Edited by: Liping Liu, Beijing Tiantan Hospital, Capital Medical University, China
Reviewed by: Teneille Emma Gofton, University of Western Ontario, Canada; Benjamin Aaron Emanuel, University of Southern California, United States
This article was submitted to Neurocritical and Neurohospitalist Care, a section of the journal Frontiers in Neurology
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Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a common cause of encephalitis in intensive care units. Until now, no reliable method has existed for predicting the outcome of anti-NMDAR encephalitis. In this study, we used quantitative electroencephalography (qEEG) to examine the brain function of anti-NMDAR encephalitis patients and assessed its predictive value. Twenty-six patients diagnosed with anti-NMDAR encephalitis were included and grouped according to whether they were treated in intensive care units (14 critically ill vs. 12 non-critically ill). All patients underwent 2-h 10-channel qEEG recordings at the acute stage. Parameters, including amplitude-integrated electroencephalogram (aEEG), spectral edge frequency 95%, total power, power within different frequency bands (δ, θ, α, and β), and percentages of power in specific frequency bands from frontal and parietal areas were calculated with NicoletOne Software and compared between groups. The short-term outcome was death or moderate/severe disability at 3 months after onset, measured with a modified Rankin Scale, and the long-term outcome was death, disability or relapse at 12 months. No differences in qEEG parameters were observed between the critically ill and non-critically ill patients. However, differential anterior-to-posterior alterations in δ and β absolute band power were observed. Logistic regression analysis revealed that a narrower parietal aEEG bandwidth was associated with favorable long-term outcomes (odds ratio, 37.9;
Anti-N-methyl-D-aspartate receptor encephalitis is an autoimmune encephalitis involving antibodies directed against the NR1 subunit of the NMDA receptor (NMDAR), and is often associated with ovarian teratomas (
Early identification of neurological outcomes is important in terms of therapeutic options. Several prognostic measures have been evaluated in anti-NMDAR encephalitis, including the Glasgow Coma Scale (GCS) score, number of complications, catatonia-predominant type, and electroencephalogram (EEG) (
A variety of quantitative EEG (qEEG) parameters have been developed in neurocritical care practice, including some applied to the diagnosis of viral encephalitis (
This single-center retrospective observational study was approved by the ethics committee of Peking Union Medical College Hospital. Eligible patients were enrolled from April 2014 to May 2017. Patient consent was not required because de-identified data were used in this study.
All enrolled patients met the diagnostic criteria for anti-NMDAR encephalitis introduced in 2016 (
We collected the demographic, clinical and laboratory data of patients, including symptoms at the acute stage, serum and CSF studies, and therapeutic regimens. The short-term outcome for this study was death or degrees of disability, which was evaluated with the mRS at 3 months after onset. Patients were considered to have a favorable short-term outcome when their mRS scores were ≤ 2 without increasing compared with baseline, and poor short-term outcome if the mRS scores were ≥3 or had increased. Long-term outcomes were obtained from hospital medical records or face-to-face interviews 12 months after onset. Patients with mRS scores ≥3 for the whole experimental period or experienced relapse events and received another episode of first-line immunotherapy were defined as experiencing poor long-term outcomes. Patients were considered to have favorable long-term outcomes if their mRS scores were ≤ 2 with no relapse events.
In the final analysis, a total of 26 patients completed 1-year follow-up. Of these, 25 patients were diagnosed with definite anti-NMDAR encephalitis, and 1 patient met the diagnostic criteria for antibody-negative anti-NMDAR encephalitis. We also recruited 10 healthy volunteers with similar ages and collected their qEEG information as a control group.
qEEG monitoring was performed for at least 2 h for each patient during the first week after admission to our center. Medication administrations during EEG recording were noted and intravenous anti-epileptic agents as well as sedatives were suspended before the start of qEEG monitoring. Silver-chloride disc electrodes were placed according to the International 10–20 System, with a 10-channel layout at the Fp1, F3, C3, P3, O1, and Fp2, F4, C4, P4, O2 sites. The reference electrode was located at Cz, and Fz was used as ground. A 1.0-Hz low- and 35-Hz high-frequency filter was used. Impedances were maintained below 10 kΩ. The qEEG recording was performed using a NicoletOne EEG monitor (VIASYS Healthcare Inc.), and both raw EEG and processed qEEG tracings were sampled simultaneously. The aEEG, spectral edge frequency 95% (SEF-95), total power, power within δ, θ, α, and β frequency bands, as well as relative percentages of power in specific frequency bands were calculated automatically and exported via NicoletOne system.
