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Objective To investigate factors influencing the occurrence of early haematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH), to develop a predictive model and evaluate its predictive efficacy. Methods A retrospective cohort of 238 patients with sICH, admitted to General Hospital of Western Theater Command between January 2017 and December 2022, was analyzed. Patients were categorized into two groups based on the criteria of HE exceeding 33% in relative volume or 6 ml in absolute volume: HE group (n=62) and non-haematoma expansion (NHE) group (n=176). Clinical characteristics, laboratory findings, Non-contrast Computed Tomography (NCCT) imaging, and Glasgow Coma Scale (GCS) scores were compared between the two groups. Multifactorial logistic regression analysis was employed to identify risk factors for HE and to model the probability of its occurrence. The R language rms package was utilized to construct a nomogram model for predicting HE in sICH patients, Additionally, the related clinical, NCCT, and GCS models were constructed. The predictive efficacy of each model for HE in sICH patients was evaluated using area under Receive Operative Characteristic (ROC) curve (AUC), and the clinical application value of each model was assessed using accuracy, sensitivity, specificity, and Jordon's index. The Delong test was applied to analyze differences in the predictive values of the models. Results Significant differences in satellite sign, vortex sign, and history of anticoagulant treatment were observed between two groups (P<0.05). Multifactorial logistic regression analysis revealed independent risk factors for HE in sICH patients, including the first CT examination time, homogeneity, history of anticoagulant medication, volume, maximal diameter, hypodensity sign, island sign, satellite sign, and vortex sign (P<0.05). The AUCs for the constructed clinical model, NCCT model, GCS model and nomogram model in predicting the occurrence of HE in sICH patients were 0.672, 0.706, 0.518 and 0.754, respectively. The nomogram model demonstrated higher accuracy, sensitivity, Jordon's index and AUC compared with those in the clinical and NCTT models. Conclusions The first CT examination time, homogeneity, history of anticoagulant treatment, volume, maximum diameter, hypodensity sign, island sign, satellite sign, and vortex sign are independent predictors of early HE in sICH patients. The nomogram model, constructed with the above parameters, demonstrated high predictive efficacy for HE and holds potential for clinical application.
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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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