Objective To analysis correlation factors for preoperative sudden death of patients with type A aortic dissection in order to determine clinical management strategy.?Methods?We retrospectively analyzed clinical data of 52 patients with type A aortic dissection who were admitted in Department of Cardiothoracic Surgery of the Affiliated Drum Tower Hospital of Nanjing University Medical School from January 2003 to January 2010. According to the presence of preoperative death, all the patients were divided into two groups, 9 patients in the preoperative sudden death （PSD）group including 7 males and 2 females with their mean age of 52.0±12.1 years；43 patients in the control group including 31 males and 12 females with their mean age of 51.5±10.9 years. Univariate and multivariate logistic regression analysis were used for analysis of preoperative factors related to sudden death.?Results?Univariate analysis result showed 7 candidate variables：body mass index （BMI, Wald χ2=2.150, P=0.143）, time of onset （Wald χ2=2.711, P= 0.100）, total cholesterol （TC, Wald χ2=1.444, P=0.230）, low density lipoprotein cholesterol （L-C, Wald χ2=1.341, P=0.247）, aortic insufficiency （AI, Wald χ2=2.093, P=0.148）, aortic sinus involvement （Wald χ2=3.386, P=0.066）and false lumen thrombosis （Wald χ2=7.743, P=0.005）. Multivariate logistic regression analysis showed that BMI （Wald χ2=4.215, P=0.040, OR=1.558）and aortic sinus involvement （Wald χ2=4.592, P=0.032, OR=171.166 ）were preoperative risk factors for sudden death, and thrombosed false lumen （Wald χ2=5.097, P=0.024, OR=0.011）was preoperative protective factor for sudden death.?Conclusion?Type A aortic dissection patients with large BMI and/or aortic sinus involvement should receive operation more urgently than others and patients with thrombosed false lumen may have relatively low risk of preoperative sudden death.
ObjectiveImpaired breathing during and following seizures is an important cause of sudden unexpected death in epilepsy (SUDEP), but the network mechanisms by which seizures impair breathing have not been thoroughly investigated. Progress would be greatly facilitated by a model in which breathing could be investigated during seizures in a controlled setting. MethodRecent work with an acute Long-Evans rat model of limbic seizures has demonstrated that depression of brainstem arousal systems may be critical for impaired consciousness during and after seizures. We now utilize the same rat model to investigate breathing during partial seizures with secondary generalization. ResultBreathing is markedly impaired during seizures(P < 0.05;n=21), and that the severity of breathing impairment is strongly correlated with the extent of seizure propagation (Pearson R=-0.73;P < 0.001;n=30). ConclusionSeizure propagation could increase the severity of breathing impairment caused by seizures. Based on these results, we suggest that this animal model would help us to improve understanding of pathways involved in impairment of breathing caused by seizures and this is an important initial step in addressing this significant cause of SUDEP in people living with epilepsy.
ObjectiveSeizure-related respiratory or cardiac dysfunction was once thought to be the direct cause of sudden unexpected death in epilepsy (SUDEP), but both may be secondary to postictal cerebral inhibition. An important issue that has not been explored to date is the neural network basis of cerebral inhibition. Our aim was to investigate the features of neural networks in patients at high risk for SUDEP using a blood oxygen level-dependent (BOLD) resting-state functional MRI (Rs-fMRI) technique. MethodsRs-fMRI data were recorded from 13 patients at high risk for SUDEP and 12 patients at low risk for SUDEP. The amplitude of low-frequency fluctuations (ALFF) values were compared between the two groups to decipt the regional brain activities. ResultsCompared with patients at low risk for SUDEP, patients at high risk exhibited significant ALFF reductions in the right superior frontal gyrus, the left superior orbital frontal gyrus, the left insula and the left thalamus; and ALFF increase in the right middle cigulum gyrus, the right supplementary motor area and the left thalamus. ConclusionsThese findings highlight the need to understand the fundamental neural network dysfunction in SUDEP, which may fill the missing link between seizure-related cardiorespiratory dysfunction and SUDEP, and provide a promising neuroimaging biomarker for risk prediction of SUDEP.