All qEEG recordings were analyzed off-line. For each patient, we selected one 30-min artifact-free epoch manually from the F3-F4 and P3-P4 montage for further quantitative analysis. We selected F3-F4 as the representative area of the anterior cross-cerebral EEG signal, and P3-P4 as the representative area of the posterior cross-cerebral EEG signal. Because signals from Fp1-Fp2 and O1-O2 often contain more artifacts or higher impedances, we did not select these recordings for final quantitative analysis.
Descriptive data are presented as mean ± standard deviation (SD) or median with interquartile ranges (IQR). We use Student's
Of the 26 patients, 11 (42.3%) were male and 15 (57.7%) were female. The median age was 20 (IQR: 16–27) years. No tumor was found in any of the male patients. Eight (53%) female patients had underlying tumors, which were pathologically confirmed as ovarian teratomas. All patients received intravenous immunoglobulin (2 g/kg divided for 5 days), 25 (96.2%) treated with methylprednisolone (1 g/d for at least 3 days), and 14 (53.8%) patients received second-line immunotherapy. Table
Comparison of demographic, clinical, and CSF characteristics between critically ill and non-critically ill patients.
Gender(female) | 8/14 | 7/12 | 1 |
Age | 20(15,26) | 20.5(17,31) | 0.502 |
Fever | 13/14 | 4/12 | 0.003 |
Headache | 10/14 | 4/12 | 0.113 |
Psychiatric behaviour | 12/14 | 11/12 | 1 |
Cognition dysfunction | 4/14 | 6/12 | 0.422 |
Memory impairment | 4/14 | 9/12 | 0.047 |
Speech dysfunction | 4/14 | 8/12 | 0.113 |
Seizures | 13/14 | 11/12 | 1 |
Movement disorder | 9/14 | 6/12 | 0.692 |
Central hypoventilation | 7/14 | 0/12 | 0.006 |
Autonomic dysfunction | 10/14 | 5/12 | 0.233 |
Decreased consciousness | 11/14 | 3/12 | 0.016 |
Glasgow Coma Scale | 5(3,6) | 11.5(7,15) | 0.005 |
Days till diagnosis | 20(15,25) | 17.5(13.5,34.5) | 0.959 |
Days in hospital | 61(54,120) | 16.5(13,27.5) | 0.0002 |
Mechanical ventilation | 10/14 | 0/12 | 0.0001 |
Tumor | 7/14 | 1/12 | 0.036 |
Tumor in female | 7/8 | 1/7 | 0.01 |
Elevated CSF protein | 2/14 | 5/12 | 0.19 |
CSF leukocyte | 0.728 | ||
~5 | 8/14 | 6/12 | |
6~50 | 5/14 | 5/12 | |
51~ | 1/14 | 1/12 | |
Oligoclonal band | 0.642 | ||
Negative | 6/13 | 3/10 | |
Suspected | 0/13 | 5/10 | |
Positive | 7/13 | 2/10 | |
Antibody titers in CSF | 0.324 | ||
~1:10 | 1/14 | 1/12 | |
1:32 | 3/14 | 5/12 | |
1:100~ | 10/14 | 6/12 | |
Antibody titers in serum | 0.040 |
||
Negative | 3/14 | 7/12 | |
1:10 | 2/14 | 1/12 | |
1:32~ | 9/14 | 4/12 | |
MMF | 9/14 | 4/12 | 0.238 |
MTX | 4/14 | 0/12 | 0.100 |
CTX | 1/14 | 0/12 | 1 |
RTX | 1/14 | 1/12 | 1 |
No 2nd-line Immunotherapy | 4/14 | 8/12 | 0.113 |
Patient short-term outcomes were as follows: favorable outcome, 7 of 26 (26.9%); and poor outcome, 19 of 26 (73.1%). There was no significant difference between the two subgroups in terms of demographic information, CSF profiles, concomitant tumors, or immunotherapy regimens. All patients in the favorable short-term outcome subgroup had symptoms of impaired memory at admission (7/7 vs. 6/19,
There were 10 (38.5%) patients experiencing poor long-term outcomes, including 5 with mRS ≥3 and 7 with relapse events within 12 months. One patient died because of complications due to infection. There were no significant differences between the favorable long-term outcome group and poor long-term outcome group in terms of sex, age, clinical symptoms, severity of onset, duration of hospital stay, profiles in CSF, concomitant tumors, or immunotherapy regimens during hospitalization.
