The Journal of Practical Medicine >

2026 , Vol. 42 >Issue 8: 1471 - 1478

DOI: https://doi.org/10.3969/j.issn.1006-5725.2026.08.023

Treatise: Clinical Practice

Risk prediction of postoperative cerebral infarction in elderly patients with intertrochanteric femoral fracture： A comparative study of decision tree and logistic regression model

  • Hongyu WU ,
  • Yupeng WU ,
  • Shaojuan HUANG ,
  • Suzhen HUANG ,
  • Yingjie HOU ,
  • Yi YANG ,
  • Zhirong ZHANG ,
  • Qingxi LI ,
  • Zhixin WU
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  • 1.The Eighth Clinical Medical College，Guangzhou University of Chinese Medicine，Foshan 528000，Guangdong，China
    2.Surgery Center，Foshan Hospital of Traditional Chinese Medicine，Foshan 528000，Guangdong，China
    3.Nursing Department，Foshan Hospital of Traditional Chinese Medicine，Foshan 528000，Guangdong，China
    4.Medical Records Room，Foshan Hospital of Traditional Chinese Medicine，Foshan 528000，Guangdong，China
    5.Neuro?Intensive Care Unit，Foshan Hospital of Traditional Chinese Medicine，Foshan 528000，Guangdong，China

Received date: 2025-12-05

  Online published: 2026-04-28

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Abstract

Objective To analyze the risk factors for cerebral infarction after surgery for intertrochanteric fracture of the femur （IFF） among elderly patients and establish a decision-tree risk prediction model， so as to provide a scientific theoretical basis for clinical medical staff to formulate targeted prevention and treatment plans. Methods Clinical data of 215 elderly patients who underwent surgery for IFF at our hospital from November 2017 to December 2023 were retrospectively selected. The patients were divided into the cerebral infarction group and the non-cerebral infarction group according to the postoperative occurrence of cerebral infarction. Univariate and multivariate logistic regression analyses were conducted to identify the risk factors for cerebral infarction after IFF surgery in elderly patients. A decision tree model and logistic regression were utilized to construct a risk prediction model， and the predictive values of both models for cerebral infarction after IFF surgery were compared. Results Among the 215 elderly patients who underwent surgery for IFF， 61 suffered from cerebral infarction post-operatively， with an incidence rate of 28.37%. Multivariate logistic regression analysis indicated that hypertension， diabetes， the duration of hospital bed rest， homocysteine， and D-dimer were risk factors for cerebral infarction after IFF surgery in elderly patients， while quality nursing was identified as a protective factor （P < 0.05）. A decision tree model was constructed based on these risk factors， with diabetes， the duration of hospital bed rest， Hcy， D-D， and quality nursing selected as explanatory variables. The model had a total of 4 layers and 15 nodes. The duration of hospital bed rest was the most significant influencing factor for cerebral infarction after IFF surgery in elderly patients. The area under the curve （AUC） value of the decision tree model for cerebral infarction after IFF surgery in elderly patients was 0.964 （95%CI： 0.930 - 0.985）， whereas the AUC value of the logistic regression model was 0.896 （95%CI： 0.847 - 0.933）. The DeLong test result for the two models was Z = 3.401， P = 0.000 7. Conclusions Hypertension， diabetes， the duration of hospital bed rest， Hcy， and D-D are identified as risk factors for cerebral infarction after IFF surgery in elderly patients， while quality nursing is recognized as a protective factor. The decision tree risk prediction model constructed based on these risk factors demonstrates significantly higher predictive efficacy compared to the logistic regression model.

Key words： decision tree model; logistic regression model; elderly intertrochanteric femoral fracture; cerebral infarction; influencing factors

Cite this article

Hongyu WU , Yupeng WU , Shaojuan HUANG , Suzhen HUANG , Yingjie HOU , Yi YANG , Zhirong ZHANG , Qingxi LI , Zhixin WU . Risk prediction of postoperative cerebral infarction in elderly patients with intertrochanteric femoral fracture： A comparative study of decision tree and logistic regression model[J]. The Journal of Practical Medicine, 2026 , 42(8) : 1471 -1478 . DOI: 10.3969/j.issn.1006-5725.2026.08.023

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