Department of Psychology, Health and Technology/Centre for eHealth Research and Disease Management, Faculty of Behavioural Sciences, University of Twente, Enschede, The Netherlands.
Department of Earth Observation Sciences, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands.
PLoS One. 2020 Oct 28;15(10):e0240995. doi: 10.1371/journal.pone.0240995. eCollection 2020.
The objective of this study was to identify risk factors for surgical site infection from digestive, thoracic and orthopaedic system surgeries using clinical and data-driven cut-off values. A second objective was to compare the identified risk factors in this study to risk factors identified in literature.
Retrospective data of 3 250 surgical procedures performed in large tertiary care hospital in The Netherlands during January 2013 to June 2014 were used.
Potential risk factors were identified using a literature scan and univariate analysis. A multivariate forward-step logistic regression model was used to identify risk factors. Standard medical cut-off values were compared with cut-offs determined from the data.
For digestive, orthopaedic and thoracic system surgical procedures, the risk factors identified were preoperative temperature of ≥38°C and antibiotics used at the time of surgery. C-reactive protein and the duration of the surgery were identified as a risk factors for digestive surgical procedures. Being an adult (age ≥18) was identified as a protective effect for thoracic surgical procedures. Data-driven cut-off values were identified for temperature, age and CRP which can explain the SSI outcome up to 19.5% better than generic cut-off values.
This study identified risk factors for digestive, orthopaedic and thoracic system surgical procedures and illustrated how data-driven cut-offs can add value in the process. Future studies should investigate if data-driven cut-offs can add value to explain the outcome being modelled and not solely rely on standard medical cut-off values to identify risk factors.
本研究旨在使用临床和数据驱动的截断值确定消化系统、胸外科和骨科手术部位感染的风险因素。第二个目标是将本研究中确定的风险因素与文献中确定的风险因素进行比较。
使用荷兰一家大型三级保健医院 2013 年 1 月至 2014 年 6 月期间进行的 3250 例手术的回顾性数据。
通过文献扫描和单变量分析确定潜在的风险因素。使用多元向前逐步逻辑回归模型确定风险因素。将标准医学截断值与从数据中确定的截断值进行比较。
对于消化系统、骨科和胸外科手术，确定的风险因素为术前体温≥38°C 和手术时使用抗生素。C 反应蛋白和手术持续时间被确定为消化系统手术的风险因素。成人（年龄≥18 岁）被确定为胸外科手术的保护因素。确定了体温、年龄和 CRP 的基于数据的截断值，可将 SSI 结果的解释提高多达 19.5%，优于通用截断值。
本研究确定了消化系统、骨科和胸外科手术的风险因素，并说明了数据驱动的截断值如何在该过程中增加价值。未来的研究应调查基于数据的截断值是否可以增加价值，以解释正在建模的结果，而不仅仅依赖于标准的医学截断值来识别风险因素。
