Efficacy of a pressure-sensing mattress cover system for reducing interface pressure: study protocol for a randomized controlled trial

Characterising mobility and pressure exposure in community dwelling residents with pressure ulcers using monitoring technology and intelligent algorithm

AIM

Individuals in the community with reduced mobility are at risk of exposure to prolonged lying and sitting postures, which may cause pressure ulcers. The present study combines continuous pressure monitoring technology and intelligent algorithms to evaluate posture, mobility, and pressure profiles in a cohort of community dwelling patients, who had acquired pressure ulcers.

MATERIALS AND METHODS

This study represents a secondary analysis of the data from the Quality Improvement project 'Pressure Reduction through COntinuous Monitoring In the community SEtting (PROMISE)'. 22 patients with pressure ulcers were purposely selected from 105 recruited community residents. Data were collected using a commercial continuous pressure monitoring system over a period of 1-4 days, and analysed with an intelligent algorithm using machine learning to determine posture and mobility events. Duration and magnitude of pressure signatures of each static posture and exposure thresholds were identified based on a sigmoid relationship between pressure and time.

RESULTS

Patients revealed a wide range of ages (30-95 years), BMI (17.5-47 kg/m2) and a series of co-morbidities, which may have influenced the susceptibility to skin damage. Posture, mobility, and pressure data revealed a high degree of inter-subject variability. Largest duration of static postures ranged between 1.7 and 19.8 h, with 17/22 patients spending at least 60 % of their monitoring period in static postures which lasted >2 h. Data revealed that many patients spent prolonged periods with potentially harmful interface pressure conditions, including pressure gradients >60 mmHg/cm.

CONCLUSION

This study combined posture, mobility, and pressure data from a commercial pressure monitoring technology through an intelligent algorithm. The community residents who had acquired a pressure ulcer at the time of monitoring exhibited trends which exposed their skin and subdermal tissues to prolonged high pressures during static postures. These indicators need further validation through prospective clinical trials.

Sitting Pressure Measurements in Wheelchair Users-Can the Effects of Daily Relief Activities Be Depicted?

Seat pressure measurements in wheelchair users have been available for some time; however, repeated measurements from a commercially available pressure mat over 90 min did not differ in the pressure-loaded measurement area or the coordinates of the center of pressure, even in participants who were able to reposition themselves in the wheelchair. The question therefore arises: to what extent are there other parameters that reflect the activity of wheelchair users with the pressure mat? To investigate this, a commercial pressure mat (BodiTrak®) was used to perform the measurements of pressure of 33 adult wheelchair-dependent people with spinal cord injury after 30 and 90 min sitting on the cushion. In addition to the standard output of the pressure mat, graph-based surface analyses (calculation of the area of maximum pressure, calculation of the pressure-loaded measurement area, and pressure-area ratio) was performed retrospectively using Python 3.7. The analysis of the measurements after 30 and 90 min was performed by distinguishing the participants between those who could actively change their position (N = 24) and those who could not (N = 9). The parameters of the pressure mat and the graph-based analyses remained unchanged for active participants. In participants who were unable to actively change their position, the area of maximum pressure and the pressure-area ratio (ratio of maximum pressure area and total pressure-loaded area) increased. Significant differences between minutes 30 and 90 are only found for the pressure-area ratio. Thus, when measuring the seat pressure of wheelchair users, the pressure-area ratio should be taken into account as it reflects the daily relief activities of wheelchair users.

How Do Everyday Life Activities Affect Seating Pressure Measurements?

BACKGROUND

Pressure measurements to detect risks for pressure injuries in wheelchair users with spinal cord injury (SCI) have been available for quite some time. Unfortunately, knowledge of how postural changes during longer periods of daily life activity affecting the results is still limited. In the present study, the authors expected to note shifts in the center of pressure as well as in the pressure distribution, especially in patients who were able to change their position actively.

METHODS

A seat pressure mat (BodiTrak2) was used to perform measurements of 34 SCI wheelchair users after initial transfer into the wheelchair as well as 30 and 90 minutes later. Mean pressure, maximum pressure, pressure-loaded measuring surface, and the coordinates of the center of pressure were analyzed, and findings were statistically analyzed using the t test and Intraclass Correlation Coefficient. To quantify the drift of the measurement system, recordings with a reference weight were performed.

