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Genetic etiology of congenital/infantile nystagmus remains largely unknown. This study aimed to identify genomic mutations in patients with infantile nystagmus and an associated disease network. Patients with inherited and sporadic infantile nystagmus were recruited for whole-exome and Sanger sequencing. β-Mannosidase activities were measured. Gene expression, protein–protein interaction, and nystagmus-associated lysosomal storage disease (LSD) genes were analyzed. A novel heterozygous mutation (c.2013G>A; p.R638H) of MANBA, which encodes lysosomal β-mannosidase, was identified in patients with autosomal-dominant nystagmus. An additional mutation (c.2346T>A; p.L749H) in MANBA was found by screening patients with sporadic nystagmus. MANBA was expressed in the pretectal nucleus of the developing midbrain, known to be involved in oculomotor and optokinetic nystagmus. Functional validation of these mutations demonstrated a significant decrease of β-mannosidase activities in the patients as well as in mutant-transfected HEK293T cells. Further analysis revealed that nystagmus is present in at least 24 different LSDs involving the brain. This is the first identification of MANBA mutations in patients with autosomal-dominant nystagmus, suggesting a new clinical entity. Because β-mannosidase activities are required for development of the oculomotor nervous system, our findings shed new light on the role of LSD-associated genes in the pathogenesis of infantile nystagmus. Genet Med 17 12, 971–979.

Background and purpose Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches. Methods Using a hospital’s electronic health record(EHR) data from 2007-2017, 13,930 eligible patients with acute ischaemic stroke (AIS) were identified to build and evaluate the models (85% of the patients were used for training, and 15% were used for testing). In total, 1012 patients (7.23%) contracted pneumonia during hospitalization. A number of machine learning methods were developed and compared to predict pneumonia in the stroke population in China. In addition to the classic methods (i.e., logistic regression (LR), support vector machines (SVMs), extreme gradient boosting (XGBoost)), methods based on multiple layer perceptron (MLP) neural networks and recurrent neural network (RNNs) (i.e., attention-augmented gated recurrent unit (GRU)) are also implemented to make use of the temporal sequence information in electronic health record (EHR) systems. Prediction models for pneumonia were built for two time windows, i.e., within 7 days and within 14 days after stroke onset. In particular, pneumonia occurring within the 7-day window is considered highly associated with stroke (stroke-associated pneumonia, SAP). Main findings The attention-augmented GRU model achieved the best performance based on an area under the receiver operating characteristic curve (AUC) of 0.928 for pneumonia prediction within 7 days and an AUC of 0.905 for pneumonia prediction within 14 days. This method outperformed the other machine learning-based methods and previously published pneumonia risk score models. Considering that pneumonia prediction after stroke requires a high sensitivity to facilitate its prevention at a relatively low cost (i.e., increasing the nursing level), we also compared the prediction performance using other evaluation criteria by setting the sensitivity to 0.90. The attention-augmented GRU achieved the optimal performance, with a specificity of 0.85, a positive predictive value (PPV) of 0.32 and a negative predictive value (NPV) of 0.99 for pneumonia within 7 days and a specificity of 0.82, a PPV of 0.29 and an NPV of 0.99 for pneumonia within 14 days. Conclusions The deep learning-based predictive model is feasible for stroke patient management and achieves the optimal performance compared to many classic machine learning methods.

Object Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to identify the ischemic stroke readmission risk factors and establish a 90-day readmission prediction model for first-time ischemic stroke patients. Methods The readmission prediction model was developed using the extreme gradient boosting (XGboost) model, which can generate an ensemble of classification trees and assign a predictive risk score to each feature. The patient data were split into a training set (5159) and a validation set (911). The prediction results were evaluated with the receiver operating characteristic (ROC) curve and time-dependent ROC curve, which were compared with the outputs from the logistic regression (LR) model. Results A total of 6070 adult patients (39.6% female, median age 67 years) without any ischemic attack (IS) history were included, and 520 (8.6%) were readmitted within 90 days. The XGboost-based prediction model achieved a standard area under the curve (AUC) value of .782 (.729-.834), and the best time-dependent AUC value was .808 in 54 days for the validation set. In contrast, the LR model yielded a standard AUC value of .771 (.714-.828) and best time-dependent AUC value of .797. Conclusions The XGboost model obtained a better risk prediction for 90-day readmission for first-time ischemic stroke patients than the LR model. This model can also reveal the high risk factors for stroke readmission in first-time ischemic stroke patients.