{"title":"印度孤立性中重度创伤性脑损伤初级减压开颅术不良后果预测模型--一项前瞻性观察研究。","authors":"Kirandeep Kaur, Nidhi Bidyut Panda, Shalvi Mahajan, Narender Kaloria, Venkata Ganesh, M Karthigeyan","doi":"10.1016/j.wneu.2024.11.006","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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-.</p>","PeriodicalId":23906,"journal":{"name":"World neurosurgery","volume":" ","pages":"123423"},"PeriodicalIF":1.9000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction Model for Unfavorable Outcome in Primary Decompressive Craniectomy for Isolated Moderate to Severe Traumatic Brain Injury in India: A Prospective Observational Study.\",\"authors\":\"Kirandeep Kaur, Nidhi Bidyut Panda, Shalvi Mahajan, Narender Kaloria, Venkata Ganesh, M Karthigeyan\",\"doi\":\"10.1016/j.wneu.2024.11.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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-.</p>\",\"PeriodicalId\":23906,\"journal\":{\"name\":\"World neurosurgery\",\"volume\":\" \",\"pages\":\"123423\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World neurosurgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.wneu.2024.11.006\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.wneu.2024.11.006","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的:近年来,创伤性脑损伤(TBI)预测模型因其有助于临床决策的潜力而备受关注。现有的模型,如重大头部损伤后皮质类固醇随机化模型(CRASH)和临床试验预后与分析国际任务模型(IMPACT),目前正在失去外部有效性和性能,这可能是因为它们的纳入标准多种多样,而且多年来治疗方式也发生了变化。目前还缺乏能严格预测创伤性脑损伤后原发性减压疗效的模型。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创伤性脑损伤而接受原发性颅骨减压术(DC)的成年患者出院后 3 个月出现不良功能预后的风险:我们在一家三级医院开展了一项前瞻性观察研究。我们对多个预后逻辑回归模型进行了训练和测试,并进行了十倍交叉验证,以选择阿凯克信息准则(Akaike Information Criterion)最低、灵敏度和阳性预测值(PPV)最高的模型。利用最终模型,我们生成了一个提名图,用于预测出院三个月后格拉斯哥结果量表扩展版(GOSE)1-4级的风险:本研究共纳入 215 名患者。按三个月后格拉斯哥预后量表(GOSE)1-4/5-8分组时,绝对标准化差异大于0-25的变量被纳入多变量模型。所选模型的准确率为 87-91%(95% CI 为 82-78-91-95%),灵敏度为 84-42%,特异性为 89-86%,PPV 为 82-28%(72-06-89-96%),NPV 为 91-18%(85-09-95-36%),LR+ 为 8-32(5-02-13-80),LR- 为 0-17(0-10-0-29):我们的研究提供了一个可随时使用的预后提名图,该提名图来源于前瞻性数据,可预测原发性 DC 术后三个月时出现 1-4 级 GOSE 的风险,具有高灵敏度、高 PPV 和低 LR-。
Prediction Model for Unfavorable Outcome in Primary Decompressive Craniectomy for Isolated Moderate to Severe Traumatic Brain Injury in India: A Prospective Observational Study.
Objective: 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.
Methods: 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.
Results: 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).
Conclusions: 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-.
期刊介绍:
World Neurosurgery has an open access mirror journal World Neurosurgery: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
The journal''s mission is to:
-To provide a first-class international forum and a 2-way conduit for dialogue that is relevant to neurosurgeons and providers who care for neurosurgery patients. The categories of the exchanged information include clinical and basic science, as well as global information that provide social, political, educational, economic, cultural or societal insights and knowledge that are of significance and relevance to worldwide neurosurgery patient care.
-To act as a primary intellectual catalyst for the stimulation of creativity, the creation of new knowledge, and the enhancement of quality neurosurgical care worldwide.
-To provide a forum for communication that enriches the lives of all neurosurgeons and their colleagues; and, in so doing, enriches the lives of their patients.
Topics to be addressed in World Neurosurgery include: EDUCATION, ECONOMICS, RESEARCH, POLITICS, HISTORY, CULTURE, CLINICAL SCIENCE, LABORATORY SCIENCE, TECHNOLOGY, OPERATIVE TECHNIQUES, CLINICAL IMAGES, VIDEOS