Severity Identification of Graves Orbitopathy via Random Forest Algorithm.

This study aims to establish a random forest model for detecting the severity of Graves Orbitopathy (GO) and identify significant classification factors. This is a hospital-based study of 199 patients with GO that were collected between December 2019 and February 2022. Clinical information was collected from medical records. The severity of GO can be categorized as mild, moderate-to-severe, and sight-threatening GO based on guidelines of the European Group on Graves' orbitopathy. A random forest model was constructed according to the risk factors of GO and the main ocular symptoms of patients to differentiate mild GO from severe GO and finally was compared with logistic regression analysis, Support Vector Machine (SVM), and Naive Bayes. A random forest model with 15 variables was constructed. Blurred vision, disease course, thyroid-stimulating hormone receptor antibodies, and age ranked high both in mini-decreased gini and mini decrease accuracy. The accuracy, positive predictive value, negative predictive value, and the F1 Score of the random forest model are 0.83, 0.82, 0.86, and 0.82, respectively. Compared to the three other models, our random forest model showed a more reliable performance based on AUC (0.85 vs. 0.83 vs. 0.80 vs. 0.76) and accuracy (0.83 vs. 0.78 vs. 0.77 vs. 0.70). In conclusion, this study shows the potential for applying a random forest model as a complementary tool to differentiate GO severity.

Cerebral glucose hypometabolism and hypoperfusion of cingulate gyrus: an imaging biomarker of autoimmune encephalitis with psychiatric symptoms.

BACKGROUND: About 60% of autoimmune encephalitis (AE) patients present psychiatric symptoms, but the underlying mechanism remains unknown. This study examined the role of the cingulate cortex in such patients to identify predictive poor psychiatric factors. METHODS: In this study, 49 AE patients and 39 healthy controls were enrolled. AE patients were further divided into two groups based on the presence/absence of psychiatric symptoms. The ratio of the standardized uptake value (SUVR) and relative cerebral blood flow (rCBF) in different regions of the cingulate cortex were calculated through positron emission tomography-computed tomography (PET/CT) and arterial spin labeling (ASL) MRI, and the results were compared among the three groups. In addition, we followed-up on the psychiatric outcomes and identified the risk factors for poor psychiatric prognosis, focusing on the cingulate cortex. RESULTS: More than half of the AE patients (27/49) exhibited psychiatric symptoms. Agitation and thought blocking were typical psychiatric phenotypes, except for anti-glutamic acid decarboxylase 65 (GAD65) encephalitis, which mainly presented with catatonia and a depressed mood. AE patients with psychiatric symptoms experienced reduced metabolism and perfusion of the anterior cingulate cortex (ACC), midcingulate cortex (MCC), and posterior cingulate cortex (PCC). The SUVR of ACC can be used as an independent risk factor of poor psychiatric outcomes, which had an area under the ROC curve (AUC) of 0.865. CONCLUSION: Impaired cingulate cortex function in AE may be the potential mechanism of psychiatric symptoms. Hypometabolism of ACC is an independent prognostic factor predicting an unfavorable psychiatric prognosis in AE.

Causal effect of psychiatric disorders on epilepsy: A two-sample Mendelian randomization study.

BACKGROUND: This study aims to explore the relationship between psychiatric disorders and the risk of epilepsy using Mendelian randomization (MR) analysis. METHODS: We collected summary statistics of seven psychiatric traits from recent largest genome-wide association study (GWAS), including major depressive disorder (MDD), anxiety disorder, autism spectrum disorder (ASD), bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), and insomnia. Then, MR analysis estimates were performed based on International League Against Epilepsy (ILAE) consortium data (ncase  = 15,212 and ncontrol  = 29,677), the results of which were subsequently validated in FinnGen consortium (ncase  = 6260 and ncontrol  = 176,107). Finally, a meta-analysis was conducted based on the ILAE and FinnGen data. RESULTS: We found significant causal effects of MDD and ADHD on epilepsy in the meta-analysis of the ILAE and FinnGen, with corresponding odds ratios (OR) of 1.20 (95% CI 1.08-1.34, p = .001) and 1.08 (95% CI 1.01-1.16, p = .020) by the inverse-variance weighted (IVW) method respectively. MDD increases the risk of focal epilepsy while ADHD has a risk effect on generalized epilepsy. No reliable evidence regarding causal effects of other psychiatric traits on epilepsy was identified. CONCLUSIONS: This study suggests that major depressive disorder and attention deficit hyperactivity disorder may causally increase the risk of epilepsy.

