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Ikuta K, Fukuoka K, Kimura Y, Nakagaki M, Ohga M, Suyama Y, Morita M, Umeda R, Konishi M, Nishikawa H, Yagi S. An ingenious deep learning approach for pressure injury depth evaluation with limited data. J Tissue Viability 2024:S0965-206X(24)00071-8. [PMID: 38825443 DOI: 10.1016/j.jtv.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 04/05/2024] [Accepted: 05/21/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND The development of models using deep learning (DL) to assess pressure injuries from wound images has recently gained attention. Creating enough supervised data is important for improving performance but is time-consuming. Therefore, the development of models that can achieve high performance with limited supervised data is desirable. MATERIALS AND METHODS This retrospective observational study utilized DL and included patients who received medical examinations for sacral pressure injuries between February 2017 and December 2021. Images were labeled according to the DESIGN-R® classification. Three artificial intelligence (AI) models for assessing pressure injury depth were created with a convolutional neural network (Categorical, Binary, and Combined classification models) and performance was compared among the models. RESULTS A set of 414 pressure injury images in five depth stages (d0 to D4) were analyzed. The Combined classification model showed superior performance (F1-score, 0.868). The Categorical classification model frequently misclassified d1 and d2 as d0 (d0 Precision, 0.503), but showed high performance for D3 and D4 (F1-score, 0.986 and 0.966, respectively). The Binary classification model showed high performance in differentiating between d0 and d1-D4 (F1-score, 0.895); however, performance decreased with increasing number of evaluation steps. CONCLUSION The Combined classification model displayed superior performance without increasing the supervised data, which can be attributed to use of the high-performance Binary classification model for initial d0 evaluation and subsequent use of the Categorical classification model with fewer evaluation steps. Understanding the unique characteristics of classification methods and deploying them appropriately can enhance AI model performance.
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Affiliation(s)
- Kento Ikuta
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Kohei Fukuoka
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Yuka Kimura
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Makoto Nakagaki
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Makoto Ohga
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Yoshiko Suyama
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Maki Morita
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Ryunosuke Umeda
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan
| | - Mamoru Konishi
- Focus Systems Corporation, 2-7-8 Higashi Gotanda, Shinagawa-ku, Tokyo, 141-0022, Japan
| | - Hiroyuki Nishikawa
- Focus Systems Corporation, 2-7-8 Higashi Gotanda, Shinagawa-ku, Tokyo, 141-0022, Japan
| | - Shunjiro Yagi
- Department of Plastic and Reconstructive Surgery, Tottori University Hospital, 36-1 Nishicho, Yonago, Tottori, 683-8504, Japan.
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Reese TJ, Domenico HJ, Hernandez A, Byrne DW, Moore RP, Williams JB, Douthit BJ, Russo E, McCoy AB, Ivory CH, Steitz BD, Wright A. Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation. JMIR Med Inform 2024; 12:e51842. [PMID: 38722209 PMCID: PMC11094428 DOI: 10.2196/51842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/08/2024] [Accepted: 03/10/2024] [Indexed: 05/18/2024] Open
Abstract
Background Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.
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Affiliation(s)
- Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Henry J Domenico
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Antonio Hernandez
- Department of Anesthesiology, Division of Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Daniel W Byrne
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ryan P Moore
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jessica B Williams
- Department of Nursing, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Brian J Douthit
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elise Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Catherine H Ivory
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bryan D Steitz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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Mansouri M, Krishnan G, McDonagh DC, Zallek CM, Hsiao-Wecksler ET. Review of assistive devices for the prevention of pressure ulcers: an engineering perspective. Disabil Rehabil Assist Technol 2024; 19:1511-1530. [PMID: 37101406 DOI: 10.1080/17483107.2023.2204127] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 04/13/2023] [Indexed: 04/28/2023]
Abstract
PURPOSE Pressure ulcers (PUs) are prevalent among immobile bed or wheelchair-reliant individuals who experience prolonged sedentary positions. Pressure relief and frequent repositioning of body posture help to mitigate complications associated with PUs. Adherence with regular repositioning is difficult to maintain due to nursing labour shortages or constraints of in-home caregivers. Manual repositioning, transferring, and lifting of immobile patients are physically demanding tasks for caregivers. This review aimed to explore and categorize these devices, discuss the significant technical challenges that need addressing, and identify potential design opportunities. MATERIALS AND METHODS In this review, a literature search was conducted using PubMED, Science Direct, Google Scholar and IEEE Xplore databases including studies from 1995 until Feb 2023 with keywords such as pressure ulcer, assistive device, pressure relief, repositioning, transfer, etc. Both commercial and research-level devices were included in the search. RESULTS 142 devices or technologies were identified and classified into four main categories that were further subcategorized. Within each category, the devices were investigated in terms of their mechanical design, actuation methods, control strategies, sensing technologies, and level of autonomy. Limitations of current technologies are design complexity, lack of patient comfort, and a lack of autonomy requiring caregivers frequent intervention. CONCLUSIONS Several devices have been developed to help with prevention and mitigation of PUs. There remain challenges that hinder the widespread accessibility and use of current technologies. Advancements in assistive technologies for pressure ulcer mitigation could lie at the intersection of robotics, sensors, perception, user-centered design, and autonomous systems.