RESULTS

The analyzed parameters from the initial measurement differed significantly from the later measurements at 30 and 90 minutes, whereas the parameters were stable after 30 minutes. The measurements with the reference weight showed the same measurement course.

CONCLUSIONS

The measurements after 30 and 90 minutes were consistent, contrary to expectations. The activity of the participant between measurements did not appear to be of much importance.

CLINICAL RELEVANCE

Relief maneuvers appear to provide relief only while the maneuver is being performed. Thus, relief activities must be frequent enough and long enough to allow adequate blood flow to the tissues.

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

BACKGROUND

Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.

OBJECTIVE

This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.

METHODS

Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model's performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.

RESULTS

Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.

CONCLUSIONS

The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

Exploring data reduction strategies in the analysis of continuous pressure imaging technology

BACKGROUND

Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries.

OBJECTIVE

To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data.

METHODS

Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots.

RESULTS

A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome.

CONCLUSIONS

Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

Electronic Alert Signal for Early Detection of Tissue Injuries in Patients: An Innovative Pressure Sensor Mattress

Monitoring the early stage of developing tissue injuries requires intact skin for surface detection of cell damage. However, electronic alert signal for early detection is limited due to the lack of accurate pressure sensors for lightly pigmented skin injuries in patients. We developed an innovative pressure sensor mattress that produces an electronic alert signal for the early detection of tissue injuries. The electronic alert signal is developed using a web and mobile application for pressure sensor mattress reporting. The mattress is based on body distributions with reference points, temperature, and a humidity sensor to detect lightly pigmented skin injuries. Early detection of the pressure sensor is linked to an electronic alert signal at 32 mm Hg, a temperature of 37 °C, a relative humidity of 33.5%, a response time of 10 s, a loading time of 30 g, a density area of 1 mA, and a resistance of 7.05 MPa (54 N) at 0.87 m³/min. The development of the innovative pressure sensor mattress using an electronic alert signal is in line with its enhanced pressure detection, temperature, and humidity sensors.

Healthcare provider and patient/family perceptions of continuous pressure imaging technology for prevention of pressure injuries: A secondary analysis of patients enrolled in a randomized control trial

INTRODUCTION

Despite the availability of various pressure injury (PI) prevention strategies (e.g., risk identification, use of pressure re-distribution surfaces, frequent repositioning), they persist as a significant issue for healthcare systems worldwide. Continuous pressure imaging (CPI) is a novel technology that could be integrated within a comprehensive approach to the prevention of PIs. We studied the perceptions of healthcare providers and patients/families to identify facilitators and barriers to the use of this technology.

METHODS

Hospitalized patients/family members from a randomized controlled trial assessing the efficacy of CPI in preventing PIs completed a survey after 72 hours (or upon discharge from hospital) of CPI monitoring. They were asked questions about prior and current experience with CPI technology. For healthcare providers, perceptions on the use of the device and its impact on care were explored through a survey distributed by email or hard copies.

RESULTS

A total of 125 healthcare providers and 525 patients/family members completed the surveys. Of the healthcare providers, 95% either agreed/strongly agreed that the CPI technology was easy to use and 65% stated that the device improved how they provided pressure relief for patients. Identified issues with the device were cost, the fitting of the mattress cover, and the fixation of the patients/families on the device. Over a quarter of the patient/family respondents agreed/strongly agreed that the device influenced how pressure relief was provided. This response was statistically associated with whether the monitor was turned on (intervention arm; 52.7%) or off (control arm; 4.2%).

DISCUSSION AND CONCLUSION

CPI technology was positively perceived by healthcare providers. Most patients/families felt it influenced care when the CPI monitor was turned on. Concerns raised around cost and the ease of use of these devices by healthcare providers may affect the decisions of healthcare system administrators to adopt and implement this technology.

