Predicting long-term risk of reoperations following abdominal and pelvic surgery: a nationwide retrospective cohort study

医学 列线图 外科 回顾性队列研究 弗雷明翰风险评分 风险因素 队列 风险评估 疾病 内科学 计算机安全 计算机科学
作者
Masja K. Toneman,Pepijn Krielen,Ahmed Jaber,Tjitske D. Groenveld,Martijn W.J. Stommel,Ewen A. Griffiths,M. C. Parker,Nicole D. Bouvy,Harry van Goor,Richard P. G. ten Broek
出处
期刊:International Journal of Surgery [Wolters Kluwer]
卷期号:109 (6): 1639-1647 被引量:1
标识
DOI:10.1097/js9.0000000000000375
摘要

Background: The risk of reoperations after abdominal and pelvic surgery is multifactorial and difficult to predict. The risk of reoperation is frequently underestimated by surgeons as most reoperations are not related to the initial procedure and diagnosis. During reoperation, adhesiolysis is often required, and patients have an increased risk of complications. Therefore, the aim of this study was to provide an evidence-based prediction model based on the risk of reoperation. Materials and methods: A nationwide cohort study was conducted including all patients undergoing an initial abdominal or pelvic operation between 1 June 2009 and 30 June 2011 in Scotland. Nomograms based on multivariable prediction models were constructed for the 2-year and 5-year overall risk of reoperation and risk of reoperation in the same surgical area. Internal cross-validation was applied to evaluate reliability. Results: Of the 72 270 patients with an initial abdominal or pelvic surgery, 10 467 (14.5%) underwent reoperation within 5 years postoperatively. Mesh placement, colorectal surgery, diagnosis of inflammatory bowel disease, previous radiotherapy, younger age, open surgical approach, malignancy, and female sex increased the risk of reoperation in all the prediction models. Intra-abdominal infection was also a risk factor for the risk of reoperation overall. The accuracy of the prediction model of risk of reoperation overall and risk for the same area was good for both parameters ( c -statistic=0.72 and 0.72). Conclusions: Risk factors for abdominal reoperation were identified and prediction models displayed as nomograms were constructed to predict the risk of reoperation in the individual patient. The prediction models were robust in internal cross-validation.
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