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Affiliation(s)
- Mahshid Mansouri
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Girish Krishnan
- Department of Industrial & Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Deana C McDonagh
- School of Art + Design and Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Elizabeth T Hsiao-Wecksler
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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Toledo LV, Bhering LL, Ercole FF. Artificial intelligence to predict bed bath time in Intensive Care Units. Rev Bras Enferm 2024; 77:e20230201. [PMID: 38422311 PMCID: PMC10895787 DOI: 10.1590/0034-7167-2023-0201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVES to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. METHODS a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. RESULTS among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. CONCLUSIONS the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.
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Wang I, Walker RM, Gillespie BM, Scott I, Sugathapala RDUP, Chaboyer W. Risk factors predicting hospital-acquired pressure injury in adult patients: An overview of reviews. Int J Nurs Stud 2024; 150:104642. [PMID: 38041937 DOI: 10.1016/j.ijnurstu.2023.104642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUND Hospital-acquired pressure injuries remain a significant patient safety threat. Current well-known pressure injury risk assessment tools have many limitations and therefore do not accurately predict the risk of pressure injury development over diverse populations. A contemporary understanding of the risk factors predicting pressure injury in adult hospitalised patients will inform pressure injury prevention and future researchers considering risk assessment tool development may benefit from our summary and synthesis of risk factors. OBJECTIVE To summarise and synthesise systematic reviews that identify risk factors for hospital-acquired pressure injury development in adult patients. DESIGN An overview of systematic reviews. METHODS Cochrane and the Joanna Briggs Institute methodologies guided this overview. The Cochrane library, CINAHL, MEDLINE, and Embase databases were searched for relevant articles published in English from January 2008 to September 2022. Two researchers independently screened articles against the predefined inclusion and exclusion criteria, extracted data and assessed the quality of the included reviews using "a measurement tool to assess systematic reviews" (AMSTAR version 2). Data were categorised using an inductive approach and synthesised according to the recent pressure injury conceptual frameworks. RESULTS From 11 eligible reviews, 37 risk factors were categorised inductively into 14 groups of risk factors. From these, six groups were classified into two domains: four to mechanical boundary conditions and two to susceptibility and tolerance of the individual. The remaining eight groups were evident across both domains. Four main risk factors, including diabetes, length of surgery or intensive care unit stay, vasopressor use, and low haemoglobin level were synthesised. The overall quality of the included reviews was low in five studies (45 %) and critically low in six studies (55 %). CONCLUSIONS Our findings highlighted the limitations in the methodological quality of the included reviews that may have influenced our results regarding risk factors. Current risk assessment tools and conceptual frameworks do not fully explain the complex and changing interactions amongst risk factors. This may warrant the need for more high-quality research, such as cohort studies, focussing on predicting hospital-acquired pressure injury in adult patients, to reconsider these risk factors we synthesised. REGISTRATION This overview was registered with the PROSPERO (CRD42022362218) on 27 September 2022.
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Affiliation(s)
- Isabel Wang
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Nursing and Midwifery, Griffith University, Gold Coast, Australia.
| | - Rachel M Walker
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; The Princess Alexandra Hospital, Brisbane, Australia. https://twitter.com/rachelmwalker
| | - Brigid M Gillespie
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; Gold Coast University Hospital, Gold Coast, Australia. https://twitter.com/bgillespie6
| | - Ian Scott
- The Princess Alexandra Hospital, Brisbane, Australia; School of Clinical Medicine, University of Queensland, Brisbane, Queensland, Australia
| | | | - Wendy Chaboyer
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia. https://twitter.com/WendyChaboyer
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Ho JC, Sotoodeh M, Zhang W, Simpson RL, Hertzberg VS. An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations. Comput Biol Med 2024; 168:107754. [PMID: 38016372 PMCID: PMC10843556 DOI: 10.1016/j.compbiomed.2023.107754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/07/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure injury prediction algorithms have been developed to tackle this problem, but these models assume a consensus, gold-standard label (i.e., presence of pressure injury or not) is present for all training data. Existing definitions for identifying hospital-acquired pressure injuries are inconsistent due to the lack of high-quality documentation surrounding pressure injuries. To address this issue, we propose in this paper an ensemble-based algorithm that leverages truth inference methods to resolve label inconsistencies between various case definitions and the level of disagreements in annotations. Application of our method to MIMIC-III, a publicly available intensive care unit dataset, gives empirical results that illustrate the promise of learning a prediction model using truth inference-based labels and observed conflict among annotators.
