Identifying surgical site infections in electronic health data using predictive models
Identifying surgical site infections in electronic health data using predictive models
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DOI：
10.1093/jamia/ocy075
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发表时间：
2018-09-01
影响因子：
6.4
通讯作者：
Coffin, Susan E.
中科院分区：
文献类型：
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作者：
Grundmeier, Robert W.;Xiao, Rui;Coffin, Susan E.
目的：目的是前瞻性地推导和验证的预测规则，用于检测的情况下进行调查的手术部位感染（SSI）门诊surgery.Methods：我们分析了电子健康记录（EHR）数据的儿童进行门诊手术在4个门诊手术设施之一。使用正则化逻辑回归和随机森林，我们使用30个月的数据（推导集）推导出SSI预测规则，并使用随后10个月的数据（验证集）评估性能。模型的开发都有和没有从自由文本中提取的数据。我们还评估了术后60天内抗生素处方的存在作为SSI证据的独立指标。我们的目标是超过80%的灵敏度和10%的阳性预测值（PPV）。结果：我们确定了234例手术的SSI证据中的7910手术可供分析。我们推导并验证了一个最佳的预测规则，其中包括使用随机森林模型的自由文本数据（灵敏度= 0.9，PPV = 0.28）。抗生素处方的存在有差的灵敏度（0.65）时，应用到派生数据，但执行更好的应用时，验证数据（灵敏度= 0.84，PPV = 0.28）。结论：电子病历数据可以促进SSI监测具有足够的灵敏度和PPV。
Objective: The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery.Methods: We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV).Results: We identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity = 0.9, PPV = 0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity = 0.84, PPV = 0.28).Conclusions: EHR data can facilitate SSI surveillance with adequate sensitivity and PPV.