Clinical Signs in 166 Beagles with Different Genotypes of Lafora.

Lafora disease (LD) is a genetic disease affecting beagles, resulting in seizures in combination with other signs. The aim of this study was to describe the clinical signs of LD in beagles with different NHLRC1 genotypes. One hundred and sixty-six beagles were tested for an NHLRC1 gene defect: L/L (n = 67), N/L (n = 32), N/N (n = 67). Owners were asked to participate in a survey about the clinical signs of LD in their dogs. These were recorded for the three possible genotypes in the two age groups, <6 years and ≥6 years. In all genotypes, nearly all the signs of LD were described. In the age group ≥ 6 years, however, they were significantly more frequent in beagles with the L/L genotype. If the following three clinical signs occur together in a beagle ≥ 6 years-jerking of the head, photosensitivity and forgetting things he/she used to be able to do-98.2% of these dogs are correctly assigned to the L/L genotype. If one or two of these signs are missing, the correct classification decreases to 92.1% and 13.2%, respectively. Only the combination of certain signs truly indicates the L/L genotype. Yet, for many dogs, only genetic testing will provide confirmation of the disease.

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.

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.

Biopsy Characteristics, Subtypes, and Prognostic Features in 107 Cases of Feline Presumed Immune-Mediated Polyneuropathy.

Inflammatory polyradiculoneuropathy (IMPN) is one of the causes of sudden onset of neuromuscular signs such as para-/tetraparesis in young cats. Even though most cases have a favorable outcome, persistent deficits, relapses, and progressive courses are occasionally seen. As clinical presentation does not always appear to predict outcome and risk of recurrence, this study was initiated to screen for prognostic biopsy findings in a large cohort of histologically confirmed IMPN cases with clinical follow-up. In total, nerve and muscle specimens of 107 cats with biopsy diagnosis of presumed autoreactive inflammatory polyneuropathy and 22 control cases were reviewed by two blinded raters for a set of 36 histological parameters. To identify patterns and subtypes of IMPN, hierarchical k-means clustering of 33 histologic variables was performed. Then, the impact of histological parameters on IMPN outcome was evaluated via an univariate analysis to identify variables for the final multivariate model. The data on immediate outcome and follow-up were collected from submitting neurologists using a purpose-designed questionnaire. Hierarchical k-means clustering sorted the tissues into 4 main categories: cluster 1 (44/129) represents a purely inflammatory IMPN picture, whereas cluster 2 (47/129) was accompanied by demyelinating features and cluster 3 (16/129) by Wallerian degeneration. Cluster 4 (22/129) reflects normal tissues from non-neuropathic control cats. Returned questionnaires provided detailed information on outcome in 63 animals. They were categorized into recovered and non-recovered. Thereby, fiber-invasive infiltrates by mononuclear cells and mild fiber loss in intramuscular nerve branches correlated with higher probabilities of recovery. Remyelination in semithin sections, on the other hand, is correlated with a less favorable outcome. Animals grouping in cluster 1 had a tendency to a higher probability of recovery compared to other clusters. In conclusion, diagnosis of feline IMPN from nerve and muscle biopsies allowed for the identification of histologic features that were positively or negatively correlated with outcome.

A retrospective case series of clinical signs in 28 Beagles with Lafora disease.

BACKGROUND: Clinical signs and their progression in Beagles with Lafora disease are poorly described. OBJECTIVES: To describe clinical signs in Beagles with Lafora disease. ANIMALS: Twenty-eight Beagles with Lafora disease confirmed by genetic testing or histopathology. METHODS: Retrospective multicenter case series. Data regarding signalment, clinical signs, diagnostic tests and treatment were retrieved from hospital data files. A questionnaire was sent to owners asking about neurological deficits, changes in cognitive functions, behavioral changes, response to treatment and survival time. RESULTS: Onset of clinical signs was 8.3 years (mean; range, 6.3-13.3). All dogs had myoclonic episodes as an initial clinical sign with tonic-clonic seizures in n = 11/28 (39%) and n = 12/28 (43%) later developing tonic-clonic seizures. Deficits of coordination (n = 21/25; 84%), impaired vision (n = 15/26; 58%), and impaired hearing (n = 13/26; 50%) developed later. Mental decline was observed as loss of house training (urination; n = 8/25; 32%), difficulties performing learned tasks (n = 9/25; 36%), and difficulties learning new tasks (n = 7/23; 30%). Common behavioral changes were: increased photosensitivity (n = 20/26; 77%), staring into space (n = 16/25; 64%), reduced stress resistance (n = 15/26; 58%), increased noise sensitivity (n = 14/26; 54%), and separation anxiety (n = 11/25; 44%). Twenty-one dogs were alive (median age 11.9 years; range, 9.8-18.6), and 7 dogs were dead (mean age 12.1 years; SD: 1.3; range, 10.5-12.6) at time of writing. CONCLUSIONS AND CLINICAL IMPORTANCE: Lafora disease in Beagles causes significant behavioral changes, and mental decline as well as neurological deficits in addition to myoclonic episodes and generalized tonic-clonic seizures. Nevertheless, a relatively normal life span can be expected.