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Preoperative anxiety risk factors identified in elective surgery patients at a tertiary hospitalNew Tool Predicts Which Surgical Patients Will Face High Anxiety

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Key Takeaway
Consider using identified risk factors to support early screening for preoperative anxiety in elective surgery patients.

This retrospective observational cohort study included 425 consecutively eligible adult patients who underwent elective surgery at a tertiary teaching hospital in Southwest China between January 2021 and October 2024. The study used retrospective determination of preoperative anxiety based on standardized cut-off values of validated anxiety assessment instruments.

Multivariable analysis identified female sex, lower body mass index, higher ASA physical status (III–IV), longer expected operative time, poorer sleep quality, higher depressive symptom scores, night-time smartphone use for at least 1 h, longer daily smartphone use duration, and higher trait anxiety scores as independent risk factors for preoperative anxiety. Greater social support was independently protective. The model had an area under the ROC curve of 0.848 (95% confidence interval, 0.811–0.884).

In absolute terms, 168 of 425 patients (39.5%) met the criteria for preoperative anxiety. No adverse events, serious adverse events, discontinuations, or tolerability data were reported, as this was a retrospective analysis.

Key limitations include the retrospective observational design, single-center setting, and lack of external validation. The nomogram may support early psychological screening and targeted perioperative support in similar elective surgical settings, but causation cannot be inferred, and findings should not be generalized without external validation in independent multicenter cohorts.

Imagine you’re scheduled for surgery. You might feel nervous, but what if your anxiety is so high it could affect your recovery? A new study from China has developed a tool to predict which patients are most likely to face this challenge.

Preoperative anxiety is more than just butterflies. It’s a real medical concern that can lead to complications during and after surgery. About 40% of surgical patients experience clinically significant anxiety, which can affect heart rate, blood pressure, and even how well anesthesia works.

This anxiety doesn’t just impact the patient. It can delay recovery, increase pain, and even lead to longer hospital stays. For doctors and nurses, it’s a daily challenge to identify who needs extra support before they enter the operating room.

Current methods often rely on a nurse’s intuition or a quick questionnaire. But these can miss people who are struggling silently. That’s why a reliable, data-driven tool could make a big difference.

Traditionally, doctors have used general guidelines to manage preoperative anxiety. They might offer reassurance or a mild sedative, but they don’t always know who needs more intensive help.

But here’s the twist: this new model uses specific patient data to create a personalized risk score. It’s not a one-size-fits-all approach. Instead, it looks at factors like sleep quality, smartphone use, and social support to paint a clearer picture.

Think of the model like a weather forecast for anxiety. Just as meteorologists use data like temperature and humidity to predict rain, this tool uses patient data to predict the likelihood of high anxiety.

The model acts like a smart filter. It sifts through dozens of factors—like age, health status, and daily habits—to find the ones that truly matter. For example, it found that using a smartphone for more than an hour at night is a red flag for anxiety.

Another key factor is social support. Having strong friends or family can act like a safety net, lowering anxiety risk. The model combines all these pieces into a single, easy-to-read score.

Researchers reviewed records from 425 adult patients who had elective surgery at a hospital in Southwest China between 2021 and 2024. All patients had completed a standard preoperative psychological assessment. The team used this data to build and test their prediction model.

About 40% of patients had high preoperative anxiety. The model identified several key risk factors: being female, having a lower body mass index, and facing a longer surgery were all linked to higher anxiety. Poor sleep, depression, and heavy smartphone use also increased risk.

On the other hand, strong social support was a protective factor. The model was highly accurate, correctly identifying high-risk patients 73% of the time and low-risk patients 82% of the time.

But there’s a catch.

This model is a promising step toward personalized preoperative care. It gives doctors a practical way to identify patients who might need extra support, like counseling or relaxation techniques. However, it’s not a crystal ball. Anxiety is complex, and no model can capture every factor.

If you’re facing surgery, this tool isn’t available yet. But it highlights the importance of speaking up about your anxiety. Talk to your doctor or nurse if you’re feeling overwhelmed. They can connect you with resources to help you feel more prepared and calm.

This study has some important limitations. It was conducted at a single hospital in China, so the results might not apply to other populations. The model also needs to be tested in larger, more diverse groups before it can be used widely.

The next step is to test this model in other hospitals and countries. If it continues to perform well, it could become a standard tool in preoperative care. Researchers also hope to explore whether using the model to guide interventions actually improves patient outcomes.

For now, it’s a reminder that understanding a patient’s mental state is just as important as checking their physical health before surgery.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
BackgroundPreoperative anxiety is common among surgical patients and is associated with adverse perioperative outcomes. Early identification of patients at elevated risk of clinically significant preoperative anxiety may facilitate timely psychological screening, support, and targeted interventions. This study aimed to identify factors associated with preoperative anxiety and to develop and internally validate a multivariable risk stratification model for individualized preoperative anxiety assessment.MethodsThis retrospective observational study included all consecutively eligible adult patients who underwent elective surgery at a tertiary teaching hospital in Southwest China between January 2021 and October 2024 and had a routinely documented preoperative psychological assessment in the medical record. Preoperative anxiety was retrospectively determined based on standardized cut-off values of validated anxiety assessment instruments documented in preoperative records. Demographic, clinical, perioperative, psychological, behavioral, and social variables were extracted from electronic medical records and routine preoperative assessments. Univariate and multivariable logistic regression analyses were performed to identify independent predictors of preoperative anxiety. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis, calibration was assessed using calibration plots with bias correction, and a nomogram was constructed based on the final multivariable model.ResultsAmong 425 eligible patients, 168 (39.5%) met the criteria for preoperative anxiety. Multivariable analysis identified female sex, lower body mass index, higher ASA physical status (III–IV), longer expected operative time, poorer sleep quality, higher depressive symptom scores, night-time smartphone use for at least 1 h, longer daily smartphone use duration, and higher trait anxiety scores as independent risk factors for preoperative anxiety, whereas greater social support was independently protective. The prediction model demonstrated good discriminative performance, with an area under the ROC curve of 0.848 (95% confidence interval, 0.811–0.884). At the optimal cut-off value, the model achieved a sensitivity of 0.73, a specificity of 0.82, and an overall accuracy of 0.76. Calibration analysis showed good agreement between predicted and observed risks. A nomogram was developed to facilitate individualized risk prediction.ConclusionThis multivariable risk stratification model showed good discrimination and calibration for identifying surgical patients at risk of preoperative anxiety. The nomogram provides a practical tool for individualized preoperative risk stratification and may support early psychological screening and targeted perioperative support within similar elective surgical settings. External validation in independent multicenter cohorts is warranted before broader implementation.
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