Generalized myoclonic epilepsy with photosensitivity in juvenile dogs caused by a defective DIRAS family GTPase 1.

The clinical and electroencephalographic features of a canine generalized myoclonic epilepsy with photosensitivity and onset in young Rhodesian Ridgeback dogs (6 wk to 18 mo) are described. A fully penetrant recessive 4-bp deletion was identified in the DIRAS family GTPase 1 (DIRAS1) gene with an altered expression pattern of DIRAS1 protein in the affected brain. This neuronal DIRAS1 gene with a proposed role in cholinergic transmission provides not only a candidate for human myoclonic epilepsy but also insights into the disease etiology, while establishing a spontaneous model for future intervention studies and functional characterization.

Prevalence of nonconvulsive seizures and nonconvulsive status epilepticus in dogs and cats with a history of cluster seizures: A retrospective study.

BACKGROUND: Nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE) are frequently observed in human patients. Diagnosis of NCS and NCSE only can be achieved by the use of electroencephalography (EEG). Electroencephalographic monitoring is rare in veterinary medicine and consequently there is limited data on frequency of NCS and NCSE. OBJECTIVES: Determine the prevalence of NCS and NCSE in dogs and cats with a history of cluster seizures. ANIMALS: Twenty-six dogs and 12 cats. METHODS: Retrospective study. Medical records of dogs and cats with cluster seizures were reviewed. Electroencephalography was performed in order to identify electrographic seizure activity after the apparent cessation of convulsive seizure activity. RESULTS: Nonconvulsive seizures were detected in 9 dogs and 2 cats out of the 38 patients (29%). Nonconvulsive status epilepticus was detected in 4 dogs and 2 cats (16%). Five patients had both NCS and NCSE. A decreased level of consciousness was evident in 6/11 patients with NCS, 3/6 also had NCSE. Mortality rate for patients with NCS (73%) and NCSE (67%) was much higher than that for patients with no seizure activity on EEG (27%). CONCLUSION AND CLINICAL IMPORTANCE: Prevalence of NCS and NCSE is high in dogs and cats with a history of cluster seizures. Nonconvulsive seizures and NCSE are difficult to detect clinically and are associated with higher in hospital mortality rates. Results indicate that prompt EEG monitoring should be performed in dogs and cats with cluster seizures.

Machine learning algorithms predict canine structural epilepsy with high accuracy.

Introduction: Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures. Materials and methods: Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features. Results: A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year. Conclusion: Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.

Client's understanding of instructions for small animals in a veterinary neurological referral center.

BACKGROUND: It is not known how much information clients retrieve from discharge instructions. OBJECTIVE: To investigate client's understanding of discharge instructions and influencing factors. ANIMALS: Dogs and cats being hospitalized for neurological diseases. METHODS: Clients were presented questionnaires regarding their pet's disease, diagnostics, treatments, prognosis and discharge instructions at time of discharge and 2 weeks later. The same questions were answered by discharging veterinarians at time of discharge. Clients answered additional questions regarding the subjective feelings during discharge conversation. Data collected included: data describing discharging veterinarian (age, gender, years of clinical experience, specialist status), data describing the client (age, gender, educational status). Raw percentage of agreement (RPA) between answers of clinicians and clients as well as factors potentially influencing the RPA were evaluated. RESULTS: Of 230 clients being approached 151 (65.7%) and 70 (30.4%) clients responded to the first and second questionnaire, respectively (130 dog and 30 cat owners). The general RPA between clinician's and client's responses over all questions together was 68.9% and 66.8% at the 2 time points. Questions regarding adverse effects of medication (29.0%), residual clinical signs (35.8%), and confinement instructions (36.8%) had the lowest RPAs at the first time point. The age of clients (P = .008) negatively influenced RPAs, with clients older than 50 years having lower RPA. CONCLUSIONS AND CLINICAL IMPORTANCE: Clients can only partially reproduce information provided at discharge. Only clients' increasing age influenced recall of information. Instructions deemed to be important should be specifically stressed during discharge.