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

Abstract

Background: Elderly patients undergoing hip fracture repair surgery are at increased risk of delirium due to aging, comorbidities, and frailty. But current methods for identifying the high risk of delirium among hospitalized patients have moderate accuracy and require extra questionnaires. Artificial intelligence makes it possible to establish machine learning models that predict incident delirium risk based on electronic health data.Methods: We conducted a retrospective case-control study on elderly patients (≥65 years of age) who received orthopedic repair with hip fracture under spinal or general anesthesia between June 1, 2018, and May 31, 2019. Anesthesia records and medical charts were reviewed to collect demographic, surgical, anesthetic features, and frailty index to explore potential risk factors for postoperative delirium. Delirium was assessed by trained nurses using the Confusion Assessment Method (CAM) every 12 h during the hospital stay. Four machine learning risk models were constructed to predict the incidence of postoperative delirium: random forest, eXtreme Gradient Boosting (XGBoosting), support vector machine (SVM), and multilayer perception (MLP). K-fold cross-validation was deployed to accomplish internal validation and performance evaluation.Results: 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.Conclusion: 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.

Development and Validation of a Nomogram for Predicting Postoperative Delirium in Patients With Elderly Hip Fracture Based on Data Collected on Admission

“…The clinical data of 404 patients were included in the present study. The incidence of POD in elderly hip fracture patients was 13.6% (55/404), which was consistent with Zhang’s study (14.3%; Zhang et al, 2019 ) and Zhao’s study (12.2%; Zhao et al, 2021 ), but lower than Wang’s study (19.12%; Wang Y. et al, 2021 ) and Guo’s study (21%; Guo et al, 2016 ). This may be related to delirium diagnostic strategies.…”

Derivation and validation of a prediction score for postoperative delirium in geriatric patients undergoing hip fracture surgery or hip arthroplasty

“…High CCI has been proved (32) to be related to higher mortality and complications after hip fracture surgery. The CCI evaluated the number of comorbidities and the severity of the concomitant diseases, which was consistent with the result of Zhao et al that the frailty of patients was a predictor of delirium after surgery (33). Another identified predictor in our study was active smoking.…”

“…Similarly, in our cohort, active smokers were liable to new-onset delirium, which may be attributed to the acetylcholine deficiency (35) caused by acute abstinence before surgery. In addition, disturbances of the internal environment caused by comorbidities or stress from the injury, presented with factors like high creatine levels, may also be interfering with the normal functioning of neurons and cause delirium (21,33).…”

Unsupervised Data Mining and Effect of Fast Rehabilitation Nursing Intervention in Fracture Surgery

“…As a comprehensive nursing intervention, rapid rehabilitation nursing intervention is highly recognized by clinicians and fracture patients due to its own advantages. It can significantly alleviate the clinical symptoms of patients, decrease the number of hospital stays, and reduce the incidence of complications [ 6 ].…”