卒中后感染对急性缺血性卒中患者出院结局的影响 Effects of Post-stroke Infection on Discharge Outcomes in Patients with Acute Ischemic Stroke
Bibliographic record
Abstract
目的 探讨急性缺血性卒中（acute ischemic stroke，AIS）不良出院结局的影响因素，以及感染对出院结局的影响。 方法 回顾性连续纳入2019年6月—2022年6月在首都医科大学附属北京天坛医院神经内科住院治疗的AIS患者，根据出院结局分为结局良好组（mRS评分＜3分）和结局不良组（mRS评分≥3分或住院期间死亡），比较两组患者的临床特点和卒中后感染（肺部感染、尿路感染及中枢神经系统感染）等指标的差异。进一步采用多因素logistic回归分析AIS患者不良出院结局的影响因素。 结果 共纳入AIS患者1024例，平均年龄（60.3±12.4）岁，其中男性788例（77.0%），出院结局良好组761例（74.3%），结局不良组263例（25.7%）。85例患者出现卒中后感染，总体感染发生率为8.3%，其中69例（6.7%）为肺部感染，16例（1.6%）为尿路感染，3例（0.3%）为中枢神经系统感染。多因素分析结果显示，卒中后出现肺部感染（OR 2.522，95%CI 1.318～4.828，P=0.005）、合并糖尿病（OR 1.486，95%CI 1.048～2.106，P=0.026）、入院NIHSS评分升高（OR 1.286，95%CI 1.233～1.342，P＜0.001）和血白细胞计数升高（OR 1.094，95%CI 1.014～1.180，P=0.020）为AIS患者不良出院结局的危险因素。 结论 肺部感染可显著增加AIS患者出院结局不良的风险。 Abstract: Objective To explore the risk factors of adverse discharge outcomes in patients with acute ischemic stroke (AIS), and the effects of post-stroke infection on discharge outcomes. Methods AIS patients who were hospitalized in the Department of Neurology, Beijing Tiantan Hospital, Capital Medical University from June 2019 to June 2022 were retrospectively included in this study. The patients were divided into good outcome group (mRS score<3) and poor outcome group (mRS score≥3 or death during hospitalization) according to discharge outcome. The clinical characteristics and post-stroke infection(pulmonary infection, urinary tract infection and central nervous system infection) were compared between the two groups. Multivariate logistic regression was used to analyze the risk factors for adverse discharge outcomes in AIS patients. Results A total of 1024 patients with AIS were included in this study, with a mean age of (60.3±12.4) years old and 788 males (77.0%). There were 761 (74.3%) patients in the good outcome group and 263(25.7%) patients in the poor outcome group. 85 (8.3%) patients developed infection after stroke, of which 69 (6.7%) were pulmonary infections, 16 (1.6%) were urinary tract infections, and 3 (0.3%) were central nervous system infections. Multivariate analysis showed that pulmonary infection (OR 2.522, 95%CI 1.318-4.828, P=0.005), diabetes (OR 1.486, 95%CI 1.048-2.106, P=0.026), increased NIHSS score on admission (OR 1.286, 95%CI 1.233-1.342, P<0.001) and increased white blood cell count (OR 1.094, 95%CI 1.014-1.180, P=0.020) were risk factors for poor discharge outcomes in AIS patients. Conclusions Pulmonary infection may significantly increase the risk of poor discharge outcomes in patients with AIS.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".