Musculoskeletal morbidity in adults with spinal cord injuries: A nationwide cohort study.

BACKGROUND: An increase in the demand for quality of life following spinal cord injuries (SCIs) is associated with an increase in musculoskeletal (MSK) pain, highlighting the need for preventive measure research. OBJECTIVE: This study aimed to evaluate the incidence and hazards of MSK morbidities among Korean adults with SCIs, as well as the influence of SCI location on MSK morbidities. METHODS: Patient populations were selected from Korean National Health Insurance Service data (n = 276). The control group included individuals without SCIs (n = 10,000). We compared the incidences and determined the unadjusted and adjusted hazard ratios (HRs) of common MSK morbidities (osteoarthritis, connective tissue disorders, sarcopenia, myalgia, neuralgia, rheumatoid arthritis, myositis, and musculoskeletal infections) based on the location of injury (cervical, thoracic, or lumbar). RESULTS: Adults with SCIs had a higher incidence of MSK morbidity (48.45% vs. 36.6%) and a lower survival probability than those without SCIs. The incidence of MSK morbidity and survival probabilities were not significantly different for cervical cord injuries, whereas both measures were significantly different for thoracic and lumbar injuries. CONCLUSION: SCI increases the risk of MSK morbidity. Lumbar SCI is associated with a higher incidence and risk of MSK morbidity than are cervical or thoracic SCIs.

Cerebrovascular Complications in Spinal Fusion Surgery: A Nationwide 8-year Follow-up Study in South Korea.

This retrospective study investigated the long-term incidence and risk of postoperative complications following spinal fusion. This study included 640,366 participants from a National Health Examination cohort in the Republic of Korea. Among them, 11,699 individuals underwent spinal fusion, and 56,667 individuals who underwent non-fusion spinal procedures served as controls. Propensity score matching was used to account for patient characteristics including demographic factors, comorbidities, and other relevant variables. The participants were followed for 8 years to assess the occurrence of cerebrovascular disease (CVD), hemorrhagic infarction (HA), ischemic infarction (II), occlusion and stenosis, and ischemic heart disease (IHD). The incidence rates of CVD and IHD were found to be 27.58 and 31.45 per 1,000 person-years in the spinal fusion group compared to 18.68 and 25.73 per 1,000 person-years in the control group (p < .001), respectively. Patients who underwent spinal fusion had a higher risk of CVD, HA, and IHD than those in the control group (all p < .001). In the subgroup analysis, thoracolumbar and noncervical spinal fusion were associated with a higher risk of CVD, II, and IHD (all p < .005). Patients undergoing thoracolumbar fusion may have an increased association with CVD, II in cerebral arteries, and IHD. This suggests a need for careful consideration of vascular risks in such patient populations.

Coincidental Nasopharyngeal Carcinoma and Ventral Foramen Magnum Meningioma: Case Report and Review of the Literature.

Multiple primary tumors at adjacent site are rare. We report a rare case of coincidentally found nasopharyngeal cancer and ventral foramen magnum meningioma. The 68-year-old male patient presented with a year history of ataxia. Radiological examination revealed lesions in the nasopharyngeal space and ventral foramen magnum. A needle aspiration biopsy for nasopharyngeal space and surgical removal for foramen magnum lesion were performed. The pathological diagnoses were nasopharyngeal cancer and meningioma, respectively. The concomitant occurrence of these two tumors is very rare and there is no known association between these two tumors. We report a case of ventral foramen magnum meningioma simultaneously present with nasopharyngeal carcinoma.

Hyperattenuations on flat-panel computed tomography after successful recanalization of mechanical thrombectomy for anterior circulation occlusion.

BACKGROUND: To evaluate intraparenchymal hyperattenuation (IPH) on flat-panel computed tomography (FPCT) findings and their clinical usefulness for predicting prognosis after successful mechanical thrombectomy (MT) for acute occlusion of anterior circulation. METHODS: A retrospective review was conducted for 158 consecutive patients undergoing mechanical thrombectomy during the last six years. After excluding those with posterior circulation occlusion or incomplete recanalization and those without FPCT, 82 patients were finally included. Immediate post-procedural IPH on FPCT was categorized into four patterns (none, striatal, cortical, or combined pattern). Follow-up magnetic resonance images or CT scans after 48 hours from MT were analyzed according to FPCT findings. The existence of hemorrhagic transformation, intracerebral hemorrhage, and brain swelling was evaluated. Functional clinical outcomes were accessed with post-procedural 3-month modified Rankin scales (mRS). RESULTS: Of 82 patients, 34 patients were found to have IPH (16 with a striatal pattern, 8 with a cortical pattern, and 10 with a combined pattern). Hemorrhagic complication (P<0.001), brain swelling (P<0.001), and poor mRS scores (P=0.042) showed significant differences according to IPH patterns. Multivariate logistic regression analysis revealed that the presence of a striatal pattern (OR: 13.26, P<0.001), cortical pattern (OR: 11.61, P=0.009), and combined pattern (OR: 45.34, P<0.001) independently predicted hemorrhagic complications. The location of the occlusion (OR: 4.13, P=0.034), cortical pattern (OR: 5.94, P=0.039), and combined pattern (OR: 39.85, P=0.001) predicted brain swelling. Age (OR: 1.07, P=0.006) and the presence of a combined pattern (OR: 10.58, P=0.046) predicted poor clinical outcomes. CONCLUSIONS: FPCT is a rapid and effective tool for a prompt follow-up just after MT to predict prognosis. Those with striatal patterns showed relatively good clinical outcomes despite significant hemorrhage. Cortical IPH patterns independently predicted a high rate of post-procedural hemorrhage or brain swelling. Combined pattern is a strong predictor for both radiologic and poor clinical outcomes.

Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning.

BACKGROUND AND AIMS: Although grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent implantation, detection of vulnerable plaques by IVUS remains challenging. We aimed to develop machine learning (ML) models for predicting optical coherence tomography-derived thin-cap fibroatheromas (OCT-TCFAs). METHODS: In 517 patients with angina, 414 and 103 coronary lesions were randomized into training vs. test sets. Each of the IVUS-OCT co-registered frames (including 32,807 for training and 8101 for test) was labeled according to the presence vs. absence of OCT-TCFA. Among 1449 computed IVUS features based on two-dimensional geometry and texture, 17 features were finally selected and used in supervised ML with artificial neural network (ANN), support vector machine (SVM), and naïve Bayes. RESULTS: IVUS sections with (vs. without) OCT-TCFA showed a larger plaque burden, and a smaller and eccentric lumen. TCFA-containing sections were characterized by increased ratios of variance, entropy, and kurtosis; reduced ratio of homogeneity within the superficial to the deeper plaque; and decreased smoothness within the fibrous cap. In addition, OCT-TCFA was associated with low ratios of gamma-beta, Nakagami-µ and Nakagami-ω, and a high ratio of Rayleigh-b within the superficial to the deeper region. With a 5-fold cross-validation, the averaged accuracies were 81 ±â€¯5% for ANN (area under the curve [AUC] = 0.80 ±â€¯0.08), 77 ±â€¯4% for SVM (AUC = 0.74 ±â€¯0.05), and 78 ±â€¯2% for naïve Bayes (AUC = 0.77 ±â€¯0.04) for predicting OCT-TCFA. In the test set, ANN and naïve Bayes showed the overall accuracies of >80%. CONCLUSIONS: Supervised ML algorithms with computed IVUS features predicted the presence of OCT-TCFA. This data-driven approach may help clinicians in recognizing high-risk coronary lesions.

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.

BACKGROUND: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80. METHODS AND FINDINGS: A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84-0.87, 95% confidence intervals 0.71-0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application. CONCLUSION: We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study.

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.

Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.

Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism.

OBJECTIVE: Detection and diagnosis based on extracting features and classification using electroencephalography (EEG) signals are being studied vigorously. A network analysis of time series EEG signal data is one of many techniques that could help study brain functions. In this study, we analyze EEG to diagnose alcoholism. APPROACH: We propose a novel methodology to estimate the differences in the status of the brain based on EEG data of normal subjects and data from alcoholics by computing many parameters stemming from effective network using Granger causality. MAIN RESULTS: Among many parameters, only ten parameters were chosen as final candidates. By the combination of ten graph-based parameters, our results demonstrate predictable differences between alcoholics and normal subjects. A support vector machine classifier with best performance had 90% accuracy with sensitivity of 95.3%, and specificity of 82.4% for differentiating between the two groups.

Association of Polysensitization, Allergic Multimorbidity, and Allergy Severity: A Cross-Sectional Study of School Children.

BACKGROUND: Aeroallergen sensitization is related to the coexistence of allergic diseases, but the nature of this relationship is poorly understood. The aim of this study was to clarify the relationship of polysensitization with allergic multimorbidities and the severity of allergic diseases. METHODS: This study is a cross-sectional analysis of 3,368 Korean children aged 6-7 years-old. We defined IgE-mediated allergic diseases based on structured questionnaires, and classified the sensitivity to 18 aeroallergens by logistic regression and the Ward hierarchical clustering method. The relationship of polysensitization (positive IgE responses against 2 or more aeroallergens classes) with allergic multimorbidities (coexistence of 2 or more of the following allergic diseases: asthma, rhinitis, eczema, and conjunctivitis) and severity of allergic diseases was determined by ordinal logistic regression analysis. RESULTS: The rate of polysensitization was 13.6% (n = 458, 95% CI 12.4-14.8) and that of allergic multimorbidity was 23.5% (n = 790, 95% CI 22.0-24.9). Children sensitized to more aeroallergens tended to have more allergic diseases (rho = 0.248, p < 0.001), although the agreement between polysensitization and multimorbidity was poor (kappa = 0.11, p < 0.001). The number allergen classes to which a child was sensitized increased the risk of wheezing attacks (1 allergen: adjusted odds ratio [aOR] 2.22, 4 or more allergens: aOR 9.39), absence from school (1 allergen: aOR 1.96, 3 allergens: aOR 2.08), and severity of nasal symptoms (1 allergen: aOR 1.61, 4 or more allergens: aOR 4.38). CONCLUSION: Polysensitization was weakly related to multimorbidity. However, the number of allergens to which a child is sensitized is related to the severity of IgE-mediated symptoms.