Establishment of a nomogram model for predicting hematoma expansion in intracerebral hemorrhage and its multidimensional evaluation

Objective: To establish a nomogram model for hematoma expansion (HE) prediction after intracerebral hemorrhage (ICH) and evaluate its performance in a multidimensionally way. Methods: A total of 348 ICH patients who were firstly diagnosed and hospitalized in the Second Affiliated Hospital of Soochow University from January 2017 to December 2019 were collected retrospectively. There were 236 males and 112 females, and their age ranged from 18 to 94 (62.0±14.6) years. All patients were divided into HE group (n=121) or non-HE group (n=227) according to the presence or absence of HE. The clinical and imaging features were compared between the two groups. Multivariate logistic regression analysis was performed for determining the independent predicting factors for HE prediction and a Nomogram model was established by using these factors. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction effectiveness, accuracy and clinical practicability of the model, respectively. Bootstrap method was used for internal validation. Results: There were significant differences in onset time, swirl sign, history of anticoagulants administrations, systolic blood pressure when admission, Glasgow coma scale (GCS) scores and RBC distribution width between the two groups[(1.77(1.0, 2.5) h vs 2(1, 3) h, 72 cases (59.5%) vs 94 cases (41.4%), 17 cases (14.0%) vs 15 cases (6.6%), (170.69±29.19) mmHg(1 mmHg=0.133 kPa) vs (163.84±26.07) mmHg, 11(8, 14) scores vs 14(10, 15) scores, 44.3% (41.2%, 46.8%) vs 42.4% (40.1%, 45.3%);respectively, all P<0.05]. Multivariate logistic regression analysis demonstrated that onset time (OR=0.809, 95%CI: 0.682-1.961, P=0.015), swirl sign (OR=0.562, 95%CI:0.349-0.905, P=0.018), history of anticoagulants administrations (OR=0.394, 95%CI: 0.180-1.861, P=0.020), and GCS (OR=0.881, 95%CI: 0.815-1.952, P=0.001) were the predicting factors for HE. The area under the curve (AUC) of the Nomogram model was 0.735(95%CI: 0.687-0.805), which demonstrated that the model has an ideal prediction effectiveness. The calibration curve showed that the prediction probability of HE of the model fits well with the actual probability, and with high calibration. DCA showed relatively wide range of optional threshold probability of the model (ranging from 14% to 72%), the clinical practicability of this model was high. The internal validation results showed a C-index of 0.703, indicated a good discrimination power. Conclusion: The established Nomogram model can predict the HE of ICH with good prediction effectiveness, discrimination power and with good clinical practicability, which can be capable of providing an intuitive and visual guidance tool for timely identifying ICH patients who may have HE.