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自身免疫性脑炎患者早期 ICU 入住风险预测模型:整合疾病严重程度的量表评估。

Risk Prediction Models for Early ICU Admission in Patients With Autoimmune Encephalitis: Integrating Scale-Based Assessments of the Disease Severity.

机构信息

Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Front Immunol. 2022 Jun 10;13:916111. doi: 10.3389/fimmu.2022.916111. eCollection 2022.

Abstract

BACKGROUND

In patients with autoimmune encephalitis (AE), the prediction of progression to a critically ill status is challenging but essential. However, there is currently no standard prediction model that comprehensively integrates the disease severity and other clinical features. The clinical assessment scale in autoimmune encephalitis (CASE) and the modified Rankin Scale (mRS) have both been applied for evaluating the severity of AE. Here, by combining the two scales and other clinical characteristics, we aimed to investigate risk factors and construct prediction models for early critical care needs of AE patients.

METHODS

Definite and probable AE patients who were admitted to the neurology department of Tongji Hospital between 2013 and 2021 were consecutively enrolled. The CASE and mRS scores were used to evaluate the overall symptom severity at the time of hospital admission. Using logistic regression analysis, we analyzed the association between the total scores of the two scales and critical illness individually and then we evaluated this association in combination with other clinical features to predict early intensive care unit (ICU) admission. Finally, we constructed four prediction models and compared their performances.

RESULTS

Of 234 patients enrolled, forty developed critical illness and were early admitted to the ICU (within 14 days of hospitalization). Four prediction models were generated; the models were named CASE, CASE-plus (CASE + prodromal symptoms + elevated fasting blood glucose + elevated cerebrospinal fluid (CSF) white blood cell (WBC) count), mRS and mRS-plus (mRS + prodromal symptoms + abnormal EEG results + elevated fasting blood glucose + elevated CSF WBC count) and had areas under the ROC curve of 0.850, 0.897, 0.695 and 0.833, respectively. All four models had good calibrations. In general, the models containing "CASE" performed better than those including "mRS", and the CASE-plus model demonstrated the best performance.

CONCLUSION

Overall, the symptom severity at hospital admission, as defined by CASE or mRS, could predict early ICU admission, especially when assessed by CASE. Adding other clinical findings, such as prodromal symptoms, an increased fasting blood glucose level and an increased CSF WBC count, could improve the predictive efficacy.

摘要

背景

在自身免疫性脑炎(AE)患者中,预测病情进展至危重症状态具有挑战性,但至关重要。然而,目前尚无综合评估疾病严重程度和其他临床特征的标准预测模型。临床自身免疫性脑炎评估量表(CASE)和改良 Rankin 量表(mRS)均已用于评估 AE 的严重程度。在这里,我们通过结合这两个量表和其他临床特征,旨在探讨 AE 患者早期需要重症监护的风险因素,并构建预测模型。

方法

连续纳入 2013 年至 2021 年期间入住同济大学附属同济医院神经内科的明确和可能的 AE 患者。入院时使用 CASE 和 mRS 评分评估总体症状严重程度。采用 logistic 回归分析,分别分析两个量表总分与危重症的关系,然后评估与其他临床特征相结合对早期入住重症监护病房(ICU)的预测价值。最后,构建了四个预测模型并比较了它们的性能。

结果

在纳入的 234 例患者中,有 40 例患者发展为危重症,并在入院后 14 天内(早期)入住 ICU。生成了四个预测模型,分别命名为 CASE、CASE-Plus(CASE+前驱症状+空腹血糖升高+脑脊液白细胞计数升高)、mRS 和 mRS-Plus(mRS+前驱症状+异常脑电图结果+空腹血糖升高+脑脊液白细胞计数升高),ROC 曲线下面积分别为 0.850、0.897、0.695 和 0.833。所有四个模型均具有良好的校准度。总体而言,包含“CASE”的模型比包含“mRS”的模型性能更好,而 CASE-Plus 模型表现最佳。

结论

总体而言,入院时的症状严重程度(由 CASE 或 mRS 定义)可以预测早期 ICU 入住,尤其是使用 CASE 评估时。添加其他临床发现,如前驱症状、空腹血糖升高和脑脊液白细胞计数升高,可提高预测效能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e71d/9226454/5e7f9403a1d5/fimmu-13-916111-g001.jpg

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