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Objective:To explore the predictive value of amplitude integrated electroencephalography(aEEG)in the neurological prognosis of children with neonatal bacterial meningitis(NBM).Methods:The clinical data and aEEG results from 148 children diagnosed with NBM who completed aEEG examinations in the Department of Neonatology at Kunming Children′s Hospital from January 2018 to December 2019 were retrospectively analyzed.According to whether aEEG is abnormal, the children were divided into aEEG abnormal group and aEEG non-abnormal group.According to the degree of aEEG abnormality, children with aEEG abnormality were divided into aEEG mild abnormal group and aEEG severe abnormal group.The abnormal rate and abnormal characteristics of aEEG were analyzed; The clinical data of two groups were compared.Results:(1)Among the 148 children with NBM, 49 children had abnormal aEEG, 99 children had no abnormality, and the aEEG abnormal rate was 33.1%.The abnormal aEEG was manifested as delayed sleep-wake cycle maturation in 39 (26.3%) cases, abnormal discharge in eight (5.4%) cases, and abnormal background activity in one (0.6%) case.(2)The proportion of children with convulsive seizures and refractory NBM in aEEG abnormal group were significantly higher than those in aEEG non-abnormal group ( P<0.05). In the routine and biochemical abnormal indexes of cerebrospinal fluid, the proportion of protein >3 g/L, cerebrospinal fluid leukocyte>500×10 6/L, cerebrospinal fluid glucose<1.5 mmol/L, positive cerebrospinal fluid culture, positive blood and cerebrospinal fluid culture, abnormal head MRI in aEEG abnormal group significantly increased ( P<0.05); While there was no significant difference regarding blood routine leukocyte abnormality, CRP increase, and positive blood culture ratio between two groups ( P>0.05). (3) 148 cases of NBM children were followed up to 15 months old, 119 (80.4%) cases completed the follow-up, the loss rate was 19.6%, three cases died, and 11 cases had psychomotor retardation.Compared with the children with abnormal aEEG, the prognosis of children with NBM was significantly different, the Spearman rank correlation coefficient r was 0.315 ( P<0.05). COX regression was used to analyze the predictive value of each index for adverse outcomes. Abnormal aEEG was an independent risk factor for adverse outcomes in children with NBM ( OR=7.452, 95% CI 1.605-34.591, P<0.05). Conclusion:The aEEG monitoring of children with NBM, if abnormal, may indicate severe NBM, which is likely to be transformed into refractory NBM or has a poor prognosis.
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Objective:To explore the significance of multimodal monitoring in the monitoring and treatment of neurocritical care patients.Methods:104 neurocritical care patients admitted to the department of Critical Care Medicine of Fujian Provincial Hospital from March 2019 to January 2020 were enrolled. Patients were randomly assigned into two groups, with 52 in each group. In the routine monitoring treatment group, heart rate, blood pressure, respiratory rate and the changes in consciousness and pupils were monitored after operation. The patients were treated with routine medicine to reduce intracranial pressure (ICP), maintain proper cerebral perfusion pressure (CPP), balance fluid intake and output, and maintain the airway clear. Patients in the multimodal monitoring treatment group were treated with invasive ICP monitoring, ultrasound to assess brain structure, ultrasound to measure optic nerve sheath diameter (ONSD), transcranial color doppler (TCCD), internal jugular venous blood oxygen saturation monitoring, near-infrared spectroscopy (NIRS), non-invasive cerebral blood oxygen saturation monitoring and quantitative electroencephalogram monitoring. According to the monitoring results, the patients were given targeted treatment with the goal of controlling ICP and improving brain metabolism. The length of intensive care unit (ICU) stay, the incidences of neurological complications (secondary cerebral infarction, cerebral hemorrhage, high intracranial pressure, etc.), and the incidences of poor prognosis [6 months after the onset of Glasgow outcome score (GOS) 1 to 3] were compared between the two groups. Spearman rank correlation analysis of the