Nevin Manimala Statistics

Novel Smart Sensor Platform for Monitoring Multiple Pressure Injury Risk Factors: A Feasibility Study in a Post-Acute Care Facility

Surg Technol Int. 2022 Aug 30;41:sti41/1602. Online ahead of print.


INTRODUCTION: Posture, temperature, and moisture have been identified as critical modifiable risk factors in pressure injury (PI) development. Microclimate is defined as temperature and humidity at the interface of the support surface and body. To our knowledge, no studies have used sensor technology to measure these parameters simultaneously in real time. Continuous monitoring of repositioning and microclimate provide real-time actionable insights to help deliver personalized care and measure the effectiveness of interventions.

OBJECTIVE: To evaluate the ability of a smart surface platform to collect and document clinical data on monitoring patients’ movement and microclimate simultaneously and to compare data generated to nursing observations in order to construct an algorithm that is expected to evolve over time: (1) comparing the blinded data from nurses interacting with the patients and the system; and (2) data being collected is validating an algorithm that is expected to become more accurate over time.

MATERIALS AND METHODS: This prospective, descriptive single-site trial was conducted at a tertiary care facility in a large urban centre in Canada. Patients identified at risk of PIs received standard of care while placed on the smart surface for timed intervals. Nurses’ assessment data were collected at three hourly timepoints using a comprehensive tool developed for the study. Sensors monitored patients’ interface pressure moisture and temperature every four seconds. A comparative statistical analysis was conducted between the two datasets retrospectively.

RESULTS: The study included a total of 104 participants; mean age of 59 years (range 21-92, ± 19.15). Sensor monitoring hours (1,407) generated 1,101,780 frames of surface data. A total of 511 nursing assessments were recorded during the study period. Sensor-generated data correlated strongly with nurse-collected data at cross-sectional intervals. There was a high level of agreement between information collected from sensors and nursing assessments: 94.7% for moisture (p<0.05), and 87.1% for temperature (p<0.05). Nurse-recorded posture assessments were compared to the smart surface platform interface pressure visualizations to determine the device’s posture detection, resulting in a 92% accordance (matching 552 out of 600 nurse postures), with a binomial test determining the posture results to be statistically significant (p<0.05) (CI 95%). In addition, moisture events were matched to nurse assessments with 94.7% in accordance, identifying 39 bladder incontinence and 93 non-urinary moisture events (125 total events captured out of 132).

CONCLUSION: The technology’s ability to capture PI risk factors supports nursing practice. Supplementary data generated has the potential to improve efficiency of professional caregiver workflow and patient outcomes by informing targeted microclimate management strategies and decreasing unnecessary interventions. The large volume of data collected will be used as a basis for artificial intelligence applications with the potential to inform other clinical decision-making areas.


By Nevin Manimala

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