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

Abstract

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=13.13~149.32), rest time (OR=38.50, 95% CI=10.43~142.19), epidemic prevention area (OR=10.16, 95% CI=3.51~29.40), epidemic prevention equipment (OR=15.24, 95% CI=5.73~40.55), family support (OR=9.63, 95% CI=3.55~26.11), colleague infection (OR=6.25, 95% CI=2.18~19.11), and gender (OR=3.30, 95% CI=1.15~9.47) 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.

Keywords:

• nomogram
• anxiety
• COVID-19
• medical staff

Ethical Statement

The “Branch approved the study for Biomedicine Study, Henan University ethics committee”, and participants obtained consent. Moreover, the study has adopted all the guidelines outlined in the declaration of Helsinki’s ethical principles.

Acknowledgments

This study was supported by Henan Provincial Medical science and Technology research Projects (No.2018020407, No.LHGJ20190625). The authors would like to thank professor Yudong Miao, at the Department of Health Policy and Management, Zhengzhou University, for advising on the statistical analyses.

Disclosure

The authors declare no competing interest in this work.