A retrospective analysis of recurrent pressure ulcer in a burn center in Northeast China

Zhang, Nan; Yu, Xiang; Shi, Kai; Shang, Fengjia; Liu, Hong; Yu, Jun

doi:10.1016/j.jtv.2019.07.002

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…The majority of the seven empirical studies published in 2019 were retrospective designs leveraging clinical data from hospitals, except for Duvall et al's 126 feasibility pilot study. The sample sizes ranged from 10 to 2062 patients [121][122][123][124][125][126] and 396 images of PI. 127 Various data science methods have been used to detect or predict PIs, including logistic regression, 121,122 nonlinear regression, 124 a univariate Cox regression, 125 and three data mining algorithms (decision trees, neural networks, and SVMs).…”

Section: Discussionmentioning

confidence: 99%

“…The sample sizes ranged from 10 to 2062 patients [121][122][123][124][125][126] and 396 images of PI. 127 Various data science methods have been used to detect or predict PIs, including logistic regression, 121,122 nonlinear regression, 124 a univariate Cox regression, 125 and three data mining algorithms (decision trees, neural networks, and SVMs). 121 The significant predictive factors comprised PI history, 121 without cancer, 121 excretion, 121 activity/mobility, 121 skin condition/circulation, 121 estimated surgery time, 122 serum albumin level, 122,125 multiple ulcers, 125 and presence of a single caregiver.…”

Section: Discussionmentioning

confidence: 99%

“…127 Various data science methods have been used to detect or predict PIs, including logistic regression, 121,122 nonlinear regression, 124 a univariate Cox regression, 125 and three data mining algorithms (decision trees, neural networks, and SVMs). 121 The significant predictive factors comprised PI history, 121 without cancer, 121 excretion, 121 activity/mobility, 121 skin condition/circulation, 121 estimated surgery time, 122 serum albumin level, 122,125 multiple ulcers, 125 and presence of a single caregiver. 125 Logistic regression analysis was also used to determine the utility of three different PI risk assessment scales (ie, the Spinal Cord Injury Pressure Ulcer Scale, Braden Scale, and Functional Independence Measure]) for identifying individuals at risk for developing PI.…”

Section: Discussionmentioning

confidence: 99%

“…121 The significant predictive factors comprised PI history, 121 without cancer, 121 excretion, 121 activity/mobility, 121 skin condition/circulation, 121 estimated surgery time, 122 serum albumin level, 122,125 multiple ulcers, 125 and presence of a single caregiver. 125 Logistic regression analysis was also used to determine the utility of three different PI risk assessment scales (ie, the Spinal Cord Injury Pressure Ulcer Scale, Braden Scale, and Functional Independence Measure]) for identifying individuals at risk for developing PI. 123 Duvall et al 126 used a threshold-based detection algorithm and a K-nearest neighbor classification approach to investigate the feasibility of a sensor technology (ie, the E-scale system) for detecting and classifying movements in bed (ie, roll, turn in place, extremity movements, and assisted turn), which are relevant for PI risk assessment.…”

Section: Discussionmentioning

confidence: 99%

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Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes

Schultz¹,

Walden²,

Cato³

et al. 2021

CIN: Computers, Informatics, Nursing

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Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes

Schultz¹,

Walden²,

Cato³

et al. 2021

CIN: Computers, Informatics, Nursing

View full text Add to dashboard Cite

show abstract

“…The study concluded that the Nursing knowledge was insufficient and that there would be a risk in the quality of the care plan for the prevention of PIs. As in Brazil, the high incidence of PIs made the government support effective actions for its prevention and treatment (8,26) .…”

Section: Discussionmentioning

confidence: 99%

Application of the educational method of realistic simulation in the treatment of pressure injuries

Baracho

Chaves

Lucas

2020

Rev. Latino-Am. Enfermagem

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Objective: to evaluate the use of realistic simulation as a strategy to promote teaching about pressure injuries. Method: This is a quasi-experimental study. A modified and translated version of the Pieper Pressure Ulcer knowledge test was applied. Kappa statistical analysis was used to assess the professionals’ knowledge in the realistic simulation using the SPSS software. A p-value <0.05 was considered significant. Results: Seventy-seven nursing professionals participated in the realistic simulation, the majority (72.7%) being nursing technicians. Regarding the knowledge of primary and secondary coverage techniques, the Kappa index went from 0.56 (p=0.002) in the pre-test to 0.87 (p=0.001) in the post-test. As for the sterile dressing technique, there was a variation from 0.55 (p=0.002) in the pre-test to 0.91 (p=0.001) in the post-test. Regarding the cleaning of pressure injuries, there was a variation from 0.81 (CI: 0.62-0.84) in the pre-test to 0.91 (0.85-0.97) in the post-test. The knowledge about the use of a sterile spatula to distribute the dressing in the wound increased from an agreement index from regular to good. Conclusion: The introduction of the realistic simulation in the clinical practice has created quality assessment indicators for the prevention and treatment of pressure injuries.

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Risk prediction models for the development of oral-mucosal pressure injuries in intubated patients in intensive care units: A prospective observational study

Choi

Kim

Kim³

2020

Journal of Tissue Viability

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A retrospective analysis of recurrent pressure ulcer in a burn center in Northeast China

Cited by 6 publications

References 31 publications

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes

Application of the educational method of realistic simulation in the treatment of pressure injuries

Risk prediction models for the development of oral-mucosal pressure injuries in intubated patients in intensive care units: A prospective observational study

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