基于临床特征、SPECT/CT脑灌注显像参数和颈动脉超声斑块特征的多因素模型对无症状性颈动脉狭窄患者认知功能下降的风险评估(Risk Assessment of Cognitive Decline in Asymptomatic Carotid Artery Stenosis Patients: A Multifactor Model Based on Clinical Features, SPECT/CT Cerebral Perfusion Imaging Parameters, and Carotid Ultrasound Plaque Characteristics)
Bibliographic record
Abstract
目的 构建一个整合临床特征、SPECT/CT脑灌注显像参数和颈动脉超声斑块特征的多因素预测模型，用于评估无症状性颈动脉狭窄患者的认知功能下降风险。 方法 回顾性分析2021年1月1日—2022年12月31日在山东第二医科大学附属医院就诊的无症状性颈动脉狭窄患者，所有患者在就诊时均完成以下评估：采用SPECT/CT脑灌注显像评估全脑平均局部脑血流量（regional cerebral blood flow，rCBF），通过颈动脉超声检查评估斑块面积及回声特征，使用MoCA进行认知功能评估并在随访1年后评估认知功能变化。以1年随访期间MoCA评分下降2分或以上为认知功能下降，将患者分为认知功能下降组和非认知功能下降组。进行单因素和多因素logistic回归分析以识别认知功能下降的独立危险因素，构建预测模型，并应用ROC曲线评估模型的预测效能。 结果 共纳入80例患者，其中35例（43.75%）在1年随访期间出现认知功能下降。高血压病史比例（62.9% vs. 35.6%，P=0.016）、全脑平均rCBF（0.82±0.09 vs. 0.93±0.08，P<0.001）、不对称指数（asymmetry index，AI）[（8.2±2.1）% vs.（5.9±1.8）%，P<0.001]、斑块面积[（24.3±7.6）mm2 vs.（17.8±6.5）mm2，P<0.001]和低回声斑块比例（62.9% vs. 24.4%，P=0.012）在认知功能下降组与非认知功能下降组间的差异具有统计学意义。多因素logistic回归分析显示，有高血压病史（OR 2.68，95%CI 1.07～6.71，P=0.035）、rCBF<0.85（OR 2.79，95%CI 1.08～7.21，P=0.034）、AI>7%（OR 3.00，95%CI 1.15～7.82，P=0.025）、斑块面积≥20 mm2（OR 2.86，95%CI 1.09～7.52，P=0.033）和存在低回声斑块（OR 2.95，95%CI 1.17～7.44，P=0.022）是认知功能下降的独立危险因素。基于上述5个因素构建的认知功能下降风险预测模型，其ROC曲线的AUC为0.836（95%CI 0.752～0.920，P<0.001），敏感度为0.725，特异度为0.850。危险因素组合分析显示，四因素模型（有高血压病史、rCBF<0.85、AI>7%和斑块面积≥20 mm2）与五因素模型（有高血压病史、rCBF<0.85、AI>7%、斑块面积≥20 mm2、存在低回声斑块）的预测效能相近（AUC分别为0.821和0.836）。 结论 本研究建立的多因素预测模型，整合了临床特征、SPECT/CT脑灌注显像参数和颈动脉超声斑块特征，可有效预测无症状性颈动脉狭窄患者认知功能下降风险，为临床风险评估和早期干预提供参考。Abstract: Objective To construct a multifactor prediction model integrating clinical features, SPECT/CT cerebral perfusion imaging parameters, and carotid ultrasound plaque characteristics for assessing the risk of cognitive decline in patients with asymptomatic carotid artery stenosis. Methods A retrospective analysis was conducted on patients with asymptomatic carotid artery stenosis treated at the Affiliated Hospital of Shandong Second Medical University from January 1, 2021, to December 31, 2022. All patients underwent the following assessments: SPECT/CT cerebral perfusion imaging to assess the whole brain mean regional cerebral blood flow (rCBF), carotid ultrasound to assess the plaque area and echogenicity features, and the MoCA to assess cognitive function, with a 1-year follow-up to evaluate changes in cognitive function. A decline of 2 points or more in the MoCA score during the 1-year follow-up period was defined as cognitive decline, and patients were divided into the cognitive decline and the non-cognitive decline groups based on their scores. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for cognitive decline. A prediction model was constructed, and its performance was evaluated using the ROC curve. Results A total of 80 patients were included, among whom 35 (43.75%) experienced cognitive decline during the 1-year follow-up. The differences in the percentage of hypertension history (62.9% vs. 35.6%, P=0.016), whole brain mean rCBF (0.82±0.09 vs. 0.93±0.08, P<0.001), asymmetry index (AI) [(8.2±2.1)% vs. (5.9±1.8)%, P<0.001], plaque area [(24.3±7.6) mm2 vs. (17.8±6.5) mm2, P<0.001], and the percentage of hypoechoic plaque (62.9% vs. 24.4%, P=0.012) between the cognitive decline group and the non-cognitive decline group were statistically significant. Multivariate logistic regression analysis revealed that the history of hypertension (OR 2.68, 95%CI 1.07-6.71, P=0.035), rCBF<0.85 (OR 2.79, 95%CI 1.08-7.21, P=0.034), AI>7% (OR 3.00, 95%CI 1.15-7.82, P=0.025), plaque area≥20 mm2 (OR 2.86, 95%CI 1.09-7.52, P=0.033), and the presence of hypoechoic plaque (OR 2.95, 95%CI 1.17-7.44, P=0.022) were independent predictors of cognitive decline. The ROC curve’s AUC for the risk prediction model of cognitive decline constructed based on the above five factors was 0.836 (95%CI 0.752-0.920, P<0.001), with a sensitivity of 0.725 and a specificity of 0.850. Risk factor combination analysis showed that the predictive efficacy of the four-factor model (history of hypertension, rCBF<0.85, AI>7%, and plaque area≥20 mm2) was similar to that of the five-factor model (history of hypertension, rCBF<0.85, AI>7%, plaque area≥20 mm2, and presence of hypoechoic plaque), with AUCs of 0.821 and 0.836, respectively. Conclusions The multifactor prediction model developed in this study, integrating clinical features, SPECT/CT cerebral perfusion imaging parameters, and carotid ultrasound plaque characteristics, effectively predicts the risk of cognitive decline in patients with asymptomatic carotid artery stenosis, providing a reference for clinical risk assessment and early intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".