Dermatology
COHORT
● Cohort
Researchers develop tool to predict skin irritation risk for nurses
Frontiers in Medicine
Published April 1, 2026
Yi Xu, Ting Lu, Lixiang Feng, Yingying Chen, Siyan Chen, Rongsheng Xiong
Researchers studied 2,852 nurses working in 40 hospitals across China to understand who might develop occupational contact dermatitis. This is a type of skin irritation that healthcare workers can get from frequent hand washing and exposure to chemicals. The study created a prediction tool called a nomogram that looks at nine different factors to estimate a nurse's risk.
The tool performed very well in identifying nurses at higher risk, with accuracy scores of 0.925 and 0.931 in different tests. These scores suggest the model could be useful for identifying nurses who might benefit from extra preventive measures. The nine factors included things like work habits, personal health factors, and workplace conditions.
It's important to understand this was an observational study that looked at data at one point in time. This means the research shows associations between certain factors and skin irritation risk, but doesn't prove these factors cause the condition. The tool hasn't been tested outside of this specific group of nurses yet, so we don't know how well it would work in other hospitals or countries.
For now, this research shows promise for helping hospitals identify nurses who might need extra skin protection support. However, more testing in different settings would be needed before this tool could be widely used in practice.
View Original Abstract ↓
Occupational contact dermatitis (OCD) is a prevalent work-related skin condition among nurses and remains a significant occupational health issue due to its impact on well-being, productivity, and workforce sustainability. However, reliable tools for early risk stratification in this population are lacking. This study aimed to develop and validate a nomogram-based prediction model to estimate the individual risk of OCD among nurses.
A multicenter cross-sectional survey was conducted among 2,852 nurses from 40 hospitals across China. Participants were randomly assigned to a training cohort (n = 2,000) and a validation cohort (n = 852). Independent predictors were identified using univariate and multivariable logistic regression analyses. A nomogram was constructed based on the final multivariable model. Model performance was assessed using the area under the ROC curve (AUC), bootstrapped calibration plots, and decision curve analysis (DCA).
Nine predictors were independently associated with OCD: age, dermatitis history, glove type, glove-wearing hours, handwashing frequency during work, hospital level, hand-cream habit, baseline skin condition, and sleep duration. The model showed excellent discrimination (AUC = 0.925 in the training set; 0.931 in the validation set). Calibration curves demonstrated close agreement between predicted and observed risks. DCA indicated consistently higher net benefit compared with the “treat-all” and “treat-none” strategies across wide threshold probability ranges (0.01–0.98 in the training set; 0.02–0.96 in the validation set). The resulting nomogram provides an intuitive, point-based tool for individualized OCD risk prediction.
A robust, well-validated prediction model and nomogram were developed to estimate OCD risk among nurses. This tool may support occupational health screening, early risk identification, and targeted preventive strategies in healthcare institutions.