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Affiliation(s)
- Joyce C Ho
- Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, 30322, GA, USA.
| | - Mani Sotoodeh
- Canadian Institute for Health Information, 495 Richmond Road, Suite 600 - WS-602, Ottawa, K2A 4H6, Ontario, Canada
| | - Wenhui Zhang
- Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, 30322, GA, USA
| | - Roy L Simpson
- Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, 30322, GA, USA
| | - Vicki Stover Hertzberg
- Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, 30322, GA, USA
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Tehrany PM, Zabihi MR, Ghorbani Vajargah P, Tamimi P, Ghaderi A, Norouzkhani N, Zaboli Mahdiabadi M, Karkhah S, Akhoondian M, Farzan R. Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm. Int Wound J 2023; 20:3768-3775. [PMID: 37312659 PMCID: PMC10588304 DOI: 10.1111/iwj.14275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 06/15/2023] Open
Abstract
Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.
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Affiliation(s)
- Pooya M. Tehrany
- Department of Orthopaedic Surgery, Faculty of MedicineNational University of MalaysiaBaniMalaysia
| | - Mohammad Reza Zabihi
- Department of Immunology, School of MedicineTehran University of Medical SciencesTehranIran
| | - Pooyan Ghorbani Vajargah
- Burn and Regenerative Medicine Research CenterGuilan University of Medical SciencesRashtIran
- Student Research Committee, Department of Medical‐Surgical Nursing, School of Nursing and MidwiferyGuilan University of Medical SciencesRashtIran
| | - Pegah Tamimi
- Center for Research and Training in Skin Diseases and LeprosyTehran University of Medical SciencesTehranIran
| | - Aliasghar Ghaderi
- Center for Research and Training in Skin Diseases and LeprosyTehran University of Medical SciencesTehranIran
| | - Narges Norouzkhani
- Department of Medical Informatics, Faculty of MedicineMashhad University of Medical SciencesMashhadIran
| | | | - Samad Karkhah
- Burn and Regenerative Medicine Research CenterGuilan University of Medical SciencesRashtIran
- Student Research Committee, Department of Medical‐Surgical Nursing, School of Nursing and MidwiferyGuilan University of Medical SciencesRashtIran
| | - Mohammad Akhoondian
- Department of Physiology, School of Medicine, Cellular and the Molecular Research CenterGuilan University of Medical ScienceRashtIran
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of MedicineGuilan University of Medical SciencesRashtIran
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Pouzols S, Despraz J, Mabire C, Raisaro JL. Development of a Predictive Model for Hospital-Acquired Pressure Injuries. Comput Inform Nurs 2023; 41:884-891. [PMID: 37279051 DOI: 10.1097/cin.0000000000001029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Hospital-acquired pressure injuries are a challenge for healthcare systems, and the nurse's role is essential in their prevention. The first step is risk assessment. The development of advanced data-driven methods based on machine learning techniques can improve risk assessment through the use of routinely collected data. We studied 24 227 records from 15 937 distinct patients admitted to medical and surgical units between April 1, 2019, and March 31, 2020. Two predictive models were developed: random forest and long short-term memory neural network. Model performance was then evaluated and compared with the Braden score. The areas under the receiver operating characteristic curve, the specificity, and the accuracy of the long short-term memory neural network model (0.87, 0.82, and 0.82, respectively) were higher than those of the random forest model (0.80, 0.72, and 0.72, respectively) and the Braden score (0.72, 0.61, and 0.61, respectively). The sensitivity of the Braden score (0.88) was higher than that of long short-term memory neural network model (0.74) and the random forest model (0.73). The long short-term memory neural network model has the potential to support nurses in clinical decision-making. Implementation of this model in the electronic health record could improve assessment and allow nurses to focus on higher-priority interventions.
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Affiliation(s)
- Sophie Pouzols
- Author Affiliations: Healthcare Direction (CHUV) (Ms Pouzols and Pr Mabire); Biomedical Data Science Center (Mr Despraz and Dr Raisaro), and Institute of Higher Education and Research in Healthcare (Pr Mabire), Lausanne University Hospital; and University of Lausanne (Pr Mabire), Lausanne, Switzerland
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10
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Barghouthi ED, Owda AY, Asia M, Owda M. Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms. Diagnostics (Basel) 2023; 13:2739. [PMID: 37685277 PMCID: PMC10486671 DOI: 10.3390/diagnostics13172739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.
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Affiliation(s)
- Eba’a Dasan Barghouthi
- Health Sciences Department, Arab American University, Ramallah P600, Palestine; (E.D.B.); (M.A.)
| | - Amani Yousef Owda
- Department of Natural Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine
| | - Mohammad Asia
- Health Sciences Department, Arab American University, Ramallah P600, Palestine; (E.D.B.); (M.A.)
| | - Majdi Owda
- Faculty of Data Science, Arab American University, Ramallah P600, Palestine;
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11
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Toffaha KM, Simsekler MCE, Omar MA. Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review. Artif Intell Med 2023; 141:102560. [PMID: 37295900 DOI: 10.1016/j.artmed.2023.102560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. OBJECTIVE This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. METHODS A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. RESULTS The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. CONCLUSIONS There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.