The detailed results for qEEG parameters are presented in Table
Comparisons of qEEG parameters between patients and controls, critically ill patients and non-critically ill patients as well as patients with different outcomes.
Frontal area | aEEG Upper Margin (μV) | 10.9(9.1,12.4) | 10.8(10,11.4) | 0.860 | 11.4(8.2,12.8) | 10.8(9.7,11.9) | 0.918 | 11(9.1,12.1) | 10.7(8.8,12.8) | 0.563 | 10.2(8.3,12.25) | 11(10,12.5) | 0.257 |
aEEG Lower Margin(μV) | 8.95(7.4,10.3) | 8.9(7.9,9) | 0.671 | 9.3(6.7,11) | 8.8(7.9,9.8) | 0.959 | 9(7.4,9.7) | 8.9(7.2,11) | 0.544 | 8.4(6.7,10.1) | 9.2(8.2,10.3) | 0.304 | |
aEEG Bandwidth(μV) | 1.7(1.6,2) | 1.9(1.8,2.1) | 0.069 | 1.65(1.5,2) | 1.7(1.65,1.95) | 0.405 | 1.8(1.6,2) | 1.7(1.6,2) | 0.539 | 1.65(1.55,1.85) | 1.8(1.7,2) | 0.150 | |
SEF-95(Hz) | 1.31(1.24,1.42) | 1.39(1.35,1.4) | 0.304 | 1.32(1.29,1.44) | 1.31(1.21,1.41) | 0.552 | 1.4(1.31,1.44) | 1.31(1.17,1.4) | 0.117 | 1.32(1.3,1.43) | 1.3(1.15,1.4) | 0.290 | |
Total Power(μV) | 27.86(19.25,61.56) | 18.92(16.88,26.01) | 0.120 | 27.04(11.21,64.15) | 29.58(20.85,48.95) | 0.662 | 27.86(16.76,35.09) | 27.86(19.41,64.59) | 0.272 | 24.82(15.23,50.87) | 31.475(25,66.96) | 0.257 | |
δ RBP(%) | 56.45(40.9,65.39) | 54.54(41.08,55.98) | 0.397 | 49.22(39.46,58.37) | 61.46(52.31,69.94) | 0.143 | 54.75(39.46,57.53) | 58.05(40.9,68.09) | 0.563 | 55.235(40.18,66.43) | 57.615(49.86,65.39) | 0.732 | |
θ RBP(%) | 15.11(11.76,21.13) | 16.21(15.32,20.44) | 0.572 | 19.27(11.6,23.9) | 14.28(12.54,19.18) | 0.700 | 14.25(11.6,20.61) | 17.74(11.76,23.9) | 0.603 | 17.42(12.04,22.515) | 14.28(11.21,20.61) | 0.544 | |
α RBP(%) | 6.18(4.31,10.08) | 8.59(7.4,11.56) | 0.072 | 6.59(5.25,11.16) | 6.18(4.24,8.56) | 0.625 | 7.86(6.11,10.08) | 5.98(4.08,10.42) | 0.203 | 6.03(4.195,8.84) | 7.61(5.98,11.16) | 0.236 | |
β RBP(%) | 10.68(7.44,22.25) | 16.38(11.55,18.34) | 0.340 | 12.07(7.54,22.57) | 10.60(6.52,16.15) | 0.440 | 14.8(12.01,22.28) | 8.34(7.34,22.25) | 0.272 | 10.93(7.49,23.705) | 10.175(5.43,20.77) | 0.399 | |
δ ABP(μV) | 15.01(8.71,24.34) | 8.36(7.03,14.01) | 0.148 | 10.83(6.45,37.88) | 16.54(9.88,20.75) | 0.520 | 9.39(8.71,21.6) | 16.5(8.67,37.88) | 0.418 | 12.92(7.9,22.97) | 16.59(8.71,37.88) | 0.510 | |
θ ABP(μV) | 4.125(2.5,6.93) | 3.575(2.39,4.36) | 0.427 | 4.48(1.87,10.84) | 4.13(2.58,6.17) | 0.939 | 3.05(2.48,5.57) | 4.23(2.5,10.84) | 0.355 | 3.28(2.18,8.16) | 4.30(3.06,5.57) | 0.580 | |
α ABP(μV) | 1.915(0.79,4.82) | 2.03(1.34,2.64) | 0.659 | 2.17(0.56,5.26) | 1.92(0.89,2.51) | 0.857 | 2.26(0.84,2.68) | 1.08(0.78,5.26) | 0.885 | 0.89(0.59,4.35) | 2.43(1.08,4.82) | 0.197 | |
β ABP(μV) | 3.1(1.64,4.25) | 3.01(2.01,4.27) | 0.646 | 3.43(1.55,5.18) | 2.14(1.70,4.11) | 0.738 | 3.08(1.98,4.25) | 3.12(1.55,5.18) | 0.862 | 2.58(1.60,4.18) | 3.37(1.98,5.18) | 0.693 | |
