Evidence map›Paper›PMID 42426824›Full record

ArticleBMC medical informatics and decision making2026

Prediction of spinal cord injury after thoracoabdominal aortic repair by machine learning method.

Jian Song, Kai Xu, Jingyu Wang, Tianxia Gu, Wenyuan Lu, Yumeng Ji, Yunpeng Ling, Juntao Qiu, Cuntao Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jian Song *Department of Cardiac Surgery, Peking University Third Hospital, Peking University Health Science Center, Beijing, 100037, China.
Kai Xu *Department of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Jingyu Wang *Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Tianxia GuBusiness School, University of Shanghai for Science & Technology, Shanghai, 200093, China.
Wenyuan LuDepartment of Cardiac Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Yumeng JiDepartment of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
Yunpeng LingDepartment of Cardiac Surgery, Peking University Third Hospital, Peking University Health Science Center, Beijing, 100037, China.
Juntao QiuDepartment of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China. qiujt0328@163.com.
Cuntao YuDepartment of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China. cuntaoyu_fuwai@163.com.

Funding

Fundamental Research Funds for the Central Universitie 3332024035Fundamental Research Funds for the Central Universities 3332024026National Natural Science Foundation of China 82400565Natural Science Foundation of Shandong Province ZR2024QH366
6 · The paper itself

Abstract

backgroundSpinal cord injury (SCI) is a severe complication after thoracoabdominal aortic repair (TAAR), substantially increasing postoperative mortality and disability. This study aimed to develop an improved machine learning model using comprehensive perioperative data to predict SCI risk.

methods270 patients were included in the study, and 66 potentially meaningful variables were selected from 84 original variables as input data to drive machine learning. An optimized machine learning algorithm (PSO-FLXGBoost) was applied to stratify and rank predictors, and Shapley additive explanations (SHAP) were used to interpret feature contributions.

resultsThe PSO-FLXGBoost model achieved strong discrimination with an AUC of 0.895. SHAP analysis highlighted four key predictors: intraoperative hemoglobin < 70 g/L, preoperative D-dimer > 6 µg/mL, platelet count > 250 × 10^9/L, and operation time > 500 min, all of which significantly increased SCI risk.

conclusionsThe optimized PSO-FLXGBoost model reliably predicts SCI after TAAR, emphasizing intraoperative hemoglobin, coagulation status, and operative duration as critical risk factors. These insights may guide tailored perioperative strategies to improve patient outcomes.

Indexed as

Aortic Aneurysm, ThoracoabdominalMachine LearningPostoperative ComplicationsSpinal Cord InjuriesAgedBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRisk FactorsMachine learningPredictorsSpinal cord injuryThoracoabdominal aortic repair

Identifiers

PMID42426824
PMCPMC13632400

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.