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Affiliation(s)
- Khaled M Toffaha
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mecit Can Emre Simsekler
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Mohammed Atif Omar
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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Rêgo ADS, Furtado GE, Bernardes RA, Santos-Costa P, Dias RA, Alves FS, Ainla A, Arruda LM, Moreira IP, Bessa J, Fangueiro R, Gomes F, Henriques M, Sousa-Silva M, Pinto AC, Bouçanova M, Sousa VIF, Tavares CJ, Barboza R, Carvalho M, Filipe L, Sousa LB, Apóstolo JA, Parreira P, Salgueiro-Oliveira A. Development of Smart Clothing to Prevent Pressure Injuries in Bedridden Persons and/or with Severely Impaired Mobility: 4NoPressure Research Protocol. Healthcare (Basel) 2023; 11:healthcare11101361. [PMID: 37239647 DOI: 10.3390/healthcare11101361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/25/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Pressure injuries (PIs) are a major public health problem and can be used as quality-of-care indicators. An incipient development in the field of medical devices takes the form of Smart Health Textiles, which can possess innovative properties such as thermoregulation, sensing, and antibacterial control. This protocol aims to describe the process for the development of a new type of smart clothing for individuals with reduced mobility and/or who are bedridden in order to prevent PIs. This paper's main purpose is to present the eight phases of the project, each consisting of tasks in specific phases: (i) product and process requirements and specifications; (ii and iii) study of the fibrous structure technology, textiles, and design; (iv and v) investigation of the sensor technology with respect to pressure, temperature, humidity, and bioactive properties; (vi and vii) production layout and adaptations in the manufacturing process; (viii) clinical trial. This project will introduce a new structural system and design for smart clothing to prevent PIs. New materials and architectures will be studied that provide better pressure relief, thermo-physiological control of the cutaneous microclimate, and personalisation of care.
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Affiliation(s)
- Anderson da Silva Rêgo
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - Guilherme Eustáquio Furtado
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
- Polytechnic Institute of Coimbra, Applied Research Institute, Rua da Misericórdia, Lagar dos Cortiços-S. Martinho do Bispo, 3045-093 Coimbra, Portugal
| | - Rafael A Bernardes
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - Paulo Santos-Costa
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - Rosana A Dias
- International Iberian Laboratory of Nanotechnology (INL), 4715-330 Braga, Portugal
| | - Filipe S Alves
- International Iberian Laboratory of Nanotechnology (INL), 4715-330 Braga, Portugal
| | - Alar Ainla
- International Iberian Laboratory of Nanotechnology (INL), 4715-330 Braga, Portugal
| | - Luisa M Arruda
- Fibrenamics, Institute of Innovation on Fibre-Based Materials and Composites, University of Minho, 4800-058 Guimaraes, Portugal
- Centre for Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimaraes, Portugal
| | - Inês P Moreira
- Fibrenamics, Institute of Innovation on Fibre-Based Materials and Composites, University of Minho, 4800-058 Guimaraes, Portugal
- Centre for Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimaraes, Portugal
| | - João Bessa
- Fibrenamics, Institute of Innovation on Fibre-Based Materials and Composites, University of Minho, 4800-058 Guimaraes, Portugal
- Centre for Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimaraes, Portugal
| | - Raul Fangueiro
- Fibrenamics, Institute of Innovation on Fibre-Based Materials and Composites, University of Minho, 4800-058 Guimaraes, Portugal
- Centre for Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimaraes, Portugal
| | - Fernanda Gomes
- CEB-Centre of Biological Engineering, LIBRO-Laboratório de Investigação em Biofilmes Rosário Oliveira, University of Minho, 4710-057 Braga, Portugal
- LABBELS-Associate Laboratory, 4710-057 Braga, Portugal
| | - Mariana Henriques
- CEB-Centre of Biological Engineering, LIBRO-Laboratório de Investigação em Biofilmes Rosário Oliveira, University of Minho, 4710-057 Braga, Portugal
- LABBELS-Associate Laboratory, 4710-057 Braga, Portugal
| | - Maria Sousa-Silva
- CEB-Centre of Biological Engineering, LIBRO-Laboratório de Investigação em Biofilmes Rosário Oliveira, University of Minho, 4710-057 Braga, Portugal
- LABBELS-Associate Laboratory, 4710-057 Braga, Portugal
| | - Alexandra C Pinto
- CEB-Centre of Biological Engineering, LIBRO-Laboratório de Investigação em Biofilmes Rosário Oliveira, University of Minho, 4710-057 Braga, Portugal
- LABBELS-Associate Laboratory, 4710-057 Braga, Portugal
| | - Maria Bouçanova
- Impetus Portugal-Têxteis Sa (IMPETUS), 4740-696 Barcelos, Portugal
| | - Vânia Isabel Fernande Sousa
- Physics Center of Minho and Porto Universities (CF-UM-PT), Campus of Azurém, University of Minho, 4804-533 Guimarães, Portugal
| | - Carlos José Tavares
- Physics Center of Minho and Porto Universities (CF-UM-PT), Campus of Azurém, University of Minho, 4804-533 Guimarães, Portugal
| | - Rochelne Barboza
- Centre for Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimaraes, Portugal
| | - Miguel Carvalho
- Centre for Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimaraes, Portugal
| | - Luísa Filipe
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - Liliana B Sousa
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - João A Apóstolo
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - Pedro Parreira
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
