Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study

Topic: Risk, prevention and care in population studies

About 245 patients were included and postoperative delirium affected 12.2% (30/245) of the patients. Multiple logistic regression revealed that dementia/history of stroke [OR 3.063, 95% CI (1.231, 7.624)], blood transfusion [OR 2.631, 95% CI (1.055, 6.559)], and preparation time [OR 1.476, 95% CI (1.170, 1.862)] were associated with postoperative delirium, achieving an area under receiver operating curve (AUC) of 0.779, 95% CI (0.703, 0.856). The accuracy of machine learning models for predicting the occurrence of postoperative delirium ranged from 83.67 to 87.75%. Machine learning methods detected 16 risk factors contributing to the development of delirium. Preparation time, frailty index uses of vasopressors during the surgery, dementia/history of stroke, duration of surgery, and anesthesia were the six most important risk factors of delirium. Electronic chart-derived machine learning models could generate hospital-specific delirium prediction models and calculate the contribution of risk factors to the occurrence of delirium. Further research is needed to evaluate the significance and applicability of electronic chart-derived machine learning models for the detection risk of delirium in elderly patients undergoing hip fracture repair surgeries.

Key findings

• About 245 patients were included and postoperative delirium affected 12.2% (30/245) of the patients.
• Multiple logistic regression revealed that dementia/history of stroke [OR 3.063, 95% CI (1.231, 7.624)], blood transfusion [OR 2.631, 95% CI (1.055, 6.559)], and preparation time [OR 1.476, 95% CI (1.170, 1.862)] were associated with postoperative delirium, achieving an area under receiver operating curve (AUC) of 0.779, 95% CI (0.703, 0.856).
• The accuracy of machine learning models for predicting the occurrence of postoperative delirium ranged from 83.67 to 87.75%.
• Machine learning methods detected 16 risk factors contributing to the development of delirium.
• Preparation time, frailty index uses of vasopressors during the surgery, dementia/history of stroke, duration of surgery, and anesthesia were the six most important risk factors of delirium.

Why it matters

This retrospective study built machine learning models from electronic chart data to predict postoperative delirium after hip fracture surgery, identifying dementia or stroke history among the top risk factors. It is about predicting delirium risk and does not report any treatment, cure, or prevention for Alzheimer's disease.

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Study details

• Study designcase-control study
• Participants245
• Effect size12.2%; OR 3.063; OR 2.631
• Evidence strengthModerate

Read the original

• DOI: 10.3389/fsurg.2021.634629
• PubMed: 34327210
• Original source →
• Free full text: Frontiers in Surgery →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{zhao2021-34327210,
  author  = {Zhao, H. and You, J. and Peng, Y. and Feng, Y.},
  title   = {{Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study}},
  journal = {Frontiers in surgery},
  year    = {2021},
  doi     = {10.3389/fsurg.2021.634629},
  pmid    = {34327210},
  url     = {https://doi.org/10.3389/fsurg.2021.634629}
}

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