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Войти в исследования Journal of tissue viability · 2024
Development and validation of a risk prediction model for community-acquired pressure injury in a cancer population: A case-control study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Zhang Zhi-Li, Luo Man, Sun Ru-Yin, Liu Yan
Аннотация
BACKGROUND: Patients with cancer are susceptible to pressure injuries, which accelerate deterioration and death. In patients with post-acute cancer, the risk of pressure injury is ignored in home or community settings.
OBJECTIVE: To develop and validate a community-acquired pressure injury risk prediction model for cancer patients.
METHODS: All research data were extracted from the hospital's electronic medical record system. The identification of optimal predictors is based on least absolute shrinkage and selection operator regression analysis combined with clinical judgment. The performance of the model was evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve (AUC), calibration analysis and decision curve analysis. The model was used for internal and external validation, and was presented as a nomogram.
RESULTS: In total, 6257 participants were recruited for this study. Age, malnutrition, chronic respiratory failure, body mass index, and activities of daily living scores were identified as the final predictors. The AUC of the model in the training and validation set was 0.87 (95 % confidence interval [CI], 0.85-0.89), 0.88 (95 % CI, 0.85-0.91), respectively. The model demonstrated acceptable calibration and clinical benefits.
CONCLUSIONS: Comorbidities in patients with cancer are closely related to the etiology of pressure injury, and can be used to predict the risk of pressure injury.
IMPLICATIONS FOR PRACTICE: This study provides a tool to predict the risk of pressure injury for cancer patients. This suggests that improving the respiratory function and nutritional status of cancer patients may reduce the risk of community-acquired pressure injury.
OBJECTIVE: To develop and validate a community-acquired pressure injury risk prediction model for cancer patients.
METHODS: All research data were extracted from the hospital's electronic medical record system. The identification of optimal predictors is based on least absolute shrinkage and selection operator regression analysis combined with clinical judgment. The performance of the model was evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve (AUC), calibration analysis and decision curve analysis. The model was used for internal and external validation, and was presented as a nomogram.
RESULTS: In total, 6257 participants were recruited for this study. Age, malnutrition, chronic respiratory failure, body mass index, and activities of daily living scores were identified as the final predictors. The AUC of the model in the training and validation set was 0.87 (95 % confidence interval [CI], 0.85-0.89), 0.88 (95 % CI, 0.85-0.91), respectively. The model demonstrated acceptable calibration and clinical benefits.
CONCLUSIONS: Comorbidities in patients with cancer are closely related to the etiology of pressure injury, and can be used to predict the risk of pressure injury.
IMPLICATIONS FOR PRACTICE: This study provides a tool to predict the risk of pressure injury for cancer patients. This suggests that improving the respiratory function and nutritional status of cancer patients may reduce the risk of community-acquired pressure injury.