| | - Anabela Salgueiro-Oliveira
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3000-232 Coimbra, Portugal
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13
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Luo Z, Liu S, Yang L, Zhong S, Bai L. Ambulance referral of more than 2 hours could result in a high prevalence of medical-device-related pressure injuries (MDRPIs) with characteristics different from some inpatient settings: a descriptive observational study. BMC Emerg Med 2023; 23:44. [PMID: 37098503 PMCID: PMC10127406 DOI: 10.1186/s12873-023-00815-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/19/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Medical device-related pressure injuries(MDRPI) are prevalent and attracting more attention. During ambulance transfer, the shear force caused by braking and acceleration; extensive medical equipment crowed in a narrow space add external risk factors for MDRPIs. However, there is insufficient research on the relationship between MDRPIs and ambulance transfers. This study aims to clarify the prevalence and characteristics of MDRPI during ambulance transfer. METHOD A descriptive observational study was conducted with convenience sampling. Before starting the study, six PI specialist nurses certified by the Chinese Nursing Association trained emergency department nurses for three MDRPI and Braden Scale sessions, one hour for each session. Data and images of PIs and MDRPIs are uploaded via the OA system by emergency department nurses and reviewed by these six specialist nurses. The information collection begins on 1 July 2022 and ends on 1 August 2022. Demographic and clinical characteristics and a list of medical devices were collected by emergency nurses using a screening form developed by researchers. RESULTS One hundred one referrals were eventually included. The mean age of participants was (58.3 ± 11.69) years, predominantly male (67.32%, n = 68), with a mean BMI of 22.48 ± 2.2. The mean referral time among participants was 2.26 ± 0.26 h, the mean BRADEN score was 15.32 ± 2.06, 53.46% (n = 54) of participants were conscious, 73.26% (n = 74) were in the supine position, 23.76% (n = 24) were in the semi-recumbent position, and only 3 (2.9%) were in the lateral position. Eight participants presented with MDRPIs, and all MDRPIs are stage 1. Patients with spinal injuries are most prone to MDRPIs (n = 6). The jaw is the area most prone to MDRPIs, caused by the cervical collar (40%, n = 4), followed by the heel (30%, n = 3) and nose bridge (20%, n = 2) caused by the respiratory devices and spinal board. CONCLUSION MDRPIs are more prevalent during long ambulance referrals than in some inpatient settings. The characteristics and related high-risk devices are also different. The prevention of MDRPIs during ambulance referrals deserves more research.
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Affiliation(s)
- Zhenyu Luo
- Guanyuan Central Hospital, Guanyuan, Sichuan, China.
| | - Sihui Liu
- The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Linhe Yang
- Guanyuan Central Hospital, Guanyuan, Sichuan, China
| | - Shuyan Zhong
- Guanyuan Central Hospital, Guanyuan, Sichuan, China
| | - Lihua Bai
- Guanyuan Central Hospital, Guanyuan, Sichuan, China
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14
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Dweekat OY, Lam SS, McGrath L. An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4911. [PMID: 36981818 PMCID: PMC10049700 DOI: 10.3390/ijerph20064911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Bedsores/Pressure Injuries (PIs) are the second most common diagnosis in healthcare system billing records in the United States and account for 60,000 deaths annually. Hospital-Acquired Pressure Injuries (HAPIs) are one classification of PIs and indicate injuries that occurred while the patient was cared for within the hospital. Until now, all studies have predicted who will develop HAPI using classic machine algorithms, which provides incomplete information for the clinical team. Knowing who will develop HAPI does not help differentiate at which point those predicted patients will develop HAPIs; no studies have investigated when HAPI develops for predicted at-risk patients. This research aims to develop a hybrid system of Random Forest (RF) and Braden Scale to predict HAPI time by considering the changes in patients' diagnoses from admission until HAPI occurrence. METHODS Real-time diagnoses and risk factors were collected daily for 485 patients from admission until HAPI occurrence, which resulted in 4619 records. Then for each record, HAPI time was calculated from the day of diagnosis until HAPI occurrence. Recursive Feature Elimination (RFE) selected the best factors among the 60 factors. The dataset was separated into 80% training (10-fold cross-validation) and 20% testing. Grid Search (GS) with RF (GS-RF) was adopted to predict HAPI time using collected risk factors, including Braden Scale. Then, the proposed model was compared with the seven most common algorithms used to predict HAPI; each was replicated for 50 different experiments. RESULTS GS-RF achieved the best Area Under the Curve (AUC) (91.20 ± 0.26) and Geometric Mean (G-mean) (91.17 ± 0.26) compared to the seven algorithms. RFE selected 43 factors. The most dominant interactable risk factors in predicting HAPI time were visiting ICU during hospitalization, Braden subscales, BMI, Stimuli Anesthesia, patient refusal to change position, and another lab diagnosis. CONCLUSION Identifying when the patient is likely to develop HAPI can target early intervention when it is needed most and reduces unnecessary burden on patients and care teams when patients are at lower risk, which further individualizes the plan of care.
