儿科创伤性脑损伤：精确风险评估模型和在线计算器，以增强患者护理

IF 1.9 4区医学 Q3 CLINICAL NEUROLOGY

Journal of Clinical Neuroscience Pub Date : 2025-05-09 DOI:10.1016/j.jocn.2025.111308

Foad Kazemi , Elena Ghotbi , Julian L. Gendreau , Alan R. Cohen

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引用次数: 0

摘要

背景：创伤性脑损伤（TBI）是一项重大的公共卫生挑战，需要大量的医疗资源。准确、个性化的延长住院时间（LOS）、非常规出院、ICU/OR转移和直接ED出院的风险评估对于优化患者护理至关重要，促使作者开发可靠的风险分层工具，以支持临床医生和多学科护理团队。方法采用改进的CDC框架，采用ICD-10编码对年龄≤18岁的儿童TBI病例进行电子病历回顾性分析。收集了人口统计、社区社会经济劣势（使用社会剥夺指数[SDI]评估）和伤害严重程度（使用伤害严重程度评分[ISS]评估）的数据。在多元回归分析中采用逆向消去法，得到最简洁的模型。分别采用受试者工作特征曲线下面积（AUC）和Spiegelhalter’s z检验评估模型判别和校准。结果共发现2954例TBI病例，平均年龄7.05岁。其中28.4%延长了住院时间，8.3%是非常规出院，23.4%需要转ICU/OR， 52.3%直接从急诊科出院；预测模型的auc分别为0.89、0.87、0.89和0.88，具有较好的判别性。所有参考模型的Spiegelhalter z检验p值均大于0.05，表明拟合充分。所有模型都用于开发一个开放获取的在线计算器，可在https://jhpedsnsgy.shinyapps.io/JHPedsNSGY/.ConclusionsBy上获得，这些预测模型和在线计算器整合了急诊科中易于获取的数据，使临床医生能够提供精确、个性化的风险评估，增强神经外科决策，并改善儿科TBI患者的高价值护理。

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Pediatric traumatic brain injury: precision risk assessment models and an online calculator for enhanced patient care

Background

Traumatic brain injury (TBI) is a significant public health challenge demanding extensive medical resources. Accurate, individualized risk assessments for extended length of stay (LOS), non-routine discharge, ICU/OR transfers, and direct ED discharges are crucial for optimizing patient care, prompting the authors to develop a reliable risk stratification tool to support clinicians and multidisciplinary care teams.

Methods

A retrospective review of electronic health records was conducted to identify pediatric TBI cases (age ≤18) using ICD-10 codes based on the modified CDC framework. Data on demographics, neighborhood socioeconomic disadvantage (assessed using the Social Deprivation Index [SDI]), and injury severity (assessed using Injury Severity Scores [ISS]) were collected. The backward elimination method was employed in the multivariate regression analysis to achieve the most parsimonious model. Model discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and Spiegelhalter’s z-test, respectively.

Results

A total of 2954 TBI cases were identified with an average age of 7.05 years. Of these, 28.4 % had extended LOS, 8.3 % had a non-routine discharge, 23.4 % required ICU/OR transfer, and 52.3 % were discharged directly from the ED; respective predictive models achieved AUCs of 0.89, 0.87, 0.89, and 0.88, demonstrating good discrimination. All the referenced models had a Spiegelhalter z-test p-value greater than 0.05, indicating an adequate fit. All models were used to develop an open-access online calculator available at: https://jhpedsnsgy.shinyapps.io/JHPedsNSGY/.

Conclusions

By integrating readily accessible data in the ED, these predictive models and the online calculator empower clinicians to deliver precise, individualized risk assessments, enhance neurosurgical decision-making, and improve high-value care for pediatric TBI patients.

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来源期刊

Journal of Clinical Neuroscience 医学-临床神经学

CiteScore

4.50

自引率

0.00%

发文量

402

审稿时长

40 days

期刊介绍： This International journal, Journal of Clinical Neuroscience, publishes articles on clinical neurosurgery and neurology and the related neurosciences such as neuro-pathology, neuro-radiology, neuro-ophthalmology and neuro-physiology. The journal has a broad International perspective, and emphasises the advances occurring in Asia, the Pacific Rim region, Europe and North America. The Journal acts as a focus for publication of major clinical and laboratory research, as well as publishing solicited manuscripts on specific subjects from experts, case reports and other information of interest to clinicians working in the clinical neurosciences.