30秒光电容积脉搏波可辅助识别大血管闭塞卒中。
Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers
- 用30秒光电容积脉搏波信号提取生理特征进行分类
- 模型在100次重复测试中平均准确率达77%(AUROC)
- 适合急救场景快速筛查,尤其适用于无法配合检查的患者
大血管闭塞(LVO)卒中治疗窗口极短,需尽快转诊至具备介入取栓能力的医院。目前临床评分依赖患者配合,对痴呆或意识障碍者不适用。本研究采集88名患者(25例LVO、27例卒中模拟、36例非LVO卒中)在悉尼利物浦医院急诊科的30秒光电容积脉搏波(PPG)信号,提取年龄、性别、形态特征及心率变异性指标。采用二分类模型区分LVO与非LVO+卒中模拟组(NL.SM),通过100次随机分层2:1训练-测试循环验证。最佳模型在测试集上中位AUROC达0.77(95%置信区间:0.71–0.82),表明30秒PPG记录具有识别LVO卒中的潜在价值。
原文摘要 · Abstract (English)
Objective. Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed treatment. Treatment for LVO involves highly specialized care, in particular endovascular thrombectomy, and is available only at certain hospitals. Therefore, prehospital identification of LVO by emergency ambulance services, can be critical for triaging LVO stroke patients directly to a hospital with access to endovascular therapy. Clinical scores exist to help distinguish LVO from less severe strokes, but they are based on a series of examinations that can take minutes and may be impractical for patients with dementia or those who cannot follow commands due to their stroke. There is a need for a fast and reliable method to aid in the early identification of LVO. In this study, our objective was to assess the feasibility of using 30-second photoplethysmography (PPG) recording to assist in recognizing LVO stroke. Method. A total of 88 patients, including 25 with LVO, 27 with stroke mimic (SM), and 36 non-LVO stroke patients (NL), were recorded at the Liverpool Hospital emergency department in Sydney, Australia. Demographics (age, sex), as well as morphological features and beating rate variability measures, were extracted from the PPG. A binary classification approach was employed to differentiate between LVO stroke and NL+SM (NL.SM). A 2:1 train-test split was stratified and repeated randomly across 100 iterations. Results. The best model achieved a median test set area under the receiver operating characteristic curve (AUROC) of 0.77 (0.71--0.82). \textit{Conclusion.} Our study demonstrates the potential of utilizing a 30-second PPG recording for identifying LVO stroke.
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