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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15
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Eshetie TC, Moldovan M, Caughey GE, Lang C, Sluggett JK, Khadka J, Whitehead C, Crotty M, Corlis M, Visvanathan R, Wesselingh S, Inacio MC. Development of a Multivariable Prediction Model for Risk of Hospitalization With Pressure Injury After Entering Residential Aged Care. J Am Med Dir Assoc 2023; 24:299-306.e9. [PMID: 36603825 DOI: 10.1016/j.jamda.2022.12.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/05/2022] [Accepted: 12/03/2022] [Indexed: 01/04/2023]
Abstract
OBJECTIVES Although largely preventable, pressure injury is a major concern in individuals in permanent residential aged care (PRAC). Our study aimed to identify predictors and develop a prognostic model for risk of hospitalization with pressure injury (PI) using integrated Australian aged and health care data. DESIGN National retrospective cohort study. SETTING AND PARTICIPANTS Individuals ≥65 years old (N = 206,540) who entered 1797 PRAC facilities between January 1, 2009, and December 31, 2016. METHODS PI, ascertained from hospitalization records, within 365 days of PRAC entry was the outcome of interest. Individual, medication, facility, system, and health care-related factors were examined as predictors. Prognostic models were developed using elastic nets penalized regression and Fine and Gray models. Area under the receiver operating characteristics curve (AUC) assessed model discrimination out-of-sample. RESULTS Within 365 days of PRAC entry, 4.3% (n = 8802) of individuals had a hospitalization with PI. The strongest predictors for PI risk include history of PIs [sub-distribution hazard ratio (sHR) 2.41; 95% CI 1.77-3.29]; numbers of prior hospitalizations (having ≥5 hospitalizations, sHR 1.95; 95% CI 1.74-2.19); history of traumatic amputation of toe, ankle, foot and leg (sHR 1.72; 95% CI 1.44-2.05); and history of skin disease (sHR 1.54; 95% CI 1.45-1.65). Lower care needs at PRAC entry with respect to mobility, complex health care, and medication assistance were associated with lower risk of PI. The risk prediction model had an AUC of 0.74 (95% CI 0.72-0.75). CONCLUSIONS AND IMPLICATIONS Our prognostic model for risk of hospitalization with PI performed moderately well and can be used by health and aged care providers to implement risk-based prevention plans at PRAC entry.
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Affiliation(s)
- Tesfahun C Eshetie
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; UniSA Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia.
| | - Max Moldovan
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Gillian E Caughey
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia; Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Catherine Lang
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Janet K Sluggett
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia; Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Jyoti Khadka
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; Health and Social Care Economics Group, Caring Future Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia
| | - Craig Whitehead
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia; Southern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia
| | - Maria Crotty
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia; Southern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia
| | - Megan Corlis
- UniSA Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia; Southern Adelaide Local Health Network, SA Health, Adelaide, SA, Australia
| | - Renuka Visvanathan
- National Health and Medical Research Council, Centre of Research Excellence Frailty Trans-Disciplinary Research to Achieve Healthy Ageing, University of Adelaide, Adelaide, SA, Australia; Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia; Aged and Extended Care Services, The Queen Elizabeth Hospital and Basil Hetzel Institute for Translational Research, Central Adelaide Local Health Network, SA Health, Adelaide, SA, Australia
| | - Steve Wesselingh
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Maria C Inacio
- Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia
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16
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Sotoodeh M, Zhang W, Simpson RL, Hertzberg VS, Ho JC. A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study. JMIR Med Inform 2023; 11:e40672. [PMID: 36649481 PMCID: PMC9999254 DOI: 10.2196/40672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 12/24/2022] [Accepted: 01/14/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. OBJECTIVE The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. METHODS We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. RESULTS We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. CONCLUSIONS Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.
