Nomographic Model for Predicting Severe Foot Pain in Nurses from Tertiary Hospitals in China

Abstract

To investigate the prevalence and common sites of severe foot pain among nurses, to define the risk factors of severe foot pain in nurses in tertiary hospital in China, and to construct a nomograph model for predicting individuals' risks for severe foot pain. Between August 2019 and December 2019, a stratified global sampling method was used to select 10691 nurses from 351 tertiary hospitals in China to investigate the incidence of severe foot pain among them. The variables that may affect the occurrence of severe foot pain were analyzed by single factor analysis to identify the influencing factors of severe foot pain in nurses. Furthermore, the independent risk factors of severe foot pain were analyzed by stepwise logistic regression analysis. The statistically significant factors identified in the multivariate regression analysis were incorporated into the nomograph prediction model. The predictive performance of the nomograph was measured by the consistency index (C-index) and calibrated with 1000 Bootstrap samples. A total of 3419 nurses out of the 10691 had foot pain, resulting in an incidence of 31.98%. The incidence of severe pain (VAS score 7-10) was 2.27% (243 of 10691). The locations of severe pain were more commonly found in the soles and heels of both feet. Six factors, including age, education, the material of the work shoes, comfortableness of the work shoes, number of complications, and foot injure history, were incorporated in the nomograph predicting model. The C-index value was 0.706 and the standard curve fitted well with the calibrated prediction curve. The risk prediction model constructed in this study showed sound performance in predicting the risk of severe foot pain in nurses, and all the indicators involved are simple and the relevant data are easily obtained. The model can provide reference for preventing severe foot pain in nurses.