Blood purification · 2026
Optimizing Remote Patient Monitoring in Peritoneal Dialysis: a "Less is More" approach to clinical decision support.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Milan Manani Sabrina, Virzì Grazia Maria, Santoro Andrea Giuseppe, Marcello Matteo, Lentini Paolo, Ronco Claudio, Zanella Monica
Аннотация
INTRODUCTION: Peritoneal dialysis (PD) is a home-based renal replacement therapy designed to preserve patient autonomy and enhance quality of life. The advent of telemedicine and e-health technologies has enabled the widespread adoption of Remote Patient Monitoring (RPM), particularly in automated peritoneal dialysis (APD). This study aimed to evaluate the impact of optimizing RPM strategies in clinical practice.
METHODS: This retrospective observational study compared two cohorts of patients treated with APD supported by RPM during different time periods: 35 patients monitored daily in 2019 and 62 patients managed in 2024 according to a personalized clinical surveillance protocol based on clinical stability stratification.
RESULTS: The new protocol implemented intermittent monitoring of key treatment parameters-ultrafiltration volume, alarms, bypass events, cycle times, and missed sessions-supported by an automated alert system to prompt timely clinical intervention. Both cohorts were comparable in demographic and baseline clinical characteristics. No statistically significant differences were observed between groups in terms of adverse events (peritonitis, hospitalizations) or dialysis adequacy.
CONCLUSIONS: We transitioned from a daily monitoring model to a selective, stratified approach, thereby establishing a personalized strategy tailored to the needs of individual patients. The success of this selective model can be attributed to a robust digital infrastructure, targeted education for patients and caregivers, and well-defined shared operating protocols between nurses and physicians. These findings support the effectiveness and sustainability of a "less-is-more" approach to RPM in APD patients, paving the way for future integration of predictive algorithms and artificial intelligence to enable more personalized and efficient clinical surveillance.
METHODS: This retrospective observational study compared two cohorts of patients treated with APD supported by RPM during different time periods: 35 patients monitored daily in 2019 and 62 patients managed in 2024 according to a personalized clinical surveillance protocol based on clinical stability stratification.
RESULTS: The new protocol implemented intermittent monitoring of key treatment parameters-ultrafiltration volume, alarms, bypass events, cycle times, and missed sessions-supported by an automated alert system to prompt timely clinical intervention. Both cohorts were comparable in demographic and baseline clinical characteristics. No statistically significant differences were observed between groups in terms of adverse events (peritonitis, hospitalizations) or dialysis adequacy.
CONCLUSIONS: We transitioned from a daily monitoring model to a selective, stratified approach, thereby establishing a personalized strategy tailored to the needs of individual patients. The success of this selective model can be attributed to a robust digital infrastructure, targeted education for patients and caregivers, and well-defined shared operating protocols between nurses and physicians. These findings support the effectiveness and sustainability of a "less-is-more" approach to RPM in APD patients, paving the way for future integration of predictive algorithms and artificial intelligence to enable more personalized and efficient clinical surveillance.