Journal of Clinical Medicine Research, ISSN 1918-3003 print, 1918-3011 online, Open Access
Article copyright, the authors; Journal compilation copyright, J Clin Med Res and Elmer Press Inc
Journal website https://jocmr.elmerjournals.com

Original Article

Volume 17, Number 7, July 2025, pages 398-407


Improving a Clinical Prediction Model for Computed Tomography Head Scan Use in Non-Traumatic Seizures: The SeizCT Optimized Model

Figures

Figure 1.
Figure 1. Study flow diagram. GCS: Glasgow Coma Scale.
Figure 2.
Figure 2. The area under the receiver operating characteristic curve (AuROC). CI: confidence interval.
Figure 3.
Figure 3. Calibration plot for predicted and observed probabilities of the SeizCT optimized model.
Figure 4.
Figure 4. Decision curve analysis.
Figure 5.
Figure 5. Predicted probability of positive CT brain findings plotted against the linear predictor. CT: computed tomography.

Tables

Table 1. Baseline Characteristics of Patients With Positive and Negative Findings on Computed Tomography (CT) Brain Scans
 
Baseline characteristicsMissing (n = 55), n (%)Positive CT (n = 118), n (%)Negative CT (n = 507), n (%)P value
SD: standard deviation; SBP: systolic blood pressure; DBP: diastolic blood pressure; GCS: Glasgow Coma Scale; IQR: interquartile range.
Female0 (0)40 (33.9)117 (23.1)0.018
Age (years), mean ± SD0 (0)60.4 ± 15.153.5 ± 16.9< 0.001
SBP (mm Hg), mean ± SD0 (0)145.5 ± 33.07139.4 ± 26.80.035
DBP (mm Hg), mean ± SD0 (0)85.7 ± 20.684.4 ± 17.80.481
GCS, median (IQR)0 (0)11 (7, 15)15 (11, 15)< 0.001
  Eye, median (IQR)0 (0)4 (2, 4)4 (4, 4)< 0.001
  Verbal, median (IQR)0 (0)2 (1, 5)5 (2, 5)< 0.001
  Motor, median (IQR)0 (0)5 (4, 6)6 (5, 6)< 0.001
Pupil (mm), median (IQR)39 (6.2)3 (2, 3)3 (3, 3)0.395
GCS change from baseline, median (IQR)0 (0)-4 (-8, 0)0 (-3, 0)< 0.001
Focal neurological deficit0 (0)65 (55.1)88 (17.4)< 0.001
Previous CT
  No previous CT0 (0)68 (57.6)243 (47.9)0.019
  Normal finding0 (0)11 (9.3)99 (19.5)
  Abnormal finding0 (0)39 (33.1)165 (32.5)
Prior stroke more than 3 months0 (0)16 (13.6)95 (18.7)0.228
Current cancer0 (0)17 (14.4)14 (2.8)< 0.001
Epilepsy0 (0)16(13.6)135 (26.6)0.003
End-stage renal disease0 (0)9 (7.6)35 (6.9)0.841
Chronic liver disease0 (0)3 (2.5)13 (2.6)1.000
Anticoagulant use0 (0)7 (5.9)26 (5.1)0.654
Hypertension0 (0)43 (36.4)164 (32.4)0.447
Diabetes mellitus0 (0)19 (16.1)87 (17.2)0.892
Smoking7 (1.1)21 (18.1)76(15.1)0.479
Alcohol consumption6 (1.0)39 (33.6)198 (39.4)0.290
Substance use3 (0.5)2 (1.7)20 (4.0)0.403
Alcohol withdrawal symptoms0 (0)2 (1.7)92 (18.2)< 0.001

 

Table 2. Predictors of Positive CT Head Scan Findings From Multivariable Logistic Regression
 
PredictorsPositive findings on CT head scan
mOR95% CIP value
CT: computed tomography; mOR: multivariable odds ratio; CI: confidence interval; GCS: Glasgow Coma Scale.
GCS change from baseline (points)1.141.07, 1.21< 0.001
Focal neurological deficit3.832.40, 6.11< 0.001
Current cancer4.111.79, 9.43< 0.001
Alcohol withdrawal symptoms0.120.03, 0.490.003
Epilepsy0.360.19, 0.68< 0.001
Prior stroke more than 3 months0.490.26, 0.930.030

 

Table 3. Internal Validation of the Predictive Model Using the Bootstrap Resampling Method
 
ParametersApparent performanceBootstrap performance
AuROC: area under the receiver operating characteristic curve; E:O ratio: expected-to-observed outcomes ratio; CITL: calibration-in-the-large.
AuROC0.8221 (0.7813, 0.8629)0.8160 (0.7700, 0.8550)
Slope1.0000 (0.7930, 1.2070)0.9570 (0.7440, 1.1960)
E:O ratio1.00001.0160
CITL-0.0000 (-0.2260, 0.2260)-0.0020 (-0.2300, 0.2370)
Bootstrap shrinkage-0.9570

 

Table 4. Confusion Matrices and Diagnostic Performance of the SeizCT Optimized Model at Different Probability Thresholds
 
Cut pointCT +ve (n = 118)CT -ve (n = 507)TotalSensitivitySpecificityPPVNPV
CT: computed tomography; CT +ve: positive finding on CT brain; CT -ve: negative finding on CT brain; PPV: positive predictive value; NPV: negative predictive value.
Probability > 18.9%94 (15.0%)116 (18.6%)21079.7%77.1%44.8%94.2%
Probability ≤ 18.9%24 (3.8%)391 (62.6%)415
Probability > 22.57%88 (14.1%)90 (14.4%)17874.6%82.2%49.4%93.3%
Probability ≤ 22.57%30 (4.8%)417 (66.7%)447
Probability > 25%84 (13.4%)83 (13.3%)16771.2%83.6%50.3%92.6%
Probability ≤ 25%34 (5.4%)424 (67.8%)458