In this technology driven era, contact-free monitoring of patients’ vital signs in the hospital settings is in the demand and seems to be reliable. As patients’ acuity level continues to increase, hospitals are implementing advanced technologies geared towards early detection of patient’s complications and a faster intervention once a problem is detected, to increase safety and save costs. Wireless technology offers ubiquitous computing service in hospitals. They are already in use in hospitals for continuous monitoring keeping track on vital signs of the patients. Although, heart rate (HR) and respiration rate (RR) are equally important vital signs to evaluate health status, the RR is undermined and neglected. Therefore, the primary objective of the proposed project is to gain greater knowledge about the non-invasive methods of continuous monitoring of RR and to explore the clinical significance of RR, by computing respiration rate variability (RRV) as well as Heart Rate Variability (HRV) and correlate it to the clinical adverse events. It is hypothesized that providing continuous monitoring of patients status (vital signs and motion related parameters) with relatively low alarm rate can potentially help improve detection of adverse events as well as other prevalent patients' safety and deterioration conditions. Furthermore, it will also provide healthcare professionals with additional valuable information as to the overall status of patient condition that can possibly indicate about readiness of the patient to be discharged from hospital. The value of assessing basic vital signs, such as heart rate (HR) and respiration rate (RR), in dialysis patients is well documented and constitutes one of the basic foundations of clinical medicine. However, monitoring of vital signs in hemodialysis Unit (HDU) is usually limited to occasional assessment and documentation, typically several hours apart. Since both HR and RR are essential for assessing clinical status specifically in patients with hemodynamic and/or respiratory insufficiencies, it is assumed that continuous monitoring of HR and RR will provide the clinician with a valuable tool to early detect patient deterioration, and opportunities to improve patient outcome by earlier intervention. Data has been collected from Taipei Medical University Hospital, hemodialysis Unit. Respiratory Rate and Heart Rate of about 110 patients followed during their 2-3 weekly hemodialysis sessions for a period of 6 months and the clinical events occurred during this period has been recorded. A total of 2,450 hemodialysis sessions with about 244 adverse events were recorded. This might be first study to our knowledge, to investigate the clinical significance of RRV in various clinical scenarios commonly seen in hemodialysis patients’ by utilizing contact-free method of continuous monitoring of RR. This project develops and evaluates a machine learning model by utilizing HRV and RRV that can be adopted in the clinical practice for prediction and early detection of adverse events of the dialysis patients. This study will open an array of opportunities for respiratory medicine researchers to explore further. This study has been approved by TMU-JIRB No. N201605046.
|Effective start/end date||8/1/17 → 7/31/18|
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