Elias Pierre, Khanna Raman, Dudley Adams, Davies Jason, Jacolbia Ronald, McArthur Kara, Auerbach Andrew D
Department of Medicine, Columbia University Medical Center, New York, New York, USA.
Division of Hospital Medicine, University of California San Francisco, San Francisco, CA, USA.
J Hosp Med. 2017 Apr;12(4):231-237. doi: 10.12788/jhm.2714.
Venous thromboembolism (VTE) risk scores assist providers in determining the relative benefit of prophylaxis for individual patients. While automated risk calculation using simpler electronic health record (EHR) data is feasible, it lacks clinical nuance and may be less predictive. Automated calculation of the Padua Prediction Score (PPS), requiring more complex input such as recent medical events and clinical status, may save providers time and increase risk score use.
We developed the Automated Padua Prediction Score (APPS) to auto-calculate a VTE risk score using EHR data drawn from prior encounters and the first 4 hours of admission. We compared APPS to standard practice of clinicians manually calculating the PPS to assess VTE risk.
Cohort study of 30,726 hospitalized patients. APPS was compared to manual calculation of PPS by chart review from 300 randomly selected patients.
Prediction of hospital-acquired VTE not present on admission.
Compared to manual PPS calculation, no significant difference in average score was found (5.5 vs. 5.1, P = 0.073), and area under curve (AUC) was similar (0.79 vs. 0.76). Hospital- acquired VTE occurred in 260 (0.8%) of 30,726 patients. Those without VTE averaged APPS of 4.9 (standard deviation [SD], 2.6) and those with VTE averaged 7.7 (SD, 2.6). APPS had AUC = 0.81 (confidence interval [CI], 0.79-0.83) in patients receiving no pharmacologic prophylaxis and AUC = 0.78 (CI, 0.76- 0.82) in patients receiving pharmacologic prophylaxis.
Automated calculation of VTE risk had similar ability to predict hospital-acquired VTE as manual calculation despite differences in how often specific scoring criteria were considered present by the 2 methods. Journal of Hospital Medicine 2017;12: 231- 237.
静脉血栓栓塞症（VTE）风险评分有助于医疗人员确定个体患者预防措施的相对获益。虽然利用更简单的电子健康记录（EHR）数据进行自动风险计算是可行的，但它缺乏临床细节，且预测性可能较差。帕多瓦预测评分（PPS）的自动计算需要更复杂的输入信息，如近期医疗事件和临床状况，这可能会节省医疗人员的时间并增加风险评分的使用。
我们开发了自动帕多瓦预测评分（APPS），以利用既往诊疗记录和入院后4小时内的EHR数据自动计算VTE风险评分。我们将APPS与临床医生手动计算PPS以评估VTE风险的标准做法进行了比较。
对30726例住院患者进行队列研究。通过对300例随机选择患者的病历审查，将APPS与手动计算PPS进行比较。
预测入院时不存在的医院获得性VTE。
与手动计算PPS相比，平均评分无显著差异（5.5对5.1，P = 0.073），曲线下面积（AUC）相似（0.79对0.76）。30726例患者中有260例（0.8%）发生医院获得性VTE。未发生VTE者的APPS平均为4.9（标准差[SD]，2.6），发生VTE者的APPS平均为7.7（SD，2.6）。在未接受药物预防的患者中，APPS的AUC = 0.81（置信区间[CI]，0.79 - 0.83），在接受药物预防的患者中，AUC = 0.78（CI，0.76 - 0.82）。
尽管两种方法在考虑特定评分标准出现频率方面存在差异，但VTE风险的自动计算与手动计算在预测医院获得性VTE方面具有相似的能力。《医院医学杂志》2017年；12：231 - 237。
