Liu Yufei, Hu Haofei, Han Yong, Li Zongyang, Yang Jihu, Zhang Xiejun, Chen Lei, Chen Fanfan, Li Weiping, Huang Guodong
Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, P.R. China.
Shenzhen University Health Science Center, Shenzhen University, Shenzhen, Guangdong 518000, P.R. China.
Oncol Lett. 2024 Mar 12;27(5):205. doi: 10.3892/ol.2024.14338. eCollection 2024 May.
The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.
识别开颅术后30天死亡高风险患者可能有助于实现干预措施的靶向递送。本研究旨在开发一种个性化列线图和评分系统，以预测此类患者术后30天死亡风险。在这项回顾性横断面研究中，18642例开颅患者被分为训练队列（n = 7800；手术年份，2012 - 2013年）和外部验证队列（n = 10842；手术年份，2014 - 2015年）。使用最小绝对收缩和选择算子（LASSO）模型在候选变量中选择最重要的变量。此外，基于LASSO模型选择的预测因子建立逐步逻辑回归模型以筛选出风险因素。构建了模型和列线图。采用受试者操作特征（ROC）曲线下面积（AUC）和校准图分析来评估模型的区分能力和准确性。根据临床切点对相关风险因素进行分类，以创建术后30天死亡的评分模型。总分分为四个风险类别：极高、高、中、低风险。训练队列和验证队列中术后30天死亡率分别为2.43%和2.58%。根据白细胞计数、血细胞比容和血尿素氮水平、年龄范围、功能健康状况以及播散癌细胞的发生率，开发了一种简单的列线图和评分系统来预测术后30天死亡风险。训练队列中列线图的ROC AUC为0.795（95%CI：0.764至0.826），验证队列中为0.738（95%CI：0.7091至0.7674）。校准显示预测的30天死亡风险与观察到的30天死亡风险之间具有完美的拟合。30天死亡的低、中、高和极高风险状态分别与总分（-1.5至-1）、（-0.5至0.5）、（1至2）和（2.5至9）相关。开发并验证了一种用于预测接受开颅手术的成年患者术后30天死亡的个性化列线图和评分系统，并且能够识别30天死亡高风险个体。
