Article Figures & Data
Figures
- FIG 1.
Illustration of ROI delineation in WM in children with prenatal MMC repair on a color-coded FA map. Arrows denote areas of the gCC, L PLIC, and R PLIC. L PLIC indicates left posterior limb of the internal capsule; R PLIC, right posterior limb of the internal capsule.
- FIG 2.
A, Comparison of the predicted probability using the CDSI model based on stepwise logistic regression (solid circles) and using a logistic regression model with a single predictor (the MOMS Index, in empty circles). The data are plotted in ascending order of the predicted probability using the CDSI model, which had 3 false-negative (red solid circles) and 2 false-positive cases (blue solid circles). The single-predictor logistic regression model using the MOMS Index yielded 5 false-negative (red empty circles) and 3 false-positive cases (blue empty circles). B, The receiver operating characteristic (ROC) curve demonstrating sensitivity and specificity based on the CDSI approach for predicting the need for CSF diversion surgery. AUC indicates area under the curve; ID, identification.
Tables
- Table 1:
Single-predictor logistic regression analysis for predicting patients who required CSF diversion surgerya
Predictor β SE Wald χ2 df P Exp(β) OR Sensitivity Specificity Overall Accuracy MOMS Index 2.262 0.841 7.236 1 .007 9.600 0.643 0.842 0.757 gCC_FA –1.358 0.506 7.193 1 .007 0.257 0.572 0.842 0.727 gCC_MD 0.970 0.444 4.773 1 .029 2.638 0.571 0.737 0.667 PLIC_FA –0.953 0.482 3.905 1 .048 0.386 0.500 0.842 0.697 PLIC_MD 0.794 0.506 2.464 1 .117 2.212 0.214 0.947 0.636 Sex –0.773 0.728 1.129 1 .288 0.462 0.500 0.684 0.606 FOHR 0.173 0.362 0.230 1 .631 1.189 0.070 1.000 0.606 Note:—Sensitivity indicates the percentage of patients who were correctly predicted among patients who required surgery; Specificity, the percentage of patients who were correctly predicted among patients who did not require surgery; SE, standard error.
↵a All the predictors were tested individually with the intercept term included (not shown).
- Table 2:
Stepwise logistic regression analysis for predicting patients who required CSF diversion surgery
Predictor β 95% CI for Exp (β) SE Wald χ2 df P Exp (β) Lower Upper MOMS Index 4.331 2.125 4.154 1 .042 76.026 1.181 4.895E + 3 gCC_FA –1.976 0.943 4.390 1 .036 0.139 0.022 0.880 PLIC_FA –1.283 0.747 2.953 1 .086 0.277 0.064 1.198 Sex 3.216 1.727 3.467 1 .063 0.040 0.001 1.184 FOHR –1.335 1.078 1.508 1 .219 0.263 0.031 2.216 Constant –0.308 0.816 3.855 1 .706 0.735 NA NA Note:—NA indicates not applicable.
- Table 3:
Overall model evaluation and goodness-of-fit for the stepwise logistic regression model
χ2 df P Overall model evaluation (likelihood ratio) 25.834 5 < .001 Goodness-of-fit test (Hosmer-Lemeshow) 7.412 8 .493 Cox-Snell test (R2) 0.543 Nagelkelke test (R2) 0.730 - Table 4:
The frequency of CSF diversion surgery and the predicted frequency by stepwise logistic regressiona
Observed Predicted % Correct Yes No Yes 11 3 78.6 No 2 17 89.5 Overall % correct 84.8 ↵a Cutoff = 0.5; sensitivity = 11/(11 + 3) = 78.6%; specificity = 17/(2 + 17) = 89.5%; false-positive = 2/(2 + 17) = 10.5%; false-negative = 3/(11 + 3) = 21.4%.
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