Objectives To investigate the changes of serum monoamine neurotransmitters and myocardial enzymes in patients with refractory epilepsy (RE), and the possible effects on the cardiovascular system, which would contribute to provide help and guidance to the early warming and prevention to the sudden unexpected death in epilepsy (SUDEP). Methods We collected sixty patients with RE who admitted to Neurological department of First Hospital of Jilin University from December 2015 to December 2016. According to the exclusion criteria, we selected thirty-two patients into the study. The study included 21 males and 11 females patients. Epinephrine (EPI), norepinephrine (NE), dopamine (DA), 5-hydroxytryptamine (5-HT), creatine kinase isoenzyme (CKMB), lactate dehydrogenase (LDH) and hydroxybutyrate dehydrogenase (HBDH) were measured in peri-ictal period and the interictal period in the patients. All the data were analyzed by SPSS17.0 statistical software. Results ① Thirty two patients were eligiblefor this study and the maleto female ratio is 21:11; The age ranged from 15 to 85 years old, with the average age of 50.9±17.6 years old. Twelve (37.5%) were older than 60 years old and 20 (62.5%) were under 60 years old. The epilepsy history ranged from 1 year to 14 years, with an average of 3.75±3.12 years; ② Comparing the levels of monoamine neurotransmitters in peri-ictal period and the interictal period in the patients with RE, we found that the level of EPI and LDH was significantly lower than that in interictal period, while the levels of NE and DA were significantly increased; ③ The results showed that EPI, NE and DA levels in patients under 60 were higher than over 60; ④ Patients were divided into four groups according to the etiology of the disease: idiopathic epilepsy group (10 cases, 31.25%), post-encephalitic epilepsy group (7 cases, 21.88%), post-stroke epilepsy group (9 cases, 28.12%) and epilepsy after brain injury group (6 cases, 18.75%). The results showed that the levels of EPI, NE and DA in the post-strokeepilepsy group were significantly lower than those in the other three groups. The level of CKMB in the idiopathic epilepsy group was higher than that in post-stroke epilepsy and epilepsy induced by brain injury patients. Conclusions RE patients have a higher level of serum NE and DA interictal period, suggesting that seizures may increase sympathetic nervous excitability. The patients under 60 years-old with RE release more catecholamines than young patients, suggesting that the latterwith intractable epilepsy may have higher sympathetic nerve excitability. And it may be associated with the higher incidence of SUDEP in young patients. Post-stroke epilepsyrelease less catecholamine than others, suggesting that the sympathetic nervous excitability is relatively low, and it may have relatively little damage to heart.
ObjectiveTo systematically review the early clinical prediction value of machine learning (ML) for cardiac arrest (CA).MethodsPubMed, EMbase, WanFang Data and CNKI databases were electronically searched to retrieve all ML studies on predicting CA from January 2015 to February 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. The value of each model was evaluated based on the area under receiver operating characteristic curve (AUC) and accuracy.ResultsA total of 38 studies were included. In terms of data sources, 13 studies were based on public database, and other studies retrospectively collected clinical data, in which 21 directly predicted CA, 3 predicted CA-related arrhythmias, and 9 predicted sudden cardiac death. A total of 51 models had been adopted, among which the most popular ML methods included artificial neural network (n=11), followed by random forest (n=9) and support vector machine (n=5). The most frequently used input feature was electrocardiogram parameters (n=20), followed by age (n=12) and heart rate variability (n=10). Six studies compared the ML models with other traditional statistical models and the results showed that the AUC value of ML was generally higher than that in traditional statistical models.ConclusionsThe available evidence suggests that ML can accurately predict the occurrence of CA, and the performance is significantly superior to traditional statistical model in certain cases.