Hospital-Acquired Pressure Ulcers

Perneger, Thomas; Heliot, Céliane; Raë, Anne-Claire; Borst, F.; Gaspoz, Jean‐Michel

doi:10.1001/archinte.158.17.1940

Cited by 86 publications

(16 citation statements)

References 26 publications

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“…However, no single factor explains PU risk, but rather a complex interaction of factors increases the probability of PU development [8]. Surgical intervention itself is a risk factor [9], and surgical duration is significantly positively associated with PU development [10]. Some studies have shown that PU development is negatively correlated with mobility and activity [8]; neurological, motor impairment and activity level [11]; mobility, activity, and sensory perception [12]; functional abilities such as getting out of bed, walking, and feeding [13]; and laboratory values such as albumin [14].…”

Section: Introductionmentioning

confidence: 99%

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On-Admission Pressure Ulcer Prediction Using the Nursing Needs Score

Nakamura¹,

Ghaibeh²,

Setoguchi³

et al. 2015

JMIR Med Inform

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BackgroundPressure ulcers (PUs) are considered a serious problem in nursing care and require preventive measures. Many risk assessment methods are currently being used, but most require the collection of data not available on admission. Although nurses assess the Nursing Needs Score (NNS) on a daily basis in Japanese acute care hospitals, these data are primarily used to standardize the cost of nursing care in the public insurance system for appropriate nurse staffing, and have never been used for PU risk assessment.ObjectiveThe objective of this study was to predict the risk of PU development using only data available on admission, including the on-admission NNS score.MethodsLogistic regression was used to generate a prediction model for the risk of developing PUs after admission. A random undersampling procedure was used to overcome the problem of imbalanced data.ResultsA combination of gender, age, surgical duration, and on-admission total NNS score (NNS group B; NNS-B) was the best predictor with an average sensitivity, specificity, and area under receiver operating characteristic curve (AUC) of 69.2% (6920/100), 82.8% (8280/100), and 84.0% (8400/100), respectively. The model with the median AUC achieved 80% (4/5) sensitivity, 81.3% (669/823) specificity, and 84.3% AUC.ConclusionsWe developed a model for predicting PU development using gender, age, surgical duration, and on-admission total NNS-B score. These results can be used to improve the efficiency of nurses and reduce the number of PU cases by identifying patients who require further examination.

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Section: Introductionmentioning

confidence: 99%

“…Gender, on the other hand, is rather controversial. Some studies have reported that males are more prone to develop PUs than females [8,15], while others have reported a higher tendency among females [9]. Yet, other studies have concluded that gender is not a risk factor for PU development [17,18].…”

Section: Introductionmentioning

confidence: 99%

On-Admission Pressure Ulcer Prediction Using the Nursing Needs Score

Nakamura¹,

Ghaibeh²,

Setoguchi³

et al. 2015

JMIR Med Inform

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“…Another important factor in risk of developing pressure ulcer is whether the patient is being moved to different body positions frequently enough, especially when patient's own ability to move and mobility is impaired. This node reflects whether the staff can/do move the patient as often as the patient should be repositioned -it is important to note that detecting and preventing pressure ulcers systematically is labor intensive (Perneger, et al, 1998)-in order to reduce the risk of developing ulcer. This is also a binary node with states: Adequate frequency of move and inadequate frequency of move.…”

Section: Pressure Ulcer Bbn Developmentmentioning

confidence: 99%

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

2017

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Despite efforts to provide safe, effective medical care, adverse events still occur with some regularity. While risk cannot be entirely eliminated from healthcare activities, an important goal is to develop effective and durable mitigation strategies to render the system 'safer'. In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the healthcare domain, this can be extremely challenging due to the wide variability in the way that healthcare processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study we have developed a generic methodology for evaluating dynamic changes in adverse event risk in acute care hospitals as a function of organizational and non-organizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational level and policy level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to adverse event risk. It also captures the feedback of organizational factors and decisions over time and the non-linearities in these feedback effects. Second, Bayesian Belief Network (BBN) framework is used to represent patient-level factors and also physician level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired adverse event. The model is intended to support hospital decisions with regards to staffing, length of stay, and investment in safeties, which evolve dynamically over time. The methodology has been applied in modeling the two types of common adverse events; pressure ulcers and vascular catheter-associated infection, and has been validated with eight years of clinical data.

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“…Common indicators used by scales such as Braden, Douglas, or Cubbin and Jackson are patient mobility, skin condition, moisture (e.g., related to incontinence), nutrition and activity, and level of consciousness. The feature list generated using statistical methods during the development of ePUP is supportive of these and previous studies of alternate predictors for the risk of pressure ulcer development (Anthony, Reynolds, & Russell, 2000;Perneger, Heliot, Rae, Borst, & Gaspoz, 1998;Salzberg et al, 1996). For example, laboratory tests for serum albumin and intravascular protein binding protein levels and a diagnosis code for "severe protein-calorie malnutrition," indicating nutritional concerns, were included.…”

Section: Pressure Ulcer Risk Assessmentmentioning

confidence: 99%

“…Based on these results, computerized patient classification systems to assess acuity as a risk factor for pressure ulcer development have been described (Adams & Duchene, 1985;Harbit, 1996). In addition, several studies have used logistic (Kim & Lang, 2006) and multivariate regression techniques to assess the validity of existing assessment tools (Anthony, Reynolds, & Russell, 2003), as well as the predictive value of certain features (Anthony, Clark, & Dallender, 2000;Anthony, Reynolds, & Russell, 2000;Perneger et al, 1998).…”

Section: Use Of Machine Learning Techniquesmentioning

confidence: 99%

Evaluation of an Automated Pressure Ulcer Risk Assessment Model

Borlawsky

Hripcsak

2007

Home Health Care Management & Practice

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The key to timely interventions and reducing avoidable incidence is the early identification of patients at risk for developing pressure ulcers. To enable the automatic detection of such patients and inform acute care interdisciplinary providers, a filter feature model using heuristic statistical methods was applied to a relational database of retrospective patient data including demographics, medications, and clinical visit details. These attributes served as input for the C4.5 decision tree induction algorithm, which was used to classify patient risk. The validity of the resulting classification model, Electronic Pressure Ulcer Prediction (ePUP), was assessed using a fourfold cross-validation. The current results show a limited application of such a naïve classification algorithm for automating pressure ulcer risk assessments. Additional refinements will be necessary before the predictions of ePUP are sufficient for general clinical use and the improvement of patient safety in acute care settings, and during the transition from hospital to home.

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Hospital-Acquired Pressure Ulcers

Cited by 86 publications

References 26 publications

On-Admission Pressure Ulcer Prediction Using the Nursing Needs Score

On-Admission Pressure Ulcer Prediction Using the Nursing Needs Score

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

Evaluation of an Automated Pressure Ulcer Risk Assessment Model

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