Prediction model of early biomarkers of massive cerebral infarction caused by anterior circulation occlusion: Establishment and evaluation

Abstract

Objective: The purpose of this study is to establish and evaluate an early biomarker prediction model of massive cerebral infarction caused by anterior circulation occlusion.

Methods: One hundred thirty-four patients with acute cerebral infarction from January 2018 to October 2020 were selected to establish the development cohort for the internal test of the nomogram. Ninety-one patients with acute cerebral infarction hospitalized in our hospital from December 2020 to December 2021 were constituted the validation cohort for the external validation. All patients underwent baseline computed tomography (CT) scans within 12 h of onset and early imaging signs (hyperdense middle cerebral artery sign, obscuration of the lentiform nucleus, insular ribbon sign) of acute cerebral infarction were identified on CT by two neurologists. Based on follow-up CT images, patients were then divided into a massive cerebral infarction group and a non-massive cerebral infarction group. The nomogram model was constructed based on logistic regression analysis with R language. The nomogram was subsequently validated in an independent external validation cohort. Accuracy and discrimination of the prediction model were evaluated by a calibration chart, receiver operating characteristic (ROC) curve, and decision curve.

Results: The indicators, including insular ribbon sign, reperfusion therapy, National Institutes of Health Stroke Scale (NHISS) score, previous cerebral infarction, and atrial fibrillation, were entered into the prediction model through binary logistic regression analysis. The prediction model showed good predictive ability. The area under the ROC curve of the prediction model was 0.848. The specificity, sensitivity, and Youden index were 0.864, 0.733, and 0.597, respectively. This nomogram to the validation cohort also showed good discrimination (AUC = 0.940, 95% CI 0.894-0.985) and calibration.

Conclusion: Demonstrating favorable predictive efficacy and reproducibility, this study successfully established a prediction model of CT imaging signs and clinical data as early biomarkers of massive cerebral infarction caused by anterior circulation occlusion.

Keywords: CT; anterior circulation occlusion; early imaging signs; massive cerebral infarction; prediction model.

Figure 1

Early imaging signs on CT. (A) hyperdense middle cerebral artery sign: the M1 segment of the right middle cerebral artery shows high density (X-ray uptake value of 64 HU); (B) insular ribbon sign: the gray-white matter interface of the right insular zone disappears, and the cerebral sulcus becomes shallow; (C) obscuration of the lentiform nucleus: the right lentiform nucleus structure is blurred, and the density is reduced (the CT average value is 27.4 HU; the CT average value of the same area on the healthy side is 34 HU).

Figure 2

Nomogram for predicting the probability of MCI based on insular ribbon sign, reperfusion therapy, NHISS, previous cerebral infarction and atrial fibrillation. Nomogram calculates the occurrence of MCI by assigning each predictive variable with scales in line segments and integrating the scores.

Figure 3

Calibration plot for nomogram in the (A) development cohort and (B) validation cohort. The x-axis represents the predicted probability and y-axis represents the actual probability. A subset of various statistics useful for validating the model are also shown. Dxy: Somers' Dxy rank correlation between p (predicted possibilities) and y (actual outcome = 0 or 1). C (ROC): the ROC area. U, Unreliability index. Brier: average squared difference in p and y. (A) Dashed line (“Ideal”) represents ideal predictions. The red line represents the entire cohort (n = 134), and the blue line indicats observed nomogram performance by bias-corrected by bootstrapping (B = 1,000). (B) Dashed line denotes perfect calibration. A smoothing curve (blue) and the calibration curve (red) basically coincide.

Figure 4

Receiver operating characteristic curve analyses of prediction for MCI in the development and validation cohort.

Figure 5

Decision curve analysis comparing the clinical usefulness of MCI. The clinical usefulness is the net benefit (y-axis) of using the score to risk stratify patients relative to two extreme strategies of treating all the patients and treating none of the patients across a range of prespecified threshold probabilities (x-axis).

Figure 6

Clinical impact curve of model nomogram. The red curve (Number high risk) indicates the number of people who are. The blue curve (Number of high risk with outcome) indicates the number of true positives. When the threshold probability was >90.0% of the predicted probability value, the prediction model determined that the MCI high-risk population was highly matched with the actual MCI population, confirming the high clinical efficiency of the prediction model.