Turfa Jawad, Hijazi Ali, Fadlallah Yasser, El-Harati Melhem, Dimassi Hani, Najjar Marwan El
Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
School of Pharmacy, Lebanese American University, Byblos, Lebanon.
Neurotrauma Rep. 2024 Jul 16;5(1):660-670. doi: 10.1089/neur.2024.0039. eCollection 2024.
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.
创伤性脑损伤（TBI）是创伤患者死亡的主要原因。识别此类患者术前预测术后结果的因素可指导手术决策。本研究的目的是利用术前变量建立一个预测模型，以预测TBI后接受神经外科手术患者的30天死亡率和发病率。在2005年至2017年期间，查询了美国外科医师学会国家外科质量改进计划（ACS NSQIP）数据库，以获取18岁及以上接受TBI特异性手术的患者。主要结局是30天死亡率，次要结局是综合发病率评分。使用卡方检验进行单变量分析的显著变量用于计算两个结局的多变量逻辑回归模型，并使用Hosmer-Lemeshow检验。共有1634例患者符合纳入标准。大多数患者为老年人，年龄>60岁（74.48%），男性（63.59%），白人（73.62%），非西班牙裔（82.44%）。总体30天死亡率为20.3%。使用多变量逻辑回归，11个术前变量与30天死亡率显著相关，包括（调整后比值比，95%置信区间）70-79岁（3.38，2.03-5.62）和>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，发现12个术前变量与30天发病率显著相关，包括急性肾衰竭（7.10，1.91-26.32）、返回手术室（3.82，2.77-5.27）、败血症（3.27，1.11-9.66）、30天内既往手术（2.55，1.06-4.95）和胰岛素依赖型糖尿病（1.60，1.06-2.40）等。构建受试者工作特征曲线后，死亡率模型的曲线下面积（AUC）为0.843，而综合发病率的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诊的临床决策。
