Department of Surgery, The University of Oklahoma, College of Medicine, Tulsa, OK, USA.
Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Tulsa, OK, USA.
Am J Surg. 2019 Sep;218(3):496-500. doi: 10.1016/j.amjsurg.2018.11.045. Epub 2018 Dec 10.
The ability to predict the need for discharge of trauma patients to a facility may help shorten hospital stay. This study aimed to determine the predictors of discharge to a facility and develop and validate a predictive scoring model, utilizing the Oklahoma Trauma Registry (OTR).
A multivariate analysis of the OTR 2005-2013 determined independent predictors of discharge to a facility. A scoring model was developed, and positive and negative predictive values (PPV and NPV) were evaluated for 2014 patients.
101,656 patients were analyzed. The scoring model included age≥50 years, lower extremity fracture, ICU stay≥5 days, pelvic fracture, intracranial hemorrhage, congestive heart failure, cardiac dysrhythmia, history of CVA or TIA, and ISS≥15, spine fracture, diabetes mellitus, hypertension, ischemic heart disease, and chronic obstructive pulmonary disease. Applying the model to 2014 patients, PPV for predicting discharge to a facility was 84.9% for scores≥15, and NPV was 90.5% for scores<8.
A scoring model including age, trauma severity, types of injury, and comorbidities could predict discharge of trauma patients to a facility. Further studies are needed to refine the efficacy of the model.
能够预测创伤患者需要转至医疗机构，可能有助于缩短住院时间。本研究旨在利用俄克拉荷马州创伤登记处（OTR）确定转至医疗机构的预测因素，并开发和验证预测评分模型。
对 OTR 2005-2013 年的数据进行多变量分析，确定转至医疗机构的独立预测因素。开发了评分模型，并对 2014 年的患者评估了阳性和阴性预测值（PPV 和 NPV）。
分析了 101656 名患者。评分模型包括年龄≥50 岁、下肢骨折、入住 ICU≥5 天、骨盆骨折、颅内出血、充血性心力衰竭、心律失常、中风或 TIA 病史、ISS≥15、脊柱骨折、糖尿病、高血压、缺血性心脏病和慢性阻塞性肺疾病。将该模型应用于 2014 年的患者，对于预测转至医疗机构的评分≥15 的患者，PPV 为 84.9%，评分<8 的患者，NPV 为 90.5%。
包括年龄、创伤严重程度、损伤类型和合并症的评分模型可以预测创伤患者转至医疗机构。需要进一步的研究来完善该模型的疗效。