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Affiliation(s)
- Mani Sotoodeh
- Public Health Research Institute of University of Montreal, University of Montreal, Montreal, QC, Canada
| | - Wenhui Zhang
- School of Nursing, Emory University, Atlanta, GA, United States
| | - Roy L Simpson
- School of Nursing, Emory University, Atlanta, GA, United States
| | | | - Joyce C Ho
- Department of Computer Science, Emory University, Atlanta, GA, United States
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Dweekat OY, Lam SS, McGrath L. Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:796. [PMID: 36613118 PMCID: PMC9819814 DOI: 10.3390/ijerph20010796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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18
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Kandi LA, Rangel IC, Movtchan NV, Van Spronsen NR, Kruger EA. Comprehensive Management of Pressure Injury. Phys Med Rehabil Clin N Am 2022; 33:773-787. [DOI: 10.1016/j.pmr.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Zhou Y, Yang X, Ma S, Yuan Y, Yan M. A systematic review of predictive models for hospital-acquired pressure injury using machine learning. Nurs Open 2022; 10:1234-1246. [PMID: 36310417 PMCID: PMC9912391 DOI: 10.1002/nop2.1429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/28/2022] [Accepted: 10/11/2022] [Indexed: 02/11/2023] Open
Abstract
AIMS AND OBJECTIVES To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high-quality ML predictive models. BACKGROUND As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. DESIGN Systematic review. METHODS Relevant articles published between 2010-2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. RESULTS Twenty-three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149-75353; the prevalence of pressure injuries ranged from 0.5%-49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre-processing and model validation. CONCLUSIONS ML, as a powerful decision-making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre-processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. RELEVANCE TO CLINICAL PRACTICE This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre-processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision-making tool. A complete and rigorous model construction process should be followed in future studies to develop high-quality ML models that can be applied in clinical practice.
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Affiliation(s)
- You Zhou
- Department of Gastroenterology, Affiliated Hospital of Yangzhou UniversityYangzhou UniversityYangzhouChina,School of Nursing, School of Public HealthYangzhou UniversityYangzhouChina
| | - Xiaoxi Yang
- Department of Gastroenterology, Affiliated Hospital of Yangzhou UniversityYangzhou UniversityYangzhouChina,School of Nursing, School of Public HealthYangzhou UniversityYangzhouChina
| | - Shuli Ma
- Department of Gastroenterology, Affiliated Hospital of Yangzhou UniversityYangzhou UniversityYangzhouChina,School of Nursing, School of Public HealthYangzhou UniversityYangzhouChina
| | - Yuan Yuan
- Department of Nursing, Affiliated Hospital of Yangzhou UniversityYangzhou UniversityYangzhouChina
| | - Mingquan Yan
- Department of Gastroenterology, Affiliated Hospital of Yangzhou UniversityYangzhou UniversityYangzhouChina
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20
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Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively? Trop Med Infect Dis 2022; 7:tropicalmed7080184. [PMID: 36006276 PMCID: PMC9416261 DOI: 10.3390/tropicalmed7080184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 11/26/2022] Open
Abstract
The COVID-19 pandemic caused significant damage to global healthcare systems. Previous studies regarding COVID-19’s impact on outpatient numbers focused only on a specific department, lacking research data for multiple departments in general hospitals. We assessed differences in COVID-19’s impact on outpatient numbers for different departments to help hospital managers allocate outpatient doctor resources more effectively during the pandemic. We compared the outpatient numbers of 24 departments in a general hospital in Beijing in 2019 and 2020. We also examined an indicator not mentioned in previous studies, monthly departmental patient reservation rates. The results show that, compared with 2019, 2020 outpatient numbers decreased overall by 33.36%. Ten departments’ outpatient numbers decreased >33.36%; however, outpatient numbers increased in two departments. In 2020, the overall patient reservation rate in 24 departments was 82.22% of the 2019 reservation rate; the rates in 14 departments were <82.22%. Moreover, patient reservation rates varied across different months. Our research shows that COVID-19’s impact on different departments also varied. Additionally, our research suggests that well-known departments will be less affected by COVID-19, as will departments related to tumor treatment, where there may also be an increase in patient numbers. Patient reservation rates are an indicator worthy of attention. We suggest that hospital managers classify departments according to changes in outpatient numbers and patient reservation rates and adopt accurate, dynamic, and humanized management strategies to allocate outpatient doctor resources.
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The Utility of Nursing Notes Among Medicare Patients With Heart Failure to Predict 30-Day Rehospitalization: A Pilot Study. J Cardiovasc Nurs 2022; 37:E181-E186. [PMID: 34935742 PMCID: PMC9918309 DOI: 10.1097/jcn.0000000000000871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND For patients with heart failure (HF), there have been efforts to reduce the risk of 30-day rehospitalization, such as developing predictive models using electronic health records. Few previous studies used clinical notes to predict 30-day rehospitalization. OBJECTIVE The aim of this study was to assess the utility of nursing notes versus discharge summaries to predict 30-day rehospitalization among patients with HF. METHODS In this pilot study, we used free-text discharge summaries and nursing notes collected from a tertiary hospital. We randomly selected 500 Medicare patients with HF. We followed the natural language processing and machine learning pipeline for data analysis. RESULTS Thirty-day rehospitalization risk prediction using discharge summaries (n = 500) produced an area under the receiver operating characteristic curve of 0.74 (Bag of Words + Neural Network). Thirty-day rehospitalization risk prediction using nursing notes (n = 2046) resulted in an area under the receiver operating characteristic curve of 0.85 (Bag of Words + Neural Network). CONCLUSION Nursing notes provide a superior input to risk models for 30-day rehospitalization in Medicare patients with HF compared with discharge summaries.
