.7° (95% CI 69.5-84.5, P < .0001) with sensitivity 75.9%, specificity 78.6%, positive predicted value 74.6%, and negative predicted value 79%. CONCLUSIONS: The distribution of impedance vectors is associated with the SGA classification of patients. The change in body hydration in each SGA category can be detected with BIVA.