Parietal area | aEEG Upper Margin (μV) | 10.2(8.8,12.4) | 11.4(10.2,13.6) | 0.223 | 9.1(8,12.1) | 10.5(9.6,12.6) | 0.537 | 10.8(8.9,12.8) | 10.1(8,12.1) | 0.506 | 9.35(7.8,12.4) | 10.95(10.3,12.4) | 0.108 |
aEEG Lower Margin(μV) | 8.45(6.8,10.2) | 9.45(8.2,11.4) | 0.244 | 7.25(6.5,10.2) | 8.6(7.65,10.35) | 0.589 | 9.1(7.2,10.7) | 8.3(6.5,10.2) | 0.623 | 7.45(6.35,10.45) | 9.2(8.6,10) | 0.170 | |
aEEG Bandwidth(μV) | 1.7(1.5,2) | 1.9(1.8,2.1) | 0.130 | 1.65(1.5,2.1) | 1.8(1.66,2) | 0.393 | 1.9(1.6,2) | 1.7(1.5,2) | 0.398 | 1.6(1.5,1.8) | 1.95(1.7,2.1) | 0.030 | |
SEF-95(Hz) | 1.29(1.19,1.4) | 1.38(1.35,1.4) | 0.082 | 1.36(1.24,1.4) | 1.23(1.19,1.34) | 0.104 | 1.33(1.22,1.4) | 1.29(1.19,1.4) | 0.622 | 1.36(1.21,1.43) | 1.24(1.18,1.29) | 0.057 | |
Total Power(μV) | 18.88(10.58,33.86) | 23.57(14.63,35.69) | 0.698 | 16.51(10.58,43.39) | 22.68(10.38,27.9) | 0.857 | 13.84(10.13,28.43) | 24.2(10.63,43.39) | 0.470 | 10.68(9.33,26.47) | 27.9(21.16,43.39) | 0.022 | |
δ RBP(%) | 49.75(41.61,65.05) | 39.715(29.78,48.56) | 0.024 |
51.05(41.61,66.5) | 49.75(38.29,61.95) | 0.625 | 49.47(33.66,51.63) | 53.04(42.91,67.91) | 0.184 | 49.75(42.66,61.95) | 57.11(39.87,67.91) | 0.732 | |
θ RBP(%) | 16.545(12.94,21.77) | 15.72(14.95,17.62) | 0.724 | 16.55(12.94,21.77) | 16.69(13.31,21.06) | 0.857 | 15.84(12.07,21.93) | 17.04(13.44,21.77) | 0.686 | 16.55(13.19,22.19) | 16.69(12.07,20.18) | 0.772 | |
α RBP(%) | 7.995(5.26,14.93) | 13.865(10.14,14.9) | 0.066 | 7.09(4.91,11.94) | 12.68(6.78,21.14) | 0.143 | 13.02(10.5,27.48) | 7.53(4.43,14.37) | 0.073 | 7.66(4.84,14.65) | 9.64(6.65,20.46) | 0.414 | |
β RBP(%) | 9.765(6.82,15.27) | 17.44(14.79,26.7) | 0.006 |
11.9(8.79,16.62) | 8.575(5.865,12.57) | 0.165 | 10.83(6.82,14.89) | 9.47(6.11,15.65) | 0.644 | 10.68(8.09,18.48) | 8.17(6.11,13.87) | 0.114 | |
δ ABP(μV) | 10.095(5.6,13.72) | 7.015(3.63,13.56) | 0.458 | 11.5(5.7,20.86) | 8.46(5.39,12.73) | 0.520 | 6.61(4.74,11.73) | 11.26(5.7,17.38) | 0.272 | 6.48(4.82,12.18) | 11.76(9.73,20.86) | 0.054 | |
θ ABP(μV) | 2.89(1.47,6.32) | 3.675(2.36,5.61) | 0.778 | 2.39(1.35,6.45) | 3.48(1.74,6.14) | 0.857 | 2.83(1.35,7.68) | 2.95(1.58,6.32) | 0.908 | 1.95(1.34,6.39) | 3.83(2.83,6.01) | 0.257 | |
α ABP(μV) | 1.755(0.67,4.95) | 3.17(1.56,8.07) | 0.148 | 1.16(0.55,4.38) | 2.19(1.2,5.63) | 0.396 | 2.38(1.4,6.31) | 1.45(0.55,4.38) | 0.355 | 1.17(0.5,4.67) | 2.61(1.99,6.31) | 0.133 | |
β ABP(μV) | 1.75(1.04,3.05) | 4.23(2.63,5.97) | 0.008 |
2.15(1.04,3.05) | 1.33(1.04,2.77) | 0.410 | 2.48(1.43,3.05) | 1.58(1.03,3.05) | 0.402 | 1.56(1.01,2.82) | 2.24(1.22,3.1) | 0.292 |
We also investigated whether the differences in qEEG parameters between anterior and posterior areas were correlated with severities or outcomes (see Table
The anterior-to-posterior gradient of qEEG parameters in patients and control group.
