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Automated peritoneal dialysis (APD) is a safe and effective therapy for patients requiring renal replacement treatment. Utilizing Remote Monitoring (RM) for (APD) patients allows for the precise collection of treatment data, which the medical team can use to establish management protocols, facilitating early identification of complications and proactive intervention. The objective of this study is to illustrate the impact of a comprehensive follow-up program for APD patients supported by remote monitoring (RM)
A retrospective study considered data from 40 patients who initiated Automated Peritoneal Dialysis (APD). These patients were evaluated, and their treatment data compared from day 1 (D1) to day 90 (D90) using a two-way remote monitoring platform. This study employed an algorithmic evaluation program based on a two-way remote monitoring platform. Patient data were collected from a cohort of participants in the APD program with RM surveillance. The outcome variables include body composition, ultrafiltration, prescribed volumes, and the presence of flags or alarms during treatment. The limits of the yellow and red flags were defined by the nephrologist responsible for the program. The interventions of the clinical team were defined according to the importance of the flags, with red flags taking priority. Qualitative variables were presented using simple frequencies and percentages, while quantitative variables are described in terms of central tendency and dispersion. For the initial analysis, a Shapiro-Wilk normality test was conducted. The Student's t-test was applied to related variables exhibiting homogeneity, while the Wilcoxon test was used for heterogeneous data. Furthermore, to explore correlations between alarms and flags, Spearman's Rho was employed, given the nature of the variables. All statistical analyses were carried out using SPSS v25 software.
The mean age of the cohort was 65±13 SD. In terms of gender, there was a predominance of men, accounting for 25 (65.8%) of the cohort. Regarding to the main etiology of the disease, diabetic nephropathy was identified as the leading cause in 28 (73.6%) of the patients. When we compare the number of alarms and the number of flags between D1 and D90, from the Sharesource® platform, a lower number of yellow flags was observed (D1: Σ=19; D90: Σ=07), as well as red flags (D1: Σ=49; D90: Σ=10), and alarms (D1: Σ=322; D90: Σ=138). This difference was notably significant during the analysis (Yellow flags: 0.30 ± 0.91, p= 0.044; Red flags: 0.97 ± 1.19, p<0.001; Alarms: 4.6 ± 11.22, p<0.013). When correlating the type of alarms, a stronger correlation is observed between the number of alarms and the presence of red flags on both day 1 (ρ = 0.752, p < 0.001) and on day 90 (ρ = 0.468, p < 0.003). A significant difference was found in nocturnal UF, being higher in the evaluation at day 90 (t=-3.125; p=0.003). This increase was also reflected in the UF on a wet day, showing an increase on day number 90 (t=-2.806; p=0.008). The difference between TBW approached the limit of significance, with a p-value of 0.05.
Remote monitoring (MR) provides the medical team with accurate information about the patient's treatment. The implementation of daily evaluation algorithms and comprehensive management programs improves the identification and resolution of potential treatment problems during APD