Right-to-Left Displacement of an Airgun Lead Bullet after Transorbital Entry into the Skull Complicated by Posttraumatic Epilepsy : A Case Report.

Penetrating head injury is a serious open cranial injury. In civilians, it is often caused by non-missile, low velocity flying objects that penetrate the skull through a weak cranial structure, forming intracranial foreign bodies. The intracranial foreign body can be displaced due to its special quality, shape, and location. In this paper, we report a rare case of right-to-left displacement of an airgun lead bullet after transorbital entry into the skull complicated by posttraumatic epilepsy, as a reminder to colleagues that intracranial metal foreign bodies maybe displaced intraoperatively. In addition, we have found that the presence of intracranial metallic foreign bodies may be a factor for the posttraumatic epilepsy, and their timely removal appears to be beneficial for epilepsy control.

Risk factors assessment and a Bayesian network model for predicting ischemic stroke in patients with cardiac myxoma.

Objective: This study aims to identify relevant risk factors, assess the interactions between variables, and establish a predictive model for ischemic stroke (IS) in patients with cardiac myxoma (CM) using the Bayesian network (BN) approach. Methods: Data of patients with CM were collected from three tertiary comprehensive hospitals in Beijing from January 2002 to January 2022. Age, sex, medical history, and information related to CM were extracted from the electronic medical record system. The BN model was constructed using the tabu search algorithm, and the conditional probability of each node was calculated using the maximum likelihood estimation method. The probability of each node of the network and the interrelationship between IS and its related factors were qualitatively and quantitatively analyzed. A receiver operating characteristic (ROC) curve was also plotted. Sensitivity, specificity, and area under the curve (AUC) values were calculated and compared between the BN and logistic regression models to evaluate the efficiency of the predictive model. Results: A total of 416 patients with CM were enrolled in this study, including 61 with and 355 without IS. The BN model found that cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility were directly associated with the occurrence of IS in patients with CM. The BN model for predicting CM-IS achieved higher scores on AUC {0.706 [95% confidence interval (CI), 0.639-0.773]} vs. [0.697 (95% CI, 0.629-0.766)] and sensitivity (99.44% vs. 98.87%), but lower scores on accuracies (85.82% vs. 86.06%) and specificity (6.56% vs. 11.48%) than the logistic regression model. Conclusion: Cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility are candidate predictors of IS in patients with CM. The BN model was superior or at least non-inferior to the traditional logistic regression model, and hence is potentially useful for early IS detection, diagnosis, and prevention in clinical practice.

Clinical features, risk factors and survival in cardiac myxoma-related ischemic stroke: A multicenter case-control study.

BACKGROUND: Cardiac myxoma (CM) is an important etiology of stroke in young adults, but studies on CM-related ischemic stroke (CM-IS) are limited and conflicting. Hence, we investigated clinical characterizations, risk factors of CM-IS, and short-term survival after surgical resection. METHODS: We performed a retrospective analysis of data from all CM patients at three referral management centers and conducted follow-up examination. RESULTS: Among 414 CM patients, 402 were recruited for further analysis, including 54 patients with CM-IS and 348 patients with CM without stroke (Non-stroke). In the acute phase, patients presented with NIHSS 3 (interquartile range: 0-10) and clinical presentation comprising neurological, cardiac and constitutional symptoms. Multivariate analysis showed that the factors associated with an increased risk of CM-IS were tumor width < 30 mm [OR = 2.652, 95% CI: 1.061-6.627, P = 0.037], tumors with high-mobility (OR = 2.700, 95% CI: 1.357-5.371, P = 0.005), thrombus on the tumor surface (OR = 1.856, 95% CI: 1.003-3.434, P = 0.049), and lower B-type natriuretic peptide (BNP) levels (OR = 0.995, 95% CI: 0.989-0.999, P = 0.047). The overall three-year survival rate was 95.7% (95% CI: 94.9-96.5) in CM-IS patients who underwent surgery. CONCLUSIONS: CM-IS patients had mild or moderate neurologic deficits with various presentations at disease onset. Narrower tumor width, tumors with high-mobility, thrombus on the tumor surface, and lower BNP levels are potential predictors of CM-IS development. Surgical removal of CM is safe and efficacious in patients with CM-IS.

Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy.

Objective: This study aimed to identify risk factors and create a predictive model for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM) using the Bayesian network (BN) approach. Materials and methods: We collected clinical data of 634 patients with DCM treated at three referral management centers in Beijing between 2016 and 2021, including 127 with and 507 without IS. The patients were randomly divided into training (441 cases) and test (193 cases) sets at a ratio of 7:3. A BN model was established using the Tabu search algorithm with the training set data and verified with the test set data. The BN and logistic regression models were compared using the area under the receiver operating characteristic curve (AUC). Results: Multivariate logistic regression analysis showed that hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis were associated with IS. The BN model found that hyperlipidemia, atrial fibrillation (AF) or atrial flutter, eGFR, and intracardiac thrombosis were closely associated with IS. Compared to the logistic regression model, the BN model for IS performed better or equally well in the training and test sets, with respective accuracies of 83.7 and 85.5%, AUC of 0.763 [95% confidence interval (CI), 0.708-0.818] and 0.822 (95% CI, 0.748-0.896), sensitivities of 20.2 and 44.2%, and specificities of 98.3 and 97.3%. Conclusion: Hypertension, hyperlipidemia, AF or atrial flutter, low eGFR, and intracardiac thrombosis were good predictors of IS in patients with DCM. The BN model was superior to the traditional logistic regression model in predicting IS in patients with DCM and is, therefore, more suitable for early IS detection and diagnosis, and could help prevent the occurrence and recurrence of IS in this patient cohort.