Aims: We sought to explore factors associated with early pressure injury progression and build a model for predicting these outcomes using a machine learning approach.
Design: A retrospective cohort study.
Methods: In this study, we recruited paediatric patients, with hospital-acquired stage I pressure injury or suspected deep tissue injury, who met the inclusion criteria between 1 January 2015-31 October 2018. We divided patients into two groups, namely healing or delayed healing, then followed them up for 7 days. We analysed patient pressure injury characteristics, demographics, treatment, clinical situation, vital signs, and blood test results, then build prediction models using the Random Forest and eXtreme Gradient Boosting approaches.
Results: The best prediction model, trained and tested using Random Forest with 10 variables, achieved an accuracy, sensitivity, specificity, and area under the curve of 0.82 (SD 0.06), 0.80 (SD 0.08), 0.84 (SD 0.08), and 0.89 (SD 0.06), respectively. The most contributing variables, in order of importance, included serum creatinine, red blood cell, and haematocrit.
Conclusion: An awareness of specific conditions and areas that could lead to delayed healing pressure injury in paediatric patients is needed.
Impact: This evidence-based prediction model, coupled with the aforementioned clinical indicators, is expected to enhance early prediction of outcomes in paediatric patients thereby improve the quality of care and the outcome of children with PIs.
目的: 研究旨在探索与早期压力损伤进展相关的因素, 并利用机器学习方法建立结果预测模型。 设计: 回顾性列队研究。 方法: 本研究中, 成功招募2015年1月1日至2018年10月31日期间符合纳入标准的儿科患者, 此类患者经诊断为患有I期压疮或疑似患有深部组织损伤。我们将患者分为康复组和延迟康复组, 随访7天。研究过程中, 我们对患者的压力损伤特征、人口统计学特征、治疗、临床情况、生命体征和血液检测结果等进行分析,随后利用随机森林算法和极端梯度提升方法建立预测模型。 结果: 利用含10个变量的随机森林算法, 以便对最佳预测模型进行应用训练和检验, 其准确度、灵敏度、特异度和曲线下面积分别为0.82(SD 0.06) 、0.80(SD 0.08) 、0.84(SD 0.08) 和0.89(SD 0.06) 。根据重要性排序, 影响最大的变量包括血清肌酐、红细胞和红细胞比容。 结论: 应了解可致儿科患者压疮延迟愈合的具体情况和部位。 影响: 此预测模型以询证为基础,综合上述临床指标, 可增强儿科患者预后的早期预测, 提高治疗质量和压力损伤患儿的预后。.
Keywords: machine learning; nursing; outcome; paediatric; prediction model; pressure injury; random forest.
© 2020 John Wiley & Sons Ltd.