Development and temporal validation of a prediction model for emergence agitation in older patients undergoing orthopedic surgery with general anesthesia: a single-center retrospective cohort study
This single-center retrospective cohort study developed and temporally validated a logistic regression model using seven clinical predictors to accurately stratify the risk of emergence agitation in older patients undergoing orthopedic surgery under general anesthesia, demonstrating strong discrimination performance that warrants further multicenter external validation.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
When an older patient wakes up from major surgery, the goal is a smooth, quiet return to consciousness. However, for some, the transition is jarring. Instead of waking calmly, they may become suddenly confused, restless, and physically agitated, often trying to pull out tubes or IV lines. This state, known as emergence agitation, is more than just a momentary nuisance; it poses real dangers. In the critical hours after surgery, a patient who is thrashing about risks injuring themselves, dislodging vital medical equipment, or causing bleeding at the surgical site. This is especially concerning for older adults undergoing orthopedic procedures, such as hip or knee replacements, where the body is already under significant stress. Because these patients have less physiological reserve and often manage multiple chronic conditions, predicting who might experience this agitation before it happens could allow medical teams to prepare better, offering closer monitoring and faster intervention to keep everyone safe.
A team of researchers at Hainan General Hospital set out to solve this problem by building a tool to forecast which older patients are most likely to become agitated after orthopedic surgery. They looked back at the medical records of nearly 2,000 patients treated over a two-year period, analyzing a wide array of information available before the patient even woke up. This included their medical history, the specific drugs used during anesthesia, the type of surgery performed, and various blood test results taken while they were still under. The researchers tested several different mathematical approaches to see which one could best spot the patterns leading to agitation. They found that a relatively simple method, which weighs seven specific factors, performed just as well as much more complex computer algorithms. The final model identified that patients with a history of drinking alcohol, a history of stroke, or high blood pressure were at higher risk, as were those who received a specific type of inhaled anesthetic called sevoflurane or had a urinary catheter inserted. Conversely, patients who had a pain pump set up to deliver medication automatically were less likely to become agitated. The model also noted that higher levels of carbon dioxide in the blood before waking were a warning sign.
The researchers first trained their model on data from 1,339 patients treated in 2025. In this group, about 30 percent of the patients experienced agitation. The model learned to distinguish between those who would remain calm and those who would not by focusing on seven key predictors: a history of drinking, a history of high blood pressure, a history of stroke, the use of sevoflurane, the presence of a patient-controlled pain pump, the use of a urinary catheter, and the level of carbon dioxide in the blood. To ensure the model was not just memorizing the data but actually learning a reliable pattern, the team tested it on a completely separate group of 670 patients treated in the first half of 2026. This second group had a slightly lower rate of agitation, at about 22 percent. When the model was applied to these new patients without any changes, it successfully identified the high-risk individuals with a high degree of accuracy, correctly distinguishing between those who would and would not become agitated in roughly 90 percent of cases.
While the model proved very good at ranking patients from low risk to high risk, the researchers observed that it tended to overestimate the actual likelihood of agitation in the new group. In other words, if the model said a patient had a 40 percent chance of agitation, the real chance was likely lower. This suggests that while the tool is excellent for flagging who needs extra attention, the specific numbers it generates should be treated as a guide rather than a precise prediction of what will happen. The study also highlighted that the most powerful factor in the prediction was not a single medical condition, but rather the setup for pain management. Patients who had a pump ready to deliver pain relief were significantly less likely to become agitated, reinforcing the idea that unmanaged pain is a major driver of this behavior.
The team concluded that this prediction tool could serve as a valuable aid for anesthesiologists and nurses in the recovery room. By reviewing a patient's chart before they wake up, the medical team could identify those with a high risk of agitation and ensure they are watched more closely or treated with extra care. However, the researchers were careful to note that this tool is not a replacement for human judgment or standard monitoring. Because the study was conducted at a single hospital, the model needs to be tested in other locations with different patient populations and medical practices before it can be used widely. The findings suggest that with the right information available before a patient wakes, it is possible to anticipate and mitigate the risks of a difficult recovery, turning a potentially chaotic moment into a managed, safer transition.
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