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22
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Zhou Y, Gao J. Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway? Front Psychiatry 2022; 13:1037503. [PMID: 36405901 PMCID: PMC9667021 DOI: 10.3389/fpsyt.2022.1037503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/17/2022] [Indexed: 01/24/2023] Open
Abstract
The exact pathogenesis of autism spectrum disorder (ASD) is still unclear, yet some potential mechanisms may not have been evaluated before. Cuproptosis is a novel form of regulated cell death reported this year, and no study has reported the relationship between ASD and cuproptosis. This study aimed to identify ASD in suspected patients early using machine learning models based on biomarkers of the cuproptosis pathway. We collected gene expression profiles from brain samples from ASD model mice and blood samples from humans with ASD, selected crucial genes in the cuproptosis signaling pathway, and then analysed these genes with different machine learning models. The accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves of the machine learning models were estimated in the training, internal validation, and external validation cohorts. Differences between models were determined with Bonferroni's test. The results of screening with the Boruta algorithm showed that FDX1, DLAT, LIAS, and ATP7B were crucial genes in the cuproptosis signaling pathway for ASD. All selected genes and corresponding proteins were also expressed in the human brain. The k-nearest neighbor, support vector machine and random forest models could identify approximately 72% of patients with ASD. The artificial neural network (ANN) model was the most suitable for the present data because the accuracy, sensitivity, and specificity were 0.90, 1.00, and 0.80, respectively, in the external validation cohort. Thus, we first report the prediction of ASD in suspected patients with machine learning methods based on crucial biomarkers in the cuproptosis signaling pathway, and these findings may contribute to investigations of the potential pathogenesis and early identification of ASD.
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Affiliation(s)
- Yu Zhou
- Department of Child Rehabilitation Division, Huai'an Maternal and Child Health Care Center, Huai'an, China.,Affiliated Hospital of Yang Zhou University Medical College, Huai'an Maternal and Child Health Care Center, Huai'an, China
| | - Jing Gao
- Department of Child Rehabilitation Division, Huai'an Maternal and Child Health Care Center, Huai'an, China.,Affiliated Hospital of Yang Zhou University Medical College, Huai'an Maternal and Child Health Care Center, Huai'an, China
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23
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Abstract
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.
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24
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Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities. INFORMATICS 2021. [DOI: 10.3390/informatics8040076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.
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25
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Falcone M, De Angelis B, Pea F, Scalise A, Stefani S, Tasinato R, Zanetti O, Dalla Paola L. Challenges in the management of chronic wound infections. J Glob Antimicrob Resist 2021; 26:140-147. [PMID: 34144200 DOI: 10.1016/j.jgar.2021.05.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/21/2021] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVES Chronic wound infections may delay the healing process and are responsible for a significant burden on healthcare systems. Since inappropriate management may commonly occur in the care of these patients, this review aims to provide a practical guide underlining actions to avoid in the management of chronic wound infections. METHODS We performed a systematic review of the literature available in PubMed in the last 10 years, identifying studies regarding the management of patients with chronic wound infections. A panel of experts discussed the potential malpractices in this area. A list of 'Don'ts', including the main actions to be avoided, was drawn up using the 'Choosing Wisely' methodology. RESULTS In this review, we proposed a list of actions to avoid for optimal management of patients with chronic wound infections. Adequate wound bed preparation and wound antisepsis should be combined, as the absence of one of them leads to delayed healing and a higher risk of wound complications. Moreover, avoiding inappropriate use of systemic antibiotics is an important point because of the risk of selection of multidrug-resistant organisms as well as antibiotic-related adverse events. CONCLUSION A multidisciplinary team of experts in different fields (surgeon, infectious disease expert, microbiologist, pharmacologist, geriatrician) is required for the optimal management of chronic wound infections. Implementation of this approach may be useful to improve the management of patients with chronic wound infections.
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Affiliation(s)
- Marco Falcone
- Division of Infectious Diseases, Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy.
| | - Barbara De Angelis
- Surgical Science Department, Plastic and Reconstructive Surgery, University of Rome 'Tor Vergata', Rome, Italy
| | - Federico Pea
- Alma Mater Studiorum, University of Bologna, University Hospital IRCCS Policlinico Sant'Orsola Malpighi, Bologna, Italy
| | - Alessandro Scalise
- Clinic of Plastic and Reconstructive Surgery, Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona, Italy
| | - Stefania Stefani
- Department of Biomedical and Biotechnological Sciences, Biological Tower, University of Catania, Catania, Italy
| | - Rolando Tasinato
- Azienda Sanitaria Locale 3 Serenissima del Veneto, Department of General and Vascular Surgery, Venice, Italy
| | - Orazio Zanetti
- Alzheimer Unit, IRCCS S. Centro Giovanni di Dio 'Fatebenefratelli', Brescia, Italy
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