Using a Two-Sample Mendelian Randomization Study Based on Genome-Wide Association Studies to Assess and Demonstrate the Causal Effects of Allergic Rhinitis on Chronic Lower Respiratory Diseases and Lung Function.

INTRODUCTION: Observational studies have reported that allergic rhinitis (AR) was associated with chronic lower respiratory diseases (CLRDs) and lung function; however, their causal effects remain elusive. Therefore, to investigate the causal effects of AR on CLRDs and lung function, we conducted the two-sample Mendelian randomization (MR) study. METHODS: The data for AR, asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, idiopathic pulmonary fibrosis (IPF), and the forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio were obtained from genome-wide association studies, which were large sample studies on people of European ancestry. In this study, single-nucleotide polymorphisms associated with AR were considered instrumental variables. We employed the inverse-variance weighted (IVW) method with random effects to evaluate causal effects, and the weighted median and MR-Egger methods were used for sensitivity analyses. Significant causal associations were attempted for replication and meta-analysis. RESULTS: In the discovery stage, we found that AR exhibited a significant causal effect on asthma (IVW, odds ratio [OR] = 16.91, 95% CI, 8.03-35.65, p < 0.001) and a suggestive effect on FEV1/FVC ratio (IVW, OR = 0.82, 95% CI, 0.68-0.99, p = 0.039). No causal effect of AR was observed on COPD, bronchiectasis, and IPF. In the replication stage, the causal effect of AR on asthma was replicated (IVW, OR = 11.57, 95% CI, 4.90-27.37, p < 0.001). The meta-analysis demonstrated that the combined OR of AR on asthma was 14.37 (IVW, 95% CI, 8.18-25.24, p < 0.001). CONCLUSIONS: We demonstrated and measured the causal effects of AR on asthma (OR = 14.37) and FEV1/FVC ratio (OR = 0.82), while there was no evidence to support a causal effect of AR on COPD, bronchiectasis, and IPF. These results suggest that AR tends to have a causal effect on lower airway disease of similar inflammatory types and can provide high-quality causal evidence for clinical practice as well as the pathogenesis and prevention of AR and asthma.

Causal relationships between potential risk factors and chronic rhinosinusitis: a bidirectional two-sample Mendelian randomization study.

PURPOSE: Smoking, alcohol consumption, allergic rhinitis (AR), asthma, and obesity are associated with chronic rhinosinusitis (CRS), albeit the causal relationships between them remain elusive. Therefore, we conducted a bidirectional two-sample Mendelian randomization (MR) study to investigate the bidirectional causal effects between these potential risk factors and CRS. METHODS: The data for daily cigarette consumption, age of smoking initiation, weekly alcohol consumption, AR, asthma, body mass index (BMI), and CRS were drawn from large sample size genome-wide association studies. Single-nucleotide polymorphisms associated with each exposure were considered instrumental variables in this study. We investigated causal effects by using the inverse-variance weighted (IVW) method with random effects, and weighted median and MR-Egger methods were used for sensitivity analyses. Pleiotropic effects were detected and corrected by the MR pleiotropy residual sum and outlier test and MR-Egger model. RESULTS: We found the causal effects of daily cigarette consumption (IVW, OR = 1.15, 95% CI 1.00-1.32, p = 0.046), AR (IVW, OR = 4.77, 95% CI 1.61-14.13, p = 0.005), asthma (IVW, OR = 1.45, 95% CI 1.31 - 1.60, p < 0.001), and BMI (IVW, OR = 1.05, 95% CI 1.00-1.09, p = 0.028) on CRS. Furthermore, we found a causal effect of CRS on asthma (IVW OR = 1.08, 95% CI 1.05-1.12, p < 0.001). CONCLUSIONS: We confirmed the causal effects of daily cigarette consumption, AR, asthma, and BMI on CRS, and the causal effect of CRS on asthma, while no causal relationship between age of smoking initiation, weekly alcohol consumption, and CRS was found. These findings are expected to provide high-quality causal evidence for clinical practice and the pathogenesis of CRS and asthma.

