Table 3 Descriptive univariate analysis and machine-learning estimators of high relapse risk after the first episode of psychosis (HRR-FEP).
From: Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis
Descriptive univariate analysis | Machine learning | |
|---|---|---|
Clinical variables | ||
Schizoaffective disorder | HR = 3.6, P = 0.046 | β = +0.24 |
↓ Poor rapport (PANSS N3) | – | β = −0.01 |
↓ Difficulty in abstract thinking (PANSS N5) | HR = 0.6, P = 0.044 | β = −0.074 |
↓ Conceptual disorganization (PANSS P2) | – | β = −0.01 |
↓ Poor attention (PANSS G11) | – | β = −0.04 |
↑ Age in years | HR = 1.1, P = 0.008 | – |
↑ Long-acting injectable antipsychotic | – | β = 0.01 |
Gray matter increase | ||
↑ R Postcentral | – | Unm, [54, −6, 24], β = +0.93 |
Gray matter decrease | ||
↓ R middle temporal | – | Unm, [66, −6, −12], β = −0.43 |
↓ R inferior frontal/precentral | – | Unm, [30, 6, 36], β = −0.21 Mod, [42, 6, 36], β = −0.18 |
↓ R middle frontal | – | Unm, [30, 42, 36], β = −0.20 |
↓ R/L rectus | Unm, [−6, 30, −36], z = −2.6 | Mod, [6, 30, −24], β = −0.17 Unm, [6, 30, −24], β = −0.15 |
↓ L superior frontal | Unm, [−18, 66, −24], z = −2.8 | – |
↓ R medial frontal | Unm, [6, 78, −12], z = −2.6 | – |
↓ R Angular | – | Unm, [30, −54, 36], β = −0.05 |
White matter increase | ||
↑ R precentral | – | Unm, [42, 6, 36], β = +0.54 |
↑ L Middle frontal | – | Unm, [−42, 6, 36], β = +0.10 |
White matter decrease | ||
↓ R middle frontal | – | Unm, [30, 30, 36], β = -0.86 |
↓ L inferior frontal | – | Mod, [−42, 18, 12], β = −0.73 Unm, [−42, 18, 12], β = −0.57 |
↓ R Cuneus | – | Mod, [18, −90, 12], β = −0.18 |
↓ R superior frontal | Unm, [6, 54, 24], z = 2.9 | – |
↓ L corpus callosum | Unm, [−18, 18, 24], z = 2.7 | Unm, [−18, −30, 24], β = −0.05 |
↓ R corpus callosum | – | Mod, [6, 30, 0], β = −0.05 |
↓ L Middle frontal | – | Mod, [−30, 42, 12], β = −0.06 |
↓ R postcentral | – | Mod, [54, −6, 24], β = −0.09 |