aEEG upper margin | 10.85 |
10.2 |
0.341 | 11.4 |
9.1 |
0.245 | 10.8 |
10.5 |
1.000 | 10.8 |
11.4 |
0.103 | 11 |
10.95 |
0.959 | 10.2 |
9.35 |
0.224 |
aEEG lower margin | 8.95 |
8.45 |
0.162 | 9.3 |
7.25 |
0.124 | 8.8 |
8.6 |
0.753 | 8.9 |
9.45 |
0.047 | 9.2 |
9.2 |
0.759 | 8.4 |
7.45 |
0.127 |
aEEG Bandwidth | 1.7 |
1.7 |
0.918 | 1.65 |
1.65 |
0.777 | 1.7 |
1.8 |
0.811 | 1.9 |
1.9 |
1.000 | 1.8 |
1.95 |
0.603 | 1.65 |
1.6 |
0.498 |
SEF-95 | 1.31 |
1.29 |
0.258 | 1.32 |
1.36 |
1.000 | 1.31 |
1.23 |
0.091 | 1.39 |
1.38 |
0.959 | 1.3 |
1.24 |
0.240 | 1.32 |
1.36 |
0.569 |
Total power | 27.86 |
18.88 |
0.059 | 27.04 |
16.51 |
0.433 | 29.58 |
22.68 |
0.050 | 18.915 |
23.57 |
0.959 | 31.475 |
27.9 |
0.285 | 24.82 |
10.68 |
0.070 |
δ RBP | 56.45 |
49.75 |
0.638 | 49.22 |
51.045 |
0.158 | 61.46 |
49.75 |
0.060 | 54.535 |
39.715 |
0.005 | 57.615 |
57.11 |
0.721 | 55.235 |
49.75 |
0.756 |
θ RBP | 15.105 |
16.545 |
0.409 | 19.265 |
16.545 |
0.778 | 14.28 |
16.685 |
0.136 | 16.21 |
15.72 |
0.333 | 14.28 |
16.69 |
0.799 | 17.42 |
16.55 |
0.255 |
α RBP | 6.18 |
7.995 |
0.002 | 6.59 |
7.09 |
0.363 | 6.18 |
12.675 |
0.002 | 8.585 |
13.865 |
0.005 | 7.61 |
9.64 |
0.114 | 6.03 |
7.66 |
0.005 |
β RBP | 10.675 |
9.765 |
0.228 | 12.07 |
11.9 |
0.470 | 10.595 |
8.575 |
0.388 | 16.38 |
17.44 |
0.075 | 10.175 |
8.17 |
0.241 | 10.93 |
10.68 |
0.501 |
δ ABP | 15.01 |
10.095 |
0.025 | 10.83 |
11.495 |
0.363 | 16.535 |
8.46 |
0.015 | 8.36 |
7.015 |
0.169 | 16.59 |
11.76 |
0.169 | 12.92 |
6.48 |
0.049 |
θ ABP | 4.125 |
2.89 |
0.086 | 4.475 |
2.39 |
0.198 | 4.125 |
3.48 |
0.272 | 3.575 |
3.675 |
0.799 | 4.30 |
3.83 |
0.799 | 3.28 |
1.95 |
0.063 |
α ABP | 1.915 |
1.755 |
0.304 | 2.17 |
1.155 |
0.778 | 1.915 |
2.185 |
0.071 | 2.03 |
3.17 |
0.047 | 2.43 |
2.61 |
0.445 | 0.89 |
1.17 |
0.535 |
β ABP | 3.1 |
1.75 |
0.020 | 3.43 |
2.15 |
0.300 | 2.14 |
1.325 |
0.019 | 3.01 |
4.23 |
0.114 | 3.37 |
2.24 |
0.093 | 2.58 |
1.56 |
0.088 |
Taking the long-term outcome as dependent variable, with univariate logistic regression we screened parietal aEEG upper margin, aEEG bandwidth, SEF-95, and β relative band power as independent variables. Subsequent multivariate logistic regression analysis yielded only one predictor: the parietal aEEG bandwidth (odds ratio, 37.9; 95% confidence interval, 1.11–1295.27;
Receiver operating characteristic curve of parietal aEEG bandwidth predicting long-term outcomes of anti-NMDAR encephalitis when cutoff point is 1.7.
