Analyse Cardio-respiratory Patterns in Order to Early Detect the Clinical Complications in the Hemodialysis Patients

NCT02832518 | Unknown

• 1 other identifier: N201512031
• Study Type: observational
• Target: 140
• Locations: 0 countries
• Sites: N/A

Brief Summary

The incidence and prevalence of dialysis in Taiwan remains higher as compared to past several years. The number of dialysis reported about 6-7 million people each year and the 2014 full-year cost estimation was about 330 million NTDs. According to age-standardized population statistics in 1995, the number of dialysis per million population in 2006 year were 372.2 people, 381.9 people in 2012 year and the annual growth rate of 0.43 percent recorded. EarlySense system is certified by TFDA, FDA and many other countries, which consists of mainly two parts. One for the sensing element, which placed under the mattress and the other one is AC-powered display panel bed. This system has the ability to monitor the patients physiological functions just be in contact via bed. When the patient laid on bed, the sensor which is placed under the bed would detects heart rate, breathing rate and activity level as well as other physiological signals. The system is also able to distinguish between patients in bed and out of bed during the operation to save the patient's records and information (including heart rate, respiratory rate and level of activity, and may render trends). The system is also have capability for real-time data transmission information which includes a warning to the nurses' station or other monitoring center of the screen by providing real-time information to nurse. In this research project, we will use EarlySense equipment for continuous monitoring the dialysis patient's physiological data along with clinical data such as A. acute complications such as rapid changes in blood pressure, respiratory rate, movements of patients and Nausea or vomiting, etc phenomenon's) B. Lab examination data C. Status of patient whether patients hospitalized or not and patient death occurred or not. The study is expected to have a pilot study for more than three months' duration. Through this EarlySense continuous monitoring and gather the data, we will analyse to develop a prediction model and confirmed with indicators. Evidences from these analytics would help to propose ways to improve it and implement Positive predictive validity models.

Conditions

Chronic Kidney Disease

Outcome Measures

Primary Outcomes (1)

• Cardiac arrest

  6 months after enrollment

Study Arms (1)

patients under hemodialysis

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients under hemodialysis

You may qualify if:

• above 20 years old and under hemodialysis

You may not qualify if:

• patient with coronary artery disease or under treatment for coronary artery disease and patient with fever, any kind of infection, or taking antibiotics

Sponsors & Collaborators

• Taipei Medical University: lead
• Tatung medical & healthcare technologies: collaborator

MeSH Terms

Conditions

Renal Insufficiency, Chronic

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Shabbir Syed-Abdul, MD, MSc, PhD

  Taipei Medical University, Taiwan

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 12, 2016
• First Posted: July 14, 2016
• Study Start: March 1, 2016
• Primary Completion: September 1, 2016
• Study Completion: December 1, 2016
• Last Updated: July 14, 2016

Record last verified: 2016-07

Data Sharing

• IPD Sharing: Will not share