correlation between invasive ICP and the ICP value which was calculated by TCCD. The receiver operating characteristic (ROC) curve of invasive ICP and pulsatility index of middle cerebral artery (PI MCA) were used to predict poor prognosis. Results:The length of ICU stay in the multimodal monitoring treatment group was significantly shorter than that of the routine monitoring treatment group (days: 6.27±3.81 vs. 9.61±5.09, P < 0.01), and the incidence of neurological complications was significantly lower than that in the routine monitoring treatment group (9.62% vs. 25.00%, P < 0.05). In the multimodal monitoring treatment group, 37 cases had a good prognosis and 15 cases had a poor prognosis, while the routine monitoring treatment group had a good prognosis in 27 cases and a poor prognosis in 25 cases. The incidence of poor prognosis in the multimodal monitoring treatment group was lower than that of the routine monitoring treatment group (28.85% vs. 48.08%, P < 0.05). In the multimodal monitoring treatment group, the invasive ICP and PI MCA of patients with good prognosis were significantly lower than those of patients with poor prognosis [invasive ICP (mmHg, 1 mmHg = 0.133 kPa): 16 (12, 17) vs. 22 (20, 24), PI MCA: 0.90±0.33 vs. 1.39±0.58, both P < 0.01]. There was no significant difference in resistance index of the middle cerebral artery (RI MCA) between the good prognosis group and the poor prognosis group (0.63±0.12 vs. 0.66±0.15, P > 0.05). There was a positive correlation between the invasive ICP and the ICP value which was calculated by TCCD ( r = 0.767, P < 0.001). ROC curve analysis showed that the area under ROC curve (AUC) of invasive ICP for poor prognosis prediction was 0.906, the best cut-off value was ≥ 18 mmHg, the sensitivity was 86.49%, and the specificity was 86.67%. The AUC of PI MCA for poor prognosis prediction was 0.759, the best cut-off value was ≥ 1.12, the sensitivity was 81.08%, and the specificity was 60.00%. The AUC of invasive ICP was greater than PI MCA ( Z = 2.279, P = 0.023). Conclusion:Comprehensive analysis of multimodal monitoring indicators for neurocritical care patients to guide clinical treatment can reduce the length of hospital stay, and reduce the risk of neurosurgery complications and disability; invasive ICP can predict poor prognosis of neurocritical care patients.
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Objective To build a predictive model of depression with secondary mild cognitive impairment (MCI) in elderly patients based on current clinical diagnosis and treatment technology,and to analyze its application.Methods Elderly patients with depression hospitalized in three hospitals were consecutively included in our study from September 2013 to December 2015 for collecting relevant clinical data,and followed up for 18 months to confirm a prognosis.The follow-up results were used to predict influencing indices for secondary MCI risk,and to verify judgement effectiveness of the critical value of the relevant indices on the window of time of the secondary MCI.Results A total of 216 elderly patients with depression were included in this study,of whom 9 patients were lost to follow-up.Finally,27 patients had secondary MCI,and 180 patients had normal cognitive function during the follow-up period.Cox multiple regression analysis showed that the risk model of secondary MCI in elderly patients with depression was composed of age (HR:1.30,95 ％ CI:1.12-1.64,P =0.03),education years (HR:0.56,95 ％ CI:0.41-0.80,P =0.01),regular psychological treatment (HR:0.73,95％ CI:0.58-0.92,P=0.03),and BSSI scale (HR:1.24,95％ CI:1.08-1.56,P=0.03).Age and BSSI scale were risk factors,while education years and regular group psychotherapy were protective factors.For an elderly patient with depression who was characterized by age ≥ 72.3 years,education years ＜8.3 years,and BSSI scale ≥75.1,the window of time for secondary MCI was shorter,and these critical values of the independent factors had significant judgement effectiveness.Conclusions Age,education years,regular psychological treatment,and BSSI scale are independently influencing factors for secondary MCI in elderly patients receiving the treatment for depression.Furthermore,age ≥72.3 years,the education period ＜8.3 years,and BSSI scale ≥75.1 points are critical values of secondary MCI.