A Nomogram-Based Study: A Way Forward to Predict the Anxiety Status in Medical Staff During the COVID-19 Pandemic

Background and Objective: Anxiety influences job burnout and health. This study aimed to establish a nomogram to predict the anxiety status of medical staff during the coronavirus disease (COVID-19) pandemic. Methods: A total of 600 medical members were randomized 7:3 and divided into training and validation sets. The data was collected using a questionnaire. Logistic regression analysis and Akaike information criterion (AIC) were applied to investigate the risk factors for anxiety. Odds ratio (OR) and 95% confidence interval (95% CI) were calculated to establish a nomogram. Results: Participation time (OR=44.28, 95% CI=13.13 similar to 149.32), rest time (OR=38.50, 95% CI=10.43 similar to 142.19), epidemic prevention area (OR=10.16, 95% CI=3.51 similar to 29.40), epidemic prevention equipment (OR=15.24, 95% CI=5.73 similar to 40.55), family support (OR=9.63, 95% CI=3.55 similar to 26.11), colleague infection (OR=6.25, 95% CI=2.18 similar to 19.11), and gender (OR=3.30, 95% CI=1.15 similar to 9.47) were the independent risk factors (P<0.05) for anxiety in medical staff. The areas under the receiver operating characteristic (ROC) curves of the training and validation sets were 0.987 and 0.946, respectively. The decision curve's net benefit shows the nomogram's clinical utility. Conclusion: The nomogram established in this study exhibited an excellent ability to predict anxiety status with sufficient discriminatory power and calibration. Our findings provide a protocol for predicting and identifying anxiety status in medical staff during the COVID-19 pandemic.