Kaur Kirandeep, Bidyut Panda Nidhi, Mahajan Shalvi, Kaloria Narender, Ganesh Venkata, Karthigeyan M
Department of Anesthesia and Intensive Care, Division of Neuroanesthesia, Post Graduate Institute of Medical Education and Research, Chandigarh, India.
Department of Anesthesia and Intensive Care, Division of Neuroanesthesia, Post Graduate Institute of Medical Education and Research, Chandigarh, India.
World Neurosurg. 2025 Feb;194:123423. doi: 10.1016/j.wneu.2024.11.006. Epub 2024 Dec 10.
Traumatic brain injury (TBI) prediction models have gained significant attention in recent years because of their potential to aid in clinical decision making. Existing models, such as Corticosteroid Randomization after Significant Head Injury and International Mission for Prognosis and Analysis of Clinical Trials, are currently losing external validity and performance, probably because of their diverse inclusion criteria and changes in treatment modalities over the years. There is a lack of models that predict outcomes strictly pertaining to primary decompression after TBI. In this study, we aimed to develop an easy-to-use prediction model for predicting the risk of poor functional outcomes at 3 months after hospital discharge in adult patients who had undergone primary decompressive craniectomy for isolated moderate-to-severe TBI.
We conducted a prospective observational study at our tertiary care hospital. We trained and tested multiple prognostic logistic regression models with ten-fold cross validation to choose the model with the lowest Akaike information criterion, high sensitivity, and positive predictive value (PPV). Using the final model, we generated a nomogram to predict the risk of having a Glasgow outcome scale-extended (GOSE) 1-4 at three months after hospital discharge.
A total of 215 patients were included in this study. Variables with an absolute standardized difference >0·25 when grouped by GOSE 1-4/5-8 at three months were included in multivariable modeling. The model of choice had an accuracy of 87·91% (95% confidence interval of 82·78%-91·95%), a sensitivity of 84·42%, specificity of 89·86%, PPV of 82·28% (72·06%-89·96%), negative predictive value of 91·18% (85·09%-95·36%), LR+ of 8·32 (5·02-13·80), and LR-of 0·17 (0·10-0·29).
Our study provides a ready-to-use prognostic nomogram derived from prospective data that can predict the risk of having a GOSE of 1-4 at three months following primary decompressive craniectomy with high sensitivity, PPV, and low LR-.
近年来，创伤性脑损伤（TBI）预测模型因其在临床决策中提供帮助的潜力而备受关注。现有的模型，如重度颅脑损伤后皮质类固醇随机试验模型和国际临床试验预后与分析任务组模型，目前正失去外部有效性和性能，这可能是由于其不同的纳入标准以及多年来治疗方式的变化。缺乏严格预测TBI后初次减压相关结果的模型。在本研究中，我们旨在开发一种易于使用的预测模型，用于预测因孤立性中重度TBI接受初次减压颅骨切除术的成年患者出院后3个月功能预后不良的风险。
我们在三级医疗中心进行了一项前瞻性观察研究。我们使用十折交叉验证训练和测试了多个预后逻辑回归模型，以选择具有最低赤池信息准则、高敏感性和阳性预测值（PPV）的模型。使用最终模型，我们生成了一个列线图，以预测出院后3个月格拉斯哥扩展预后量表（GOSE）评分为1 - 4的风险。
本研究共纳入215例患者。将三个月时按GOSE 1 - 4/5 - 8分组时绝对标准化差异>0·25的变量纳入多变量建模。所选模型的准确率为87·91%（95%置信区间为82·78% - 91·95%），敏感性为84·42%，特异性为89·86%，PPV为82·28%（72·06% - 89·96%），阴性预测值为91·18%（85·09% - 95·36%），阳性似然比为8·32（5·02 - 13·80），阴性似然比为0·17（0·10 - 0·29）。
我们的研究提供了一个基于前瞻性数据的即用型预后列线图，它能够以高敏感性、PPV和低阴性似然比预测初次减压颅骨切除术后3个月GOSE评分为1 - 4的风险。
