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BACKGROUND: Accurate outcome prediction can serve to approach, quantify and categorize severe traumatic brain injury (TBI) coma patients for right median electrical stimulation (RMNS) treatment, which can support rehabilitation plans. As a proof of concept for individual risk prediction, we created a novel nomogram model combining amplitude-integrated electroencephalography (AEEG) and clinically relevant parameters. METHODS: This study retrospective collected and analyzed a total of 228 coma patients after severe TBI in two medical centers. According to the extended Glasgow Outcome Scale (GOSE), patients were divided into a good outcome (GOSE 3-8) or a poor outcome (GOSE 1-2) group. Their clinical and biochemical indicators, together with EEG features, were explored retrospectively. The risk factors connected to the outcome of coma patients receiving RMNS treatment were identified using Cox proportional hazards regression. The discriminative capability and calibration of the model to forecast outcome were assessed by C statistics, calibration plots, and Kaplan-Meier curves on a personalized nomogram forecasting model. RESULTS: The study included 228 patients who received RMNS treatment for long-term coma after a severe TBI. The median age was 40 years, and 57.8% (132 of 228) of the patients were male. 67.0% (77 of 115) of coma patients in the high-risk group experienced a poor outcome after one year and the comparative data merely was 30.1% (34 of 113) in low-risk group patients. The following variables were integrated into the forecasting of outcome using the backward stepwise selection of Akaike information criterion: age, Glasgow Coma Scale (GCS) at admission, EEG reactivity (normal, absence, or the stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs)), and AEEG background pattern (A mode, B mode, or C mode). The C statistics revealed that the nomograms' discriminative potential and calibration demonstrated good predictive ability (0.71). CONCLUSION: Our findings show that the nomogram model using AEEG parameters has the potential to predict outcomes in severe TBI coma patients receiving RMNS treatment. The model could classify patients into prognostic groups and worked well in internal validation.
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Objective: To evaluate the value of the correlation coefficient between the ICP wave amplitude and the mean ICP level (RAP) and the resistance to CSF outflow (Rout) in predicting the outcome of patients with post-traumatic hydrocephalus (PTH) selected for shunting. Materials and Methods: As a training set, a total of 191 patients with PTH treated with VP shunting were retrospectively analyzed to evaluate the potential predictive value of Rout, collected from pre-therapeutic CSF infusion test, for a desirable recovery level (dRL), standing for the modified rankin scale (mRS) of 0-2. Eventually, there were 70 patients with PTH prospectively included as a validation set to evaluate the value of Rout-combined RAP as a predictor of dRL. We calculated Rout from a CSF infusion test and collected RAP during continuous external lumbar drainage (ELD). Maximum RAP (RAPmax) and its changes relative to the baseline (ΔRAPmax%) served as specific parameters of evaluation. Results: In the training set, Rout was proved to be a significant predictor of dRL to shunting, with the area under the curve (AUC) of 0.686 (p < 0.001) in receiver-operating characteristic (ROC) analysis. In the validation set, Rout alone did not present a significant value in the prediction of desirable recovery level (dRL). ΔRAPmax% after 1st or 2nd day of ELD both showed significance in predicting of dRL to shunting with the AUC of 0.773 (p < 0.001) and 0.786 (p < 0.001), respectively. Significantly, Rout increased the value of ΔRAPmax% in the prediction of dRL with the AUC of 0.879 (p < 0.001), combining with ΔRAPmax% after the 1st and 2nd days of ELD. RAPmax after the 1st and 2nd days of ELD showed a remarkable predictive value for non-dRL (Levels 3-6 in Modified Rankin Scale) with the AUC of 0.891 (p < 0.001) and 0.746 (p < 0.001). Conclusion: Both RAP and Rout can predict desirable recovery level (dRL) to shunting in patients with PTH in the early phases of treatment. A RAP-combined Rout is a better dRL predictor for a good outcome to shunting. These findings help the neurosurgeon predict the probability of dRL and facilitate the optimization of the individual treatment plan in the event of ineffective or unessential shunting.
