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To establish a nomogram predictive model for occupational blood exposures of medical staff and validate the model.
From July to December 2023, 20% of the medical staff of each sample hospital were selected for the study using multi-stage whole group probability sampling method and divided into training and validation groups in a 7:3 ratio. LASSO-Logistic regression was used to screen independent risk factors. R language was used to establish a nomogram model and verify it.
A total of 2 251 medical staff were included. There was no significant difference in general data between the two groups (P>0.05). LASSO-Logistic regression analysis showed that post (doctor: OR=3.024, 95% CI: 1.313-6.963; nurses: OR=3.837, 95% CI: 1.739-8.467), professional title (intermediate: OR=1.926, 95% CI: 1.444-2.569); advanced: OR=1.684, 95% CI: 1.052-2.697), education level (undergraduate: OR=2.076, 95% CI: 1.445-2.983); master’s degree or above: OR= 1.767, 95% CI: 1.073-2.910), psychological quality (general: OR=0.658, 95% CI: 0.443-0.987; good: OR=0.568, 95% CI: 0.368-0.879), stress level (moderate: OR=1.348, 95% CI: 1.061-1.713; severe: OR=2.109, 95% CI: 1.457-3.055), protective awareness (general: OR=0.515, 95% CI: 0.332-0.799; good: OR=0.297, 95% CI: 0.186-0.474), protective behavior (sometimes: OR=0.589, 95% CI: 0.363-0.955; always: OR=0.424, 95% CI: 0.261-0.689) was an independent influencing factor of blood-borne occupational exposure. The results of ROC curve showed that the area under the curve of the training group was 0.821 (95% CI: 0.667-0.831), and the validation group was 0.716 (95% CI: 0.618-0.715). The Hosmer-Lemeshow test showed that the calibration of the model was good (P=0.568, 0.956). The calibration curve showed that the prediction curve was basically fitted with the standard curve, and the model prediction accuracy was high. The results of the decision curve showed that the model had the best applicability when the risk threshold was about 0.1-0.8.
The nomogram model can accurately identify high-risk groups of occupational blood exposures and provide a basis for personalized risk prevention and control.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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