Trial reportMedical science monitor : international medical journal of experimental and clinical research2020
Risk Factor Analysis and Risk Prediction Model Construction of Pressure Injury in Critically Ill Patients with Cancer: A Retrospective Cohort Study in China.
Trial report in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- Nomogram for intraoperatively acquired pressure injuries in children undergoing cardiac surgery with cardiopulmonary bypass: a retrospective study.BMC pediatrics · 2024Article
- Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Review
- Risk factors for hospital acquired pressure injury in patients with spinal cord injury during first rehabilitation: prospective cohort study.Spinal cord · 2022Observational
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND The aim of this study was to analyze the risk factors of pressure injury (PI) in critically ill patients with cancer to build a risk prediction model for PI. MATERIAL AND METHODS Between January 2018 and December 2019, a total of 486 critically ill patients with cancer were enrolled in the study. Univariate analysis and binary logistic regression analysis were used to explore risk factors. Then, a risk prediction equation was constructed and a receiver operator characteristic (ROC) curve analysis model was used for prediction. RESULTS Of the 486 critically ill patients with cancer, 15 patients developed PI. Risk factors found to have a significant impact on PI in critically ill patients with cancer included the APACHE II score (P<0.001), semi-reclining position (P=0.006), humid environment/moist skin (P<0.001), and edema (P<0.001). These 4 independent risk factors were used in the regression equation, and the risk prediction equation was constructed as Z=0.112×APACHE II score +2.549×semi-reclining position +2.757×moist skin +1.795×edema-9.086. From the ROC curve analysis, the area under the curve (AUC) was 0.938, sensitivity was 100.00%, specificity was 83.40%, and Youden index was 0.834. CONCLUSIONS The PI risk prediction model developed in this study has a high predictive value and provides a basis for PI prevention and treatment measures for critically ill patients with cancer.
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