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The use of a logistic regression model to develop a risk assessment of intraoperatively acquired pressure ulcer

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  • Ling Gao
  • Lina Yang
  • Xiaoqin Li
  • Jin Chen
  • Juan Du
  • Xiaoxia Bai
  • Xianjun Yang

Abstract

Aims and objectives To screen the factors of intraoperatively acquired pressure ulcer and establish a new risk assessment model of intraoperatively acquired pressure ulcer. Design This is a prospective study. Methods A total of 1,963 patients who received neurosurgery, orthopaedics, paediatric surgery and cardiac surgery therapy in Sichuan Academy of Medical Science and Provincial People's Hospital in China from October 2015–October 2016 were enrolled in the study, and their clinical parameters were collected. Multivariable logistic regression analysis and decision tree analysis were used to analyse and screen the factors of intraoperatively acquired pressure ulcer and establish the risk assessment model of intraoperatively acquired pressure ulcer. Results The risk factors for intraoperatively acquired pressure ulcer included the application of external force during operation (β = 1.10, OR = 3.20), lean body mass (β = 1.08, OR = 2.95), time of operation ≥6 hr (β = 2.66, OR = 14.30), prone position operation (β = 1.13, OR = 3.10), cardiopulmonary bypass during operation (β = 1.72, OR = 5.59) and intraoperative blood loss (β = 0.67, OR = 1.95). The new risk assessment model showed that the AUC of ROC curve was 0.897 (p

Suggested Citation

  • Ling Gao & Lina Yang & Xiaoqin Li & Jin Chen & Juan Du & Xiaoxia Bai & Xianjun Yang, 2018. "The use of a logistic regression model to develop a risk assessment of intraoperatively acquired pressure ulcer," Journal of Clinical Nursing, John Wiley & Sons, vol. 27(15-16), pages 2984-2992, August.
    • Handle: RePEc:wly:jocnur:v:27:y:2018:i:15-16:p:2984-2992
    DOI: 10.1111/jocn.14491
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    Cited by:

    1. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(1), pages 1-19, January.
    2. Luciana Bjorklund‐Lima & Maria Müller‐Staub & Michelle Cardoso e Cardozo & Daniela de Souza Bernardes & Eneida Rejane Rabelo‐Silva, 2019. "Clinical indicators of nursing outcomes classification for patient with risk for perioperative positioning injury: A cohort study," Journal of Clinical Nursing, John Wiley & Sons, vol. 28(23-24), pages 4367-4378, December.
    3. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review," IJERPH, MDPI, vol. 20(1), pages 1-46, January.
    4. Li Zhi RN & Chang Lin, 2019. "A Review on Perioperative Pressure Ulcers," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 21(5), pages 16220-16224, October.
    5. Man-Long Chung & Manuel Widdel & Julian Kirchhoff & Julia Sellin & Mohieddine Jelali & Franziska Geiser & Martin Mücke & Rupert Conrad, 2022. "Risk Factors for Pressure Injuries in Adult Patients: A Narrative Synthesis," IJERPH, MDPI, vol. 19(2), pages 1-17, January.
    6. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(6), pages 1-18, March.

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