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Effect of a Continuous Bedside Pressure Mapping System for Reducing Interface Pressures

2023· article· en· W4379094318 on OpenAlexafffund
Chester Ho, Wrechelle Ocampo, Danielle A. Southern, Darlene Y. Sola, Barry Baylis, John Conly, David B. Hogan, Jaime Kaufman, Henry T. Stelfox, William A. Ghali

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsAlberta HealthUniversity of CalgaryUniversity of AlbertaAlberta Health Services
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of CanadaPfizerFondation Brain CanadaCraig H. Neilsen FoundationWorld Health OrganizationAlberta InnovatesUniversity of Calgary
KeywordsMedicineRandomized controlled trialBlood pressureClinical trialInternal medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

Importance: Continuous bedside pressure mapping (CBPM) technology can assist in detecting skin areas with excessive interface pressure and inform efficient patient repositioning to prevent the development of pressure injuries (PI). Objective: To evaluate the efficacy of CBPM technology in reducing interface pressure and the incidence of PIs. Design, Setting, and Participants: This parallel, 2-group randomized clinical trial was performed at a tertiary acute care center. The study started to enroll participants in December 2014 and was completed in May 2018. Participants included adults partially or completely dependent for bed mobility. Statistical analysis was performed from September 2018 to December 2022. Intervention: Nursing staff using visual feedback from CBPM technology for 72 hours. Main Outcomes and Measures: Absolute number of sensing points with pressure readings greater than 40 mm Hg, mean interface pressure across all sensing points under a patient's body, proportion of participants who had pressure readings greater than 40 mm Hg, and pressure-related skin and soft tissue changes. Results: There were 678 patients recruited. After attrition, 260 allocated to the control group (151 [58.1%] male; mean [SD] age, 61.9 [18.5] years) and 247 in the intervention group (147 [59.5%] male; mean [SD] age, 63.6 [18.1] years) were included in analyses. The absolute number of sensing points with pressures greater than 40 mm Hg were 11 033 in the control group vs 9314 in the intervention group (P = .16). The mean (SD) interface pressure was 6.80 (1.63) mm Hg in the control group vs 6.62 (1.51) mm Hg in the intervention group (P = .18). The proportion of participants who had pressure readings greater than 40 mm Hg was 99.6% in both the control and intervention groups. Conclusions and Relevance: In this randomized clinical trial to evaluate the efficacy of CBPM technology in the reduction of interface pressure and the incidence of PIs in a tertiary acute care center, no statistically significant benefit was seen for any of the primary outcomes. These results suggest that longer duration of monitoring and adequately powered studies where CBPM feedback is integrated into a multifaceted intervention to prevent PI are needed. Trial Registration: ClinicalTrials.gov Identifier: NCT02325388.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.415
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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