Multivariate linear mixture models for the prediction of febrile seizure risk and recurrence: a prospective case-control study

Abstract

Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case-control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71 females). Participants formed one case group (patients with FS) and two control groups (febrile patients without seizures and healthy controls). The impact of blood iron status, peak body temperature, and participants' demographics on FS risk and recurrence was investigated with univariate and multivariate statistics. Serum iron concentration, iron saturation, and unsaturated iron-binding capacity differed between the three investigated groups (p_FWE < 0.05). These serum analytes were key variables in the design of novel multivariate linear mixture models. The models classified FS risk with higher accuracy than univariate approaches. The designed bi-linear classifier achieved a sensitivity/specificity of 82%/89% and was closest to the gold-standard classifier. A multivariate model assessing FS recurrence provided a difference (p_FWE < 0.05) with a separating sensitivity/specificity of 72%/69%. Iron deficiency, height percentile, and age were significant FS risk factors. In addition, height percentile and hemoglobin concentration were linked to FS recurrence. Novel multivariate models utilizing blood iron status and demographic variables predicted FS risk and recurrence among infants and young children with fever.

Figure 1

Between-group differences at the univariate level. The figure-embedded table summarizes between-group differences with highlighted significant findings. Graphs show value distributions for selected variables. Automatically enumerated discriminating thresholds (dashed gray lines) and corresponding SE and SP values are displayed for satFe, Fe, and UIBC variables, which demonstrated the strongest separation between groups. 1 healthy controls, 2 febrile patients without seizures, 3 febrile patients with non-recurrent FS, 4 febrile patients with recurrent FS, GA gestational age, Age age at the first febrile seizure attack, Height height percentile, Weight weight percentile, HGB hemoglobin, Fe serum iron concentration, Fer serum ferritin concentration, FS febrile seizures, TF serum transferrin concentration, satFe iron saturation, UIBC unsaturated iron-binding capacity, thr threshold, SE sensitivity, SP specificity.

Figure 2

Cross-correlation matrix plot for investigated variables. Value in the upper-left corner of each plot is the Pearson correlation coefficient (r) for corresponding variable pairs. Value r is red-highlighted for the significant coefficient with p < 0.001. The correlation regression line is presented as a black dashed line. The values at x- and y-axes are fixed for each variable across the plot. Histograms at the main plot diagonal display the value distribution for each corresponding variable. GA gestational age (weeks), Age age at the first febrile seizure attack, Height height percentile, Weight weight percentile, HGB hemoglobin, Fe serum iron concentration, Fer serum ferritin concentration, TF serum transferrin concentration, satFe iron saturation, UIBC unsaturated iron-binding capacity.

Figure 3

Between-group differences with multivariate linear mixture models. Left-sided panels: (a-b) represent dataset 3D visualizations in the space of three significant variables (in figure (a) height, UIBC, Fe; in figure (b) height, UIBC, and satFe) with p-values for respective between-group comparisons under each panel; c shows linear dependence between height percentile and HGB evaluated with Pearson correlation coefficient (r) for subgroups of patients with non-recurrent and recurrent febrile seizures. Right-sided panels: (a-b) show distributions of regressed values for all investigated groups, c for subgroups of patients with non-recurrent and recurrent febrile seizures. Automatically enumerated discriminating thresholds are shown with dashed gray lines; corresponding SE and SP values for separation properties of control and case groups are based on model₁ (a), model₂ (b), model₃ (c). Model equations are displayed in the y-axis label descriptions. 1 healthy controls, 2 febrile patients without seizures, 3 febrile patients with non-recurrent FS, 4 febrile patients with recurrent FS, Fe serum iron concentration, satFe iron saturation, Fer serum ferritin concentration, Age age at the first febrile seizure attack, Height height percentile, FS febrile seizures, UIBC unsaturated iron-binding capacity, HGB hemoglobin, thr threshold, SE sensitivity, SP specificity, *p-values were evaluated with the Wilcoxon rank-sum test.

Figure 4

Increased specificity of the case group separation and receiver operating characteristics while combining model₁ and model₂. (a) Visualization of the mutual model₁ (x-axis)—model₂ (y-axis) projection for all investigated groups. Right panel shows the zoomed-in area (delimited by dashed grey line) of the upper-right quadrant. The bi-linear classifier represents the thresholds of each separate model₁ and model₂ derived from data distributions shown in Fig. 3a,b. Thresholds are visualized as black solid lines. (b) Receiver operating characteristics and Euclidean distance (E) between an ideal “gold standard” classifier and the optimal classifier fit for the corresponding model/variable. Fe serum iron concentration, satFe iron saturation, Height height percentile, Age age at the first febrile seizure attack, UIBC unsaturated iron-binding capacity, HGB hemoglobin, thr threshold, SE sensitivity, SP specificity, ROC receiver operating characteristics.