MO700: Outcomes of Remote Patient Monitoring among Peritoneal Dialysis Population in the Covid-19 ERA
Abstract BACKGROUND AND AIMS Automated peritoneal dialysis (APD) is a growing PD modality but as with other home dialysis methods, the lack of monitoring of patients' adherence to prescriptions is a limitation with potential negative impact on clinical outcome parameters. Remote patient monitor...
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Veröffentlicht in: | Nephrology, dialysis, transplantation dialysis, transplantation, 2022-05, Vol.37 (Supplement_3) |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Abstract
BACKGROUND AND AIMS
Automated peritoneal dialysis (APD) is a growing PD modality but as with other home dialysis methods, the lack of monitoring of patients' adherence to prescriptions is a limitation with potential negative impact on clinical outcome parameters. Remote patient monitoring (RPM-PD) allowing the clinical team to have access to dialysis data and adjust the treatment may overcome this limitation. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the importance of RPM programs has raised to allow the physicians ensure optimal care of PD patients. In addition, to avoid the increased risk of complications or technique failure, the present study sought to determine clinical outcomes associated with RPM use in patients on APD therapy.
METHOD
We performed a systematic review in PubMed, MEDLINE, Embase and Cochrane databases to select studies that met the inclusion criteria. The search terms used were: peritoneal dialysis, remote monitoring, sharesource, outcomes, peritonitis, hospitalization, technique failure and adherence. These search terms were individually used and then combined in different databases. References within the chosen studies were reviewed. We followed the recommendations of Cochrane collaboration and the Quality of Reporting of Meta-analyses guidelines. STATA package-15 was used. We combined all study-specific estimates using inverse-variant weighted averages of logarithmic relative risk in random effects model. Confidence interval including the value of 1 was used evident for statistically significant estimate. Heterogeneity was evaluated using the Higgins I² statistic. Heterogeneity was estimated when the level of P-value was |
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ISSN: | 0931-0509 1460-2385 |
DOI: | 10.1093/ndt/gfac078.037 |