Objective:To construct and validate a risk prediction model for work-related musculoskeletal disorders (WMSDs) among nurses, so as to provide a scientific and objective reference tool for the screening of high-risk groups of WMSDs among nurses.Methods:This study was a cross-sectional study. From March to July 2021, convenience sampling was used to select 1 050 nurses from 25 hospitals in Beijing as the research subject. The risk factors for WMSDs were evaluated by issuing the Musculoskeletal Disease Questionnaire. All survey subjects were randomly divided into the training set ( n=715) and the validation set ( n=304) according to the ratio of 7∶3, and the training set was used to build the model. The predictive ability of the risk model was evaluated using the area under receiver operating characteristic curve (AUC) and the validation set was used for validation. A total of 1 050 questionnaires were distributed and 1 019 valid questionnaires were recovered, with a valid recovery rate of 97.05%. Results:Among 1 019 nurses, the weekly incidence of WMSDs was 84.0% (856/1 019) and the annual incidence was 86.7% (883/1 019) . The neck was the site with the highest incidence of WMSDs, the weekly incidence and annual incidence were 70.3% (716/1 019) and 70.1% (714/1 019) , respectively. The results of multivariate Logistic regression analysis showed that the risk factors of the weekly incidence of WMSDs among nurses included age ≥40 years old ( P<0.001) , tense work atmosphere ( P<0.001) , forward neck posture ( P=0.002) , doing the same job daily ( P=0.039) , needing to dealing with patients ( P=0.012) and keeping the back in the same posture for a long time ( P=0.002) , and the risk factors of the annual incidence of WMSDs among nurses included age ≥40 years old ( P=0.001) , did not participation in safety protection training ( P=0.003) , high education ( P=0.041) , stressful work atmosphere ( P=0.005) , significantly forward neck posture ( P=0.008) , needing to dealing with patients frequently ( P=0.001) , work involving cold or temperature changes ( P=0.017) , insufficient rest time ( P=0.019) , frequent shift change ( P=0.035) , frequent repetition of the same action of the trunk at work ( P=0.025) , hand bending ( P=0.006) . Based on the above screening variables, a nomogram model was established to predict the risk of weekly and annual incidence of WMSDs among nurses, and the AUC values of the model in the training set were 0.794 [95% CI: (0.750-0.838) ] and 0.789 [95% CI: (0.718-0.860) ], respectively. The validation set further confirmed the predictive ability of the nomogram model. The AUC values for predicting the risk of weekly and annual incidence of WMSDs among nurses were 0.782 [95% CI: (0.729-0.835) ] and 0.794 [95% CI: (0.721-0.868) ], respectively. Conclusions:The nomogram model has good predictive ability for the occurrence risk of WMSDs among nurses, which can help to screen high-risk groups and give effective intervention in time.