Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury

Abstract

Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .

Fig. 1. Flowchart and study design.

Data-collection flowchart (left) and schematic illustrating study design (right). ICU, intensive care unit; DoC, disorder of consciousness; GOS-E, Glasgow Outcome Scale–Extended (levels range from 1 to 8, with higher levels indicating better outcomes); Se, sensitivity; Sp, specificity; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the receiver operating characteristic (ROC) curve.

Fig. 2. One-year functional outcome according to the DoC-team prognosis based on MMA.

One-year GOS-E scores for patients with good, poor or uncertain prognoses, for all patients (left) and after exclusion of those with WLST and those for whom the WLST decision was unknown (right). The numbers of patients with each GOS-E score are shown underneath the graphs. P values correspond to the shift analyzes (no adjustment for multiple comparisons; see Supplementary Table 2).

Fig. 3. Proportion of uncertain prognoses and accuracy according to the number of modalities included in MMA.

Left, accuracy and the percentage of uncertain prognoses for all patients. Right, accuracy and the percentage of uncertain prognoses after exclusion of those with WLST and those for whom the WLST decision was unknown. P values were calculated using Cochran–Armitage tests for trend (two-sided, no adjustment for multiple comparisons).

Fig. 4. Performances of individual prognostic markers, DoC-team prognosis and multivariable classifier in predicting favorable outcomes.

a, Performances of individual prognostic markers compared with the DoC-team prognosis performances, with all patients included (top) and after exclusion of those with WLST or for whom the WLST decision was unknown (bottom). The green color indicates the best performances (see Supplementary Table 3). Note that these figures are shown for a descriptive purpose only, because prognosis parameters have been calculated for different populations (see Supplementary Tables 4 and 5 for statistical comparisons). b, Performance of the multivariable classifier in predicting favorable outcomes (one-year GOS-E ≥ 4) compared with individual prognostic markers’ performances (left, training set ROC, patients with WLST or unknown decision excluded) and the DoC-team prognosis (right). See Supplementary Tables 4 and 5 for comparison of accuracy. FOUR, full outline of unresponsiveness score; FA, fractional anisotropy.

Extended Data Fig. 1. Distribution of DoC-team prognosis and implementation of individual markers over the study period.

When split on the median ( ≥ or < 2016), there is a significant decrease in the proportion of “uncertain” prognosis with a relative increase of “poor” prognosis (54.84% vs 36.48%, OR = 0.47 [95%CI: 0.29 - 0.76], P = 0.001 and 25.81% vs 38.99%, OR = 1.83 [95%CI: 1.11 – 3.06], P = 0.016, respectively) with no difference in terms of “good” prognosis (19.35%, vs 24.53%, OR = 1.35 [95%CI: 0.76 - 2.41], P = 0.279), accuracy (73.21% vs 78.95%, OR = 1.37 [95%CI: 0.58 – 3.16], P = 0.431) and WLST (26.5% vs 34.51%, OR = 1.46 [95%CI: 0.83 - 2.60], P = 0.179). Two-sided Fisher’s exact test used with no adjustment for multiple comparisons. CRS-r: Coma Recovery Scale revised; (q)EEG: (quantitative) Electroencephalography; SSEP: Somatosensory Evoked potential; ERP: Event Related Potential (‘Local-Global’ paradigm); FA Fractional Anisotropy; FOUR: Full Outline of UnResponsiveness Score; RS-fMRI: Resting state – functional Magnetic Resonance Imaging; PET: Positron Emission Tomography; DoC: Disorder of Consciousness; hASR: habituation of Auditory Startle Reflex; CMD: Cognitive Motor Dissociation. *: for % Accuracy and % WLST the percentages are provided on n = 277 and 259, respectively.

Extended Data Fig. 2. Disentangling the effect of the number of modalities and of time on accuracy.

Multivariable logistic regression models with splits on medians for time ( ≥ vs < 2016) and the number of modalities ( ≥ vs < 6) suggest main effects of the number of modalities on accuracy (OR = 3.13 [95%CI: 7.29 - 7.88], P = 0.013, adjusted for time: OR = 0.77 [95%CI: 0.30 - 1.88], P = 0.568); and on “uncertain” prognosis (OR = 0.45 [95%CI: 0.25 - 0.81], P = 0.007, adjusted for time: OR = 0.61 [95%CI: 0.34 - 1.08], P = 0.088). Likelihood ratio tests did not advocate for an interaction effect of the number of modalities and time in both models (LRT, accuracy: χ2(1) = 0.003, P = 0.958, “uncertain” prognosis, χ2(1) = 0.307, P = 0.579).

Extended Data Fig. 3. Individual plot (left) and loading plot (right) of the multivariable (sPLS-DA) classifier model using two components with respectively 3 and 8 selected prognostic markers.

CRS-r: Coma Recovery Scale revised; FOUR: Full Outline of UnResponsiveness; SSEP: Somatosensory Evoked Potential; ERP: Event Related Potential; EEG: Electroencephalography; FA: Fractional Anisotropy; GOS-E: Glasgow Outcome Scale Extended.