创伤性脑损伤开颅手术后 30 天死亡率和发病率的预测因素:ACS NSQIP 数据库分析。

IF 1.8 Q3 CLINICAL NEUROLOGY Neurotrauma reports Pub Date : 2024-07-16 eCollection Date: 2024-01-01 DOI:10.1089/neur.2024.0039
Jawad Turfa, Ali Hijazi, Yasser Fadlallah, Melhem El-Harati, Hani Dimassi, Marwan El Najjar
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引用次数: 0

摘要

创伤性脑损伤(TBI)是导致创伤患者死亡的主要原因。确定能预测此类患者术后结果的术前因素可以为手术决策提供指导。本研究旨在利用术前变量建立一个预测模型,以预测接受神经外科手术的 TBI 患者 30 天的死亡率和发病率。研究人员查询了美国外科学院国家外科质量改进计划(ACS NSQIP)数据库,该数据库在 2005 年至 2017 年期间收录了年龄在 18 岁以上、接受 TBI 特异性手术的患者。主要结果是30天死亡率,次要结果是综合发病率评分。通过卡方检验进行单变量分析后发现的显著变量被用于计算这两项结果的多变量逻辑回归模型,并使用Hosmer-Lemeshow检验。共有 1634 名患者符合纳入标准。大多数患者为年龄大于 60 岁的老年人(74.48%)、男性(63.59%)、白人(73.62%)和非西班牙裔(82.44%)。30 天总死亡率为 20.3%。通过多变量逻辑回归,11 个术前变量与 30 天死亡率显著相关,包括(aOR,95% CI)年龄 70-79 岁(3.38,2.03-5.62)和年龄大于 80 岁(7.70,4.74-12.51)、呼吸机依赖(6.04,4.21-8.67)、接受透析(4.97,2.43-10.18)、播散性癌症(4.42,1.50-13.0)和昏迷 >24 小时(3.30,1.40-7.80)等。同样,有 12 个术前变量与 30 天内的发病率显著相关,包括急性肾功能衰竭(7.10,1.91-26.32)、重返手术室(3.82,2.77-5.27)、败血症(3.27,1.11-9.66)、30 天内曾做过手术(2.55,1.06-4.95)和胰岛素依赖型糖尿病(1.60,1.06-2.40)等。构建接收者操作特征曲线后,死亡率模型的曲线下面积(AUC)为 0.843,而复合发病率的曲线下面积(AUC)为 0.716。该模型有助于在大规模伤亡事件中根据预后对患者进行分流的临床决策。
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Predictors of 30-Day Mortality and Morbidity Following Craniotomy for Traumatic Brain Injury: An ACS NSQIP Database Analysis.

Traumatic brain injury (TBI) is the leading cause of death among trauma patients. Identifying preoperative factors that predict postoperative outcomes in such patients can guide surgical decision-making. The aim of this study was to develop a predictive model using preoperative variables that predicts 30-day mortality and morbidity in patients undergoing neurosurgery following TBI. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database was queried between 2005 and 2017 for patients aged 18 years or older who underwent TBI-specific surgery. The primary outcome was 30-day mortality, and the secondary outcome was a composite morbidity score. Significant variables on univariate analysis with Chi-squared test were used to compute multivariable logistic regression models for both outcomes, and Hosmer-Lemeshow test was used. A total of 1634 patients met the inclusion criteria. Most patients were elderly aged >60 years (74.48%), male (63.59%), of White race (73.62%), and non-Hispanic ethnicity (82.44%). The overall 30-day mortality rate was 20.3%. Using multivariate logistic regression, 11 preoperative variables were significantly associated with 30-day mortality, including (aOR, 95% CI) age 70-79 years (3.38, 2.03-5.62) and age >80 years (7.70, 4.74-12.51), ventilator dependency (6.04, 4.21-8.67), receiving dialysis (4.97, 2.43-10.18), disseminated cancer (4.42, 1.50-13.0), and coma >24 hours (3.30, 1.40-7.80), among others. Similarly, 12 preoperative variables were found to be significantly associated with 30-day morbidity, including acute renal failure (7.10, 1.91-26.32), return to OR (3.82, 2.77-5.27), sepsis (3.27, 1.11-9.66), prior operation within 30 days (2.55, 1.06-4.95), and insulin-dependent diabetes (1.60, 1.06-2.40), among others. After constructing receiver operating characteristic curve, the model for mortality had an area under the curve (AUC) of 0.843, whereas composite morbidity had an AUC of 0.716. This model can aid in clinical decision-making for triaging patients based on prognosis in cases of mass casualty events.

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