Textile-Based Pressure Sensing Matrix for In-Bed Monitoring of Subject Sleeping Posture and Breathing Activity

According to current trends in healthcare sensing technologies, we describe a textile-based pressure sensing matrix that can be integrated in the mattress of a smart bed to characterize sleeping posture/movement of a subject and to extract breathing activity. The pressure mapping layer is developed as a matrix of 195 piezoresistive sensors, it is entirely made of textile materials, and it is the basic component of a smart bed that can perform sleep analysis, can extract physiological parameters, and can detect environmental data related to subject’s health. In this paper, we show the principle of the pressure mapping layer and the architecture of the dedicated electronic system that we developed for signal acquisition. In addition, we describe the algorithms for posture/movement classification (dedicated artificial neural network) and for extraction of the breathing rate (frequency domain analysis). We also perform validation of the system to quantify the accuracy/precision of the posture classification and the statistical analysis to compare our breathing rate estimation with the gold standard.

Effectiveness of Tragacanth Gel Cushions in Prevention of Pressure Ulcer in Traumatic Patients: a Randomized Controlled Trial

Introduction: Introduction: Pressure ulcer is one of the most common and painful complications in patients admitted to intensive care units (ICUs). This study aimed to compare the effects of tragacanth gel cushions and foam-filled ones on the prevention of pressure ulcers. Methods: This triple-blind, randomized, clinical trial was performed on 94 patients admitted to the ICU of Taleqhani Hospital of Mashhad, Iran. The participants were divided into two groups of tragacanth gel pad and foam (n=47 for each group). Both groups received all the routine care. The participants were examined on a daily basis during a ten-day period. We considered sacral region as the most affected site, and the patients with erythema were excluded from the study. To analyze the data, Chi-squared test, Fisher's exact test, and t-test were run, using Stata version 12. Results: The data showed significant differences in terms of the incidence of skin redness in the tragacanth gel cushions group and foam cushion group, respectively, 6.84 (1.58) and 5.67 (1.26) days after admission. In addition, in the tragacanth gel cushions group 14 patients (29.7%) did not have skin redness compared with the foam cushion group (8.51%), which indicated a significant difference. Conclusion: This study showed that the use of tragacanth gel cushion is effective in the prevention of pressure ulcers in ICU patients. Cushions filled with tragacanth gel also delayed the onset of erythema. Considering their cost-effectiveness and naturalness, the use of tragacanth gel cushions is recommended to improve the sacral skin health and prevent pressure ulcers.

Detection and classification methodology for movements in the bed that supports continuous pressure injury risk assessment and repositioning compliance

Pressure injuries are costly to the healthcare system and mostly preventable, yet incidence rates remain high. Recommendations for improved care and prevention of pressure injuries from the Joint Commission revolve around continuous monitoring of prevention protocols and prompts for the care team. The E-scale is a bed weight monitoring system with load cells placed under the legs of a bed. This study investigated the feasibility of the E-scale system for detecting and classifying movements in bed which are relevant for pressure injury risk assessment using a threshold-based detection algorithm and a K-nearest neighbor classification approach. The E-scale was able to detect and classify four types of movements (rolls, turns in place, extremity movements and assisted turns) with >94% accuracy. This analysis showed that the E-scale could be used to monitor movements in bed, which could be used to prompt the care team when interventions are needed and support research investigating the effectiveness of care plans.

The Effect of Support Surfaces on the Incidence of Pressure Injuries in Critically Ill Patients: A Randomized Clinical Trial

PURPOSE

To analyze whether a viscoelastic mattress support surface can reduce the incidence of stage 2 pressure injuries compared to a standard hospital mattress with pyramidal overlay in critically ill patients.

METHOD

A randomized clinical trial with intention-to-treat analysis was carried out recruiting patients with Braden scale ≤14 on intensive care unit admission from April 2016 to April 2017. Patients were allocated into two groups: intervention group (viscoelastic mattress) and control group (standard mattress with pyramidal overlay). The level of significance adopted was 5%.

RESULTS

A total of 62 patients were included in the study. There was a predominance of males (53%) and the mean age was 67.9 (SD 18.8) years. There were no differences in clinical or severity characteristics between the patients in the control group and the intervention group. Pressure injuries occurred in 35 patients, with a median time of 7 days (ITQ 4-10) from admission. The frequency of pressure injuries was higher in the control group (80.6%) compared to the intervention group (32.2%; p < 0.001).

CONCLUSIONS

Viscoelastic support surfaces reduced the incidence of pressure injuries in moderate or higher risk critically ill patients when compared to pyramidal support surfaces.