Anti-NMDAR encephalitis has been recognized as a common cause of encephalitis in ICU. Despite its responsiveness to immunotherapy and tumor removal, the mortality rate of anti-NMDAR encephalitis in the ICU is 4–25% (
aEEG is a type of processed EEG that is compressed with respect to amplitude and time, and the upper and lower margins of the aEEG reflect the maximum/minimum peak-to-peak amplitudes of the EEG signals (
Parietal aEEG bandwidth among favorable long-term patients, poor long-term patients, and healthy volunteers.
Interestingly, compared with healthy controls, the lower margins of the aEEG in both the critically ill and non-critically ill subgroup did not show any significant differences. One possible reason for this phenomenon is the small number of patients. Nevertheless, it may also suggest that brain function in anti-NMDAR encephalitis is relatively intact even in critically ill patients. This characteristic of qEEG is potentially consistent with the pathogenesis of anti-NMDAR encephalitis. NMDAR is an ionotropic glutamate receptor distributed in entire brain tissues. Antibodies directed at the NR1 subunit of the NMDA receptors act by mechanisms including the binding, capping, and cross-linking of NMDA receptors, leading to internalization from the cell membrane surface and a selective decrease in NMDA receptor currents with no effect on synapse number or other synapse proteins (
Compared with the healthy control group, there were specific anterior-to-posterior graded alterations of qEEG parameters in patients with anti-NMDAR encephalitis. In particular, there were alterations in δ and β absolute band power. δ absolute band power in the posterior area was lower than in the anterior area in the healthy control group and non-critically ill subgroup, but higher than in anterior area of the critically ill subgroup. However, this trend was reversed in the β absolute band power, which was higher in the posterior area in the healthy control group, and lower in the critically ill and non-critically ill subgroups. Increased power in slower frequency bands (δ and θ) and decreased power in faster frequency bands (α and β) are seen with reductions in brain metabolism (
The main limitation of our study is the small number of patients, which limits the power of the findings. In addition, patients requiring qEEG monitoring due to decreased consciousness or suspected seizures, but were not severe enough to require ICU admission, were enrolled as the non-critically ill subgroup. This might have led to a selection bias; however, it also enabled the analysis of the most challenging group of patients with this disease, in whom prognostic biomarkers are most needed. Additional analysis of temporal and occipital areas, as well as prolonged qEEG monitoring, are needed in the future. Furthermore, while critically ill patients are monitored, some were being administered with anti-epileptic drugs, sedatives, or antipsychotics at the same time. In our study, almost all patients in the ICU was administered at least one intravenous sedative, including midazolam, diazepam, or propofol, to control seizures and involuntary movements in the early course of the disease. Before the start of qEEG monitoring, we requested that these sedatives be suspended and restarted after the monitoring is over. However, these medications may still have an impact on EEG signals. In general, sedatives and antiepileptic medications depress the electrocortical activity and render the EEG background more discontinuous and depressed than expected; therefore, a continuous background may become slightly discontinuous (
In conclusion, the qEEG pattern in anti-NMDAR encephalitis can offer better understanding of the pathophysiological mechanisms and prognostic possibilities. A wider parietal aEEG bandwidth was associated with worse long-term outcomes, and may serve as a useful biomarker in anti-NMDAR encephalitis. Further, well-designed studies are needed to confirm this novel finding, and elucidate the underlying mechanism.
NJ wrote the initial draft of the paper. HG and HR acquisitioned patients' demographic and clinical data from encephalitis database registration. QL guided for analyzing the EEG signals. BP guided for study designation and made critical revision of draft.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We gratefully acknowledge the support from Huadong Zhu (Emergency Intensive Care Unit, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, P. R. China) and Bin Du (Medical Intensive Care Unit, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, P. R. China) for their support of this research.