Full
Outline
To develop and validate a risk prediction model for infiltration/extravasation in peripheral intravenous catheter therapy.
This retrospective study analyzed 942 patients who completed the Infiltration/Extravasation Risk Assessment Form between January and June 2023 at the First Affiliated Hospital of Army Medical University (including Departments of Neurology, Endocrinology, Gastroenterology and Hepatobiliary Surgery). Patients were allocated to a derivation cohort (n=628) and validation cohort (n=314) in a 2∶1 ratio based on catheterization chronology. The derivation cohort served for model development and internal validation, while the validation cohort underwent external validation. Logistic scoring method constructed the risk model, with Hosmer-Lemeshow (HL) test assessing goodness-of-fit and ROC curve evaluating predictive performance. Twenty-one potential risk factors were assessed, including age, gender, chronic diseases, clinician experience, treatment compliance, and total infusion volume.
Infiltration/extravasation occurred in 48 cases (5.10% incidence: 31 derivation/17 validation). Among 21 factors, 15 showed significant association (P<0.05), with 6 independent predictors: junior high school education or below (OR=5.2), chronic disease history (OR=3.1), poor compliance (OR=2.8), lower extremity venipuncture (OR=4.1), total infusion ≥1 000 mL (OR=3.5), and hyperosmotic/corrosive medications (OR=6.7). The final prediction model was: Y=2×(low education)+1×(chronic disease)+1×(poor compliance)+1×(lower extremity puncture)+1×(volume ≥1 000 mL)+2×(corrosive agents). For the derivation cohort, AUC was 0.967 (95%CI: 0.936~0.998), specificity 0.911, sensitivity 0.935, with good calibration (χ²=4.135, P=0.845). Validation cohort showed AUC 0.939 (0.853~1.000), specificity 0.919, sensitivity 0.941, and acceptable calibration (χ²=8.998, P=0.085).
This model demonstrates excellent discriminative ability and calibration, providing an effective tool for identifying high-risk patients and guiding targeted preventive strategies.
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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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