Prognostic significance and extra-hypothalamus dysfunction of hyponatremia in anti-leucine-rich glioma-inactivated protein 1 encephalitis.

This study aimed to investigate prognostic significance and brain metabolic mechanism of hyponatremia in anti-leucine-rich glioma-inactivated protein 1 (LGI1) encephalitis. After adjusting for confounders, patients with moderate and severe hyponatremia had significantly increased risk of poor functional outcome and sequelae of seizures. In addition, serum sodium was negatively correlated with normalized ratio of the standardized uptake value of medial temporal lobe (MTL), basal ganglia (BG), and hypothalamus on positron emission tomography (PET) and which was further validated using voxel-wise analysis, suggesting an extra-hypothalamus (BG and MTL) localization for hyponatremia.

Positron emission tomography in autoimmune encephalitis: Clinical implications and future directions.

18 F-fluoro-deoxyglucose position emission tomography (18 F-FDG-PET) has been proven as a sensitive and reliable tool for diagnosis of autoimmune encephalitis (AE). More attention was paid to this kind of imaging because of the shortage of MRI, EEG, and CSF findings. FDG-PET has been assessed in a few small studies and case reports showing apparent abnormalities in cases where MRI does not. Here, we summarized the patterns (specific or not) in AE with different antibodies detected and the clinical outlook for the wide application of FDG-PET considering some limitations. Specific patterns based on antibody subtypes and clinical symptoms were critical for identifying suspicious AE, the most common of which was the anteroposterior gradient in anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and the medial temporal lobe hypermetabolism in limbic encephalitis. And the dynamic changes of metabolic presentations in different phases provided us the potential to inspect the evolution of AE and predict the functional outcomes. Except for the visual assessment, quantitative analysis was recently reported in some voxel-based studies of regions of interest, which suggested some clues of the future evaluation of metabolic abnormalities. Large prospective studies need to be conducted controlling the time from symptom onset to examination with the same standard of FDG-PET scanning.

Predicting the functional outcomes of anti-LGI1 encephalitis using a random forest model.

OBJECTIVES: To establish a model in order to predict the functional outcomes of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm. METHODS: Seventy-nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy. RESULTS: A total of 79 patients were included in the final analysis. After a median follow-up of 24 months (range, 8-60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti-LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti-LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64). CONCLUSIONS: The random forest model can predict poor functional outcomes of patients with anti-LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.

18 F-fluorodeoxy-glucose positron emission tomography pattern and prognostic predictors in patients with anti-GABAB receptor encephalitis.

AIMS: To identify the metabolic pattern and prognostic predictors in anti-gamma-aminobutyric-acid B (GABAB) receptor encephalitis using 18 F-fluorodeoxy-glucose positron emission tomography (18 F-FDG-PET). METHODS: Twenty-one patients diagnosed anti-GABAB receptor encephalitis who underwent 18 F-FDG-PET at first hospitalization were retrospectively reviewed. 18 F-FDG-PET images were analyzed in comparison with controls. Further group comparisons of 18 F-FDG-PET data were carried out between prognostic subgroups. RESULTS: 18 F-FDG-PET was abnormal in 81% patients with anti-GABAB receptor encephalitis and was more sensitive than MRI (81% vs. 42.9%, p = 0.025). Alter limbic lobe glucose metabolism (mostly hypermetabolism) was observed in 14 patients (66.7%), of whom 10 (10/14, 71.4%) demonstrated hypermetabolism in the medial temporal lobe (MTL). Group analysis also confirmed MTL hypermetabolism in association with relative frontal and parietal hypometabolism was a general metabolic pattern. After a median follow-up of 33 months, the group comparisons revealed that patients with poor outcome demonstrated increased metabolism in the MTL compared to those with good outcome. CONCLUSION: 18 F-FDG-PET may be more sensitive than MRI in the early diagnosis of anti-GABAB receptor encephalitis. MTL hypermetabolism was associated with relative frontal or parietal hypometabolism and may serve as a prognostic biomarker in anti-GABAB receptor encephalitis.