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

Zhihui Liu; Nazeer Hussain Khan; Lintao Wang; Chun-Yang Zhang; Xin-Ying Ji

doi:10.2147/JMDH.S385060

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

J Multidiscip Healthc. 2022 Nov 30:15:2725-2733. doi: 10.2147/JMDH.S385060. eCollection 2022.

Authors

Zhihui Liu^#¹, Nazeer Hussain Khan^#^{2

3}, Lintao Wang⁴, Chun-Yang Zhang^{5

6}, Xin-Ying Ji^{2

7

8}

Affiliations

¹ Department of General Practice, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
² Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, People's Republic of China.
³ Section of Basic Medical Subjects, Shu-Qing Medical College of Zhengzhou, Zhengzhou, People's Republic of China.
⁴ The First Affiliated Hospital of Henan University, Kaifeng, People's Republic of China.
⁵ Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
⁶ Department of General Thoracic Surgery, Hami Central Hospital, Hami, People's Republic of China.
⁷ Kaifeng Key Laboratory for Infectious Diseases and Biosafety, Kaifeng, People's Republic of China.
⁸ Faculty of Basic Medical Subjects, Shu-Qing Medical College of Zhengzhou, Zhengzhou, People's Republic of China.

^# Contributed equally.

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: COVID-19; anxiety; medical staff; nomogram.